3 - Jacobs University

Second-Party Quality Auditing
in the Automotive Industry
In-depth Methodology Analysis
by
Borislav Hadzhiev
(borislav.hadzhiev@volkswagen.de)
a thesis for conferral of a Doctor of Philosophy in
Communications, Systems and Electronics
Dissertation Committee
Prof. Dr. Werner Bergholz • Jacobs University
Dr. Mathias Bode • Jacobs University
Prof. Dr. Roland Jochem • TU Berlin
Roberto Lotz • Volkswagen AG
Roman Mogalle-Kiebler • Volkswagen AG
Chair of the Dissertation Committee: Prof. Dr. Werner Bergholz
Date of Defense: September 21, 2012
School of Engineering and Science
Jacobs University Bremen • Campus Ring 1 • 28759 Bremen • Germany
Group Quality Assurance Supply Parts Chemistry
International Coordination Supplier Audit
Volkswagen AG • P.O. Box 011/14670 • 38436 Wolfsburg • Germany

Disclaimer
April 23, 2013
To whom it may concern,
I, Borislav Hadzhiev, hereby declare that this Ph.D. Thesis is an independent work that has not
been submitted elsewhere for conferral of a degree.
Publications about the content of this work require the written consent of Volkswagen AG.
The results, opinions and conclusions expressed in this thesis are not necessarily those of
Volkswagen AG.
Borislav Hadzhiev
i

Abstract
Today automotive producers operate on a global market with very strong competition and vast
variety of customers, each with their own preferences. Under these conditions automotive companies
need to provide products of excellent quality in order to stay in business. However, managing
quality in the automotive production is a particularly demanding task due to the fact that automotive
Original Equipment Manufacturers (OEM) have some of the most intricate production networks
which exist. They are involved in only about 20% to 40% of the actual production process and the
trend is that in the future this share will keep on shrinking. Therefore OEMs should make sure that
they cooperate only with reliable and quality capable business partners.
Supplier auditing is a very important quality management tool, which is used to assess the capability
of a particular supplier to deliver products of high quality and therefore its suitability as a
business partner. Quality auditing is employed before awarding contracts and during the series production
to assess the quality risks down the supply chain. This work studies the ability of supplier
quality auditing in the automotive industry to provide reliable process capability evaluations. The
case study presented here was carried out in cooperation with Volkswagen AG and evaluates the
effectiveness of the supplier auditing process at the German automotive manufacturer with respect
to the challenges on the global automotive market.
This paper employs a research approach for assessment of the quality auditing process, which
draws similarities to sampling – an important measurement method in the field of Electrical Engineering.
The discussion points out important aspects for the technical implementation of the
quality audit in the automotive industry as well as critical points regarding the information management
of audit evaluation records. Even though the focus of this work is on the automotive
industry, the analytical approach and the statistical methods used here can be used to assess the
effectiveness of supplier quality auditing also in other industry sectors.
ii

Contents
1 Introduction 1
2 Research Question and Research Motivation 3
3 Research Approach 8
3.1 General Strategy for Employing the Quality Audit . . . . . . . . . . . . . . . . . . 13
3.2 Effectiveness of Implementation of a Single Quality Audit . . . . . . . . . . . . . 14
4 Specifics of the Automotive Industry 17
4.1 Internationalization of the Automotive Operations . . . . . . . . . . . . . . . . . . 17
4.2 Rising Development Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3 The Automotive Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.4 Implications for the General Automotive Quality Auditing Strategy . . . . . . . . . 28
5 Fundamental Principles of the Quality Audit 32
5.1 Audit Planning, Initiation and Preparation . . . . . . . . . . . . . . . . . . . . . . 34
5.2 On-site Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.3 Reporting and Closure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
6 Empirical Data 39
6.1 Quality Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.2 Quality Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
6.3 Automation of the Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 54
6.3.1 Matching Quality Capability and Quality Performance . . . . . . . . . . . 56
6.3.2 Supplier Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
7 Results and Discussion 58
7.1 State and Quality of the Available Empirical Data . . . . . . . . . . . . . . . . . . 58
7.2 Possible Sources of Data Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
7.2.1 Revisions of Volkswagen’s Quality Auditing Process . . . . . . . . . . . . 70
7.2.2 Period of Relevance of Quality Audit Results . . . . . . . . . . . . . . . . 84
7.2.3 The Factor ”Human” and People Calibration . . . . . . . . . . . . . . . . 86
7.2.3.1 Calibration of Supplier Quality Auditors . . . . . . . . . . . . . 86
7.2.3.2 Calibration of Production Quality Assessment . . . . . . . . . . 88
7.3 Matching Quality Capability and Quality Performance . . . . . . . . . . . . . . . 95
7.4 Relation Between Number of Defective Parts and Delivery Amount . . . . . . . . 102
8 Conclusion 121
8.1 General Auditing Strategy (Sampling Frequency) . . . . . . . . . . . . . . . . . . 123
8.2 Effectiveness of Individual Audits (Accuracy of Samples) . . . . . . . . . . . . . . 124
8.3 Answers of the Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . 129
Appendices 133
A Results from the analysis presented in Section 7.2.1 133
B Results from the analysis presented in Section 7.2.3.2 155
B.1 List of Material Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
References 162
iii

Acknowledgements
I hereby want to express my sincere gratitude to the numerous people, who were involved in
this project and whose constructive input made it possible to achieve the results presented in this
paper. Even though the list is very long and I am very thankful to every single person on it, there
are a few names, to which I want to bring attention.
I would like to express my great appreciation to my supervisor Prof. Dr. Werner Bergholz for
his high commitment and incessant support throughout every single stage of the project, to Roberto
Lotz, who with his more than 30 years of experience in the automotive sector brought valuable
practical background in the discussions, to Dr. Mathias Bode for his constructive questions and
insightful remarks, to Prof. Dr. Roland Jochem for his sincere and objective input, to Roman
Mogalle-Kiebler for his committed and cooperative conduct. I want to also thank Roland Assmann
for his interest and help in initiating the project.
Furthermore, I am thankful to Carlo Boettger, Wolfgang Hering, Tanja Brueggemann, Osman
Meneses and every further colleague at Volkswagen, who contributed to the productive and comfortable
working atmosphere at any time.
And last but not least I want to express my gratefulness also to Silviya Nikolova for her patience
and strong moral support throughout the entire project.
Sincerely,
Borislav Hadzhiev
iv

A customer is the most important visitor on our premises. He is not dependent
on us. We are dependent on him. He is not an interruption on our
work. He is the purpose of it. He is not an outsider on our business. He is
a part of it. We are not doing him a favour by serving him. He is doing us
a favour by giving us an opportunity to do so.
Mahatma Gandhi
1 Introduction
The Cambridge Dictionaries Online (2012) define business as ”the activity of buying and selling
goods and services, or a particular company that does this, or work that you do to earn money”
1 . The target of each commercial organization is to maximize its long-term profits and to assure
sustainability and continuity of its operations by building a set of customers which are going to
repeat business with the organization in the future. Under free market conditions organizations can
only achieve this, if their products meet the expectations of their target customers.
The term ”quality” is defined by the international standard ISO 9000 (2005) as the ”degree to
which a set of inherent characteristics fulfils requirements” (p. 18). Quality plays a central role
in today’s business environment. If an organization manages to identify and fulfill its customers’
requirements through its products to a high degree (achieve good quality), its products will deliver
high value to its customers who will be willing to do business with the company in the future.
Thus, the good reputation of the organization and the perceived value of its products will grow.
In his work The One Number You Need to Grow, Reichheld (2003) argues that company growth
correlates very well with its customers’ readiness to recommend company’s products to other potential
customers, such as e.g. their friends or colleagues. When satisfied customers share their
good experiences with other potential customers, the customer base of the organization expands
generating business growth and larger long-term profits. If, on the other hand, an organization fails
to meet the expectations of its customers (i.e. provides poor quality), it is very likely that the latter
will not be willing to do business with the organization in the future. The pool of customers of the
1 In this paper the term product is used to denote both a physical object and/or a service.
1

organization will therefore shrink and the organization might not be able to sustain its business in
the long run.
The importance of quality is further intensified in a competitive environment. Most of the time
a number of companies (business competitors) are competing for a limited number of customers.
The company that offers the best quality will be able to capture the largest share of the customer
pool and therefore be the most profitable. For that reason quality has become a cornerstone of the
business strategies in every major industry today. It should be noted, however, that in a competitive
business environment price is also an important factor along with quality.
To remain profitable in the longer term companies need to sustain and continuously improve
quality. This vision is reflected in international standards such as the ISO 9001 (2008, p. 9). A key
concept in business management is the EFQM Excellence Model (2012) of the European Foundation
for Quality Management (EFQM), which defines a framework for assessment of company
progress, ideas communication, and achieving company’s business targets. However, modern business
world is defined by complexity and to achieve quality of your products is not always straight
forward. Definitely the ability to identify trends in customer demand and quickly and adequately
respond to these changes plays a crucial role. That is the reason why companies spend millions on
market research. Even though very important, identifying customer needs alone is not sufficient.
It is equally challenging to translate the identified customer expectations into specific companyinternal
objectives. For example, a chocolatier might know that her customers prefer the chocolate
mousse neither too sweet nor too bitter. But in order to make high quality chocolate mousse the
task of the chocolatier would be to first understand what ”neither too sweet nor too bitter” means
to her customers and then to translate it into a specific proportion of sugar and cocoa in her recipe.
The task becomes even more difficult if we move to more complex products such as a television
set or a smart phone. But probably the most complex every-day product most of us would ever
use is a car. An automobile has to meet a very broad set of requirements ranging from extremely
diverse design and performance preferences, through tax, safety and environmental regulations, to
better price for value expectations.
Companies, which offer complex products, rarely produce these entirely by themselves. Very
often one or rather several external companies are involved in the production process. In the race
for market share business competitors are put under pressure to offer superior products at lower
prices. Outsourcing part of their processes to external partners allows companies to reduce costs
and concentrate valuable resources on their most important capabilities, thus maximizing the profit
2

margins and strengthening their market position (Abele, Meyer, Näher, Strube, & Sykes, 2008). In
a number of market sectors outsourcing has become so important that doing business without it is
unthinkable.
Along with its benefits, however, outsourcing turns quality assurance into a significant challenge.
Increasing the number of parties involved in the production process raises the complexity
of the communication flow, the number of process interfaces grows (practice shows that most of
the quality related problems arise at such interfaces) and thus companies run higher risk of miscommunication
of their business objectives. Nonetheless, it is the responsibility of the outsourcing
company to make sure that their products meet customers’ expectations, which in turn means that
quality requirements must be fulfilled in the entire supply chain. van Weele (2010) writes: ”quality
of the finished product is determined to a large extent by the quality of raw materials and
components” (p. 241). Therefore, it is very important for a company to carefully select its business
partners. For the latter understanding the generally accepted quality practices and being able to
properly manage quality are a basic requirement.
This paper studies quality assurance down the supply chain of the automotive industry – a
business sector with one of the most intricate mass production supply chains which exist 2 . More
precisely, the aim of the subsequent discussion is to present and evaluate the effectiveness of supplier
quality auditing. Supplier quality auditing is a widely used quality assurance tool, which
evaluates the quality capability of production processes down the supply chain and ultimately provides
essential input for decision making in the outsourcing process.
2 Research Question and Research Motivation
Due to the highly competitive environment on the international automotive market, the growing
diversity of customer preferences, and the fact that on average 60 to 80 percent of the global automotive
production is outsourced (Veloso & Kumar, 2002), competing in the automotive business
is particularly demanding. Good management of the entire production chain and more concretely
the use of effective quality management tools is therefore of key importance for staying in the
sector. This work examines the effectiveness of second-party quality auditing in the automotive
2 The supply chains of the aircraft and ship industries are of comparable complexity as that of the automotive sector.
Their production scale is substantially smaller, however, which is the reason why QM tools in the two industries have
not been developed and implemented to the same extent as in most other mass production sectors, what represents an
additional challenge for the quality management process in those two sectors.
3

industry and especially its strategic importance as an integral part of the supplier management and
supply chain management processes. The purpose of this work is formulated within the following
research question: Can quality performance of automotive suppliers in the mass production be anticipated
based on quality evaluation of the capability of their production processes? Additionally,
the analysis presented here tries to give an answer to a second, more subjective question: What
is the quality of the quality auditing process at Volkswagen Group and what is its improvement
potential?
Naturally the quality capability of a production process is reflected in the extent to which the
delivered components comply with the relevant quality specifications. It is therefore possible to
infer the quality capability of a particular production process based on the quality of supplier’s
products. If supplier’s products are of high quality, which is stable over time, one can conclude
with relatively high level of certainty that its process has high quality capability (Figure 1). In many
cases the quality of a particular part is not only expressed in its physical characteristics but also in
the quality of services which the respective supplier offers. Thus for example, measures such as
the on-time delivery, having sufficient capacity to keep up with the customer demand, as well as
good responsiveness in cases of potential problems are quite important for the customer-supplier
relationship.
Supplier
Production
Process
Product
Customer
(OEM)
Process Quality
Capability
reactive
Product
Quality
Second-Party
Quality Audit
proactive
Figure 1: Inferring the quality capability of a supplier and ultimately its suitability as a business
partner based on the quality of its final product is relatively straight forward, but rather reactive
(only possible after the contracts are in place). Therefore companies try to proactively infer the
quality of the final products based on evaluations (second-party quality audits) of the quality capability
of the relevant production processes, which is a significantly more demanding task. The
question is how successful is quality auditing in achieving this.
4

Certainly companies want to work only with suppliers which are quality capable. Thus for example
the Volkswagen quality criteria for contract awards described in Formel Q-capability (2008)
include the following requirement: ”In order to ensure the quality of the components/modules/systems
the companies of the VOLKSWAGEN GROUP [require] the supplier to [produce] the components/modules/systems
in an A-rated [(quality capable)] production site” (p. 9). Even though
quality of final products and services is a straightforward way to measure quality capability of production
processes, making conclusions about the quality capability of a particular supplier based
on its quality performance, however, is rather reactive. Quality performance can be measured only
after the start of mass production. Something, what companies definitely want to avoid, is to receive
parts with low quality during the series production of a particular product. Such situations
would unbalance the overall business process and are therefore quite undesirable. For that reason,
it is important for companies to be able to assess the quality capability of their suppliers before
signing a contract. To do that, however, they need an alternative method, which allows them to
determine the quality capability of a particular supplier in a proactive manner.
To this end one particularly important aspect of business relationships is standardization. Given
the complexity and size of the automotive supplier network, coping with the challenges posed by
modern-day production requires establishing and maintaining well-structured systems for quality
management (QMS) and quality engineering. The latter were originally developed by companies
in an individual and informal manner, e.g. Ford’s Q101, GM’s Supplier Performance and Evaluation
Report (SPEAR) (Hoyle, 2005, p. 100). It was not before the appearance of the first quality
standards that the development and maintenance of quality management systems was formalized
and the individual implementation steps were well-documented.
Nowadays a number of international quality standards provide guidelines for the effective implementation
of a quality management system (e.g. ISO 9000, 2005; ISO/TS 16949, 2009), which
assures the quality of an organization’s products. Certification according to quality standards such
as the ISO 9001 or ISO/TS 16949 (which builds upon ISO 9001 and is of particular importance
for the automotive sector) serves to build trust in the business relationships. Companies use such
certification to show that they are quality aware, to provide proof of the effective implementation
of a quality management system, and to assure their clients that they work on the continuous
improvement of their products. This makes certified companies preferred business partners. Moreover,
even though the use of standards is of recommending character, nowadays to enter business
without a quality certification is almost impossible in a vast number of industries.
5

Quality standards build upon the Total Quality Management (TQM) principle, which postulates
that business should be sustained through continuous improvement of its products and processes.
The ISO 9000 (2005) standard defines eight quality management principles, which are fundamental
for the successful operation of an organization:
a) Customer focus
Organizations depend on their customers and therefore should
understand current and future customer needs, should meet customer
requirements and strive to exceed customer expectations.
b) Leadership
Leaders establish unity of purpose and direction of the
organization.
They should create and maintain the internal
environment in which people can become fully involved in achieving
the organization’s objectives.
c) Involvement of people
People at all levels are the essence of an organization and
their full involvement enables their abilities to be used for
the organization’s benefit.
d) Process approach
A desired result is achieved more efficiently when activities and
related resources are managed as a process.
e) System approach to management
Identifying, understanding and managing interrelated processes
as a system contributes to the organization’s effectiveness and
efficiency in achieving its objectives.
f) Continual improvement
Continual improvement of the organization’s overall performance
6

should be a permanent objective of the organization.
g) Factual approach to decision making
Effective decisions are based on the analysis of data and
information.
h) Mutually beneficial supplier relationships
An organization and its suppliers are interdependent and a
mutually beneficial relationship enhances the ability of both
to create value. (pp. 5–6)
Furthermore, Hendricks and Singhal (2000) provide statistical evidence that companies, which
have embraced the TQM philosophy and have successfully incorporated the TQM principles in
their organization show convincing long term performance. Their financial stability and good market
standing makes such companies reliable and preferred business partners. For that reason in the
automotive sector certification to the international quality standards such as the ISO 9000 family
and especially ISO/TS 16949 has become a fundamental requirement for entering business. Certification
attests the conformity of the implemented quality management system to the respective
standards.
Gropp (2009) argues, however, that possessing a valid certificate does not necessarily imply
high product quality. He points out that for a number of companies the major incentives for certification
are the desire to maintain a good corporate image as well as the practical compulsion
for certification itself imposed by the outsourcing companies. Gropp (2009) writes further that in
many cases certified organizations do not follow the TQM philosophy and show high deficits in
the implementation of the standard requirements especially on the process level, and sees this as
the main reason why automotive companies resort to additional quality control of their certified
business partners especially on the process and product levels.
A standard business practice to proactively assess the quality capability of your suppliers’ production
processes is through quality auditing (Figure 1). Such type of evaluation is based on the
assumption that process parameters are especially important and have a direct influence on the
quality of the process outputs (VDA 6.3, 2010; ISO 19011, 2011; The EFQM Excellence Model,
2012). The quality audit focuses therefore on critical processes and compares their conformity to
7

generally accepted benchmarks. Quality auditing is particularly useful for supplier evaluation, because
it additionally offers the opportunity to estimate the levels of risk associated with the quality
capability of the individual processing steps. The degree to which the according processes conform
with the state-of-the-art practices is then used to make a statement about their overall quality capability
and its sustainability in the long term. Having the focus of the quality audit on the correct
process parameters is therefore essential for its effectiveness.
There is limited scientific literature (Stroescu-Dabu, 2008; Hadzhiev, 2009), which addresses
the question whether second-party quality auditing in the automotive industry is indeed effectively
implemented in practice and whether it succeeds in providing a reliable foresight of the quality of
purchased components. On the other hand, answering such a question is very important, because
supplier process evaluations have a direct impact on a company’s sourcing decisions and ultimately
affect its market performance. The current paper was motivated by these circumstances and aims
at providing more insights on the topic. The following section presents in detail the research
approach. Section 4 provides a general description of the automotive business sector. Special
attention is paid to the trends of development of the sector and its major driving factors. The
specifics of automotive production are then put in quality perspective in order to outline the quality
risks with special focus on problems originating in the supply chain and their implications for
OEMs’ general quality auditing strategies. The rest of the paper focuses on the effective practical
implementation of quality auditing and analyses empirical data resulting from the supplier auditing
process at one of the largest automotive producers in the world – Volkswagen Group.
3 Research Approach
To address the research questions described above this study takes an empirical approach and
analyzes real-world operational business data. The analysis presented here was carried out in cooperation
with Volkswagen Group and in particular its corporate department for Quality Assurance
of Purchased Parts (from German Konzern Qualitätssicherung Kaufteile), which among others is
responsible for the international coordination of Volkswagen’s global quality auditing units. Given
the scale of the organization, the provided empirical data is particularly useful to assess challenges
faced by automotive OEMs on the major automotive markets worldwide. Volkswagen Group is
the largest European automotive manufacturer and currently among the three largest in the world,
alongside the Japanese Toyota and the American General Motors. Volkswagen Group operates
8

more than 60 production facilities on five continents and its global operations are supported by
more than 9000 direct business partners (1st-tier suppliers) 3 . Because of the large number of
suppliers, second-party auditing has received a central role in Volkswagen’s quality philosophy.
Furthermore, the scale of its global operations and the size of its supplier base provide a broad set
of empirical data, and therefore the opportunity for an in-depth analysis of the topic at hand. For
these reasons, Volkswagen Group is an ideal research object.
The essence of quality auditing at Volkswagen Group lies within regular evaluations of the
quality capability of suppliers’ production processes. As will be explained in detail later in this
paper a particular supplier quality audit lasts just several days and provides simply an assessment
of the momentary quality capability state of the evaluated production processes. On the other
hand, the long-term development of the quality capability of a particular production process can
be obtained only by a succession of quality audits at different points of time. Interestingly this
approach draws a lot of similarities with an important technique in the signal processing called
sampling. Sampling is used to convert analog to digital signals and is fundamental for areas such as
digital communications (Karrenberg, 2002; D’Antona & Ferrero, 2006). The idea behind sampling
is rather simple: ”the input signals are converted into a sequence of sampled values by means of a
sampling operation performed at given time instants, with a constant sampling period”, (D’Antona
& Ferrero, 2006, p. 33) . In practical terms this means that an analog (continuous in time) signal
(Figure 2a), such as speech for example, passes through an analog to digital (A/D) converter, which
converts it into a digitalized (discrete in time) copy of the original signal (Figure 2b). Proper
sampling reduces the amount of information contained within the original signal, but preserves the
main characteristics of the signal or system under study.
Most of the time analog signals contain a significant amount of redundant information, which
could be omitted and the core message be still transmitted. This less relevant information introduces
a computing overhead during the data processing cycle. Through sampling the amount of
information in the original signal is reduced and therefore its processing is simplified. A major
consideration in sampling, however, is to use enough samples in order to be able to completely
retrieve the essential information in the original signal from the sampled sequence (Karrenberg,
2002; D’Antona & Ferrero, 2006). Depending on the rate of variation of the individual continuoustime
signals, the frequency of the sampling measurements should be adjusted accordingly. If the
3 Source: personal communication with Volkswagen AG employees.
9

sampling frequency is not high enough, this will lead to the improper representation of the original
signal and therefore loss of information (Figure 3b (II.)). In the theory of signal processing this
concept is formalized by the sampling theorem (Karrenberg, 2002; D’Antona & Ferrero, 2006),
which states that an analog signal can only be fully reconstructed from its time-discrete representation,
if the sampling frequency f S is at least two times larger than the highest frequency f MAX
in the original signal. Here the mathematics behind this statement are omitted. For a formal mathematical
derivation of the theorem and the necessary preconditions for its validity the reader is
referred to e.g. D’Antona and Ferrero (2006, pp.35–40). What is important to understand here,
however, is that in order to get an idea about the true characteristics of a particular continuous
signal, the latter needs to be sampled with frequency sufficiently larger than the rate of its inherent
variations. Thus, signals with particularly high amount of variations need to be measured
more frequently than signals with lower amount of variations in order to preserve their essential
characteristics.
At this point one could ask what exactly the term signal means. Sinha (2010) defines a signal as
”any time-varying physical quantity” (p. 9). In this line of thoughts, when talking about sampling
signals, instead of referring to quantities commonly used in the field of Electrical Engineering
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(a) Continuous time signal
(b) Sampled signal
Figure 2: Analog to digital (AD) conversion of an analog (continuous in time) sinc signal – (a) –
to a digital (discrete in time) signal – (b) – using sampling with a constant frequency.
10

I.
f MAX = 1Hz
I.
f MAX = 6Hz
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f S = 12Hz
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f S = 48Hz
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(a) Signal y(t) = sin (2πf 1 t) sampled at 12Hz and
48Hz respectively.
(b) Signal y(t) = sin (2πf 1 t) + 0.25 sin (2πf 2 t)
sampled at 12Hz and 48Hz respectively.
Figure 3: Sampling of two analog signals. In each of the two figures the uppermost plot presents
the continuous time signal (I.). Each signal was sampled using f S = 12Hz (II.) and f S = 48Hz
(III.). The signal on the left has one frequency component f 1 = f MAX = 1Hz, while the signal on
the right has two frequency components of f 1 = 1Hz and f 2 = f MAX = 6Hz respectively.
such as voltage or current, one may as well refer to any other quantity varying over time such
as the amount of money in a particular bank account measured in EUR or the level to which a
particular supplier production process fulfills the quality requirements of the outsourcing company
(process quality capability) measured in percent. In the discussion presented in this paper, we are
of course interested in the latter. Naturally such signals would exhibit variations on significantly
different time scale than electrical signals. Nevertheless, it is still possible to measure them on a
periodic basis to gain an idea about their overall behavior.
Usually a company would want to assess the quality capability of its suppliers and its variation
over time. The ability to identify any negative trends in the development of the quality capability
of their suppliers allows companies to detect and eliminate potential problems in a timely manner.
In this regard the quality audits could be deemed as the individual sampling measurements and
the sequence of audits at a particular supplier as the overall sampling signal. The average length
of the time intervals between individual quality audits would be therefore the ”sampling period”
T S = 1/f S .
11

Two fundamental factors influence the accuracy of such assessments. As already mentioned
above, one is the frequency with which the individual audits are performed. Too frequent auditing
of suppliers with rather stable production processes is not a problem from the quality point
of view, but on the other hand would make the auditing process rather inefficient in terms of resources.
However, if suppliers experience high variation of their internal processes, it can be rather
misleading to expect that their overall quality capability will remain constant over long time periods.
Moreover, quite probably it will be subject to persistent changes. Therefore if audits at such
suppliers are not performed frequently enough, conclusions about supplier’s quality capability may
rather quickly become out-of-date and not correspond to the current state of the respective production
processes. For these reasons, to assure the effectiveness of the quality auditing as a quality
management tool, companies need to consider the specifics of the individual links down the supply
chain and accordingly adjust the auditing (”sampling”) frequency.
The second important factor, which is usually omitted in the classic discussions on sampling,
is the assumption that throughout the sampling process the individual measurements are accurate
enough to detect the correct level of the input analog signal. In the field of Electrical Engineering
such a consideration is probably rather insignificant, due to the fact that measurement of quantities
such as voltage or current is very well defined and can be achieved with very high precision. When
we talk about measuring quality capability, however, it is not always obvious that this condition
will be fulfilled. There are a number of factors which influence the correct assessment of supplier’s
quality capability during a single audit. These need to be comprehensively examined in order to
make sure that the individual quality evaluations will adequately evaluate the level to which a
particular production process fulfills customer’s quality requirements.
Consequently, in order to guarantee the effectiveness of quality auditing as a quality management
tool, companies need to address the problem from two different perspectives. On the one
hand, it is important individual quality audits to be able to adequately assess the capability of a
particular production process to provide the necessary quality, while on the other hand the general
strategy for applying and the frequency of use of this quality management tool needs to correspond
to the developments and the specifics of the according industry sector. To answer the research questions
this paper pursues a similar approach and deals with the topics from two different viewpoints.
12

3.1 General Strategy for Employing the Quality Audit
It is quite difficult to find scientific studies, which deal with the effectiveness of the quality audit
from a strategic viewpoint, especially in the automotive industry. The lack of literature is probably
partially due to the fact that if there were such studies at all, these were performed internally and
their results are kept unpublished because of their perceived value for the respective OEMs and
the potential competitive advantage they offer. On the other hand, most of the available literature
which deals with the topic of quality auditing concentrates primarily on the specifics of the implementation
and individual steps of a single audit (Arter, 1989; Parsowith, 1995; Green, 1997;
Wealleans, 2005) and very few works discuss also the strategic importance of effective quality auditing
in the overall market strategy of the corporation (Wealleans, 2005). Even when there is such
a discussion it is somewhat superficial. Thus for instance, even though works in the field of supply
chain management recognize the strategic importance of supplier evaluation (van Weele, 2010),
their discussion usually concentrates on the overall supply management process and the topic of
quality auditing is not covered in sufficient depth.
A strategy for the effective deployment of quality auditing as a major quality management tool
in the automotive industry has to address several important aspects of the automotive market and
to adequately respond to the challenges, which OEMs face in the different regions. First, it is very
important to start with the basics and consider the implications which assembly line manufacturing
alone has on the overall business process. Here it is particularly important to account for major
sources of uncertainty such as changing customer demand, inherent variations of the production
process as well as variations originating in the supply chain such as supply interruptions due to
delivery delays or inconsistent quality of the delivered components. A second major point in this
discussion are the dynamics of the global market and in particular the specifics of the developed
western markets, i.e. the Triad – USA, Japan and Western Europe, versus the emerging markets,
including among others the BRIC countries (Brazil, Russia, India, and China), South East Asia,
and Eastern Europe. An important topic in this regard is the increasing need for diverse product
portfolios, which need to be able to answer the growing diversity of customer preferences. A third
important factor, which needs to be considered, are rising development costs and the trend towards
outsourcing major portion of the production process. It is especially pressing to account for the
growing complexity not only of the supplier network but also of the sourced components, as well
as to consider topics such as sustainability of the production process quality capability.
All these factors have a strong influence on the market performance of automotive OEMs since
13

they put the quality of the final products to the challenge and are therefore relevant aspects to
be taken into account in a study about the effectiveness of quality audits. The individual points
are discussed in detail in Sections 4 and 4.4, as the discussion aims to provide the reader with
background for commonly accepted business strategies in the automotive sector. Of particular
interest are the therewith associated quality risks and the consequences these have on the overall
quality auditing strategy. As a particular example the quality auditing policy of Volkswagen Group
is presented in detail with respect to the presented quality challenges in the automotive sector.
3.2 Effectiveness of Implementation of a Single Quality Audit
The second part of this work deals with the ability of a single quality audit to accurately infer the
actual quality capability of the respective supplier at the time of the audit. If one assumes that
the quality audit is effectively implemented and can adequately infer the quality capability of a
supplier, one should expect that audit evaluation results would correspond to the actual quality
performance of the respective suppliers at the time of the audit. In the end an audit evaluation
score and the quality performance of a particular supplier are just two different ways to evaluate
the quality capability of the same supplier production process.
This assumption is confirmed in an empirical study by Wittmann and Bergholz (2006), who
analyzed the benefits of quality auditing at Infineon Technologies AG (a producer of electronics).
They found that there is ”at least qualitative” correlation between the quality performance
of individual Infineon suppliers and their audit scores (Wittmann & Bergholz, 2006). Suppliers
with higher quality evaluation scores would cause lower number of quality related problems in the
overall production process. Furthermore, the results of the analysis by Wittmann and Bergholz
(2006) also show that quality auditing has a positive effect on the long term quality capability
of suppliers’ production processes. The authors observed the tendency that suppliers’ evaluation
scores continuously improve during the subsequent audits (second and third audit). On the other
hand, the results of their study show that the quality capability of suppliers of wafer substrates was
rated significantly higher than that of suppliers of process materials such as gases and chemicals
(Wittmann & Bergholz, 2006). The authors do not provide information about the relation between
the evaluation scores of the latter and their actual quality performance. Nevertheless, such findings
suggest important differences within the individual parts of the supplier network, which have to
be studied carefully as they would eventually identify potential adjustments of the overall quality
auditing procedure specific to the respective type of industry.
14

The work of Wittmann and Bergholz (2006) provides an important method for assessment of
the effectiveness of quality auditing. An evaluation of the consistency between quality performance
of Volkswagen suppliers and their respective quality evaluation scores is therefore a good starting
point to assess the effectiveness of the implementation of quality auditing at Volkswagen Group in
particular and in the automotive industry in general. Consequently, a substantial part of this paper
is devoted to a set of studies, which investigate this relationship.
Performance information of Volkswagen Group suppliers was acquired from production plant
records and is compared to the perceived quality capability of the respective supplier production
processes provided by their quality auditing scores measured in percent. At this point the initial
hypothesis of this part of the analysis can be summarized in that suppliers which obtained higher
scores during the audit evaluation are expected to have fewer problems throughout the series delivery
and therefore better quality performance, and vice versa. Any discrepancies between the
evaluation scores of suppliers and their actual quality performance imply potential deficiencies of
the quality auditing routine and therefore identify particular areas which need closer examination.
On the other hand, best practice audit processes can be derived from cases which show good parity
between the two investigated quantities.
Two recent studies (Stroescu-Dabu, 2008; Hadzhiev, 2009) evaluate the effectiveness of
Volkswagen Group’s quality audit and employ the same analytical approach. These works already
outline important findings, which serve as a basis for the analysis presented here. The first
important point is the fact that the automotive supply network is characterized by a high level of
diversity. This is not surprising, provided its enormous scale and the great variety of individual
components, which are assembled in a car. Stroescu-Dabu (2008) uses a general supplier categorization
based on the type of industry in which each company operates. Suppliers are divided into
metal, chemical, and electrical suppliers. The analysis of Stroescu-Dabu (2008) shows that the
distribution of evaluation scores of electrical suppliers is statistically different than the evaluation
distributions of metal and chemical suppliers. The latter, on the other hand, have similar evaluation
score distributions. Furthermore, while in the electric industry auditing scores show relatively
good correspondence to the quality performance of the respective suppliers, for suppliers which
operate in the metal and chemical industries results do not provide any evidence that suppliers with
better audit scores perform better in terms of quality of the delivered components. This is a highly
undesired state of affairs and needs to be clarified.
In an attempt to narrow down the causes of these results Hadzhiev (2009) extends the findings
15

of Stroescu-Dabu (2008) and uses a more detailed data categorization, which takes into account
not only industry of operation, but also the type of delivered components as well as the processes
involved in their production. The general trends of the overall data, observed by Stroescu-Dabu
(2008), are confirmed also by the analysis of Hadzhiev (2009). Nevertheless, the results of the
second work reveal additional subgroup differences within the individual industry sectors. In
particular, certain supplier subgroups in both electric and metal industries, such as suppliers of
lighting components or metal profiles, show significantly better consistency between their quality
evaluations and actual quality performance than the rest of the suppliers in the respective industry.
Meanwhile, industry sub-groups, such as suppliers of electro-mechanic controls or cast metal
components, show a negative relation between their quality capability evaluation scores and their
quality performance. For such suppliers a higher quality evaluation score corresponds to a larger
number of quality related problems in the series production.
The studies by Stroescu-Dabu (2008) and Hadzhiev (2009) suggest potential shortcomings of
the quality auditing practice at Volkswagen Group. Even though some of their results are on the
verge of statistical significance, the findings of the two studies emphasize the need for further research
on the topic in order to understand the underlying reasons for these observations. There are a
number of factors, which are not accounted for in the two papers presented above, but which could
potentially bias the data and therefore strongly influence the analyses. One particularly important
point is consistency of the evaluated quality performance data. The supplier quality performance
indicator used in both works is ppm (defective parts per million). At the first stage of this project
it was found, however, that there are significant differences in the way ppm are collected for simple
components as opposed to more sophisticated assembly units (for a detailed discussion of this
point see the following sections). These characteristics of the data make comparisons between
records, based on ppm-values only, difficult and sometimes even meaningless. It is even possible
that, partially due to heterogeneous quality performance data, sectors such as metal and chemical
industry do not show the desired consistency between the quality capability scores and the actual
quality performance of the respective suppliers. In certain cases it is more meaningful not to rely
on ppm records alone but to use additional indicators to describe supplier’s quality performance.
Later on the author discusses also a number of additional biasing factors such as the validity
of a particular audit evaluation over time, the proper definition of product groups used for categorization
of audit evaluation scores, as well as the ”human” factor in the data including auditor and
quality inspector ”calibration”. All these influences could potentially affect the consistency of the
analyzed data and must be considered in order to assure the conclusiveness of the analysis. How-
16

ever, to improve the structure of the presented argumentation, it is meaningful to first introduce the
specifics of the automotive business sector and their implications on quality auditing.
4 Specifics of the Automotive Industry
Strategic decision making in any organization is a complex process which has multiple dimensions
besides the quality aspects (especially important are the financial incentives). It is therefore the
responsibility of quality managers to understand corporate strategies, to identify the quality challenges
resulting from them and accordingly to counteract these challenges in order to guarantee
the effectiveness of their quality management tools and eventually the high quality of final products.
For these reasons the current section introduces the major aspects of the automotive sector, its
driving factors and draws conclusions upon particularly important aspects relevant for the quality
auditing process.
4.1 Internationalization of the Automotive Operations
Until the 1980s major automotive players had their operations concentrated mainly on the local
markets and international presence, even though existing, was quite limited (Veloso & Kumar,
2002). Moreover, most of the automotive sales were generated in the Triad – USA, Western Europe,
and Japan. American automotive producers dominated the American market, Japanese car
manufacturers dominated the Japanese market and the European players were present mostly in
Europe. However, automotive producers had been facing a mature market in the Triad for decades
with limited annual market growth and were looking for ways to expand their customer base. This
is the reason why in the recent a few decades the international presence of automotive producers
increased, which intensified the competition for market share. Local brands were highly affected
by this global expansion. American producers for example lost about 20 percent of their local
market share to Japanese car-makers (Veloso & Kumar, 2002). Similar trends were observed in
Europe as well, though on smaller scale due to stricter trade regulations (Veloso & Kumar, 2002)
and higher quality standard of the European manufacturers.
The increase of international operations, however, was not limited only to the Triad. In the late
1980s and the 1990s a number of new markets outside the Triad opened and offered opportunities
to the automotive players to increase their market share. Governments in South America and Asia
17

Million Units
80
60
40
Worldwide Automotive Production
15,8 14,1 16,4 18,3 17,9 19,6 21,5 25,1 27,3 30,2 34,2
35,3
35,8
46,5
20
0
100%
39,2 39,4 39,8 40,0 38,4 39,4 39,2 39,4 39,2 39,1 39,1 35,4
25,9 31,1
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
75%
29% 26% 29% 31% 32% 33% 35% 39% 41% 44% 47% 50% 58% 60%
50%
25%
0%
71% 74% 71% 69% 68% 67% 65% 61% 59% 56% 53%
Rest of the world
50% 42% 40%
USA + Western Europe + Japan
Figure 4: Shift of the automotive production capacity from the Triad (USA, Western Europe, and
Japan) to other more cost competitive production locations. (Data Source: OICA, 1997 – 2010)
alleviated strict governmental trade policies in their attempts to attract Foreign Direct Investments
(FDI), (Veloso & Kumar, 2002). At the same time, the fall of communist regimes in the early
1990s opened Eastern European markets and granted Western companies access to new customers
also in this region.
In recent years developing countries have been the motor of growth of global sales with regions
such as China, South East Asia, Eastern Europe, South America, and India seeing the major
growth. In the 1990s almost 80% of the automotive sales were still generated in North America,
Europe and Japan (Veloso & Kumar, 2002). In the last decade, however, markets in Asia and
South America soared and their growth was the major driving factor for an overall increase of
global sales with about 55% to surpass 70 million vehicles (Veloso & Kumar, 2002). About half
of the global sales now take place outside the Triad. The more favorable conditions on the newly
opened markets prompted major automotive players to invest in these new regions and to relocate
significant part of their production close to their new customers. In 1997 a mere 29% of all vehicles
worldwide were produced outside the Triad (see Figure 4). In less than 15 years this share
more than doubled, and 60% of the global automotive production output in 2010 came from the
18

developing markets (Figure 4).
Due to the extreme geographic dispersion
of final customers nowadays it is very important
for the automotive producers to run global
operations. Usually an automotive OEM would
run a number of production facilities strategically
located within or close to its most important
markets, with the majority of its key suppliers
situated in close proximity. This not only
reduces the uncertainty of deliveries to OEMs’
final customers, but also significantly reduces
transportation costs (transportation of voluminous
products such as automobiles on long distances
is quite expensive). Therefore, carmakers’
decisions on where to position their production
facilities are governed by the development
of global markets.
Million Units
20
15
10
5
0
Growth of Automotive Production
in Developing Countries
USA
China
Eastern and Central Europe
South America
South Korea
India
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Figure 5: Growth of the automotive production
in the developing countries
(Data Source: OICA, 1997 – 2010).
All these recent changes made today’s automotive
business environment much more complex
as compared to the one just a few decades ago. To defend their market positions major players
need to adjust their strategies to the new market environment. On the one hand, market leaders
need to defend their strong positions on the developed western markets and fend off new business
rivals. Here competition does not come only from producers from other developed regions.
Automotive players with recently gained momentum, such as the Korean Hyundai-Kia, currently
the fourth largest automotive manufacturer worldwide, also strive to set strong foot on Western
markets. Meanwhile, stagnant customer demand and limited sales in these markets exacerbate
the price competition. On the other hand, automotive OEMs need to keep up with the headlong
growth of developing markets and capture as large a market share as possible on these markets.
Doing business in the newly opened regions in many cases can be particularly challenging due to
specific governmental regulations which favor local producers. Thus for example in China foreign
automotive manufacturers may only produce through local joint ventures with their participation
limited to no more than 50 percent (Economist Intelligence Unit, 2011). Moreover, to develop key
competences such as the production of smaller and cheaper cars, which correspond to the lower
19

udget levels in these regions, plays a key role in securing a share of the sales. To support this
business restructuring the supplier quality auditing process needs to be adjusted accordingly.
Most of the leading automotive producers rely on supplier networks with long history of cooperation.
The longer two parties work together, the better they know each other. Lasting business
relationships are a sign of trust and this is the reason why OEMs prefer to work with old partners on
new projects rather than enter in business relationships with new and unknown suppliers (Veloso &
Kumar, 2002). However, investment in new plants in growing markets is a cornerstone in OEMs’
business strategies for international operations. Particularly challenging for the expansion to developing
markets turns out to be finding the proper local business partners, which are going to support
their operations. Building lasting relationships with key suppliers in the new regions is therefore
vital. As already pointed out, in many cases governmental regulations set particular requirements
for the share of local content in the final product. In such cases it is necessary to source from the
local market and accordingly work with local suppliers, with which the respective OEMs have no
previous business partnerships. To mitigate the risks associated with starting up new facilities in
developing markets major players invite their key suppliers to the new regions and try to replicate
already existing supply structures (Veloso & Kumar, 2002). Automotive suppliers usually have to
choose between opening an own production facility in the new region or transferring their knowhow
to a local supplier (Veloso & Kumar, 2002). Such business moves are associated with great
deal of risk and are therefore critical for the success of the outsourcing OEM on the respective
market. This puts a particularly high responsibility on the quality audit.
However, there are a number of common factors which make quality management in developing
regions quite challenging. The high growth rates of these markets are a significant consideration.
Production facilities should not only be up and running, but also project plans need to
make room for capacity expansions and volume increases. Even when there are such plans, very
often local suppliers are overwhelmed with new projects and fail to keep the quality requirements
of their customers in focus. This leads very often to lower quality of the supplied components,
increased part rejection rates at OEM’s side and jeopardizes the integrity of the entire production
process. Avoiding such situations is crucial for OEMs in strengthening their positions on the new
markets and this needs to receive a particularly high attention in the quality auditing process.
Studies in the field of Human Resource Management show that there are several major problems
faced by multinational corporations in developing countries also on the level of personnel
management (Napier & Vu, 1998; Scullion, Collings, & Gunnigle, 2007). Scullion et al. (2007)
20

write that ”countries such as India and China face shortages of suitably qualified and skilled employees
for MNCs [multinational corporations] and local enterprises alike” (p. 311). For that
reason it is difficult to recruit locally the necessary managerial force, which has the qualification
to operate in these new environments (Scullion et al., 2007). Scullion et al. (2007) argue that
such deficiencies generate ”a growing demand for expatriate employees” (p. 314) in the emerging
markets. Furthermore, the authors write that it is difficult to persuade experts to transfer to these
regions, and even when this happens, very often due to cultural differences these experts do not
necessarily possess the required skills to manage in the new region (Scullion et al., 2007). At the
same time high growth in emerging economies and the specifics of the market situation offer a lot
of opportunities for career jumps. Very often companies in these markets face exceptionally high
levels of employee turnover, which in turn inhibits the corporate learning process and makes proper
knowledge management difficult to achieve. This leads to the conclusion that during quality audits
in such regions it is especially important to put special emphasis on the personnel qualification and
knowledge management of the suppliers.
All these factors have negative influence on the quality capability of the individual suppliers
which operate in these regions and especially on its sustainability. A number of re-qualification
quality audits conducted by Volkswagen Group in 2010 in regions such as India and China revealed
that the quality capability of their local business partners can drastically change for the
worse just within several months 4 . In many of the cases the principal causes for the rapid decline
in suppliers’ quality capability were related to production volume increases and high personnel
fluctuations. Given the fact that emerging markets are especially important for the growth of automotive
corporations and for sustaining their business, the quality assurance policies and especially
the employment of quality management tools such as supplier quality auditing need to pay special
attention and adequately respond to risks specific to growing markets.
4.2 Rising Development Costs
The next aspect of the automotive business which is relevant for quality auditing are rising development
costs and the production approaches which automotive producers employ to reduce them.
To cope with business challenges specific for the different regions it is essential for automotive
producers to establish a product portfolio, which adequately captures the abundance of customer
requirements. It is no longer sufficient to offer just a few models rather it is important to have a
4 Source: personal communication with Volkswagen AG employees.
21

wide product range. The number of market segments has significantly increased in recent years
and the separation between the different market niches has lost its sharpness. To better respond
to their customers’ requirements, OEMs constantly introduce new models, which are quite often
tailored exclusively for a specific market. Such a strategy allows automotive OEMs to have access
to a larger customer pool and therefore increase the amount of their sales. Furthermore, to
spark additional sales the generally accepted standard in automotive business is to renew the model
ranges in ever smaller time cycles which are nowadays in the order of just a few years.
Developing a larger number of models much more frequently than in the past, however, is quite
expensive. Meanwhile, even though currently rising – global automotive sales are nevertheless
limited and the growing diversity of car models translates in fewer sales per model. Thus for example
in the US the number of models offered on the market almost doubled from 550 in 1980
to 1050 in 1999, while in the same time period the average number of yearly sales per model decreased
from more than 20,000 units in 1980 to less than 15,000 sales per model in 1999 (Veloso
& Kumar, 2002) despite an increase of the overall market volume of more than 40%. The smaller
scale raises accordingly the development costs per sold unit. Given the high competitiveness characteristic
for the automotive sector, providing good price for value is crucial in the race for market
share. The rising development costs put the drive towards less expensive products to a significant
challenge. A common practice for automotive OEMs is therefore to base a number of models on
a single standardized platform such as Volkswagen’s ”A”, Fiat’s ”178” or General Motor’s ”Mid-
Range” platforms (Veloso & Kumar, 2002). Platforms are a cornerstone of the automotive business
operations for a number of reasons.
On the one hand, the use of platforms offers a number of financial benefits, which have positive
effect on a company’s market performance. Platforms allow car-makers to split development costs
over several models and therefore achieve higher economies of scale. Lowering the development
costs per sold unit respectively reduces the price of the individual products and has direct impact
on customers’ satisfaction through better price for value. Furthermore, platforms offer a lot of
flexibility and make it possible for the automotive OEMs to develop and sell a particular model
practically worldwide with small modifications defined by the specifics of the individual markets.
The use of platforms is also especially useful for the quick integration of technical advancements
in the entire product range, which in turn gives the respective OEM a competitive advantage over
its business rivals.
On the other hand, the employment of standardized platforms affects positively a number of
22

internal processes and increases organization’s operational efficiency and therefore its profitability.
Platforms reduce the number of components and therefore simplify OEMs’ inventory management
(van Weele, 2010). Meanwhile, the larger volumes per component accelerate the learning process
in production, which eventually results in improved quality. An important advantage of standardized
platforms is also risk-pooling. In cases of shortages of components for a particular model, the
OEM can quickly counteract by filling in the inventories with the same components stock-piled
for other models (Veloso & Kumar, 2002; van Weele, 2010). This is particularly useful to offset
misguided forecasts of customer demand such as unexpectedly high interest in particular products
and lack of interest in others (van Weele, 2010).
Due to the significant advantages which the platform use offers, very often the whole product
portfolio of a particular automotive group is based on just a few standardized platforms. Quite
common are also cases in which automotive business competitors would form alliances among
one another and develop joint platforms. Recently, more daring projects aim at the even further
integration of individual models in OEMs’ product ranges and therefore larger cost reductions.
One such example is Volkswagen’s MQB strategy (from German Modularer Querbaukasten or
modular toolkit). The target of Volkswagen’s new strategy is to develop a universal platform for
the construction of transverse, front-engine, front-wheel-drive vehicles, which is intended to be
highly standardized, but at the same time flexible enough to be incorporated in a very large number
of different models in several different market segments 5 . The use of the MQB is an important
pillar in Volkswagen’s business strategy. It is expected that the introduction of the MQB platform
would reduce the one-off expenditure and the unit costs by up to 20% each, and the engineered
hours per vehicle by up to 30% (Pötsch, 2011). MQB is also particularly useful for significant
weight reductions and respectively lower emissions. The first mass production models based on
the MQB concept are expected to roll off the Volkswagen production lines already in 2012.
Despite their immense advantages, standardized platforms represent still another significant
challenge for the quality management process and in particular for quality auditing. The fact
that they offer a way to suppress rising development costs, and at the same time allow OEMs to
maintain product portfolios with a large number of models, gives platforms a central position in
OEMs’ production strategies. However, potential defects in the individual standardized production
components immediately affect all models using the platform and respectively a very large amount
of vehicles. Such undesired situations not only have serious financial consequences, but also are
especially harmful for the image of the respective company. In the end of 2009 and the beginning
5 Source: personal communication with Volkswagen AG employees.
23

of 2010 there was a major recall campaign of Toyota models. One of the reported causes for the
Toyota problems was a malfunctioning accelerator pedal. Only in the USA a total of 12 different
Toyota models and accordingly almost 7 million vehicles were affected by just this single problem
(NHTSA Recall Report, 2011). The strikingly large number of vehicles, which Toyota recalled,
is mainly due to the fact that all affected models used the same standardized component. The
financial consequences of the recalls were massive. Toyota models in the US alone affected by the
acceleration problems accounted for 57% of sales in 2009 (New York Times, 2010). Even worse –
models of other manufacturers such as General Motors’ ”Pontiac Vibe [were] among the vehicles
recalled because [they were] built through a joint venture with Toyota”, (New York Times, 2010).
The Toyota recalls are a vivid example of how quickly a potential defect can propagate throughout
a major portion of the product portfolio. Therefore, the growing standardization of components in
the automotive production needs especially high attention in the quality management process and
the quality auditing strategy in particular.
4.3 The Automotive Supply Chain
Another important development of the automotive sector affecting the quality auditing process is
the growing complexity of the automotive supply chain. With the progressively larger number of
similar products and intensifying competition, the ability to properly position your products on
the heterogeneous automotive market is a key competence in the race for market share. Considering
the large diversity of customer requirements and the therewith associated differences between
individual markets, however, the proper management of the product portfolio requires a lot of resources.
That is the reason why nowadays automotive producers get continuously less involved
in manufacturing and concentrate significant portion of their capacities on enhancing the value
of intangible concepts such as their brand image, which play an increasingly central role in the
automotive business (Veloso & Kumar, 2002) OEMs can successfully restructure their core competences,
however, only given that they cooperate with a number of carefully selected business
partners, which overtake a major portion of the manufacturing process.
Outsourcing is not a new concept to automotive manufacturers, as it dates back as early as the
first years of automotive production. Nevertheless, outsourcing in the past would usually be limited
only to individual components, which companies assembled into their final products. In recent
years, however, due to the globalization of sales and the need to concentrate resources on other important
competences, the amount of outsourced automotive production has drastically increased.
24

Moreover, today OEMs tend to supply entire systems rather than individual components. As a
result suppliers of assembly units gain increasing importance and a lot of the suppliers, which produce
the individual assembly components and used to deliver these directly to the OEMs, operate
now in the 2nd or higher tiers (see Figure 6). Due to such business restructuring, in the past a few
decades the automotive supplier network has evolved into a very complex, highly interconnected,
multi-layer, global structure. Nowadays between 60 and 80 percent of automotive production is
outsourced (Veloso & Kumar, 2002). The number of just the 1st-tier suppliers the biggest automotive
producers have is in the order of several hundred up to a few thousand, while the supplier
base is growing ever bigger as the portion of outsourced production expands. In 2010 Volkswagen
Group ran several pilots on sub-supplier management, which revealed that the average Volkswagen
Group 1st-tier supplier has from 5 to 10 critical sub-suppliers, which are involved in a substantial
part of its production process 6 . The results of this evaluation show that 1st-tier suppliers represent
just the tip of the iceberg (reaching in the best case up to 10 to 20 percent) of the enormous
pool of delegated manufacturing responsibilities and underscore once again how complex the real
automotive supply chain is.
Outsourcing plays a particularly important role in price reduction as well. OEMs strive to reduce
the number of their business partners and at the same time increase the share of business
for individual suppliers. The larger order quantities allow the outsourcing companies to negotiate
better prices. However, there are particular differences between the approaches of individual
automotive players in how far they would go with reduction of the number of their direct suppliers.
Companies such as Renault and Volkswagen pursue a more conservative strategy and use the
”two-plus-one” formula (Veloso & Kumar, 2002). This means that for every component there are
two major suppliers with a third one, closely following behind. The third supplier delivers smaller
quantities but meanwhile receives enough business so that in case one of the two primary suppliers
cannot keep up with the deliveries the third one will step in and take over the orders. Ford on
the other hand pursues a more aggressive strategy and wants to have a single supplier for each
component or system (Veloso & Kumar, 2002). Currently Ford has a comprehensive catalogue of
the prices of individual components and therefore an idea about what a whole system should cost.
This gives Ford advantage in the negotiation process. In the future, however, Ford would know no
more the prices of individual components but only the prices of the whole systems, which would
give its suppliers a lot of power in the negotiations. One further advantage of the ”two-plus-one”
strategy is that working with and accordingly auditing two or three suppliers provides a much more
6 Source: personal communication with Volkswagen AG employees.
25

comprehensive overview of the the state-of-the-art production and technology of the product, than
when a company has an overview of the processes (and challenges) of a single supplier. In this
regard the monetary aspect and the aspect of security of supply are complemented through the win
of knowledge through auditing.
Even though in most of the cases production alone is cheaper at the outsourcing company
than at suppliers, companies still prefer to outsource a major portion of their production process
(van Weele, 2010). The reason for that is that development of the components is particularly cost
intensive. A major requirement for automotive suppliers is therefore that they possess the necessary
development capabilities and take over not only the production process but the development of the
respective components as well (Veloso & Kumar, 2002). Automotive OEMs expect that their
suppliers will be able to offer lower prices by splitting the development costs over several of their
customers. Furthermore, instead of sourcing individual components, the number of purchased
complete systems increases. Usually this would require from suppliers to invest a great deal in
development and be ready to first wait for several years before they can make any profit (Veloso &
Kumar, 2002). Only suppliers which have the necessary development know-how and the required
resources are therefore able to compete on the highest levels of the supply chain. This is the reason
why in recent years the automotive supply market is dominated by large international suppliers.
Suppliers with smaller financial resources cannot compete on global terms and usually restructure
their business to position themselves in the lower tiers of the supply chain as local component
suppliers of the large automotive system integrators (Veloso & Kumar, 2002, see also Figure 6).
This increase of the depth of the automotive supply chain (lower tiers appear) means that quality
auditing can achieve a representative overview of the quality risks down the supply chain only if
second party audits concentrate not only on the first-tier suppliers but also on the lower tiers.
One negative side of outsourcing is that it increases the quality risks and therefore influences
the quality management process. As already explained in detail, nowadays companies are able
to outsource significant parts of their production to external partners. Due to the high levels of
outsourcing OEMs and the quality of their products are increasingly dependent on their suppliers,
which gain an ever growing importance in the overall business process (van Weele, 2010). Any
supply shortages or poor quality of the delivered components could lead to halts of the production
and thus result into out-of-stock situations. The fact that today every automotive mass producer deploys
an assembly line, makes the individual production steps interdependent and a disturbance at
26

Tier 1
Supplier 1
Tier 2 (Tier n)
Supplier 1
Tier 1
Supplier 2
Supplier 2
Supplier 3
OEM
Supplier 3
OEM
…
Supplier N
…
Supplier N
Trend of Development
of the Automotive Supply Chain
Figure 6: Increasing complexity of the automotive supply chain.
Suppliers of simpler components and subsystems tend to position their businesses in the second and
lower tiers of the automotive supply chain. The first tier is dominated by large system integrators
(Veloso & Kumar, 2002).
any process step has a large impact on the entire production chain. Considering the extremely competitive
business environment such process variations can have significant financial repercussions
leading to lost sales opportunities and consequently decrease of market share, therefore degrading
the overall financial performance of the respective organization.
At this point it is necessary to keep in mind that suppliers are independent business units and
determine their business strategies autonomously. Very often the quality capability of suppliers
is influenced by factors, which the automotive OEMs cannot control. Different managerial decisions
within the individual suppliers such as changes of the workflow and process optimizations,
investment in new technologies, restructurings of suppliers’ supply chains and sometimes even the
relocation of entire production facilities influence the stability of suppliers’ production processes.
Nowadays such modifications happen rather frequently in many business sectors and target continuous
improvement and price reductions. Very often such changes degrade the quality of the
delivered products and endanger the continuity of OEMs’ workflow. It is therefore very important
for the outsourcing company to follow closely major changes down the supply chain and take
these factors into account in its general supply management policy and the auditing frequency in
particular.
27

The concentration of a large portion of business in a few suppliers transforms the structure of
their business operations. As indicated before, most of the 1st-tier automotive suppliers nowadays
deliver entire integrated systems, rather than single components. To develop and produce these
systems suppliers need a great deal of specific technical knowledge and therefore they, too, resort to
outsourcing. The number of business partners of the largest suppliers on the automotive market can
easily reach a few hundred. Increasingly important for such corporations is the ability to manage
the quality risks down their own supply chain. Volkswagen experience shows that a significant
portion of the problems originating in the supply chain are due to issues in the production processes
of sub-suppliers 7 . This is the reason why quality audits at such suppliers must put special emphasis
on their supplier management capabilities.
4.4 Implications for the General Automotive Quality Auditing Strategy
The previous several sections presented the specifics of the automotive business and already outlined
a number of quality related challenges, which need to be addressed by the general strategy
for quality auditing. The current section summarizes the insights gained by the discussion above
and organizes the aspects relevant for the quality auditing process, which result from the specifics
of the automotive sector. Figure 7 provides a structured summary of the said in the previous a
few sections. Based on this summary an idealized priority listing is derived, which provides a
guideline for defining the general quality auditing plan, i.e. addressing the ”sampling frequency”
aspect of quality auditing (see Section 3). At the end of the section the quality auditing process of
Volkswagen is compared to the results of the discussion.
Several important conclusions stem from the automotive discussion above. A key point is
that outsourcing has received a central role in the automotive business today and the viability
of car-makers’ operations is highly dependent on the ability of their suppliers to support OEMs’
core business processes. Quality is a determining factor for success in the extremely competitive
automotive business environment and therefore quality assurance in the entire production chain
has to be top priority for every single market player. Due to the specifics of the sector and the
increased complexity of the manufacturing process in recent years, however, assuring quality in
the automotive industry and especially in the supply chain has become particularly challenging.
Therefore suppliers’ quality capability should be followed regularly with the help of quality audits.
7 Source: personal communication with Volkswagen AG employees.
28

In this respect evaluation of the quality capability of suppliers’ production processes should be
carried out not only before awarding a particular contract but also regularly throughout the series
production. To maximize the effectiveness of quality auditing it is important that individual quality
Internationalization of Automotive Sales
Business Challenges Strategic Solutions Quality Challenges
• Mature Western market with
local presence only
• Limited opportunities for
expansion of the customer
base
• Internationalization of the
operations
• High growth in emerging
markets (opportunities for
organizational growth and
increase of market share)
• Production shifts to cost
competitive countries
• Closer to new customers
• Too high growth in emerging
markets (negative impact on
quality)
• Governmental regulations for
local content (work with new
and unknown suppliers,
increased quality risk)
• Lack of qualified personnel,
high personnel fluctuations
(inhibits corporate learning,
unstable production
processes)
Rising Development Costs
Business Challenges Strategic Solutions Quality Challenges
• Large variety of customers
requires large product portfolio
with many models
• Renew model ranges in
shorter cycles (shorter time for
return on investment)
• Fewer sales per model (larger
development cost per sold
unit)
• Strong competition (pricing
pressures)
Standardized platforms
• Split development costs over
several models (lower
development cost per unit)
• Market flexibility (same model
worldwide with market specific
modifications)
• Quick integration of technical
advancements
• Simplify inventory mgmt.
• Shorter learning cycles
• Risk pooling
• Potential defects immediately
affect all models based on the
platform
• Quality risk concentration in
very high volumes
• Quality problems have very
high financial consequences
and can damage company
image (Toyota)
The Automotive Supply Chain
Business Challenges Strategic Solutions Quality Challenges
• Very strong competition
• Product portfolio and brand
image management require
resources (determine market
success)
• Rising costs of operation
• Outsourcing gains growing
importance (60% to 80% and
growing)
• Source entire systems (free
resources)
• Fewer suppliers with larger
order quantities (price
reduction)
• Suppliers overtake also
development costs
• Growing complexity of the
supply chain (1 st -tiers
potentially only 10% to 20% of
all suppliers)
• OEM dependent on suppliers
(vulnerable to supply
shortages and poor quality)
• Suppliers autonomously
determine their business
strategies (affect OEMs
processes anyways)
• Suppliers’ ability to manage
their suppliers is critical for the
entire manufacturing process
Figure 7: Quality challenges resulting from the market strategies of automotive producers.
29

audits are properly managed and are incorporated in a well-structured quality auditing plan. It is
essential that the strategy for the employment of quality auditing adequately reflects the market
situation and responds to the individual quality challenges specific to it.
While the quality audit should address problems in the entire supply chain, parts of the business
process which are characterized with high levels of variation and increased quality risks need
particularly high attention. The latter should be audited more frequently than the rest and companies
need to concentrate more resources on early problem detection in these problematic areas (see
also the discussion on sampling in Section 3). One especially critical step is the start of business
relationships with new and unknown suppliers. These need to be audited more frequently in order
to make sure that the new business partners understand the quality requirements of the outsourcing
organization and reduce the quality risks associated with the implementation of the respective
projects. As the two business parties gain trust in each other and their business relationship stabilizes
the audit frequency can be reduced (see Section 3). This consideration is particularly relevant
for developing regions where OEMs have to work with a number of new business partners, while
at the same time rapid growth and high employee turnover have negative impact on quality sustainability
in these regions (Figure 7, see also Section 4.1). Due to the high concentration of quality
related issues, audits in emerging markets should be rather frequent.
Another particularly important point, which needs to be considered in the general quality auditing
plan, are the increasingly integrated product ranges (see Section 4.2). The use of standardized
platforms increases the volumes of certain components and therefore also the cost of potential
problems (Figure 7). Cases in the past have shown the detrimental effects, which poor quality of
standardized components has on the entire business of an organization (the Toyota recalls from
2009 and 2010 are mentioned above). For that reason suppliers which deliver standardized platform
components or entire integrated systems should be handled with particular care and be audited
quite frequently as well. The frequency of the audits should be determined by the complexity of
the delivered components, industry sector, maturity of the according technology, order volumes as
well as the specifics of the region in which the respective production facilities are located. Due to
the increase in deliveries of entire systems lately and the therewith associated increased complexity
of the sub-supplier network (see Section 4.3), it is also important to pay special attention in the
quality audit planning to sub-supplier management capabilities of the respective suppliers. This
concerns particularly suppliers with high number of sub-suppliers.
30

Finally, the quality auditing process has to encompass the entire supplier pool, meaning that
suppliers without any quality related problems and non-critical production processes should still be
subject to regular quality auditing, however, not with the same intensity as in the previous cases.
All the points mentioned above can be used to prioritize quality audits and serve as a basis for
the definition of a quality auditing plan, which accounts for the specifics of the automotive sector.
Figure 8 presents an idealized priority list for planning second-party quality audits.
High Priority
(Very Frequent Auditing)
• Suppliers with critical quality problems
• New and unknown suppliers
• Suppliers in fast developing regions
(focus on personnel qualification and knowledge management)
• Suppliers of components for standardized platforms
Medium Priority (Frequent Auditing)
• Suppliers with large supply networks such as suppliers of complex
automotive systems (focus on sub-supplier management)
• Suppliers with minor quality problems
• Suppliers with critical production processes
Low Priority
(Regular re-Auditing)
• Regular requalification of suppliers without quality problems
Figure 8: Idealized priority list for planning second-party quality audits in the automotive sector.
Figure 9 presents the average age of the audit evaluations performed by Volkswagen auditors in
different regions worldwide. The empirical data shows a lot of similarities with the recommendations
for the audit planning presented in Figure 8. Thus for example, in developed regions such as
North America and Germany the average age of the performed audits is considerably larger than
the age of audits in developing regions such as China, India, or Russia. In the light of the discussion
in Section 4.1 it is reasonable to expect that the company faces indeed a lot more quality challenges
in the latter three regions and therefore needs to evaluate its suppliers there more frequently.
Volkswagen’s auditing strategy accounts also for the growing complexity of the supplier pool
(see Section 4.3). In recent years the capability of Volkswagen’s first tier suppliers to manage
their own supply chains has received increasing importance and the number of the conducted subsupplier
audits has been steadily increasing 8 . One particular point, which definitely has to be
8 Source: personal communication with Volkswagen AG employees.
31

Average Time Period Since the Last Audit
North America
Germany
England
Spain
Eastern Europe
Mexico
Brazil
China (VGC)
Argentina
India
South Africa
Russia
Time
Figure 9: Average ”age” of the audit evaluations of Volkswagen Group suppliers sorted by region.
Note: VGC = Volkswagen Group of China
considered next in Volkswagen’s quality audit planning, is the start of production of vehicles based
on the new MQB platform, which is planned for 2012. Due to the increased weight of potential
problems originating from MQB components, the respective suppliers will have to be audited
rather frequently.
These results show that the general auditing strategy of Volkswagen adequately responds to the
quality challenges in the automotive sector (at least qualitatively). On the other hand, to determine
whether the absolute quality auditing frequency is suitable for the specifics of the respective regions,
it is required a more comprehensive analysis of the specifics of the according region, which
is not subject of this paper.
5 Fundamental Principles of the Quality Audit
After Section 4 introduced the specifics of the automotive industry and summarized the insights
resulting from this discussion with respect to the general strategy for employing quality auditing,
the current section introduces the technical aspects of the implementation of a single quality audit.
32

This information is particularly helpful for the clarity of the discussion presented in the remaining
part of this paper.
There are three types of quality audits – first-, second- and third-party. All three types assess
the capability of particular processes to provide high-quality products or services. There are certain
implementation differences, however, which distinguish the individual types of audits from
one another. A first-party, also called an internal audit, is conducted within an organization on
internal demand by internal auditors. The purpose of this type of audit is to evaluate the own
quality management system and to identify possible improvements in the work of the organization
(Parsowith, 1995; Wealleans, 2005). Second-party quality auditing is performed by an organization
to its first-tier (sometimes second- and even higher-tier) suppliers in order to ”prove the
’technical’ capability of the [supplier] to provide the product or service”, ”check that [the supplier]
has sufficient capacity and resources to cope with the customer demands”, and ”assess the rigor of
[supplier’s] operational processes” (Wealleans, 2005, p. 5). As already discussed in the previous
sections, second-party auditing in the higher tiers of the supply chain is of increasing importance
for automotive manufacturers due to the growing complexity of their supplier network. Sometimes
audits are performed by an external independent party, whose purpose is to preserve objectivity
– third-party auditing. This type of audit could be performed by a company hired by the main
contractor to review the work of its subcontractors or more commonly a company would hire an
external auditor to review its own quality management system and its conformance to the generally
accepted quality management standards (Parsowith, 1995; Green, 1997; Wealleans, 2005).
Extending the production footprint by outsourcing could be regarded as an expansion of manufacturer’s
factory. Therefore the employment of first- and second-party quality audits together
assure control over the entire production process from the development phase of a certain component
down the production chain to its placement in the final product. One could assume that
third-party quality audits could have a similar function as second-party auditing. The parties involved
in a second-party quality audit, however, are not so much interested in systems rather in the
technical and commercial specifics of their relationship (Wealleans, 2005). This makes third-party
audits more suitable for quality assurance in the sense of certifying and making sure that particular
production and business processes comply with general quality standards.
Even though all three types of quality auditing are an important part of the overall quality
assurance process, the focus of this work falls upon second-party quality audits. Second-party
quality auditing is a process, which can be divided into several individual implementation stages.
33

The audit stages include audit planning, initiation and preparation, on-site evaluation, reporting
and closure, (Arter, 1989; Parsowith, 1995; Wealleans, 2005).
5.1 Audit Planning, Initiation and Preparation
The first thing that could make the quality audit ineffective is its inadequate integration into the
quality management system in terms of organization and technical content. This makes general
audit planning a vital part of company’s quality assurance program and often it could be regarded as
a separate phase of the auditing process (Wealleans, 2005). Audit planning, in this sense, deals with
the overall quality assurance strategy. It covers scheduling quality audits within the entire planning
horizon of an organization and putting the audit into strategic and general business context. These
aspects were already discussed in the context of the automotive industry in Section 4. Naturally,
areas, which represent high risk or contain serious quality related problems, as well as areas, which
are prone to significant production process variation (e.g. suppliers in new regions with high
personnel fluctuation), will be subject to more frequent auditing (Wealleans, 2005).
In addition to scheduling, an important aspect of audit planning is scoping. Scoping identifies
the areas which will be covered by each individual audit and usually involves identifying the
facilities, processes, products, activities and quality assurance systems which will be reviewed,
(Parsowith, 1995). Defining the appropriate scope of an audit can have a strong impact on the
validity and relevance of the audit results. Making the scope too broad hides the risk of making the
audit too superficial forcing the auditors to rush through the topics under the pressure of limited
time. A narrow scope of the audit, on the other hand, allows for in-depth analysis of a particular
problem, but also hides the risk of inefficient resource allocation (Parsowith, 1995; Wealleans,
2005).
Once the general audit planning is finalized, a lead auditor will be assigned to each individual
audit and he has to take care of the rest of the preparation. His first task will usually be to select
the rest of the team members based on their competence in the audited area. Depending on the
width and depth of the audit the audit team size may vary. Most of the available sources agree
that using more than six auditors on the team will make the audit very difficult to manage (Arter,
1989; Parsowith, 1995; Wealleans, 2005). On the other hand, there are different opinions about
the minimal size of an audit team. While Parsowith (1995) recommends that the team should
comprise of at least two auditors, Wealleans (2005) does not exclude the cases in which a single
auditor performs the process quality evaluation and even argues that in some particular cases this
34

could be more efficient than having a larger team size. Given the usually limited resources in
the real business world and the large amount of work, such practice increases the efficiency of
the auditing units and saves costs. Volkswagen auditors for example perform supplier quality
evaluations individually and sometimes in teams of two. The audit teams rarely exceed two people.
By contrast, quality audits of Porsche are conducted by larger teams reaching in some cases up to
five or six people.
Furthermore, Parsowith (1995) suggests that in order to keep the objectivity of the audit and
avoid biasing of conclusions, consequent audits within a certain area to be performed by different
auditors. Wealleans (2005), by contrast, argues that assigning the same auditor for subsequent
audits saves time, since the auditor will already be acquainted with the audited area and will need
less time for preparation. Nevertheless, he acknowledges that if a particular auditor runs audits
within the same area or organization too frequently the auditing procedure would become stale.
Volkswagen pursues the strategy to build strategic audit competence, meaning that Volkswagen
quality auditors develops specific process know-how and each auditor performs audits mainly in
his area of competence (usually no more than two or three audits in a row at a particular supplier
are performed by the same auditor). This approach enables Volkswagen to have a large number of
experts possessing profound knowledge of the state-of-the-art of strategic production processes.
After the audit team has been assembled, its members will have to gather all required information
and get acquainted with the areas and processes, which have to be evaluated (Parsowith, 1995;
Green, 1997; Wealleans, 2005). The audit team has to collect information about the delivered products,
what contracts are currently in place, what the customer-supplier relationship at hand is, how
important the supplier is for the overall business of the customer, and what information is there
about past performance of the supplier (getting this information for new suppliers may not be fully
applicable), (Wealleans, 2005). Among the documents a Volkswagen auditor prepares for an audit
are technical drawings of the audited parts, specific testing requirements for the components (e.g.
flammability test (TL 1010) for interior components), applicable legal regulations and standards,
supplier’s quality performance record and current quality problems, etc. Wealleans (2005) recommends
further that even though gathering the information may start quite early in the preparation
process, the actual preparation to happen as close as possible to the audit itself. Going through the
relevant documents would help identify spots which need improvement, clarification, or in-depth
study. Preparation should result into obtaining detailed notes on the topics which need to be addressed,
which later on could be used to prepare audit checklists (Wealleans, 2005). Wealleans
(2005) does not recommend the use of standardized checklists, however, since auditors tend not to
35

prepare for an audit and rely on the predefined questions, rather than on their investigation skills.
Instead he recommends the use of the so-called process-based approach where individual steps of a
process are described on a sheet of paper together with the relevant inputs and outputs (Figure 10).
During the on-site evaluation information is gathered about all supporting elements of the process.
Enablers:
Materials
Methods
Manpower
Machines
Environment
• …
• …
• …
• …
• …
• …
• …
• …
• …
• …
Measurement and Monitoring
• …
• …
• …
Inputs
Process
Outputs
• …
• …
• …
• …
• …
• …
• …
• …
• …
• …
Figure 10: Process preparation sheet. Note: The figure was prepared according to Figure 9.2 on
page 182 in (Welleans, 2005).
Due to the limited time available for the actual audit, effective time management is a must.
Thus an important part of the preparation for the audit is designing a detailed plan of action for
the on-site evaluation (Arter, 1989; Parsowith, 1995; Wealleans, 2005). It should provide the time
frame for the individual evaluation steps. Scheduling the supplier assessment process in advance
will help team members to focus on their tasks and serve them as a reference to check on their
progress with respect to the remaining time till the end of the evaluation. This part of the audit
preparation is extremely important since good initial planning would ease the actual evaluation and
increase the efficiency of the audit and its effectiveness in identifying issues critical to quality.
Once the plan of action has been finalized a formalized copy of the audit action plan has to
be approved by customer’s management and an official contact with the supplier would follow.
36

The audit plan together with the names of all audit team members will be sent to the supplier and
the dates of the on-site evaluation will be fixed. Sending the initial contact should happen well in
advance in order to give the supplier enough time to prepare for the upcoming evaluation. Usually
a Volkswagen quality audit is scheduled from several weeks up to two or three months in advance.
It is supplier’s responsibility to organize the logistics of the on-site evaluation such as to allocate
sufficient rooms for the meetings, assign guides for the audit team members, provide transportation
between different facilities if necessary, etc.
5.2 On-site Evaluation
This is the most important part of the quality audit and covers the time span between the arrival
of the audit team at supplier’s site and their departure (Parsowith, 1995; Wealleans, 2005). This
part has a great direct impact not only on the validity of a particular process quality capability
evaluation, but also on the work of the entire customer organization. Conclusions based on the
information gathered during the on-site evaluation are decisive for the future of the manufacturersupplier
relationship and the ’health’ of the overall production process. Positive audit evaluations
are a necessary prerequisite for contract awarding in the Corporate Sourcing Committee (CSC) of
Volkswagen (see also Figure 12 in Section 6.1). The structure of a particular audit as well as the
methods used to collect data may vary depending on factors such as type of industry, scope of the
audit, available resources, etc. (Arter, 1989; Parsowith, 1995; Wealleans, 2005).
Every on-site evaluation starts with an opening meeting which includes the entire audit team,
supplier’s management, and the according staff responsible for the areas which fall within the
scope of the audit. The meeting is used to make the necessary introductions and to restate the
aims of the upcoming evaluation (Wealleans, 2005). The auditors should address topics such as
the scope of the audit as well as quality and product specifications to which the company is being
audited (Parsowith, 1995). Additionally, the logistics of the audit as well as a tentative plan of
action are negotiated taking into account supplier’s operational hours and resource availability.
Subsequently the audit team collects objective data, which is used to assess the quality capability
of supplier’s production processes (the guideline used by Volkswagen auditors for collecting
objective evidence during an audit – Formel Q-Fähigkeit (2009) – is introduced in detail in Section
6.1 below). Auditors pay particular attention to critical process steps and address known
production problems, which have been identified during the audit preparation. Among the reviewed
areas are process control tools and parameters such as SPC charts and cpk-values, results
37

from different control tests and part release protocols at various process stages, documentation
subject to legal regulations for storage (duty of documentation, TLD), emergency plans, etc. If
during the on-site evaluation particular quality-relevant weaknesses of the production processes
are identified, the supplier is responsible to define and implement the according corrective actions.
During the audit auditors also need to set aside sufficient time to discuss previous evaluations and
determine the progress of particular corrective actions defined in the past. At this stage it is very
important that the auditors are well acquainted with the state-of-the-art of the evaluated process.
This knowledge helps them to identify potential problems easier in the limited amount of time
and therefore increases the quality of the evaluation. As mentioned above Volkswagen pursues the
strategy to develop its auditors in particular areas of specialization, which gives the company the
advantage to possess profound knowledge about strategic production processes and technologies
in the automotive industry.
Several auditing approaches for data collection are employed during a particular quality audit.
The quality of the evaluation is influenced by the proper selection of an auditing approach, whose
suitability is determined by the specifics of the situation and the type of evaluated information. The
most common approaches used in the practice are tracing, corroboration, and sampling (Parsowith,
1995; Arter, 1989; Wealleans, 2005). Tracing, for instance, has several variances as the most
commonly used in second-party auditing are contract tracing and flow-of-work tracing (Wealleans,
2005). When tracing a contract the auditor would investigate the quality assurance system and
its impact on the contract of interest. On the other hand, monitoring the flow of work allows
the process interfaces to be investigated in detail. This is of particular interest due to the fact
that most quality related problems usually appear at these interfaces. This type of auditing is
beneficial primarily for the auditing organization as it provides a high degree of assurance that
the contract related job is taken proper care of (Parsowith, 1995). Volkswagen audits are usually
structured in a way that they follow the flow of work from the input of raw materials through
the process stages down to the final product (the Volkswagen auditing questionnaire is described
in detail in Section 6.1 below). Another important auditing approach is corroboration. In this
approach data sources are cross-referenced as this allows for making sure that collected data are
accurate. The facts must agree based on at least two different auditors, two different records, two
different interviews, or any combination of these (Parsowith, 1995). Finally, sampling is used for
the collection of physical data. This technique is particularly useful for controlling large sets of
data and calibration. This is the approach which is usually used by Volkswagen auditors to carry
out product audits as part of the quality audit (see Section 6.1).
38

Audit data collected during the on-site evaluation can be divided according to four main categories:
physical evidence, sensory observation, comparisons and trends, and interviews and questioning
(Parsowith, 1995). Generally speaking recorded data could be qualitative or quantitative
as both types have their advantages. The main advantage of qualitative data is that it allows for
the in-depth study of selected issues without the necessity to fit data into certain predetermined
categories (Patton, 1987). On the other hand, quantitative data is very useful when great amounts
of data have to be processed. This type of evidence offers ”statistical aggregation of the data” and
”gives a broad, generalizable set of findings” (Patton, 1987, p. 9).
5.3 Reporting and Closure
The information gathered during the on-site evaluation is used as a basis for shaping an opinion
about supplier’s capability to meet the particular quality and commercial requirements of the
outsourcing organization. Once the on-site evaluation has come to an end, the audit team has to
prepare an official report based on the factual observations gathered during the audit. All identified
problems have to be supported with substantial evidence. The report should be submitted to the
supplier shortly after the completion of the on-site evaluation. Based on the report the auditing
organization may assign follow-up audits in order to revisit areas which need improvement. The
supplier, on the other hand, should prepare a corrective action plan which describes the measures
that will be taken to remove the observed problems together with the respective deadlines and perform
the necessary quality improvements. The important aspects of the discussion presented in
this section are summarized in Figure 11.
6 Empirical Data
The preceding sections treated the conceptual and general aspects of quality auditing. Section 4
elaborated on the broad context of this discussion and derived the consequences for quality auditing
resulting from the specifics of the automotive industry, while Section 5 presented in detail the
technical structure of the second-party quality audit. Now that the general framework has been
introduced, the remaining part of the paper deals with the analysis of Volkswagen specific empirical
data and concentrates on the research questions defined in Section 2. The aim of the current section
39

Audit planning
• incorporate quality audit in the general quality assurance strategy
• prepare an audit schedule
• identify problematic areas for frequent auditing
• define audit scoping
Multiple Audits
Initiation and preparation
• determine lead auditor and the audit team
• gather initial information (technical documents, legal and quality
requirements, quality performance of the supplier)
• prepare audit plan
• schedule the quality audit with the supplier
On-site evaluation
• opening meeting
• collect objective data on-site (tracing, corroboration, sampling)
• identify problematic areas for improvement
Reporting and Closure
• evaluate collected evidence
• prepare and present the audit report
• agree upon corrective action plan
• assign follow up audits
Single Audit
Figure 11: Stages of the second-party quality auditing process.
is to introduce the types of information, which are going to be evaluated in Section 7, and to
describe the processes, which generate these data.
6.1 Quality Capability
The quality capability scores of Volkswagen suppliers result from on-site evaluations of their production
processes, i.e. second-party audits. Volkswagen quality audits are carried out according to
a product group classification, which accounts for the specifics of the individual production processes
and is maintained by the Volkswagen Quality Assurance. According to this classification,
a particular product group organizes a number of components based on similarities in their production
(e.g. light-metal alloy parts, welded parts, sensors, etc.). Due to such production process
similarities, Volkswagen assumes that if a supplier has the know-how to produce a component in
a specific product group, it is also capable to produce other components from the same product
group. Therefore, a particular Volkswagen audit result is valid for the entire range of products
within a particular product group. This means that, if a supplier is nominated for the delivery of a
new component from a product group, for which it has already been audited, no new audit will be
required. If the new component is part of a non-audited product group, however, the nomination
will not be considered unless the respective supplier achieves a satisfactory result in a quality audit
40

of the respective product group(s). The latter is very common for new suppliers, which have no
previous business relationships with Volkswagen, as well as for existing Volkswagen suppliers,
which extend their product ranges to new production areas.
The product group classification is particularly important also for this work. It is used to divide
the empirical data into subsets and account for the differences between the individual production
processes, and potentially any negative influences such process differences might have on the final
results of the analysis. In the general case, the effective definition of the product group classification
is subject to several important considerations. A too narrow classification would require a
significantly larger amount of audits and thus unnecessarily would increase the demand for auditing
manpower, due to the fact that similar production processes would be part of different product
groups. In case a certain product group is defined too generally, however, there is the risk that
suppliers, which are good at making one of the products, will get certified to deliver other products
in the same product group, but lack the necessary capability to produce them. In the latter case
a supplier would be awarded a contract for new delivery without being re-audited, even though it
may lack the necessary process know-how. Such situations are a potential reason to observe poor
correlation between a supplier’s quality evaluation and its actual quality performance.
The product group approach provides a convenient way to classify audit evaluation data and is
extensively used in the analysis presented here. Nevertheless, there are still slight differences in
the production cycles even for similar components in the same product group. In the end the implementation
of a particular production process is company specific. For that reason Volkswagen
regards the individual production processes as a combination of processing steps, e.g. painting,
electric welding, injection moulding (plastics). The latter are evaluated separately during a quality
audit and the overall quality capability score summarizes the individual process step evaluations.
Depending on the complexity of the components (and accordingly the associated production processes)
two audit evaluations of the same product group may contain different number of process
steps.
The quality capability metric used in this analysis is generated in a clearly defined algorithm
described in Volkswagen’s Formel Q-Fähigkeit (2009). Formel Q-Fähigkeit is one of a set of documents
describing the quality policy of Volkswagen, and contains among others a comprehensive
supplier evaluation questionnaire, which provides a guideline for conducting quality audits. Due
to the fact that Volkswagen cooperates closely with a number of national and international quality
organizations such as VDA (Verband der Automobilindustrie) and IATF (International Automo-
41

tive Task Force) its supplier evaluation questionnaire draws a lot of similarities with established
automotive standards such as VDA 6.3 or ISO/TS 16949. The questions in this questionnaire
are divided into three primary blocks, which address aspects, regarded as vital for maintaining a
stable, quality-capable production process at supplier’s site. Particular areas of interest during a
Volkswagen quality audit are suppliers’ capability to manage its own supply chain, the effective
application of quality management throughout the entire production process, and finally customer
care and service (Formel Q-Fähigkeit, 2009). In the general case the use of such a guideline is particularly
useful for any organization, since it enables the organization to maintain the same quality
auditing standard on an international level and helps in communicating organization’s quality requirements
to its business partners.
The focus of the first block of questions in the Volkswagen specific questionnaire falls upon
topics regarding the supply chain management capabilities of the respective supplier. Volkswagen
requires from its suppliers to regularly evaluate their own subcontractors and make sure that only
certified and approved business partners are engaged in the production processes. This part of the
questionnaire is very important, especially for suppliers with large amount of purchased components.
As already mentioned in the discussion in Section 4.3 these are usually suppliers of complex
automotive systems, which also rely on plenty of outsourcing to sustain their businesses just like
the automotive OEMs themselves. Quality of the incoming materials needs to be continuously
monitored and evaluated, and in case of deviations from the quality specifications the according
Volkswagen supplier needs to agree upon improvement actions with its own suppliers and attend to
their implementation. For that reason suppliers are required to have the necessary laboratory and
measurement facilities for incoming material inspection. Volkswagen suppliers bear the responsibility
to make sure that all purchased materials are sampled and released before they enter the
series production. Furthermore, this part of the supplier evaluation deals with material handling
and warehousing of raw materials at supplier’s site. Particularly important is to store the incoming
materials under conditions, which preserve their physical properties. The materials have to be
properly labeled to assure traceability and be used in a first-in-first-out manner (FIFO).
The second block of questions deals with the individual production steps. Focus of this part of
the supplier evaluation is personnel qualification, suitability of the processing and testing facilities
and continuous improvement, as well as the appropriateness of the in-house transportation and
warehousing for the specifics of the particular components (Formel Q-Fähigkeit, 2009). In this
regard the responsibilities of the individual operators (process owners) must be clearly defined.
Volkswagen regards it as especially important, that suppliers’ employees possess the necessary
42

qualifications to perform the tasks assigned to them. This aspect is especially critical for the stable
business operations in developing regions which are subject to high personnel turnover (see Section
4.1). Personnel need to be regularly trained and should possess sufficient know-how about
suppliers’ products, frequently occurring problems, as well as the impact of the process on the
final quality. The facilities involved in the production should also be regularly controlled and their
ability to achieve the required quality level of the final products should be continuously monitored
and guaranteed. This is achieved through regular calibration of the machines and statistical control
of the most important process parameters, e.g. temperature, pressure, moisture, time, speed, chemical
composition, etc. Suppliers are also required to have the necessary testing equipment in place
and make sure during the series production that the respective quality requirements are achieved.
Moreover, material transportation between the individual processing stations on the work floor
should be implemented in an ergonomic way and should guarantee a continuous material flow.
Parts must be transported in suitable containers, which do not have negative impact on the quality
of the products, i.e. prevent the transported goods from pollution and damage. To assure traceability
each component has to be properly labeled. Volkswagen holds it for very important to have
easily recognizable identification of components used for reference or calibration, defective parts
as well as components, which need post processing. Such labeling is vital for preventing defective
parts to reach the final customer and for the subsequent analysis of the capability of the overall
production process. Suppliers are required to analyze the yield of the individual production steps
and to define appropriate actions to minimize the scrap rates. This is an important step towards
achieving continuous improvement of their overall process and accordingly lower prices of their
final products.
The third and final block of questions in the Volkswagen auditing questionnaire deals with
customer care and customer satisfaction. Volkswagen expects from its suppliers to be responsive
and react to customer complaints on short notice. In case of customer complaints the respective
problems have to be analyzed as quickly as possible and corrective actions have to be defined and
implemented accordingly. Suppliers have to define emergency plans for alternative ways of supply,
production, and transportation, which assure the continuous delivery of high-quality components
to their final customer even in the case of unexpected situations. These aspects are an essential
precondition for a healthy customer-supplier business relationship which has to be sustained on
the long term by any organization.
During a quality audit a supplier has to provide evidence that it has considered and adequately
implemented all customer requirements part of the audit questionnaire and thus assure Volkswagen
43

Table 1: Evaluation points for a single question.
(Source: Formel Q-Capability, 2009, p. 31)
that its production processes are quality capable. Each of the questions in the individual blocks is
graded on a scale from 0 to 10 points respectively (see Table 1), depending on the level to which
the according production process fulfills the particular Volkswagen Group requirement. The first
and third blocks of the auditing questionnaire (Sub-Contractors / Purchased Material and Customer
Care / Customer Satisfaction (Service) respectively) refer to the overall production process, while
the evaluation of the second block of questions (Production) is carried out separately for each
individual processing step. The overall grade of each evaluation block is the percentage, which
the sum of the points achieved in the individual questions represent from all possible points. For
production processes which are comprised of several processing steps the evaluation result for the
second block of the questionnaire (E PG ) is obtained as an average of the evaluation scores of the
individual processing steps (E 1 , E 2 , . . ., E n ) (Formel Q-Fähigkeit, 2009):
E PG [%] = E 1 + E 2 + . . . + E n
[%] (1)
n
Here n is the number of processing steps. The process evaluation score of a quality audit is
given in percent and is computed as an average of the evaluations obtained in the first (E Z ), second
(E PG ), and third (E K ) questionnaire blocks (Formel Q-Fähigkeit, (2009)):
E P [%] = E Z + E PG + E K
[%] (2)
3
44

Apart from E P Volkswagen has defined four additional quality capability measures, calculated
based on the evaluations of the questions in the block Production. These are namely: E U1 (Personnel
/ Personnel Qualification), E U2 (Machinery / Equipment), E U3 (Transport / Parts Handling
/ Storage / Packaging), and E U4 (Failure Analysis / Corrective Measures / Continuous Improvements).
Along with the process evaluation described above a Volkswagen Group quality audit includes
also a product-audit. The product audit assesses the degree of compliance of supplier’s final products
with Volkswagen Group customer specific requirements and characteristics. Auditors measure
on-site the key characteristics of ready-for-shipment products and any deviations from the
customer requirements which are identified during the product audit have influence on the final
rating of the respective supplier. Volkswagen has defined three major classifications for deviations
detected during the product audit (Table 2).
Each quality audit should provide a statement about the quality capability of a particular supplier.
Due to the fact that this type of quality capability evaluations pursue a holistic approach
and evaluate the entire production chain at a supplier’s location, it is reasonable to expect that this
Table 2: Fault categorization relevant for a Volkswagen product audit.
(Source: Formel Q-Capability, 2009, p. 27)
45

quality management tool (if carried out properly) is able to detect the individual quality risks down
the production chain and therefore give a good predictor for supplier’s quality.
Volkswagen suppliers receive a letter-encoded quality rating – A, B, or C – depending on the
results of their process and product evaluations. These ratings are then used in the corporate sourcing
process as input for sourcing decisions. The general rules for supplier rating and the meanings
of the individual rating categories are listed in Table 3. Quality rating is governed by the so-called
hurdle principle (from the German Hürdenprinzip), however, and a supplier could receive a worse
quality rating, even though its process evaluation score covers the basic requirements for a higher
rating. A number of the requirements listed in the audit questionnaire are regarded by Volkswagen
as especially critical for the overall quality capability of the production process and therefore, in
case a supplier fails to adequately fulfill them, its overall quality rating is downgraded. This concept
makes the evaluation procedure especially flexible in the cases of serious deviations from
the quality requirements, as it gives the auditors the possibility to intervene and raise awareness
about potential problems by downgrading the supplier production process. The according questions
which evaluate the critical quality requirements are marked as *-questions (star-questions
Table 3: Rules for supplier letter-encoded rating.
(Source: Formel Q-Capability, 2009, p. 35)
46

from the German Sternchenfragen). Thus for example, a supplier could get a B quality rating, even
though its process evaluation score is greater than 91% (see Table 3). Common reasons for such
type of downgrading are deficiencies identified during the process audit such as a missing certification
of supplier’s quality management system according to the VDA 6.1 or ISO/TS 16949 quality
standards, a question in the audit questionnaire evaluated with 0 points or a * question evaluated
with 4 points, as well as an evaluation score for one of the individual components (E Z , E PG , or E K )
smaller than 80%. Reason for downgrading a supplier’s quality rating from A to B could also be
a B-type or a systematic C-type deviation identified during the product audit (Formel Q-Fähigkeit,
2009). That is why there is difference between an A-rated supplier and a B-rated supplier with an
A process evaluation score (E P ≥ 92%).
For more serious deviations from the quality requirements a supplier’s quality rating would be
downgraded to C despite a process quality evaluation score greater than 81%. Such are cases in
which a supplier is not able to meet certain project deadlines and implement the defined improvement
actions before start of series production (SOP). A C downgrading would follow also in cases
in which one or more of the individual evaluation components (E Z , a single processing step E 1 ,
E 2 , . . . , E n , E PG , E K , E U1 , E U2 , or E U3 ) are smaller than 70% or a *-question is evaluated with 0
points (Formel Q-Fähigkeit, 2009). Furthermore, if during the product audit an A-type or a systematic
B-type deviation is detected, the supplier will be C-downgraded, too. Mismatches between
the quality evaluation score and the quality rating of downgraded suppliers (B with E P ≥ 92% or
C with E P ≥ 82%) can influence negatively the correlation between quality capability and quality
performance and were therefore regarded in the calculations in the later stages of the project.
In the general case Volkswagen awards delivery contracts only to suppliers with an A or a B
quality rating, as suppliers with an A rating are usually preferred (Figure 12). Suppliers with a
C quality rating are not considered in the sourcing process. In the empirical data analyzed here,
however, there are a few cases of C-rated suppliers which deliver to Volkswagen. Such cases result
due to the fact that suppliers’ rating can be downgraded to C also post factum based on poor quality
performance in the series delivery. Subsequently the status of the Volkswagen business relationship
with such suppliers is set to ”business-on-hold”. This means that the latter can still deliver parts
for the current projects, but will not be considered for any new projects unless they manage to
improve their production process and receive a positive audit evaluation. A part of the contractual
agreement between Volkswagen and its business partners requires from every Volkswagen Group
47

supplier to achieve an A rating (from German ”Ziel-A Vereinbarung” or ”Target A Agreement”).
B- and respectively C-rated suppliers will be therefore subsequently audited, until they achieve this
target. Very often in practice due to limited auditing resources, however, A-rated and sometimes
even B rated suppliers will not be audited for considerably long periods, as long as their products
aren’t subject to any serious quality related problems. In light of the sampling discussion presented
in Section 3 this is quite undesirable. Failing to follow the developments of the quality capability
of the production processes could lead eventually to the untimely detection of serious production
problems down the supply chain. On the other hand, with respect to the analysis presented in this
paper – old evaluations, which do not correspond to the actual quality capability of the respective
suppliers, pose a significant challenge. This is an especially important factor for the subsequent
calculations and is therefore discussed once again in greater detail in Section 7.2.2 below.
6.2 Quality Performance
After Section 6.1 described the quality capability records, the current section introduces suppliers’
quality performance information – the second important type of empirical data, which is necessary
in order to perform the correlation studies suggested in Section 3 as a possible way to give an
answer to the primary research question of this paper. Quality performance data describes supplier-
Controlling
Procurement
Presenting
Legal Entity
Buyer
Head of
Procurement
(CSC)
Commodity
Manager
(VW Group
Procurement)
Quality
Assurance
(Supplier Audit)
Logistics
Volkswagen
Corporate Sourcing
Committee (CSC)
collective decision
Global
Sourcing VW
Forward
Sourcing VW
Research &
Development
In-house
Production
Local
Purchasing
Teams
Procurement of
the Brands
Right to veto
Figure 12: Participants in Volkswagen’s Corporate Sourcing Committee (CSC). An unsatisfactory
supplier evaluation in a quality audit results into a veto in the CSC process.
(Source: Volkswagen Internal Reports, 2012)
48

induced problems originating in the production halls of Volkswagen during series production. The
empirical quality performance records are organized according to a categorization, which will
hereafter be referred to as material groups (from the German term Werkstoffgruppen). The material
group classification is maintained by Volkswagen Procurement and is used not only for recording
quality performance of the respective Volkswagen suppliers, but also in the sourcing process to
classify information regarding their contracts with Volkswagen.
Unlike the product group classification, the purpose of material groups is not to account for
the specifics of parts’ manufacturing processes. Rather this type of classification is applicationoriented,
i.e. material groups include products which have similar application, even though in
certain cases they might be manufactured through significantly different production processes.
This type of classification is particularly useful for the sourcing process. One good example for
the differences between the two types of classifications is the material group of fuel tanks (material
group – 0094). Components, part of this material group, have two major variants – synthetic
material fuel tank assembly and metal fuel tank assembly (Figure 13). Despite the obvious differences
between the two types of production processes involved in parts’ manufacturing (one deals
with plastic components, and the other with metal components), the products are categorized in
the same material group, because they are used for the same purpose – to store fuel. From the
viewpoint of the product group classification, however, the two variants of fuel tanks fall in two
completely different product groups – Assembly Blow Moulded Parts (Zsb. Kunststoffblasteile –
2122) and Assembly Metal Parts (welded) (Zsb. Metallteile (geschweisst) – 1361) respectively.
Audits of suppliers of fuel tanks are therefore carried out according to the latter two classifications
(by auditors from different audit divisions), while their quality performance data is collected under
the same material group – 0094.
Product Group: 1361
Assy. Metal Parts
(welded)
Product Group: 2122
Assy. Blow Moulded
Parts
Material Group: 0094
Fuel Tanks
Product: Fuel Tank
Material: Metal
Product group: 1361
Material group: 0094
Product: Fuel Tank
Material: Synthetic
Product group: 2122
Material group: 0094
Figure 13: Products with the same application (fuel tank) are classified in the same material
group. If the production processes of the respective products differ (metal welded part vs. blow
moulded part), they are classified in different product groups.
49

There are a total of four quality performance indicators available in the analyzed empirical
data. These are namely ppm-values, production-line interferences/failures or also HSF (from the
German Hallenstörfälle), direct quality performance (direkte Leistung) and indirect performance
(indirekte Leistung). The ppm quality indicator (defective parts per million) was already mentioned
above and is one of the most commonly used quality performance measures not only in the
automotive industry but also in many other production industries. It describes deviations of the
characteristics of the delivered components from the according technical specifications (physical,
haptic, performance, etc. properties). This is probably the type of quality performance information
which is most closely related to the manufacturing quality capability of a particular production
process and is also (the only one) used in the analyses by Stroescu-Dabu (2008) and Hadzhiev
(2009). The definition of ppm is as follows:
ppm =
number of delivered defective components
total number of delivered components
× 1.000.000 (3)
HSF is another relevant quality performance metric. It is of more general character than ppm
and describes incidents in which a supplier’s products or services have negative influence on the
overall production process. HSF records result from deliveries of defective components (in which
cases the respective supplier would receive ppm as well), but can also be induced by incidents
which are not necessarily related to any defects of the delivered components. The latter situations
would increase supplier’s HSF record, but would not generate any ppm. Examples of HSF, which
do not result in ppm, are cases in which a supplier delivers the wrong type of components or the
delivered components are not properly labeled. Such cases trigger sorting campaigns and disturb
the continuity of Volkswagen’s internal processes. The time overhead which the processing of a
particular supplier-caused problem incurs for Volkswagen is measured by suppliers’ direct quality
performance in minutes. The direct performance of the suppliers together with precisely defined
costs per unit time is then used by Volkswagen to calculate the total incurred costs of a particular
problem. In case the respective problem has also a monetary dimension the according amount
enters the records as the indirect performance of the supplier in EUR. This quality performance
metric is probably not very suitable for measuring supplier performance, however, since it is dependent
on the present price levels and its consistency is subject to variation over time influenced
by varying prices, exchange rates and inflation. This supplier performance metric is used primarily
for charge-back purposes.
50

While ppm and HSF are two indicators which are consistently recorded in the empirical data,
very few of the available supplier records contain information also about their direct and indirect
performance (around 15% and 2% respectively of all available records for the time period considered
in the analysis). The lack of such information is surprising, since for every single HSF the
supplier receives recourse with the costs required by Volkswagen to process the according problem,
i.e. for every HSF the supplier should also have a direct performance record in minutes. At
this point, it is probably important to mention that the empirical data analyzed here was not obtained
directly from the system, which is used to collect the respective performance information
(KPM-Halle), rather from a secondary SAP-based database (Business Objects), which is used for
evaluation purposes and provides the available information in a format which is convenient for
analysis with statistical software. The reasons why these metrics are not consistently present in the
second system probably lie in the fact that they are almost exclusively used for supplier recourse
purposes, and are hardly used in internal Volkswagen quality reports for tracking the development
of suppliers’ quality performance over time. The fragmented information available about suppliers’
direct performance and indirect performance makes these two quality performance metrics
unreliable for the purposes of this analysis and therefore they are excluded from the subsequent
discussion.
The process of generating the quality performance metrics used in this analysis is significantly
more complex than the way quality capability scores are obtained. This is due to the large amount
of individuals involved in the assessment of suppliers’ quality performance. Even though part
of the delivered components is regularly sampled for defects through incoming inspection, a major
portion of the defect detection at Volkswagen happens at its assembly lines. Practically any
Volkswagen employee involved in the assembly process can detect and report a problem. The
detected problem is documented and the affected batch of components is sent for analysis to the
line rejects handling center (in German Regresszentrum) of the affected production facility. There
the parts are sorted out and all components, which are not subject to the encountered problem,
are then returned to the production line for assembly. This is also where the data collection takes
place. Quality performance records enter Volkswagen’s database KPM-Halle. Every instance in
KPM-Halle represents a single HSF and includes a detailed description of the reported problem,
identification codes of the affected parts, the type and number of the affected components (not provided
for problems unrelated to deviations from the technical specifications of the components),
general identification information of the respective supplier, as well as the time overhead for recourse
purposes.
51

Currently, Volkswagen works on a new concept called ”Quality Filter for Purchased Components”
(from German Warenfilter), which aims at improving the work flow in Volkswagen’s
production facilities. A particular disadvantage of the new concept from the analytical point of
view, however, is that neither ppm nor HSF will enter the quality records of suppliers included in
the quality filter program. Quality filters are external warehouses, which are located in close proximity
to Volkswagen’s production locations and whose function is to perform a 100% incoming
inspection for components with a very large amount of defects. The idea behind the quality filter
is that in the future suppliers which have poor quality performance in the series production will
not be allowed to deliver their components directly to the respective Volkswagen plants. Instead
100% of their deliveries will be first examined in the quality filters and only functional parts will
be then transported to the Volkswagen plants. Suppliers will then have to deliver through the quality
filter until their quality performance reaches satisfactory levels and they are allowed to deliver
again directly to Volkswagen’s production facilities. The employment of quality filters will have a
positive impact on the stability of Volkswagen’s production process and will reduce the number of
line rejects, due to the fact that less defective components will reach the assembly lines.
Even though the process for recording suppliers’ quality performance metrics is clearly structured,
as already mentioned in the previous sections, the meaning of the individual quality performance
indicators is variable and is influenced by the complexity of the delivered components as
well as the according delivery amounts. If a certain problem is caused by a sub-supplier, for example,
no ppm information is included in the quality performance record of the direct Volkswagen
supplier. Rather the latter is held liable only for the incurred costs (the direct supplier receives a
HSF nevertheless). For that reason suppliers of relatively complex components with several subsuppliers
will usually have smaller amounts of ppm than suppliers of simpler parts. Further, in
certain cases there is such a large variety of a particular type of assembly products that instead
of using a separate part number for the individual component variants, Volkswagen describes the
delivered units in its systems as an aggregation of the part numbers of the sub-components used in
their assembly. Good examples for such types of components are axles and seats. Whenever there
is a problem with such products, however, only the part numbers of the defective assembly subcomponents
enter the quality performance record of the respective supplier. Thus the according
ppm values are artificially reduced. For these reasons ppm is not always suitable for performance
comparison especially between suppliers which deliver products with significantly different complexity.
Such comparisons would not necessarily adequately depict the real situation and could
potentially obscure important trends in the empirical data. In the analysis a possible workaround
52

for this problem is to define classes of complexity and look at correlations only within the respective
classes. A good starting point is to use the material group classification as basis for comparison
and avoid comparing records, which are classified into different material groups.
The specifics of Volkswagen ppm records described here are not considered in the analytical
studies by Stroescu-Dabu (2008) and Hadzhiev (2009). Therefore, a potential bias of their results
is not excluded, due to the heterogeneity of the actual meaning of the ppm quality performance
indicator. This holds true especially for the more general, industry-based classification of empirical
data. Such bias could even be a possible reason, which partially explains the unsatisfactory
analytical results for the chemical and metal industries observed in these studies. This statement is
supported by the results observed by Hadzhiev (2009). His analysis shows that for a more detailed
supplier classification, which would be expected to increase the consistency between the individual
ppm records within a certain analytical supplier group, particular supplier groups also from the
metal industry such as suppliers of metal profiles indeed show better consistency between the actual
quality performance and the evaluation scores of Volkswagen suppliers. At this point it is also
important to mention, that assembly units from the electric industry such as navigation systems,
radios, and control electronics, are described with their own part numbers. Therefore ppm records
for electric suppliers are expected to be with relatively good consistency.
The other quality performance metric in the empirical data available for this analysis is HSF.
HSFs are of summarizing character and their definition is independent of the total number of
rejected parts in a particular defective batch. Thus two independent production line rejects can
concern significantly different amount of defective parts, but they will be equal in terms of HSF.
Companies, which deliver components in large batches such as screws or nuts, normally would
have a relatively low number of HSFs even in cases when their ppm records are high. On the other
hand, companies which deliver parts in smaller batches, such as assembly units like seats, are more
likely to have a relatively large number of HSFs, even if their ppm record remains low. In other
words, if the same relative number of defective parts is delivered in several smaller batches the
resulting number of HSFs will be significantly higher as compared to the case, when components
are delivered in fewer, larger batches.
One way to avoid the pitfalls posed by the diverse character of empirical data – both quality
performance records and quality capability evaluation scores – is to split records into a number
of analytical groups, which contain only suppliers with comparable quality indicators. Such classification
should not merely take into account the more general industry-based classification of
53

products (electrical, metal, and chemical parts), but has to also take into account the size of deliveries,
the manufacturing processes involved, as well as the product complexity. Thus, the quality
performance results of suppliers within a certain analytical group will be comparable to one another
based on ppm or HSF accordingly as well as on suppliers’ quality evaluation scores. On the
other hand, comparisons between the records of suppliers in different analytical groups have to be
performed with caution due to the potential differences in the character of the according empirical
data described above.
6.3 Automation of the Data Processing
In order to conduct the analysis according to such detailed criteria and reach any generalizable
conclusions, it is particularly important to consider a representative data sample. Inevitably, given
the large amounts of empirical data, a significant part of the data processing has to be performed
in an automated manner. A significant obstacle for automation of the analysis turned out to be the
structure of the internal Volkswagen databases, which are optimized for the needs of the individual
departments (Quality Assurance and Procurement respectively), and therefore do not contain the
according information in the format required for the current analysis. In most of the cases each
Volkswagen department uses a separate system which is specifically tailored for its own needs and
in many cases does not provide the data in a format suitable for analysis, other than the particular
demands of the respective department. The fact that data required for the analysis presented here
stem from several different systems is therefore related to a number of compatibility issues, which
obstruct the automated data processing.
The challenges faced during the project are expressed within the following. Suppliers’ quality
capability information is stored in the database ISQAD maintained by the Volkswagen Quality
Assurance department. The backbone of ISQAD is an Oracle database accessed via a browserbased
user interface. The system provides access to the records of individual suppliers and all
relevant documents (which is what is required in auditors’ daily routine), but the only summarizing
information, which is accessible over the user interface, is a list with the latest audit evaluations
of the suppliers – supplier catalogue (from the German Lieferantenkatalog). The list includes
the overall supplier rating (A, B, or C) for each of the evaluated product groups, together with the
overall quality evaluation scores (E P ) of the individual suppliers in percent. The supplier catalogue
does not provide any information about previous audit results at a particular location, nor any
details about the individual components of a particular evaluation such as the evaluated process
54

steps as well as the resulting partial evaluations (E 1 , E 2 , . . ., E n ). In general such information is
essential in providing an idea about the complexity of the evaluated processes as well as about the
strengths and weaknesses of suppliers’ production.
At this point it is important to mention, however, that such information is available after all,
but is not systematized in a structured list, and is rather dispersed in the individual audit reports.
Due to the specifics of the Volkswagen internal process, audit reports are saved in the system in
the form of an attachment, which in most of the cases is a scanned copy of the original report,
stored in paper form in Volkswagen’s archive . This form of storage is a particular obstacle for
an automated data processing and results into a serious manual processing overhead. Due to the
fact that a great deal of the information had to be extracted manually from the records, it was
not possible to include in the analysis a substantial amount of the available more detailed quality
evaluation data in a sufficiently large scale.
Based on these experiences already here the first important conclusion from this work can be
made. The supplier evaluation process at Volkswagen generates considerable amount of field information
which is an especially valuable input for statistical analysis of Volkswagen’s supplier
management process. Currently this information is stored in a form, which makes it particularly
difficult to access for large generalizable studies (audit reports in paper form with scanned
copies in the main electronic database). However, making the generated information available in
an electronic form may be achieved relatively straight forward. A positive aspect of the problem
is the fact that the original audit reports are created in an electronic form (Microsoft Excel forms),
which means that ideally the relevant information can be very easily exported into the main Oracle
database (ISQAD). The transfer can be completely automated and will require minimal amount
of time. This improvement will increase the efficiency of the old procedure without compromising
the quality of the available audit records. In addition to the currently available data, the new
procedure would make the rest of important information in the audit reports easy to access and
analyze.
One further challenge is posed by the fact that even though quality performance data are accessible
over an SAP software tool (Business Objects), used for quarterly and yearly reports, a
substantial amount of automated post processing was required here as well. The information included
in the analysis was processed using a Microsoft Excel-based VBA 9 software tool, developed
by the author especially for this purpose.
9 Visual Basic for Applications (a programming language)
55

In addition to the data processing obstacles described above, the following two sections describe
additional challenges and their possible solutions, which came up during the data analysis.
6.3.1 Matching Quality Capability and Quality Performance
In addition to the more trivial problems of data accessibility introduced above there is a more
profound problem: the use of two different categorization types to organize quality capability
(process-oriented product groups) and quality performance (application-oriented material groups)
data. The two categorization types were already introduced earlier in this chapter. This particular
difficulty is rather on the conceptual level than on the practical implementation of the supporting
databases. Usually product groups are more generally defined than material groups, since a particular
manufacturing process could be used to produce components with quite different applications
- e.g. moulded plastic parts for interior and exterior. Therefore, in the general case a product group
is expected to encompass components from several material groups, while a single material group
would encompass products from a single product group. However, this is not always the case. The
manufacturing of a particular component could include a number of very diverse processing steps.
One particular example are electric components such as e.g. the material group 0043 – Exterior
Lights (Außenleuchten), comprised of metal, plastic and electrical subcomponents part of several
different product groups.
Due to the different logic of the two categorizations there is no clearly defined relations matrix
between them. In other words, in a great part of the cases one cannot instantly distinguish
which one of the audit results for a certain supplier refers to the manufacturing process used to
produce a particular type of components present on supplier’s quality performance record. The
lack of product-group-material-group relation matrix makes it difficult to distinguish whether the
processes used to manufacture two components from the same material group are similar or not
and represents a significant challenge for the automation of the subsequent analysis. The only alternative
to perform the desired correlations was to manually match quality performance and quality
capability records, which due to the high processing overhead substantially limited the scale of the
analysis.
At this point it is necessary to mention that a tentative solution to the problem has already been
implemented at Volkswagen and is currently being used to upload new audit data. However, due to
the limited time window not enough empirical were data generated with the new procedure till the
end of this project, which data could serve as a sufficient base for statistical analysis. Nevertheless,
56

this improvement is of particular benefit for any future analyses on the topic.
6.3.2 Supplier Identification
The problem described in the previous section was partially mitigated by the help of the supplier
identification systems in use at Volkswagen, which served as reference to narrow down the manual
data queries. Volkswagen Group uses two code systems to identify its suppliers. These are namely
the Volkswagen internal KRIAS identification system (from the German Kreditoren-Informationsund
Abrechnungssystem or system for supplier identification and accounting) and Dun & Bradstreet’s
international numbering system – D-U-N-S R○ (Data Universal Numbering System).
The KRIAS identification system is primarily used for billing purposes and is managed internally
by Volkswagen. KRIAS numbers are assigned to Volkswagen suppliers individually for every
supplier production location. This means that if a given supplier manufactures its products at two
separate manufacturing sites and delivers its production to the same Volkswagen Group receiving
plant, each of supplier’s production facilities will be assigned with a distinct KRIAS number. The
major disadvantage of KRIAS numbers, however, is the fact that they are assigned autonomously
by the individual companies, members of the Volkswagen Group. Thus, the same supplier location
(and respectively the same production process) is very often associated with several KRIAS
numbers in the cases, in which the same supplier delivers components to different subsidiaries of
the Volkswagen Group. This makes the KRIAS number not very suitable for record identification.
Nevertheless, some of the supplier evaluation records are still associated to KRIAS numbers only,
due to the fact that the according suppliers do not possess a D-U-N-S R○ number. Such records
were excluded from the subsequent analysis.
A more suitable alternative to the KRIAS number is the second identification system in use at
Volkswagen – the D-U-N-S R○ . It was developed by Dun & Bradstreet (D&B) and first introduced
in 1962 (Dun & Bradstreet, 2012). The D-U-N-S R○ number is a freely distributed number for
unique global company identification and is currently used by more than 100 million companies
worldwide (Dun & Bradstreet, 2012). The major advantage of the D-U-N-S R○ identification in
comparison to KRIAS is that it is business unit specific, meaning that every single legal business
unit has its own, unique D-U-N-S R○ number. Generally speaking a single D-U-N-S R○ number is
usually associated with one or several KRIAS numbers. On the other hand, a KRIAS number is
most commonly associated with a single D-U-N-S R○ number. The fact that a D-U-N-S R○ number
provides unique business identification (a single number used worldwide), makes it more suitable
57

than the KRIAS number for managing operational data. This is also the reason why for the parts
of the analysis presented within the following sections the D-U-N-S R○ number was the preferred
identification and was used to more easily determine which quality capability record is relevant for
a particular quality performance entry and vice versa.
At this point it is important to mention, however, that even though D-U-N-S R○ numbers offer
better global identification, the use of the D-U-N-S R○ identification system is associated with
one particular drawback. D-U-N-S R○ numbers, unlike KRIAS numbers, are not managed by
Volkswagen itself, rather by the Dun & Bradstreet organization, making it difficult for Volkswagen
to track changes of the D-U-N-S R○ record of a certain supplier location. This state of affairs poses
serious challenges regarding information consistency within the individual Volkswagen databases.
Despite this disadvantage, D-U-N-S R○ is still the preferred identification for the purposes of this
analysis.
7 Results and Discussion
Section 6 introduced the different types of empirical data, which were subject to a number of
statistical evaluations aiming to contribute to the answer of the research questions introduced in
Section 2. After part of the technical aspects of working with the data were also covered in the
previous section, now it is time to present the results of the conducted evaluations and discuss the
insights gained from the observations. This is the aim of the current chapter.
7.1 State and Quality of the Available Empirical Data
The part of the project presented in this section provides a comprehensive assessment of the composition
of the empirical data analyzed later in this paper. This step was motivated by the fact
that the relevance for quality auditing of any insights gained from the subsequent data analysis are
strongly influenced by how accurately and how thoroughly the recorded data describe the actual
processes. Partial records do not allow to explore the process interactions to their full extent and
therefore limit the depth of the analysis. This part of the analysis has also high practical value. The
findings of this section can help improve Volkswagen’s data management. Objective operational
data is essential for decision making and completeness of the records is especially important for the
management. Any incomplete data records are of little use and are merely a source of additional
58

expenses for the organization (costs to obtain and preserve the data, locked resources and time,
financial consequences of misguided decisions etc.). Therefore it is important on the one hand to
identify and eliminate such records and on the other to improve data collection.
This section includes a set of cross-sectional as well as longitudinal evaluations, whose goal
is to identify incomplete records as well as to assess the behavior of data quality over time and
space. Due to the large overhead to compensate for any incomplete records (use other ways to
get the missing information) and since this is not the main emphasis of this work, all incomplete
records were excluded from the subsequent phases of this study. For quality capability data these
are records, which lack e.g. a D-U-N-S R○ number or have an inconsistent quality capability score
(e.g. an A-supplier with a fulfillment level E P < 90% or 92% respectively). On the other hand,
incomplete quality performance records lack one or more important statistical quantities such as
the amount of delivered components, the respective material group classification, or a D-U-N-S R○
number.
The result of the analysis of quality capability data is presented in Figure 14a. Each quality
capability entry represents the quality audit evaluation of a single product group at a particular
supplier production facility. Thus, a quality audit of a certain supplier, which manufactures components
from two different product groups at the same production site, would generate two separate
entries in the statistic – the according evaluations of each of the product groups at this location.
The results of the evaluation show that 9% of the records have an incomplete quality capability
evaluation score (Figure 14a). In these cases the percent score of the according records is subject
to system input errors and is not entered correctly in the database. In most of the cases it is simply
missing, while in the rest the evaluation percent value does not correspond to the quality capability
rating (A or B) of the respective supplier (most probably a typo). Additional 8% of the quality
capability records have no D-U-N-S R○ number associated to them. It is necessary to mention, however,
that the supplier catalogue (used as basis for the statistics) includes quality capability records
over a large time period and most of the identified incomplete records refer to suppliers, which
are no longer active or have never been assigned a contract. Since such cases concern particularly
old evaluations, which are not considered in suppliers’ rating, they also have little influence on
the quality assurance process of Volkswagen. For the analysis presented in this paper it is only
meaningful to concentrate on the records of active suppliers, therefore incomplete records of the
former two types of suppliers are not of interest for this study and have been excluded.
The number of evaluated quality performance records was significantly larger than the amount
59

9%
8%
1,1% 1,3% 0,9%
1,3%
83%
Records with incomplete QC score
QC records without a DUNS-Nr.
Complete QC records
96%
Missing Material Group Information
Missing D&B DUNSâ
Missing Delivery Quantity
Operational Material
Complete Records
(a) Quality of the quality capability data.
(b) Quality of the quality performance data.
Figure 14: Quality of the available empirical data.
Note that in the case of quality performance records the individual percentages sum up to more than 100% because
part of the evaluated records miss more than one type of relevant information.
of quality capability evaluations. The records were generated on a monthly basis and a single
record includes information about components from a particular material group, which have been
delivered from a single supplier production location to a single Volkswagen plant. Slightly above
1% of all records contain incomplete material group information (see Figure 14b). 1.3% of all
quality performance records do not contain D-U-N-S R○ information, and additional 0.8% lack information
about the number of delivered components. 1.3% of the records contain information
about operational material (from German Betriebsmaterial), which is not relevant for the analysis.
The latter are supplies, which are not used in the Volkswagen Group production processes (do not
end up in the final products), and are auxiliary materials such as protective wear for the employees,
cleaning supplies, etc. All incomplete quality perfomance records were excluded from the
analysis, presented in the subsequent chapters.
To narrow down the sources of particular data non-integrities, the quality performance information
was divided into several groups according to the location of the Volkswagen production
plants, which reported the data. This step was motivated by the discussion on the trends of the
automotive industry in the previous sections. Automotive production is spread over a number
of regions which differ substantially in their manufacturing history and level of experience with
60

20%
9%
57%
7% 2,4%
2,2%
1,9%
1,3%
0,4%
0,1%
Germany
Spain
England
Mexico
India
Czech Republic
Brazil
Belgium
Argentina
Russia
Figure 15: Amount of quality capability records, reported by the individual Volkswagen production
facilities sorted by region. The presented numbers are monthly averages over the period January,
2008 – December, 2010.
modern production management methods. It is therefore anticipated that historical and cultural
differences influence quality awareness in these regions. As consequence this leads to potentially
different perception of the importance of operational data and ultimately different data quality.
Volkswagen Group has concentrated its production capacities into ten different regions – Germany
(a total of 33 independent production units are located in this region), Argentina (2), Belgium (1),
Brazil (4), Czech Republic (8), England (1), Mexico (1), Russia (1), India (1), and Spain (5). Other
Volkswagen Group production facilities such as the ones in USA and China are not included in this
analysis either due to the fact that they were not active throughout the evaluated time period or because
the relevant performance information from the respective regions was not accessible through
Volkswagen’s databases, which were available for this analysis.
The amount of quality performance records generated by the individual regions is significantly
different (see Figure 15), provided the uneven distribution of production capacities. As expected
Germany contributed the largest number of records – comprising more than 56% of all records.
Other regions, which generated particularly large amounts of quality performance information,
are the Czech Republic (20% of all records) and Spain (9%). The available empirical data shows
regional variations not only of the amount of incomplete records, but also of the proportion of
factors, which influence data integrity (see Figure 16). However, the overall integrity of quality
performance data, generated in the three regions 10 mentioned above, is exceptionally good and the
10 These are also among the regions, in which Volkswagen has been present the longest and has established stable
business processes. On the other hand, regions, which show certain deficits in data’s integrity, are mostly among the
regions, in which Volkswagen has relatively small amount of experience.
61

1,1% 1,4% 0,9% 1,3%
0,4% 0,3% 0,2% 0,0%
0,7% 0,8%
0,4% 0,2%
0,3% 1,1% 0,6% 0,0%
0,5% 0,1% 0,9% 2,2%
3,0%
2,0%
1,9%
0,1%
99%
Region 2 Region 4 Region 7 Region 6 Region 8
3,4% 4,0%1,0%
0,1%
98%
0,0% 1,8% 6,3% 0,0%
98%
14%
5,3%
1,5%
0,0%
97%
0,3%
32%
94%
6,8%
38%
96%
Worldwide
0,2%
92%
92%
82%
68%
0,0%
56%
0,2%
0,2%
Region 3 Region 9 Region 5 Region 10 Region 1
Missing Material Group Info Missing Delivery Quantity Complete Records
Missing DUNS Number
Operational Material
Figure 16: Quality of the supplier performance data reported by the individual Volkswagen production
facilities. The data are sorted according to region. The numbers under each pie chart
represent the average monthly amount of records reported by the according region over the period
January, 2008 – December, 2010. Note that there are cases in which the individual percentages
sum up to more than 100%. This is due to the fact that part of the evaluated records are missing
more than one type of relevant information. The percentages for the amount of complete records
in all cases are exact and can be used to deduce the overall amount of incomplete records.
total amount of incomplete records remain below the overall average of 4.3%. This fact is very
positive considering that recods coming from these regions comprise more than 85% of all monthly
records. The observed differences between the quality of data records in the different regions show
that splitting the records into regional datasets was indeed very meaningful.
Note that not only the amount, but also the nature of data inconsistencies in the individual
regions varies significantly. Thus for example, while almost 90% of all incomplete records in
Region 1 are due to missing D-U-N-S R○ supplier identification, D-U-N-S R○ related data fragmentation
is responsible for just below 30% of all problematic cases in Region 5. By contrast, almost
80% of all incomplete records from this region are subject to incomplete material group information
(some of the records are subject to both). In Region 6 60% of the incomplete data entries are
subject to missing information about the amounts of delivered components. Significant variations
in the extent to which certain factors influence data consistency, were identified not only on the regional,
but also on the level of individual production units. On the one hand, such information can
be quite useful to address the sources of differences and accordingly improve the coherence of the
data collection process at different Volkswagen locations. This will ultimately improve the overall
data consistency in the databases. On the other hand, the influence of the incomplete records on
the subsequent steps of the analysis is limited only to reducing the available base of empirical data,
since as already mentioned previously they are excluded from the calculations in the following
62

sections. However, this is not particularly critical for the overall analysis since they represent less
than 5% of the total available records (Figure 16).
The numbers in Figure 16 are an average over the entire three-year time period covered by
the analysis (from 2008 to 2010) and provide important information about the proportion of the
major sources of data inconsistencies. However, it is equally important to also assess whether
the influence of these factors on data consistency has diminished or increased over the according
time period. Figures 17 through 21 present the longitudinal development of data quality in the
individual regions. The graphs include the regional quality performance records on a monthly time
scale and identify trends in the development of their quality.
Worldwide
Amount of Incomplete Records [%]
10,00%
9,00%
8,00%
7,00%
6,00%
5,00%
4,00%
3,00%
2,00%
1,00%
Missing Material Group Information [%] Missing DUNS [%] Missing Delivery Quantity [%]
0,00%
2008 2009 2010
Figure 17: Development over time of the quality of quality performance records, reported by all
Volkswagen production facilities worldwide.
63

Region 1
60,00%
Missing Material Group Information [%] Missing DUNS [%] Missing Delivery Quantity [%]
Amount of Incomplete Records [%]
40,00%
20,00%
0,00%
2008 2009 2010
Figure 18: Development over time of the quality of quality performance records, reported by
Volkswagen’s production facilities in Region 1.
Region 2
Amount of Incomplete Records [%]
10,00%
9,00%
8,00%
7,00%
6,00%
5,00%
4,00%
3,00%
2,00%
1,00%
Missing Material Group Information [%] Missing DUNS [%] Missing Delivery Quantity [%]
0,00%
2008 2009 2010
Figure 19: Development over time of the quality of quality performance records, reported by
Volkswagen’s production facilities in Region 2.
64

Region 3
Amount of Incomplete Records [%]
10,00%
9,00%
8,00%
7,00%
6,00%
5,00%
4,00%
3,00%
2,00%
1,00%
Missing Material Group Information [%] Missing DUNS [%] Missing Delivery Quantity [%]
Data point: 100%
0,00%
2008 2009 2010
Figure 20: Development over time of the quality of quality performance records, reported by
Volkswagen’s production facilities in Region 3.
Region 4
Amount of Incomplete Records [%]
10,00%
9,00%
8,00%
7,00%
6,00%
5,00%
4,00%
3,00%
2,00%
1,00%
Missing Material Group Information [%] Missing DUNS [%] Missing Delivery Quantity [%]
0,00%
2008 2009 2010
Figure 21: Development over time of the quality of quality performance records, reported by
Volkswagen’s production facilities in the Region 4.
65

Region 5
30,00%
Missing Material Group Information [%] Missing DUNS [%] Missing Delivery Quantity [%]
Amount of Incomplete Records [%]
20,00%
10,00%
0,00%
2008 2009 2010
Figure 22: Development over time of the quality of quality performance records, reported by
Volkswagen’s production facilities in Region 5.
Region 6
Amount of Incomplete Records [%]
10,00%
9,00%
8,00%
7,00%
6,00%
5,00%
4,00%
3,00%
2,00%
1,00%
Missing Material Group Information [%] Missing DUNS [%] Missing Delivery Quantity [%]
0,00%
2008 2009 2010
Figure 23: Development over time of the quality of quality performance records, reported by
Volkswagen’s production facilities in Region 6.
66

Region 7
10,00%
Missing Material Group Information [%] Missing DUNS [%] Missing Delivery Quantity [%]
Amount of Incomplete Records [%]
9,00%
8,00%
7,00%
6,00%
5,00%
4,00%
3,00%
2,00%
1,00%
0,00%
2008 2009 2010
Figure 24: Development over time of the quality of quality performance records, reported by
Volkswagen’s production facilities in Region 7.
67

Region 8
10,00%
Missing Material Group Information [%] Missing DUNS [%] Missing Delivery Quantity [%]
Amount of Incomplete Records [%]
9,00%
8,00%
7,00%
6,00%
5,00%
4,00%
3,00%
2,00%
1,00%
0,00%
2008 2009 2010
Figure 25: Development over time of the quality of quality performance records, reported by
Volkswagen’s production facilities in Region 8.
The number of incomplete records with missing D-U-N-S R○ identification has increased slightly
on a global scale during the first two years included in the evaluation – namely 2008 and 2009,
while records with missing information about the quantity of delivered products or their material
group classification have decreased over the same time period (Figure 17). The consistency of
quality performance records marks an improvement during 2010. However, the overall result of
the analysis shows that the cummulative quality performance dataset has a stable amount of affected
records, which even slightly decreases over time. The total amount of complete records is
particularly high – above 95%.
On the regional level, the analysis shows that records from the plants in Region 6 and Region
2 exhibit particularly stable data consistency with low share of incomplete records (see Figures
23 and 19). Excellent examples for data quality improvement are the records from Volkswagen
Group’s plants in Region 8 and Region 5. The plant in Region 8 managed to reduce the amount of
records with missing D-U-N-S R○ information more than 10 times over a period of only three years
(Figure 25). Similarly the production location in Region 5, managed to handle data inconsistency
problems and drastically reduced the number of incomplete records – both for records with missing
material group information as well as for records, which lack a D-U-N-S R○ number (Figure 22).
Other regions also show positive development of data quality, even though not with the same pace.
68

Thus for example, in Region 1 (Figure 18) the absolute number of records with missing D-U-N-S R○
number show an overall improvement after a slight increase in 2008.
From this several important conclusions can be derived both for quality management and the
research questions of this study. On the one hand, benchmarking locations with positive development,
such as Region 5 and Region 8, and applying their approaches for reducing the amount
of incomplete records to other production locations, will improve the consistency of quality performance
information in Volkswagen’s databases. As a result a number of Volkswagen internal
processes, which need this information as input, will benefit from this improvement. Additionally,
frequent monitoring of the quality of available data would also allow for the timely detection of
potential negative trends. On the other hand, these observations show that the operational information,
used to support Volkswagen’s business management process, with small exceptions shows
good overall consistency especially given the size of the organization. Data management is a particularly
critical issue and any organization has to take the due diligence to avoid data inconsistencies
– most of all large international organizations.
Generally speaking, integrity of the operational data is of extreme importance, not only for the
purposes of the subsequent analysis, but also for the overall production process of Volkswagen
Group (as well as for the quality management system of any other organization). Conclusions
based on a reliable set of production records have a particularly important role in closing the PDCA
(Plan-Do-Check-Act) feedback cycle, which is vital for the continuous improvement of business
processes (ISO 9001, 2008, p. 9).
69

7.2 Possible Sources of Data Bias
Given the results on data quality, presented in the previous section, the next step is to analyze for
potential sources of bias resulting from the found facts or other more general factors. This is the
subject of Section 7.2. The following several sections discuss in detail a number of biasing factors
and present possible approaches to account for and mitigate their influences.
7.2.1 Revisions of Volkswagen’s Quality Auditing Process
The first of the general influencing factors stems from the changes the auditing process has undergone
over time. Quality auditing is not a new concept to Volkswagen (and other midsize and large
companies) – the company conducted its first supplier quality evaluations already in the 1980s.
Volkswagen’s quality audits were initially performed according to a number of process-specific
lists of quality requirements, which later on were summarized into a single standardized quality
auditing checklist 11 . The latter served as a basis for the first edition of Formel Q-Fähigkeit, which
was released in 1991. Over the years the concept of quality auditing evolved and Volkswagen’s
quality auditing policy was adjusted accordingly. There have been a total of seven editions of the
document until now, as the most recent was issued in January, 2012 12 .
All changes introduced by the subsequent editions of Formel Q-Fähigkeit aimed at optimizing
Volkswagen’s auditing process and providing more reliable supplier quality capability evaluations.
For example, one especially important change in the past is raising the requirements for a positive
quality evaluation. The minimum quality capability fulfillment level for an A-rating was increased
from 90% to 92% with the release of the fifth edition of Formel Q-Fähigkeit in January, 2005.
The minimum requirement for a B-rating was also adjusted in the same edition and changed accordingly
from 80% to 82%. Another example is the hurdle principle, which was introduced in
the sixth edition of the document, released in August, 2009. Such modifications of the auditing
requirements and respectively the auditing process can result into significant differences of the
meaning of suppliers’ quality capability scores. However, comparisons between datasets which
contain such differences would be undesirable. Therefore, an important part of the current analysis
includes a number of comparisons between the distributions of quality capability scores performed
according to the different editions of Formel Q-Fähigkeit. The purpose of these comparisons is
11 Source: personal communication with Volkswagen AG employees.
12 Empirical data included in this analysis does not include any supplier evaluations performed according to the
requirements listed in Formel Q-Fähigkeit 7.0.
70

to evaluate the extent to which changes introduced with the document revisions affect empirical
data. Any significant variations in the distribution of supplier rankings suggest important differences
and consequently in such cases it is meaningful to analyze quality capability data from the
different periods separately.
To determine what kind of tests are suitable for this part of the analysis – parametric or nonparametric
statistical tests – it is first important to determine whether the analyzed data has a
normal distribution or not. An important assumption of the parametric statistical tests is that the
analyzed data is normally distributed. Thus, if the data are not normally distributed parametric tests
will not be applicable and it will be necessary to use non-parametric distribution comparison tests.
There are a number of methods to test for normality. Among those are the use of the one-sample
Kolmogorov-Smirnov goodness of fit test performed with respect to the theoretical distribution of
a normally distributed random variable (σ = 1 and µ = 0), the Liliefors test for normality, and the
Shapiro-Wilk test for normality (Marques de Sá, 2007). This analysis employs the Shapiro-Wilk
test since it is considered to be better for small sample sizes as compared to the two other tests
mentioned here (Marques de Sá, 2007). The Shapiro-Wilk test ”is based on the observed distance
between symmetrically positioned data values” (Marques de Sá, 2007, p. 187) and its statistic is
defined as (Marques de Sá, 2007, p. 187):
W =
[ k∑
i=1
a i (x n−i+1 − x i )] 2
/
⎧
n∑
⎨
(x i − x) 2 with k =
⎩
i=1
n + 1
2
, if k is odd
n
2 , if k is even (4)
The coefficients a i in (4) and the critical values of W can be obtained from table look-up
(Marques de Sá, 2007). However, the normality test results presented here were obtained using
the statistical software R 13 and no look-up was required. The R distribution comes with a ready
implementation of the Shapiro-Wilk test (shapiro.test()). The R implementation of the test
uses the null-hypothesis that sample data is normally distributed. In addition to the test statistic the
software provides a p-value, which can be used to determine whether the null-hypothesis can be
rejected at a particular significance level α, i.e. p < α.
The available quality capability information was split into a number of subsets. Due to the
great diversity of production processes the first criterion used to divide empirical data was sector
of operation of the individual suppliers. Accordingly, three major sets of data resulted – quality
13 All test results obtained in R and presented in this paper were generated using the software version 2.13.0 from
13 th April 2011.
71

evaluation scores of suppliers operating in the chemical, metal, and electrical industries accordingly.
The resulting three sets of data were further divided based on which edition of the Formel
Q-Fähigkeit was used as reference at the time the respective supplier evaluations were conducted.
As a result, each of the three major evaluation score datasets was divided further into four subsets
corresponding to evaluations performed according to Formel Q-Fähigkeit 3.0 (covering the time
period between January 1997 and March 2000), Formel Q-Fähigkeit 4.0 (April 2000 – December
2004), Formel Q-Fähigkeit 5.0 (January 2005 – July 2009), and Formel Q-Fähigkeit 6.0 (August
2009 – December 2011).
A Shapiro-Wilk normality test was performed on each of the resulting twelve groups of evaluation
scores. Additionally, in order to visually ”double-check” the results of the normality tests for
each dataset a Q-Q plot was generated. On the y-axis of each Q-Q plot are plotted the normalized
quantiles of the given sample (µ = 0 and σ = 1), while on the x-axis are plotted the theoretical
quantiles of a normal distribution with zero-mean and standard deviation of 1 (µ = 0 and σ = 1).
If a particular sample is normally distributed, the according data points in the Q-Q plot would lie
on the line y = x. Any systematic deviations from this line indicate that the data sample is not
distributed normally (Marques de Sá, 2007). In each of the following Q-Q plots the line y = x
was added (plotted in red). The results of the normality test on each of the twelve initial evaluation
score samples are presented together with the respective Q-Q plots in Figures 26 through 28
below. Each plot includes the Shapiro-Wilk test statistic W and the according p-value along with
the sample size n.
72

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−3 −2 −1 0 1 2 3
−8 −6 −4 −2 0
Normal Q−Q Plot for TOTAL_Chemie_FQF3
Theoretical Quantiles
Normalized Sample Quantiles ( σ = 1; µ = 0)
Shapiro−Wilk Test
W = 0.7665
p = 4.6244e−32
n = 793
(a) Evaluations performed according to FQF 3.0.
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−3 −2 −1 0 1 2 3
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Normal Q−Q Plot for TOTAL_Chemie_FQF4
Theoretical Quantiles
Normalized Sample Quantiles ( σ = 1; µ = 0)
Shapiro−Wilk Test
W = 0.8535
p = 9.3982e−30
n = 1022
(b) Evaluations performed according to FQF 4.0.
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−3 −2 −1 0 1 2 3
−6 −4 −2 0 2
Normal Q−Q Plot for TOTAL_Chemie_FQF5
Theoretical Quantiles
Normalized Sample Quantiles ( σ = 1; µ = 0)
Shapiro−Wilk Test
W = 0.9026
p = 3.7678e−30
n = 1531
(c) Evaluations performed according to FQF 5.0.
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−3 −2 −1 0 1 2 3
−6 −4 −2 0 2
Normal Q−Q Plot for TOTAL_Chemie_FQF6
Theoretical Quantiles
Normalized Sample Quantiles ( σ = 1; µ = 0)
Shapiro−Wilk Test
W = 0.8953
p = 1.6998e−30
n = 1469
(d) Evaluations performed according to FQF 6.0.
Figure 26: Shapiro-Wilk normality test on evaluation scores of chemical part suppliers. The samples
include quality auditing data from all geographic regions.
73

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Normal Q−Q Plot for TOTAL_Elektrik_FQF3
Normal Q−Q Plot for TOTAL_Elektrik_FQF4
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−5 −4 −3 −2 −1 0 1
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Shapiro−Wilk Test
W = 0.8226
p = 8.8423e−16
n = 238
Normalized Sample Quantiles ( σ = 1; µ = 0)
−6 −4 −2 0
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W = 0.8785
p = 2.2182e−18
n = 453
−3 −2 −1 0 1 2 3
Theoretical Quantiles
(a) Evaluations performed according to FQF 3.0.
−3 −2 −1 0 1 2 3
Theoretical Quantiles
(b) Evaluations performed according to FQF 4.0.
Normal Q−Q Plot for TOTAL_Elektrik_FQF5
Normal Q−Q Plot for TOTAL_Elektrik_FQF6
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−4 −2 0
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Shapiro−Wilk Test
W = 0.9116
p = 2.9814e−19
n = 667
Normalized Sample Quantiles ( σ = 1; µ = 0)
−6 −4 −2 0
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W = 0.8523
p = 2.274e−20
n = 460
−3 −2 −1 0 1 2 3
Theoretical Quantiles
(c) Evaluations performed according to FQF 5.0.
−3 −2 −1 0 1 2 3
Theoretical Quantiles
(d) Evaluations performed according to FQF 6.0.
Figure 27: Shapiro-Wilk normality test on evaluation scores of electrical part suppliers. The
samples include quality auditing data from all geographic regions.
74

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−3 −2 −1 0 1 2 3
−8 −6 −4 −2 0 2
Normal Q−Q Plot for TOTAL_Metal_FQF3
Theoretical Quantiles
Normalized Sample Quantiles ( σ = 1; µ = 0)
Shapiro−Wilk Test
W = 0.8511
p = 6.6782e−36
n = 1551
(a) Evaluations performed according to FQF 3.0.
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−3 −2 −1 0 1 2 3
−6 −4 −2 0 2
Normal Q−Q Plot for TOTAL_Metal_FQF4
Theoretical Quantiles
Normalized Sample Quantiles ( σ = 1; µ = 0)
Shapiro−Wilk Test
W = 0.8829
p = 2.2353e−34
n = 1743
(b) Evaluations performed according to FQF 4.0.
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−3 −2 −1 0 1 2 3
−8 −6 −4 −2 0
Normal Q−Q Plot for TOTAL_Metal_FQF5
Theoretical Quantiles
Normalized Sample Quantiles ( σ = 1; µ = 0)
Shapiro−Wilk Test
W = 0.8376
p = 4.1559e−46
n = 2682
(c) Evaluations performed according to FQF 5.0.
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−3 −2 −1 0 1 2 3
−6 −4 −2 0 2
Normal Q−Q Plot for TOTAL_Metal_FQF6
Theoretical Quantiles
Normalized Sample Quantiles ( σ = 1; µ = 0)
Shapiro−Wilk Test
W = 0.846
p = 8.5925e−40
n = 1932
(d) Evaluations performed according to FQF 6.0.
Figure 28: Shapiro-Wilk normality test on evaluation scores of metal part suppliers. The samples
include quality auditing data from all geographic regions.
75

The performed normality test gives strong statistical evidence that the respective distributions
of the quality capability scores are not normally distributed. In all cases the p-value of each set
of evaluation scores is substantially smaller than the 5%-significance level. The Shapiro-Wilk test
results are also in accordance with the corresponding Q-Q plots, which show that the distribution
of each group of points consistently deviate from the straight line representing y = x. Given
these results, a parametric statistical test would not be suitable to compare the distributions of the
individual sample groups. Alternatively, a two-sample Kolmogorov-Smirnov test was used for the
comparisons.
The two-sample Kolmogorov-Smirnov test ” is used to assess whether two independent samples
were drawn from the same population or from populations with the same distribution” (Marques
de Sá, 2007, p. 201). The test statistic measures the maximal deviation between the cumulative
sample distributions of the two sets of data and is given by (Marques de Sá, 2007, p. 201):
D m,n = max |S n (x) − S m (x)| (5)
where S n (x) and S m (x) are the cumulative sample distributions of the two samples with sizes
n and m respectively. Even for small samples the Kolmogorov-Smirnov test has a high powerefficiency
14 of about 95% when compared to its parametric counterpart – the t-test (Marques de
Sá, 2007, p. 201). The two-sample Kolmogorov-Smirnov test is implemented in R and can be
executed using the function ks.test(). The null-hypothesis of the test is that the two samples
have the same distributions. Each run of the Kolmogorov-Smirnov test in R provides the test
statistic D along with the corresponding p-value, which is used to accept or alternatively reject
the null-hypothesis at a particular significance level. In addition to the test statistic D, its p-value,
and the sizes of the two compared samples n 1 and n 2 , each Kolmogorov-Smirnov test performed
in this analysis is presented along with three plots. The first two plots present the distributions of
each of the two samples, while the third plot superimposes their cumulative distribution functions.
These graphs are particularly useful to understand the differences between the two samples and
to identify regions in which they are similar. Figure 29 presents the results of the two-sample
Kolmogorov-Smirnov test on evaluation score samples of chemical-part suppliers. The results for
metal-part and electrical-part suppliers are presented in Figures 30 and 31 accordingly.
14 The power efficiency of a non-parametric test η BA is the ratio between the sample size n A needed by the parametric
test A and the sample size n B needed by its non-parametric counterpart B to achieve the same power at the
same significance level, i.e. η BA = n A
n B
(Marques de Sá, 2007, p. 171).
76

Frequency
0 50 100 150
TOTAL_Chemie_FQF3
TOTAL_Chemie_FQF4
TOTAL_Chemie_FQF5
TOTAL_Chemie_FQF6
TOTAL_Chemie_FQF3
TOTAL_Chemie_FQF4
TOTAL_Chemie_FQF5
TOTAL_Chemie_FQF6
Distribution of TOTAL_Chemie_FQF3
30 40 50 60 70 80 90 100
Frequency
0 20 40 60 80 100
Distribution of TOTAL_Chemie_FQF4
40 50 60 70 80 90 100
Frequency
0 20 40 60 80 100
Distribution of TOTAL_Chemie_FQF4
30 40 50 60 70 80 90 100
Frequency
0 40 80 120
Distribution of TOTAL_Chemie_FQF5
40 50 60 70 80 90 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for TOTAL_Chemie_FQF3
and TOTAL_Chemie_FQF4
Two−sample Kolmogorov−Smirnov test
D = 0.15
p = 3.72e−09
n_1 = 793
n_2 = 1022
TOTAL_Chemie_FQF3
TOTAL_Chemie_FQF4
30 40 50 60 70 80 90 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for TOTAL_Chemie_FQF4
and TOTAL_Chemie_FQF5
Two−sample Kolmogorov−Smirnov test
D = 0.0714
p = 0.003863
n_1 = 1022
n_2 = 1531
TOTAL_Chemie_FQF4
TOTAL_Chemie_FQF5
40 50 60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 3.0 and FQF 4.0.
(b) Evaluations according to FQF 4.0 and FQF 5.0.
Frequency
0 40 80 120
Frequency
0 50 100 150
Distribution of TOTAL_Chemie_FQF5
40 50 60 70 80 90 100
Distribution of TOTAL_Chemie_FQF6
40 50 60 70 80 90 100
Shapiro-Wilk Normality test p-values
p-values
4,6E-32 9,4E-30 3,8E-30 1,7E-30
Samples 793 1022 1531 1469
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for TOTAL_Chemie_FQF5
and TOTAL_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.03117
p = 0.4602
n_1 = 1531
n_2 = 1469
TOTAL_Chemie_FQF5
TOTAL_Chemie_FQF6
40 50 60 70 80 90 100
Evaluation Scores [%]
(c) Evaluations according to FQF 5.0 and FQF 6.0.
TOTAL_Chemie_FQF3 1,000 3,7E-09 0 0
TOTAL_Chemie_FQF4 3,7E-09 1,000 0,004 0,127
TOTAL_Chemie_FQF5 0 0,004 1,000 0,460
TOTAL_Chemie_FQF6 0 0,127 0,460 1,000
significant on the 10% significance level
significant on the 5% significance level
(d) Distribution comparisons summary
Figure 29: Distribution comparisons between the evaluation scores of chemical-part suppliers,
performed according to different versions of Formel Q-Fähigkeit. The samples include quality
auditing data from all geographic regions.
77

In the case of chemical-part suppliers above (Figure 29) the obtained p-values are smaller than
the significance level α = 5% in the cases in which results according to FQF 3.0 were compared
to results according to FQF 4.0, as well as the comparison between results according to FQF 4.0
and FQF 5.0. On the other hand, the p-value is greater than the 5%-significance level for the
comparison between FQF 5.0 and FQF 6.0 evaluations. Thus, in the first two cases it is concluded
that there is statistical significance and the null-hypothesis, that the two samples have the same
distribution, is rejected, i.e. the distributions of the evaluations according to FQF 3.0, FQF 4.0 and
FQF 5.0 are different from one another. The test results provide also statistical evidence that the
evaluations performed according to FQF 5.0 and FQF 6.0 have similar distributions. In the latter
case the null-hypothesis is not rejected.
Similar conclusions can be made in the case of metal-part suppliers (Figure 30). Here again
the p-values are smaller than the 5%-significance level for the comparisons between FQF 3.0 an
FQF 4.0, as well as FQF 4.0 and FQF 5.0, meaning that the null-hypothesis, that the according
samples have the same distributions, is rejected. Here again, when evaluations according to FQF
5.0 are compared to evaluations according to FQF 6.0 the respective p-value is greater than the
5%-significance level, meaning that the null-hypothesis is not rejected and that the two samples
have the same distribution.
78

Distribution of TOTAL_Metal_FQF3
Distribution of TOTAL_Metal_FQF4
Frequency
0 50 150 250
40 50 60 70 80 90 100
Distribution of TOTAL_Metal_FQF4
Frequency
0 50 100 150
Frequency
0 50 100 150
40 60 80 100
Distribution of TOTAL_Metal_FQF5
Frequency
0 50 150 250
TOTAL_Metal_FQF3
TOTAL_Metal_FQF4
TOTAL_Metal_FQF5
TOTAL_Metal_FQF6
TOTAL_Metal_FQF3
TOTAL_Metal_FQF4
TOTAL_Metal_FQF5
TOTAL_Metal_FQF6
40 50 60 70 80 90 100
40 60 80 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for TOTAL_Metal_FQF3
and TOTAL_Metal_FQF4
Two−sample Kolmogorov−Smirnov test
D = 0.09903
p = 2.044e−07
n_1 = 1551
n_2 = 1743
TOTAL_Metal_FQF3
TOTAL_Metal_FQF4
40 50 60 70 80 90 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for TOTAL_Metal_FQF4
and TOTAL_Metal_FQF5
Two−sample Kolmogorov−Smirnov test
D = 0.06194
p = 0.0006026
n_1 = 1743
n_2 = 2682
TOTAL_Metal_FQF4
TOTAL_Metal_FQF5
40 60 80 100
Evaluation Scores [%]
(a) Evaluations according to FQF 3.0 and FQF 4.0.
(b) Evaluations according to FQF 4.0 and FQF 5.0.
Shapiro-Wilk Normality test p-values
Distribution of TOTAL_Metal_FQF5
Frequency
0 50 150 250
40 60 80 100
Distribution of TOTAL_Metal_FQF6
p-values
6,7E-36 2,2E-34 4,2E-46 8,6E-40
Samples 1551 1743 2682 1932
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Frequency
0 50 100 200
40 60 80 100
TOTAL_Metal_FQF3 1,000 2,0E-07 0 0
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for TOTAL_Metal_FQF5
and TOTAL_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.03381
p = 0.1533
n_1 = 2682
n_2 = 1932
TOTAL_Metal_FQF5
TOTAL_Metal_FQF6
40 60 80 100
Evaluation Scores [%]
TOTAL_Metal_FQF4 2,0E-07 1,000 0,001 0,001
TOTAL_Metal_FQF5 0 0,001 1,000 0,153
TOTAL_Metal_FQF6 0 0,001 0,153 1,000
significant on the 10% significance level
significant on the 5% significance level
(c) Evaluations according to FQF 5.0 and FQF 6.0.
(d) Distribution comparisons summary
Figure 30: Distribution comparisons between the evaluation scores of metal-part suppliers, performed
according to different versions of Formel Q-Fähigkeit. The samples include quality auditing
data from all geographic regions.
79

Distribution of TOTAL_Elektrik_FQF3
Distribution of TOTAL_Elektrik_FQF4
Frequency
0 5 10 20 30
60 70 80 90 100
Distribution of TOTAL_Elektrik_FQF4
Frequency
0 10 20 30 40 50 60
Frequency
0 10 20 30 40 50 60
60 70 80 90 100
Distribution of TOTAL_Elektrik_FQF5
Frequency
0 20 40 60 80
TOTAL_Elektrik_FQF3
TOTAL_Elektrik_FQF4
TOTAL_Elektrik_FQF5
TOTAL_Elektrik_FQF6
TOTAL_Elektrik_FQF3
TOTAL_Elektrik_FQF4
TOTAL_Elektrik_FQF5
TOTAL_Elektrik_FQF6
60 70 80 90 100
60 70 80 90 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for TOTAL_Elektrik_FQF3
and TOTAL_Elektrik_FQF4
Two−sample Kolmogorov−Smirnov test
D = 0.05722
p = 0.6866
n_1 = 238
n_2 = 453
TOTAL_Elektrik_FQF3
TOTAL_Elektrik_FQF4
60 70 80 90 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for TOTAL_Elektrik_FQF4
and TOTAL_Elektrik_FQF5
Two−sample Kolmogorov−Smirnov test
D = 0.07125
p = 0.1292
n_1 = 453
n_2 = 667
TOTAL_Elektrik_FQF4
TOTAL_Elektrik_FQF5
60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 3.0 and FQF 4.0.
(b) Evaluations according to FQF 4.0 and FQF 5.0.
Shapiro-Wilk Normality test p-values
Distribution of TOTAL_Elektrik_FQF5
Frequency
0 20 40 60 80
50 60 70 80 90 100
Distribution of TOTAL_Elektrik_FQF6
p-values
8,8E-16 2,2E-18 3,0E-19 2,3E-20
Samples 238 453 667 460
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Frequency
0 20 40 60
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
50 60 70 80 90 100
CDFs for TOTAL_Elektrik_FQF5
and TOTAL_Elektrik_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.0518
p = 0.4582
n_1 = 667
n_2 = 460
TOTAL_Elektrik_FQF5
TOTAL_Elektrik_FQF6
50 60 70 80 90 100
Evaluation Scores [%]
(c) Evaluations according to FQF 5.0 and FQF 6.0.
TOTAL_Elektrik_FQF3 1,000 0,687 0,091 0,094
TOTAL_Elektrik_FQF4 0,687 1,000 0,129 0,157
TOTAL_Elektrik_FQF5 0,091 0,129 1,000 0,458
TOTAL_Elektrik_FQF6 0,094 0,157 0,458 1,000
significant on the 10% significance level
significant on the 5% significance level
(d) Distribution comparisons summary
Figure 31: Distribution comparisons between the evaluation scores of electrical-part suppliers,
performed according to different versions of Formel Q-Fähigkeit. The samples include quality
auditing data from all geographic regions.
80

Things look a bit different in the case of electrical-part suppliers (Figure 31). Nevertheless,
certain parallels to the previous two cases can be drawn. Here the Kolmogorov-Smirnov test
does not show any statistically significant differences between the individual datasets on the 5%-
significance level. However, p-values in the comparisons between the distributions of audit results
according to the FQF 4.0 and the FQF 5.0 (p=0.129), the FQF 4.0 and FQF 6.0 (p=0.157), as well
as in the cases in which audit result according to the FQF 3.0 are compared to audits according to
FQF 5.0 (p=0.091) and FQF 6.0 (p=0.094) respectively, are significantly lower than the p-values
for the comparisons between audits according to FQF 3.0 and FQF 4.0 (p=0.687) and between
FQF 5.0 and FQF 6.0 (p=0.458). These results suggest that there are more parallels between the
audits performed according to FQF 3.0 and FQF 4.0 and respectively FQF 5.0 and FQF 6.0 than
between these two groups of results.
The results of the Kolmogorov-Smirnov test show that indeed changes in the requirements defined
in subsequent editions of Formel Q-Fähigkeit significantly influence the characteristics of
empirical data. Evaluation score limits, which Volkswagen defines to differentiate between A-,
B-, and C-suppliers, are potentially one of the major sources of difference between the individual
groups of evaluations. This is supported by the fact that there is no statistically significant difference
between the evaluation scores according to FQF 5.0 and FQF 6.0. The latter have identical
requirements for an A- and a B-rating. On the other hand, the major differences in the empirical
data occur between evaluations to FQF 3.0 and FQF 4.0 and the latter two compared to FQF 5.0
and FQF 6.0 accordingly. The two editions – FQF 3.0 and FQF 4.0 – define also different limits
for an A- and a B-rating respectively.
The evaluations presented above analyze the similarities between twelve generalized sets of
empirical data, which include audit results from a number of different regions. However, the
results of the analysis presented in Section 7.1 identified considerable regional differences of the
composition of empirical data. It is therefore meaningful to evaluate also the influence of the
changes of Formel Q-Fähigkeit on the regional level. For the purpose, the initial twelve sets of
empirical data were divided into a number of subsets based on the region in which the audits were
performed. Using the same methodology as above, for each subset a Shapiro-Wilk normality test
was performed. Since in almost all of the cases the data was found to be non-normally distributed a
two-sample Kolmogorov-Smirnov test was used to identify any statistically significant differences
between the distributions of the newly formed datasets. The results of the performed analysis are
presented in Figures 58 through 78 in Section A in the Appendix.
81

Chemical‐part Suppliers
KS test p‐value
Region
Data Series
FQF 4.0
vs FQF 5.0
FQF 5.0
vs FQF 6.0
Region A Team_A_Chemie_FQFx ‐ 0,046
Region B Team_B_Chemie_FQFx 0,850 0,915
Region C Team_C_Chemie_FQFx ‐ 1,000
Region D Team_D_Chemie_FQFx ‐ 2,4E‐04
Region E Team_E_Chemie_FQFx ‐ 0,011
Region F Team_F_Chemie_FQFx 0,006 0,684
Region G Team_G_Chemie_FQFx ‐ 0,006
Region H Team_H_Chemie_FQFx ‐ 0,556
Region I Team_I_Chemie_FQFx ‐ 0,661
Metal‐part Suppliers
KS test p‐value
Region
Data Series
FQF 4.0
vs FQF 5.0
FQF 5.0
vs FQF 6.0
Region A Team_A_Metal_FQFx ‐ 0,104
Region B Team_B_Metal_FQFx 0,041 0,531
Region C Team_C_Metal_FQFx ‐ 3,3E‐04
Region D Team_D_Metal_FQFx ‐ 1,5E‐05
Region E Team_E_Metal_FQFx ‐ 0,472
Region F Team_F_Metal_FQFx 5,4E‐06 0,448
Region G Team_G_Metal_FQFx ‐ 0,446
Region H Team_H_Metal_FQFx ‐ 1,5E‐07
Region I Team_I_Metal_FQFx 0,177 0,993
Region J Team_J_Metal_FQFx ‐ 0,374
Electrical‐part Suppliers
KS test p‐value
Region
Data Series
FQF 4.0
vs FQF 5.0
FQF 5.0
vs FQF 6.0
Region C Team_C_Elektrik_FQFx ‐ 0,456
Region E Team_E_Elektrik_FQFx ‐ 0,050
Region F Team_F_Elektrik_FQFx 0,010 0,133
Legend:
significant on the 5% level
x = 4, 5, or 6 ‐ FQF Version
Figure 32: Summary of the two-sample Kolmogorov-Smirnov test p-values obtained by comparisons
between the distributions of supplier evaluation scores according to the Formel Q-Fähigkeit
versions 4.0, 5.0, and 6.0 respectively. The results are sorted by commodity type as well as region 1 .
1 Note that the empirical information in some of the regional datasets refers to different supplier pools than the
regional division used in Section 7.1. For this reason the region identification in this and the following sections does
not correspond to the identification used in Section 7.1. However, this is irrelevant for the statistical analysis and its
results.
82

Figure 32 presents a summary of the performed Kolmogorov-Smirnov tests on the sets of regional
empirical data. The test results show that also on the regional level evaluation scores obtained
with respect to FQF 4.0 are significantly different from the evaluations performed according
to FQF 5.0. The only two cases in which no statistical significance was observed are for the evaluations
of metal-part suppliers in Region I, performed by the Volkswagen audit team in this region,
and evaluations of chemical-part suppliers in Region B performed by the local audit team. In the
first case the lack of statistical significance is most probably due to the relatively small sizes of
the data samples – 19 (FQF 4.0) and 28 (FQF 5.0) accordingly. Moreover, the p-values of the
Kolmogorov-Smirnov test for comparisons between audits to FQF 4.0 and FQF 5.0 (p=0.177) and
between FQF 4.0 and FQF 6.0 (p=0.063) are much lower than the p-value of the Kolmogorov-
Smirnov test comparing the evaluation results of metal-part suppliers in Region I with respect to
FQF 5.0 and FQF 6.0 (p=0.993). These results suggest that there are definitely more parallels
between the evaluations according to FQF 5.0 and FQF 6.0 than between these two and the audits
performed according to FQF 4.0, which situation is in accordance with the observations in the rest
of the regions. On the other hand, there is no obvious reason why the evaluations of chemical-part
suppliers performed by the audit team in Region B do not differ for FQF 4.0 and FQF 5.0, especially
provided that in the rest of the analyzed cases there is statistical evidence for such difference.
Moreover, unlike the case with metal-part suppliers in Region I, the sample sizes in the second case
are considerably larger – 69 evaluations for FQF 4.0 and 103 evaluations for FQF 5.0. The p-value
of the test is also very high – p=0.850.
The statistical test results show also that in the majority of cases there is no statistically significant
difference between the evaluation scores performed according to FQF 5.0 and FQF 6.0
accordingly. In the cases, in which statistical significance is observed, the differences in the empirical
data are very probably not due to the changes in the Formel Q-Fähigkeit, and are rather due
to regional trends in the supplier development. Region D for example is one of the markets with
high strategic importance for Volkswagen Group but is subject to high personnel fluctuations and
lack of quality sustainability 15 . This is the reason why Volkswagen has been running an intensive
supplier qualification program in this region for some years now. The program aims at improving
the quality capability of the local suppliers. It is therefore normal to expect that the overall quality
capability of the supplier base in Region D will improve over time. This is indeed what is observed
in Figures 60 and 72 in Section A in the Appendix. The quality audits performed to FQF 6.0 reveal
higher evaluation scores compared to evaluations to FQF 5.0. This trend is consistent both for
15 Source: personal communication with Volkswagen AG employees.
83

chemical-part as well as metal-part suppliers.
Other regions which show positive trends of development of the supplier audit evaluations
are Region A (Figure 57) and Region G (Figure 63) for chemical-part suppliers, and Region C
(Figure 71) and Region H (Figure 76) for metal-part suppliers. It is anticipated that in most of
these cases the positive shift of the evaluation scores is due to improved quality performance of
suppliers. It is not excluded, however, that other factors could also play a role and influence
positively the supplier evaluation scores.
7.2.2 Period of Relevance of Quality Audit Results
The observations in the previous section lead to the next potential source of data bias - development
of supplier’s quality capability over time. On the one hand, a particular supplier evaluation by
Volkswagen auditors lasts on average several days, while the time between quality audits of the
same supplier is of the order of months or even years (this point was already mentioned in the
sampling discussion above). On the other hand, process variation is natural for all productions,
as the intensity of variations is influenced by factors such as maturity of the production process,
implementation of quality improvement programs, period of operation of the production facility,
changes down the supply chain, and many others. This naturally poses the question: Even if at the
time of the audit a quality evaluation result accurately reflects the quality capability of a particular
supplier, how long is this audit result relevant?
The answer to this question is quite important for the subsequent steps of the analysis. As
proposed in the beginning of this paper, a possible way to assess the effectiveness of Volkswagen’s
audit evaluations is to compare the quality capability scores of suppliers to their quality performance
records, under the initial assumption that, if audit evaluations are performed effectively,
suppliers with better audit scores would have better quality performance and vice versa. However,
such comparison is only meaningful, if the quality capability evaluation of each supplier
adequately reflects supplier’s quality capability at the time its quality performance was recorded.
More precisely, the audit result provides only an assessment of the degree, to which a particular,
temporary state of the constantly changing supplier production system meets Volkswagen Group
customer requirements. By contrast, supplier’s quality performance is at any given point of time
directly related to the actual state of the production process. It is influenced by the error sources
down the supplier production chain, which can vary significantly over time. Thus, if the time separation
between the last audit of a supplier and its quality performance record is large, it is highly
84

probable that supplier’s quality capability, at the moment its quality performance was recorded,
does not match its quality capability record in Volkswagen’s database.
Let’s illustrate this with a concrete example – in this case a best-case scenario. Suppose the
supplier A-Supplier was audited several years ago and received an A-rating. Let’s also suppose its
production process fulfilled Volkswagen’s quality requirements to 93%. If we now assume that the
quality audit has adequately evaluated supplier’s quality capability, this means that in a particular
time window around the audit the supplier delivered to Volkswagen indeed with the performance
of an A-93%-supplier. Suppose now that after its last audit the supplier implements a one-year
improvement program, aiming at elimination of major sources of process variation. As a result,
today A-Supplier employs a significantly better production process and does not fulfill any more
just 93% of Volkswagen Group’s customer specific requirements – rather 98%. However, supplier’s
official quality capability record of A 93% in Volkswagen’s database will not change, since no new
audit at supplier’s site has been conducted. Thus, regardless of the fact that an effective supplier
evaluation was performed, the quality audit score of A-Supplier does not match its actual quality
capability any more. On the other hand, due to the implemented improvement measures the quality
of supplier’s final products would increase and the amount of rejects at Volkswagen’s production
lines would drop considerably. Consequently, even though its ”official” quality rating is A 93%, A-
Supplier would supply with the quality of an A-98% rated supplier. It is very difficult to distinguish
such a case from a case, in which the according supplier quality capability is indeed A 93% and
matches its quality capability rating in Volkswagen’s database.
Such mismatches of the measured quality capability (through an audit) and supplier’s actual
quality performance introduce noise in the empirical data and could therefore distort analytical
results. A possible workaround would be to introduce a suitable weighing function, so that quality
performance data obtained around the time of the audit is given more importance than the rest of the
empirical data. Finding the proper definition of the weighing function, however, is extremely difficult,
as variations even for similar production processes are company-specific and could happen at
substantially different time scales. Nevertheless, such a weighing function is especially important
for the analysis of empirical data generated in regions characterized with unstable supplier quality
capability and high process fluctuations. Suitable input for the definition of a weighing function
are supplier-specific process variation indicators such as cpk-values, company-specific KPIs, etc.
However, the empirical data analyzed here does not include any such information. This is the
reason why, the comparisons between quality capability and quality performance presented later
in this paper are restricted mainly to regions with relatively high stability of suppliers’ production
85

processes.
7.2.3 The Factor ”Human” and People Calibration
When considering reliability of recorded data one must also take into account the influence of
the ”human” element. The fact that data is recorded by human beings, each with their individual
perceptions and judgments, can lead to significant variations. One of the important tasks in this
project is to account for the latter when performing the analysis. The large number of people,
responsible for data collection, naturally poses the problem of calibration. There are roughly about
a hundred Volkswagen auditors worldwide. On the other hand, there are several thousand people
involved in the evaluation of suppliers’ quality performance. The complexity of calibration is further
increased also by the type of evaluations the two groups of quality inspectors should perform.
In this function people can be regarded as measurement gauges, which have to provide the according
reproducibility and repeatability of measurements. In order to do so they have to be properly
calibrated and their ability to generate consistent measurements constantly monitored. In general
it is meaningful to define calibration measures analogous to the Cg and Cgk indicators used for
assessing a systems’ measuring capability as well as devise people calibration methods. Given
the importance of people calibration, the next part of this analysis tests for differences in people’s
judgments, i.e. differences in the measurement results of the ”human gauges”.
7.2.3.1 Calibration of Supplier Quality Auditors
Due to their relatively small number, calibration of quality auditors seems to be easier as
compared to calibration of the large number of people, who evaluate suppliers’ quality performance.
Nevertheless, deviations might occur and it is therefore necessary to evaluate their
influence on the empirical data. Suppliers’ quality capability is evaluated by auditors, who have
been particularly trained for the purpose. Each of Volkswagen’s auditors has to pass a number of
quality management trainings before he is authorized to perform audits. Furthermore, the use of
the standardized auditing questionnaire in Formel Q-Fähigkeit worldwide contributes significantly
to a coherent supplier evaluation process. Additionally, Volkswagen requires from its auditors to
perform supplier quality evaluations at least 125 days annually in order to keep their know-how
up-to-date with the constant changes in the sector. Auditor calibration is aided also by a number
of communication channels on the local and international level, which facilitate the exchange of
know-how between the individual quality auditors. These include internal meetings on a weekly
86

asis within the individual auditing units, as well as various regular trainings, workshops, and
seminars (e.g.
Internationale Tagung der Auditoren Leiter (ITAL), Jahres Auditoren Tagung),
which cover important topics of the every-day auditing activities.
Such know-how transfer
suggests relatively good consistency of the auditing results and it is expected that, if two auditors
evaluate the same supplier at the same point of time, their final evaluations will be coherent.
However, in order to assess the effectiveness of auditor calibration in practice, a direct comparison
between the audit results obtained from different auditors at the same production location
is not informative, due to the fact that these are usually separated in time and in the meantime
supplier’s quality capability is influenced by process variation as already discussed in detail in the
previous section. This is the reason why here a different approach was used to evaluate how good
the calibration of Volkswagen auditors is. Rather than concentrating on single suppliers, this part
of the analysis considered entire groups of suppliers based on their location, under the assumption
that the latter are subject to similar influencing factors common to the respective region – for example
suppliers in India. Thus, if two groups of auditors 16 perform a large number of audits in the
same supplier pool in the same time window, it is expected that their evaluations will have similar
distributions, assuming that the two audit groups are equally calibrated. In the previous sections it
was observed that in certain regions such as Region D or Region H, for example, there has been a
positive development of the overall quality capability of suppliers in these regions from the time
period of FQF 5.0 to the time period of FQF 6.0. To minimize the influence of such market-specific
developments the analysis discussed in this section restricted the time period considered for each
comparison to the period of relevance of a single edition of Formel Q-Fähigkeit.
The analytic method used for the comparisons in this section is identical to the one introduced
in Section 7.2.1. For the purpose the empirical quality capability evaluation data was divided into
a number of datasets based on the audit team, which performed the evaluations, and the region
in which the according suppliers are located. For each set of data a Shapiro-Wilk normality test
was conducted. Subsequently the two-sample Kolmogorov-Smirnov test was used to identify any
potential differences in the evaluation distributions. At this point it is necessary to mention that
comparisons were carried out only for part of the regions introduced in the preceding sections. The
only reason why audit data from the remaining regions, analyzed in the previous sections, were not
included in this part of the analysis is because they do not contain large enough samples of quality
evaluations performed by two different auditor teams. In such regions the only available datasets
16 Note that evaluations focused on single auditors were not conducted due to an internal Volkswagen restriction,
which forbids any kind of individual-related performance evaluations.
87

large enough for statistical analysis are those generated by the respective local auditor teams. In
total 10 different sets of empirical data over the three commodities were evaluated.
The majority of the evaluated audit records showed very good consistency of the audit results,
which implies that Volkswagen’s audit teams are well calibrated. In the few cases, in which statistically
significant difference between the compared datasets was observed, either the number
of observations was so low, that the small size of the datasets is the most probable reason for the
statistical significance of the Kolmogorov-Smirnov test, or the empirical data were subject to specific
regional influences and such differences had to be expected. Thus the observed deviations do
not necessarily indicate different calibration of the audit teams. Moreover, these results show that
the evaluations of the individual auditor teams can be subject to the influence of a small cultural
human factor, even when the auditors are equally well calibrated. Based on the observations of
this part of the analysis, to avoid any influence of potentially different supplier audit evaluations,
the subsequent analysis evaluates audit results from different auditor teams separately.
7.2.3.2 Calibration of Production Quality Assessment
If quality capability data are to be compared to quality performance data, one has to account
for any potential biases in suppliers’ quality performance as well. The evaluation process
in the latter case is again highly influenced by the judgments of human beings and therefore also
here it is important to make sure that the same evaluation standards are applied. However, the
number of people involved in the evaluation process of supplier quality performance is much
larger than the total number of Volkswagen auditors conducting quality audits worldwide. Apart
from quality inspectors and supplier quality engineers, practically every single employee involved
in the assembly process is also involved in the detection of eventual quality-related problems of
the supplied components and consequently in the assessment of suppliers’ quality performance.
Moreover, calibration of all participants in the performance evaluation process is particularly
demanding due to the extremely heterogeneous character of the assessed quality. Calibrating the
assessment of quality for products, whose quality can be measured using quantitative parameters
such as length, weight, temperature, and other measurable physical characteristics, is much easier
as compared to calibrating the evaluation of haptic quality for product characteristics such as
color, look, feel, etc. Often, in the latter cases calibration is only possible with the help of shared
master and limit samples. Their use, however, can still be subject to individual influences. For this
reason, empirical data originating from different Volkswagen production plants might be subject
88

to differences even for the same types of products.
To assess the extent to which quality performance data from the different Volkswagen production
facilities differ from one another, the supplier quality performance records from a number of
different plants were compared. The available empirical data from each plant included in the analysis
were divided into datasets based on the type of delivered products using the material group
differentiation (used by the Volkswagen Procurement Department) already introduced in the previous
sections. Similar to the analysis in Section 7.2.3.1 for each dataset a Shapiro-Wilk normality
test was conducted and the individual sets of empirical data were compared pairwise with the help
of a two-sample Kolmogorov-Smirnov test. The quality performance records of 98 suppliers were
evaluated for two different material groups – 0058 (”Moulded parts < DIN A4 for body”) and
0068 (”Moulded parts > DIN A4 for body”). The according empirical data was reported by 11
different Volkswagen production facilities. Suppliers selected for the evaluation deliver the same
type of products to two different Volkswagen plants. Each of the conducted pairwise comparisons
includes the quality performance of all suppliers, which deliver components from a single material
group simultaneously to two Volkswagen production facilities. Figure 33, for example, presents the
test results for the comparison of two datasets – namely A_M_WSGR_0058_Plant_A_ppm and
A_M_WSGR_0058_Plant_M_ppm. The two datasets include the quality performance records of
only those suppliers (in the particular case 10), which deliver parts from the material group 0058 simultaneously
to both Plant A and Plant M. The first dataset – A_M_WSGR_0058_Plant_A_ppm
– contains the quality performance records of these suppliers for their deliveries to Plant A, while
the second dataset – A_M_WSGR_0058_Plant_M_ppm – contains the quality performance
records of the exactly same suppliers for their deliveries to Plant M. Using this methodology a
total of seven different pairs of data were generated. The results from the comparisons of the remaining
pairs of performance evaluations are presented in Figures 79 through 84 in Section B in
the Appendix.
This approach allows to directly compare the quality performance evaluations based on the
same products coming from the same production processes conducted by two independent groups
of evaluators in the respective Volkswagen production facilities. Any differences in the distributions
of the compared pairs of data could suggest discrepancies in the evaluation methods.
The conducted analysis shows, however, that the evaluations of the individual plants are rather
consistent. In all cases the resulting p-values of the Kolmogorov-Smirnov test are greater than
89

Distribution of A_M_WSGR_0058_Plant_A_ppm
Frequency
0.0 0.5 1.0 1.5 2.0
Frequency
0.0 1.0 2.0 3.0
Plant: A
WSGR: 0058
Plant: M
WSGR: 0058
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
Plant: A
WSGR: 0058
Plant: M
WSGR: 0058
1 2 3 4
Distribution of A_M_WSGR_0058_Plant_M_ppm
1 2 3 4
CDFs for A_M_WSGR_0058_Plant_A_ppm
and A_M_WSGR_0058_Plant_M_ppm
Two−sample Kolmogorov−Smirnov test
D = 0.3077
p = 0.5696
n_1 = 13
n_2 = 13
A_M_WSGR_0058_Plant_A_ppm
A_M_WSGR_0058_Plant_M_ppm
1 2 3 4
ppm Scores [log]
Shapiro-Wilk Normality test p-values
p-values 0,180 0,159
Samples 13 13
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Plant: A
WSGR: 0058
Plant: M
WSGR: 0058
1,000 0,570
0,570 1,000
significant on the 10% significance level
significant on the 5% significance level
(a) Performance records comparison based on ppm
(b) Distribution comparison summary
Figure 33: Comparison between the quality performance records of the same 13 suppliers reported
by two different plants – Plant A and Plant M. The reported defective parts per million (ppm) refer
to quality problems of components from the material group 0058 (”Moulded parts < DIN A4 for
body”).
0.05, which in statistical terms means that the null-hypothesis of the non-parametric test is not
rejected on the 5%-significance level. This result is not surprising given that the evaluated parts
are metal components used in the raw construction of the automobile, for which the most important
characteristic is parts’ geometry, i.e. parts’ quality is defined by quantifiable metrics.
Even though the analytical results above do not suggest any discrepancies between the evaluation
methods of the individual Volkswagen production plants (at least not for the evaluated product
groups), empirical data from the different Volkswagen locations may still contain certain differences.
This is demonstrated by the analytical results presented in Figures 34 through 36. Here the
quality performance records of suppliers which deliver a particular type of products reported by
the different Volkswagen locations are compared to one another. The statistical evaluation contains
performance records for three different material groups – 0058 (Figure 34), 0068 (Figure 35,
and 0096 – ”Reservoirs, covers, pipes, wires” (Figure 36). Each figure presents a summary of the
conducted Shapiro-Wilk normality test and the two-sample Kolmogorov-Smirnov test accordingly.
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Plant: A
WSGR: 0058
Plant: B
WSGR: 0058
Plant: E
WSGR: 0058
Plant: G
WSGR: 0058
Plant: H
WSGR: 0058
Plant: K
WSGR: 0058
Plant: L
WSGR: 0058
Plant: M
WSGR: 0058
Plant: N
WSGR: 0058
Plant: O
WSGR: 0058
Plant: P
WSGR: 0058
Plant: A
WSGR: 0058
Plant: B
WSGR: 0058
Plant: E
WSGR: 0058
Plant: G
WSGR: 0058
Plant: H
WSGR: 0058
Plant: K
WSGR: 0058
Plant: L
WSGR: 0058
Plant: M
WSGR: 0058
Plant: N
WSGR: 0058
Plant: O
WSGR: 0058
Plant: P
WSGR: 0058
Shapiro-Wilk Normality test p-values
p-values 0,048 0,235 0,056 0,050 0,103 0,008 0,005 0,001 0,003 0,001 0,008
Samples 25 17 20 23 32 27 27 31 24 22 29
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Plant: A
WSGR: 0058
Plant: B
WSGR: 0058
Plant: E
WSGR: 0058
Plant: G
WSGR: 0058
Plant: H
WSGR: 0058
Plant: K
WSGR: 0058
Plant: L
WSGR: 0058
Plant: M
WSGR: 0058
Plant: N
WSGR: 0058
Plant: O
WSGR: 0058
Plant: P
WSGR: 0058
1,000 0,755 0,711 0,107 0,707 0,012 0,632 0,345 0,327 0,085 0,010
0,755 1,000 0,804 0,208 0,632 0,026 0,807 0,984 0,386 0,552 0,215
0,711 0,804 1,000 0,579 0,458 0,001 0,228 0,269 0,857 0,338 0,015
0,107 0,208 0,579 1,000 0,093 4,9E-06 0,173 0,131 0,553 0,081 0,008
0,707 0,632 0,458 0,093 1,000 0,004 0,193 0,222 0,228 0,043 0,005
0,012 0,026 0,001 4,9E-06 0,004 1,000 0,004 0,003 2,7E-04 6,4E-05 1,1E-06
0,632 0,807 0,228 0,173 0,193 0,004 1,000 0,786 0,139 0,474 0,121
0,345 0,984 0,269 0,131 0,222 0,003 0,786 1,000 0,112 0,615 0,173
0,327 0,386 0,857 0,553 0,228 2,7E-04 0,139 0,112 1,000 0,014 0,002
0,085 0,552 0,338 0,081 0,043 6,4E-05 0,474 0,615 0,014 1,000 0,147
0,010 0,215 0,015 0,008 0,005 1,1E-06 0,121 0,173 0,002 0,147 1,000
significant on the 10% significance level
significant on the 5% significance level
Figure 34: Comparison between the supplier quality performance records reported by eleven different
plants based on ppm. The reported defective parts per million (ppm) refer to quality problems
of components from the material group 0058 (”Moulded parts < DIN A4 for body”).
91

Plant: B
WSGR: 0068
Plant: G
WSGR: 0068
Plant: H
WSGR: 0068
Plant: I
WSGR: 0068
Plant: K
WSGR: 0068
Plant: L
WSGR: 0068
Plant: N
WSGR: 0068
Plant: O
WSGR: 0068
Plant: P
WSGR: 0068
Plant: B
WSGR: 0068
Plant: G
WSGR: 0068
Plant: H
WSGR: 0068
Plant: I
WSGR: 0068
Plant: K
WSGR: 0068
Plant: L
WSGR: 0068
Plant: N
WSGR: 0068
Plant: O
WSGR: 0068
Plant: P
WSGR: 0068
Shapiro-Wilk Normality test p-values
p-values 0,610 0,987 0,450 0,430 0,261 0,244 0,023 0,092 0,022
Samples 25 19 28 24 35 37 26 23 21
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Plant: B
WSGR: 0068
Plant: G
WSGR: 0068
Plant: H
WSGR: 0068
Plant: I
WSGR: 0068
Plant: K
WSGR: 0068
Plant: L
WSGR: 0068
Plant: N
WSGR: 0068
Plant: O
WSGR: 0068
Plant: P
WSGR: 0068
1,000 0,973 0,257 0,349 0,927 0,960 0,216 0,714 0,016
0,973 1,000 0,145 0,124 0,290 0,961 0,316 0,445 0,010
0,257 0,145 1,000 0,865 0,285 0,029 0,026 0,111 0,001
0,349 0,124 0,865 1,000 0,952 0,273 0,080 0,089 0,012
0,927 0,290 0,285 0,952 1,000 0,401 0,134 0,215 0,020
0,960 0,961 0,029 0,273 0,401 1,000 0,053 0,414 0,005
0,216 0,316 0,026 0,080 0,134 0,053 1,000 0,976 0,029
0,714 0,445 0,111 0,089 0,215 0,414 0,976 1,000 0,018
0,016 0,010 0,001 0,012 0,020 0,005 0,029 0,018 1,000
significant on the 10% significance level
significant on the 5% significance level
Figure 35: Comparison between the supplier quality performance records reported by nine different
plants based on ppm. The reported defective parts per million (ppm) refer to quality problems
of components from the material group 0068 (”Moulded parts > DIN A4 for body”).
For material group 0058 supplier quality performance data from Plant K stands out as systematically
different from the rest of the analyzed data. In all cases the Kolmogorov-Smirnov
test’s low p-value for this particular dataset indicates statistically significant difference on the 5%-
significance level. Other plants which show potential differences between the quality performance
of their suppliers and the rest of the delivering companies are Plant O and Plant P. Plant P stands
out as systematically different also in the case of material group 0068. Here the p-values of the
92

Kolmogorov-Smirnov test are below the 5%-significance level in all 8 comparisons (Figure 35).
Other plants for which the Kolmogorov-Smirnov test revealed statistically significant results are
Plant H and Plant I as well as Plant N. Apart from the identified differences the rest of the evaluations
were found to be statistically similar, meaning that suppliers which deliver the material
groups 0058 and 0068 to the different Volkswagen plants have consistent quality.
Shapiro‐Wilk Normality test p‐values
Plant: C
WSGR: 0096
Plant: D
WSGR: 0096
Plant: F
WSGR: 0096
Plant: H
WSGR: 0096
Plant: J
WSGR: 0096
Plant: L
WSGR: 0096
p‐values 0,088 0,475 0,086 0,112 0,060 0,575
Samples 17 15 24 23 15 15
Kolmogorov‐Smirnov Distribution Comparison test
p‐values matrix
Plant: C
WSGR: 0096
Plant: D
WSGR: 0096
Plant: F
WSGR: 0096
Plant: H
WSGR: 0096
Plant: J
WSGR: 0096
Plant: L
WSGR: 0096
Plant: C
WSGR: 0096
Plant: D
WSGR: 0096
Plant: F
WSGR: 0096
Plant: H
WSGR: 0096
Plant: J
WSGR: 0096
Plant: L
WSGR: 0096
1,000 0,531 0,013 0,026 0,134 0,604
0,531 1,000 7,8E‐05 1,1E‐04 0,003 0,026
0,013 7,8E‐05 1,000 0,097 0,854 0,009
0,026 1,1E‐04 0,097 1,000 0,105 0,409
0,134 0,003 0,854 0,105 1,000 0,028
0,604 0,026 0,009 0,409 0,028 1,000
significant on the 10% significance level
significant on the 5% significance level
Figure 36: Comparison between the supplier quality performance records reported by six different
plants based on ppm. The reported defective parts per million (ppm) refer to quality problems of
components from the material group 0096 (”Reservoirs, covers, pipes, wires”).
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Things are a bit different, however, for the case of material group 0096 (Figure 36). Here more
than half of the performed comparisons revealed statistically significant differences between the
individual datasets which made this particular case suitable for closer examination (a ”technical
drill down”). The most probable reason for these differences is the high diversity of components
which comprise this material group. A closer look at the available information revealed that material
group 0096 has a total of 15 different subgroups in contrast to material group 0058 comprising
of only 2 subgroups and material group 0068, which has only 4 subgroups 17 . The differences of
the individual types of products, whose quality performance was evaluated, can also be derived
from the fields of operation of the individual plants. Among the evaluated plants are plants, which
build gearboxes, motors, after-sales replacement components, as well as plants involved in the final
assembly of automobiles for different market segments.
The analytical results included in this section underline important differences also in the character
of quality performance data. Such discrepancies between the individual supplier records need
to be handled with care, as in particular cases direct comparisons between the separate datasets are
not desirable. To avoid potential negative influences of such different character of the empirical
data on the final analytical results, quality performance data not only for different material groups
but also data from the same material group reported by different plants of the Volkswagen Group
are evaluated separately in the subsequent analysis.
17 Note that the empirical data included in this analysis are not detailed enough and do not provide any information
about the subgroups of the individual material groups.
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7.3 Matching Quality Capability and Quality Performance
The steps of the project presented in Section 7.1 were necessary to eliminate any incomplete
records and thus assure a reliable set of empirical data, while in Section 7.2 it was evaluated
under which pre-conditions and circumstances it is reasonable to correlate quality capability and
quality performance data. In this section the quality performance records of two groups of suppliers
are compared to their quality audit scores in an attempt to give a possible answer to the
research questions defined in the beginning of this paper. The two datasets were built considering
the possible sources of data bias presented in the discussion in Section 7.2. However, to obtain the
quality evaluations of the respective supplier production processes, which are relevant for the considered
components, turned out to be a significant challenge and cost a significant amount of effort
and time. This is because, while quality performance data are structured according to material
groups defined by the Volkswagen Group Procurement department (Konzern Beschaffung), suppliers’
quality capability is assessed with respect to the product groups defined by the Group Quality
Assurance department (Konzern Qualitätssicherung). Even though the two types of categorization
cover the same spectrum of components, there are little parallels between the two (as already discussed
in one of the previous sections), and the only way to match them was to manually process
the records (no automation of the process was possible). This was necessary due to the fact that
at the time the analysis was performed there was no correspondence matrix, which described the
relations between the two types of categorization. Defining such a matrix is a challenge in itself,
due to the fact that the criteria used to define the two categorizations are of completely different
character. While material groups use application as their main differentiating criterion, product
groups are primarily based on the type of process, which is required to manufacture the according
components.
In the majority of cases each supplier production location has several quality capability evaluations
according to different product groups. The number of evaluations depends on the diversity
of produced components and accordingly the variety of production processes in the according facility.
However, to tell which exactly of these audit evaluations assesses the quality capability of
the production process, used to manufacture a particular component, which appears in supplier’s
quality performance record, is not possible directly given the available data identification system
currently in use at Volkswagen. Therefore, the relevant capability score had to be manually identified
for every single supplier quality performance record included in the analysis. Given the high
processing overhead, the analysis presented here was limited to the two presented cases.
95

At this point it is necessary to mention that this particular challenge is relevant not only for
the purposes of this analysis but also for Volkswagen’s internal processes such as the contract
awarding by the Corporate Sourcing Committee (CSC), which needs inputs both from the quality
assurance and procurement departments for the individual sourcing decisions. The problem is not
new to Volkswagen, but at the time the evaluations were carried out there was still no functioning
solution to it. In 2010 the Group Procurement, Volkswagen Research and Development, as well as
the Group Quality Assurance departments discussed possible ways to unify the different product
identification systems. One possible solution includes the definition of a relatively complex identification
system (with several levels of identification), which will allow for the seamless interchange
of information between the different databases of the according departments. The proposed solution
has already been partially implemented. Once this concept is fully functional, it will allow for
a more comprehensive, largely automated analysis of the type presented here.
The first set of analyzed data contains the
quality performance records of 31 suppliers,
which deliver products from the material group
0096 (”Reservoirs, covers, pipes, wires”) to Plant
H. The quality performance records include the
number of ppm each supplier accumulated over
the period of three years – 2008, 2009, and 2010
respectively. However, not all suppliers were active
throughout the entire time period. What is
also important to mention is that not all of the
suppliers included in the analysis have had quality
related problems in the respective period of
time. On the contrary, the quality records of 18
suppliers, representing almost 60% of the evaluated
records, do not contain any reports of delivery
of defective components. At this point, considering
the hypothesis defined in the beginning
of this paper, it is reasonable to expect, that suppliers
which have no ppm have also better quality
capability evaluations compared to suppliers,
which experienced quality related problems. The
Frequency
0 1 2 3 4
Frequency
0 1 2 3 4
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
Distribution of Ep no ppms
85 90 95 100
Distribution of Ep with ppms
85 90 95 100
CDFs for Ep no ppms
and Ep with ppms
Two−sample Kolmogorov−Smirnov test
D = 0.1667
p = 0.9848
n_1 = 18
n_2 = 13
Ep no ppms
Ep with ppms
85 90 95 100
Evaluation Scores [%]
Figure 37: Distribution comparison between
the evaluation scores of suppliers without reported
quality related problems in the series
production (E P no ppms) and suppliers with
reported problems (E P with ppms). Both
groups of suppliers delivered components from
the material group 0096 (”Reservoirs, covers,
pipes, wires”) to Plant H.
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overall quality capability evaluations of the two groups of suppliers – with and without ppm - are
compared in Figure 37 using a two-sample Kolmogorov-Smirnov test. For both distributions a
Shapiro-Wilk normality test revealed statistically significant evidence that they are non-normally
distributed on the 5%-significance level with p-values of 0.021 in the case of suppliers without
ppm, and 0.019 in the case of suppliers with ppm accordingly. The performed Kolmogorov-
Smirnov test reveals no statistically significant result on the 5%-significance level, contrary to
the expectations. Moreover, the according p-value of 0.985 of the non-parametric test is rather
high, indicating that the evaluations of the two groups of suppliers are almost identical.
Figure 38 presents the results of a linear regression
between the quality performance of suppliers
with ppm (the respective ppm records were
evaluated on a logarithmic scale with base 10)
and their quality capability evaluations. The estimated
parameters of the least square linear fit are
an intercept of β 0 = −6.439 (std. error= 3.977)
and a slope of β 1 = 0.0939 (std. error= 0.044).
Additionally an F-test was conducted to evaluated
the significance of the estimated parameters
of the least squares fit. The null-hypothesis of the
performed test is that β 1 = 0, i.e. there is no correlation
between the two compared datasets, with
alternative hypothesis that β 1 ≠ 0. The p-value
of the F-test of 0.056 on 1 and 11 degrees of freedom
indicates a marginally significant result on
the 5%-significance level, i.e. the null-hypothesis
of the F-test can be rejected and the least squares
fit shows indeed a positive correlation between
PPM [log]
20 50 100 200
●
Quality Capability vs PPM
●
●
●
●
84 86 88 90 92
Quality Capability Evaluation [%]
Material Group: 0096 (Reservoirs, covers, pipes, wires)
Plant: Plant H
Figure 38: A linear regression fit between
the quality performance and quality capability
records of suppliers, which delivered products
from the material group 0096 to Plant H and experienced
quality related problems in the time
period between the years 2008 and 2010.
the quality performance records and quality capability scores of the respective suppliers (β 1 ≠ 0).
The positive slope indicates that suppliers, which received higher quality capability evaluation during
an audit, experience a larger number of quality related problems. These results are in contrast
with the initial hypothesis presented in the beginning of this paper, which states that suppliers with
higher capability scores are expected to have better quality performance and thus lower number of
ppm respectively.
●
●
●
●
●
●
●
●
97

The second set of empirical data analyzed in this section comprises of the quality performance
records of suppliers, which deliver parts from the material group 0337 (”Light metal cast gearbox
parts”) to the Volkswagen production facility Plant C. The data contains the records of 44 different
suppliers, as in this case again around 60% (26 suppliers) of all evaluated supplier records contain
no information about any quality related problems in the respective time period between 2008 and
2010. Note that also here not all suppliers were active during the entire period of evaluation.
Distribution of Ep no ppms
Quality Capability vs PPM
●
Frequency
0 2 4 6 8
Frequency
0 1 2 3 4 5 6
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
85 90 95 100
Distribution of Ep with ppms
85 90 95 100
CDFs for Ep no ppms
and Ep with ppms
Two−sample Kolmogorov−Smirnov test
D = 0.235
p = 0.5994
n_1 = 26
n_2 = 18
Ep no ppms
Ep with ppms
85 90 95 100
Evaluation Scores [%]
Figure 39: Distribution comparison between
the evaluation scores of suppliers without reported
quality related problems in the series
production (E P no ppms) and suppliers with reported
problems (E P with ppms). Both groups
of suppliers delivered components from the
material group 0337 (”Light metal cast gearbox
parts”) to Plant C.
PPM [log]
1 10 100 1000 10000
●
●
●
●
●
●
●
●
●
●
●
86 88 90 92 94
Quality Capability Evaluation [%]
Material Group: 0337 (Light metal cast gearbox parts)
Plant: Plant C
Figure 40: A linear regression fit between
the quality performance and quality capability
records of suppliers, which delivered products
from the material group 0337 to Plant C and experienced
quality related problems in the time
period between the years 2008 and 2010.
Figure 39 presents the results of a two-sample Kolmogorov-Smirnov comparison between the
quality capability scores of suppliers with and respectively without ppm, which deliver parts from
the material group 0337 to Plant C. Additionally, for each set of evaluation scores a Shapiro-Wilk
normality test was performed. The resulting Shapiro-Wilk test’s p-values are 0.008 for suppliers
without ppm and 0.031 for suppliers with ppm, indicating statistical significance on the 5%-
significance level in both cases. Based on these p-values it is concluded that both datasets are
non-normally distributed. The Kolmogorov-Smirnov comparison between the two distributions
reveals a particularly high p-value of 0.599, which is above the 5%-significance level, meaning
●
●
●
●
●
98

that the null-hypothesis of the test is not rejected and the two datasets are found to have statistically
similar distributions. These results are in accordance with the accompanying plot of the two
cumulative distribution functions included in Figure 39.
Similar to the previous case, also here a linear regression was used to determine the relation
between the quality performance records (on a decimal logarithmic scale) of suppliers, which were
reported to have quality related problems, and their respective quality capability scores. The results
of the linear regression are presented in Figure 40. The estimated parameters of the least square
fit are accordingly an intercept β 0 = 8.212 (std. error= 9.61083) and a slope β 1 = −0.058 (std.
error= 0.107). The slightly negative slope of the least squares fit suggests that suppliers with
higher audit evaluation score would have less ppm and accordingly better quality performance.
However, a F-test with the null-hypothesis of β 1 = 0 and alternative hypothesis β 1 ≠ 0 was used
to check the significance of the obtained regression parameter estimates and revealed a p-value
of 0.594. The high p-value of the F-test indicates that its null-hypothesis is not rejected on the
5%-significance level, i.e. there is no statistically significant evidence supporting the claim that the
linear fit has a negative slope indeed.
For the second set of empirical data, apart from the comparison between the overall evaluation
scores of suppliers with and respectively without ppm, the individual evaluation components (already
presented in detail in Section 6.1), which comprise a particular audit evaluation, were also
compared. The purpose of these comparisons is to possibly find potential reasons why these two
groups of suppliers perform differently on the level of quality performance, while there is little
difference between their quality capability evaluations. Note that the supplier catalogue, which as
already described in Section 6.3 was the major source of audit evaluation data for the analysis,
provides only the overall evaluation scores and does not contain any information about the individual
evaluation elements of the audit results. For that reason here again the according scores
had to be obtained manually from the audit report hard copies. For each of the resulting datasets a
Shapiro-Wilk normality test was performed. The evaluation results were then compared pairwise
with respect to the individual elements of a quality audit using a two-sample Kolmogorov-Smirnov
test. The test results are presented in Figures 41 and 42 below.
99

Distribution of Ez no ppms
Distribution of Ek no ppms
Frequency
0 2 4 6 8
80 85 90 95 100
Distribution of Ez with ppms
Frequency
0 1 2 3 4
Frequency
0 1 2 3 4 5 6 7
80 85 90 95 100
Distribution of Ek with ppms
Frequency
0 1 2 3 4
80 85 90 95 100
80 85 90 95 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Ez no ppms
and Ez with ppms
Two−sample Kolmogorov−Smirnov test
D = 0.07692
p = 1
n_1 = 26
n_2 = 18
Ez no ppms
Ez with ppms
80 85 90 95 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Ek no ppms
and Ek with ppms
Two−sample Kolmogorov−Smirnov test
D = 0.2009
p = 0.7841
n_1 = 26
n_2 = 18
Ek no ppms
Ek with ppms
80 85 90 95 100
Evaluation Scores [%]
(a) Comparison for the element Purchased Material.
(b) Comparison for the element Customer Care / Customer
Satisfaction.
Distribution of EU1 no ppms
Distribution of EU2 no ppms
Frequency
0 1 2 3 4
85 90 95 100
Frequency
0.0 1.0 2.0 3.0
75 80 85 90 95 100
Distribution of EU1 with ppms
Distribution of EU2 with ppms
Frequency
0 1 2 3 4
85 90 95 100
Frequency
0.0 1.0 2.0 3.0
75 80 85 90 95 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for EU1 no ppms
and EU1 with ppms
Two−sample Kolmogorov−Smirnov test
D = 0.1709
p = 0.9151
n_1 = 26
n_2 = 18
EU1 no ppms
EU1 with ppms
85 90 95 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for EU2 no ppms
and EU2 with ppms
Two−sample Kolmogorov−Smirnov test
D = 0.188
p = 0.8463
n_1 = 26
n_2 = 18
EU2 no ppms
EU2 with ppms
75 80 85 90 95 100
Evaluation Scores [%]
(c) Comparison for the element Personnel / Personal
Qualification.
(d) Comparison for the element Machinery / Equipment.
Figure 41: Distribution comparison between the individual components of the evaluation scores
of suppliers without reported quality related problems in the series production (E P no ppms) and
suppliers with reported problems (E P with ppms). Both groups of suppliers delivered components
from the material group 0337 (”Light metal cast gearbox parts”) to Plant C.
100

Distribution of EU3 no ppms
Distribution of EU4 no ppms
Frequency
0 1 2 3 4 5 6
Frequency
0 2 4 6 8
85 90 95 100
80 85 90 95 100
Distribution of EU3 with ppms
Distribution of EU4 with ppms
Frequency
0 1 2 3 4 5
85 90 95 100
Frequency
0.0 1.0 2.0 3.0
80 85 90 95 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for EU3 no ppms
and EU3 with ppms
Two−sample Kolmogorov−Smirnov test
D = 0.1966
p = 0.8056
n_1 = 26
n_2 = 18
EU3 no ppms
EU3 with ppms
85 90 95 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for EU4 no ppms
and EU4 with ppms
Two−sample Kolmogorov−Smirnov test
D = 0.1624
p = 0.9418
n_1 = 26
n_2 = 18
EU4 no ppms
EU4 with ppms
80 85 90 95 100
Evaluation Scores [%]
(a) Comparison for the element Transport / Parts Handling
/ Storage / Packaging.
(b) Comparison for the element Failure Analysis / Corrective
Measures / Continuous Improvements.
Figure 42: Distribution comparison between the individual components of the evaluation scores
of suppliers without reported quality related problems in the series production (E P no ppms) and
suppliers with reported problems (E P with ppms). Both groups of suppliers delivered components
from the material group 0337 (”Light metal cast gearbox parts”) to Plant C.
The conducted Kolmogorov-Smirnov tests reveal no statistically significant differences at the
5%-significance level also for the individual components of the audit evaluations. In all cases
the resulting p-values are considerably high ranging from 0.784 for the element ”Customer Care/-
Customer Satisfaction” to 1.000 for the element ”Suppliers / Purchased Materials” 18 , for which
element the audit scores of the two groups of suppliers are practically identical. A look on the
cumulative distribution functions of each pair of datasets confirms the results of the Kolmogorov-
Smirnov test. In almost all cases the compared pairs of distributions overlap completely. The only
case in which the suppliers without ppm possibly have an advantage in terms of evaluation scores
is for the element ”Customer Care/Customer Satisfaction”, where their distribution shows slightly
better evaluation results up to the 91%-mark (Figure 41b). This is probably due to the fact that
during a quality audit, the quality performance, which a supplier has shown up to the time of the
audit, is also considered in the final evaluation.
18 The names of the respective audit evaluation elements are used here as they appear in the Formel Q-Fähigkeit 6.0
(2009). In the currently valid Formel Q-Fähigkeit 7.0 these may vary.
101

7.4 Relation Between Number of Defective Parts and Delivery Amount
The two case studies presented in Section 7.3 above do not provide any statistically significant
evidence that suppliers, which have quality related problems expressed in terms of ppm, are rated
differently in their quality capability evaluations than suppliers without any reported quality problems.
The reader is reminded that the conducted evaluations are based on empirical data, which
account for a number of influencing factors presented in Section 7.2. Therefore, these particular biasing
factors could have little influence on the observed non-correlation between the two statistical
quantities.
Nevertheless, there is one particular indicator (other than ppm), based on which in both of the
presented case studies suppliers with ppm records turn out to be very different from suppliers without
quality problems. This is namely the number of components they delivered. Figure 43 presents
the results of a two-sample Kolmogorov-Smirnov test, which compares the delivery amounts (on
a decimal logarithmic scale) of the two groups of suppliers in each of the above cases. The test
Distribution of Records w/o ppm (MG 0096/Plant H)
Distribution of Records w/o ppm (MG 0337/Plant C)
Frequency
0 1 2 3 4 5
3.5 4.0 4.5 5.0 5.5 6.0 6.5
Frequency
0 1 2 3 4 5
2 3 4 5 6
Distribution of Records with ppm (MG 0096/Plant H)
Distribution of Records with ppm (MG 0337/Plant C)
Frequency
0 1 2 3 4 5
3.5 4.0 4.5 5.0 5.5 6.0 6.5
Frequency
0 2 4 6 8
2 3 4 5 6
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0096/Plant H)
and Records with ppm (MG 0096/Plant H)
Two−sample Kolmogorov−Smirnov test
D = 0.5598
p = 0.009848
n_1 = 18
n_2 = 13
Records w/o ppm (MG 0096/Plant H)
Records with ppm (MG 0096/Plant H)
3.5 4.0 4.5 5.0 5.5 6.0 6.5
Delivery Amount [log]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0337/Plant C)
and Records with ppm (MG 0337/Plant C)
Two−sample Kolmogorov−Smirnov test
D = 0.9444
p = 1.149e−08
n_1 = 26
n_2 = 18
Records w/o ppm (MG 0337/Plant C)
Records with ppm (MG 0337/Plant C)
2 3 4 5 6
Delivery Amount [log]
(a) Suppliers, which delivered parts from the material
group 0096 to Plant H.
(b) Suppliers, which delivered parts from the material
group 0337 to Plant C.
Figure 43: Comparison between the delivery amounts of suppliers with and without reported quality
related problems.
102

esults for material group 0096 are presented in Figure 43a and the test results for material group
0337 – in Figure 43b respectively. The resulting p-values of the Kolmogorov-Smirnov test are
0.010 in the case of material group 0096 and 1.15E-8 for material group 0337. Both p-values are
below the 5%-significance level meaning that the null-hypothesis of the Kolmogorov-Smirnov test
assuming similarity between the distributions of the compared data is rejected, as in the case of
material group 0337 the especially low p-value indicates particularly large difference between the
two distributions. The Kolmogorov-Smirnov test results are in accordance also with the accompanying
cumulative distribution function plots in Figure 43. For suppliers which deliver components
from the material group 0337 the ranges of the delivery amounts of the two groups barely overlap.
The results presented in Figure 43 were not anticipated in the beginning of this project. However,
given the fact that they identify a second important factor, which differentiates between suppliers
with good quality performance and suppliers with quality related problems, this finding gives
significant information, which can be used to improve the effectiveness of the quality auditing process
employed by Volkswagen to manage the production quality down its supply chain. Therefore
the topic was further investigated and the respective analytic results are presented in the current
section.
At this point it is important to answer the following questions: What is the relation between
the number of components a supplier delivered and the amount of ppm it scored? And what information
does this relation give about the quality capability of supplier’s production process?
To provide an answer to these questions here first a mathematical approach is used to derive the
relation between the delivery amount and ppm. Subsequently a number of empirical data sets are
analyzed and the results are discussed with respect to the theoretical derivation.
Let k ∈ N be the number of defective parts in a batch of N components. Then the amount of
ppm in this batch is defined as follows:
ppm = F (k, N) = k N × 106 (6)
Note that ppm ∈ Q + . Let the event X refer to the absolute number of defective components k
a supplier delivers in a batch of N components, and the event Y refer to the fraction of defective
components in the same batch expressed in ppm. Further, let p = const 19 be the probability that
19 It is important to keep in mind that the quality capability of a particular production process and accordingly the
probability to produce functional parts can vary over time. In this calculation it is assumed that the supplier has a stable
process over the time period in which the N components are produced and the according probability for no defects or
defects accordingly remains constant.
103

the supplier produces a functional part, and accordingly q = 1−p the probability that the produced
part is defective. Note that according to this definition q is the failure rate of supplier’s production
process. Under this assumption the event X would have a binomial distribution with N trials and
success rate q, i.e. X ∼ B(N, q). Furthermore, the events X and Y are related as follows:
Y = g(X) = βX, where β = 106
N
(7)
Now let Ω Y = {kβ | k ∈ [0; N]} be the domain of all possible discrete values of ppm’s for a batch
of N parts. The expected ppm-value for suppliers which have ppm (quality related problems) can
be written as:
E [Y | Y > 0] =
∑
y P (Y = y | Y > 0) (8)
y ∈ Ω Y
(
P (Y = y | Y > 0) = P (βX = y | βX > 0) = P X = y )
β ∣ X > 0 (9)
(
(
P X = y ) P X > 0
β ∣ X > 0 ∣ X = y ) (
P X = y )
β
β
=
(10)
P (X > 0)
Using (10) one can write (8) as follows:
E [Y | Y > 0] =
∑
y
y ∈ Ω Y
= 1, for y > 0
and 0 otherwise
{ ( }} {
P X > 0
∣ X = y β
P (X > 0)
)
P
(
X = y )
β
(11)
N∑
E [Y | Y > 0] = kβ
k = 1
P (X = k)
P (X > 0)
(12)
N∑
E [Y | Y > 0] = kβ
k = 1
P (X = k)
1 − P (X = 0)
Now using the fact that X has a binomial distribution, the above equation is reduced to:
N∑
E [Y | Y > 0] = kβ
k = 1
( N
k
)
q k p N−k
1 − p N = β
1 − p N N ∑
k = 1
( N
k
)
kq k p N−k =
(13)
βNq
1 − p N (14)
104

E [Y | Y > 0] =
q
1 − p N × 106 (15)
Analogically one can find the conditional expectation for the logarithm of the obtained ppm, only
for suppliers with ppm, i.e. E [Z | Y > 0], where
Z = log 10 (βX), with β = 106
N
(16)
Since here only suppliers, which have ppm are of interest, the domain of the event Z is defined as
Ω Z = {log 10 (kβ) | k ∈ [1; N]}.
E [Z | Y > 0] =
∑
z P (Z = z | Y > 0) (17)
z ∈ Ω Z
P (Z = z | Y > 0) = P (log 10 (βX) = z | βX > 0) = P (βX = 10 z | X > 0) (18)
(
)
)
)
P (Z = z | Y > 0) = P
(X = 10z
P X > 0
β ∣ X > 0 ∣ X = 10z P
(X = 10z
β
β
=
P (X > 0)
(
)
) (19)
E [Z | Y > 0] =
∑ P X > 0
∣ X = 10z P
(X = 10z
β
β
z
(20)
1 − P (X = 0)
z ∈ Ω Z
N∑
E [Z | Y > 0] = log 10 (kβ)
k = 1
( ) N
q k p N−k
k
N∑
E [Z | Y > 0] = log 10 (kβ)
k = 1
= 1, for k > 0
{ }} {
P (X > 0 | X = k ) P (X = k)
1 − P (X = 0)
(21)
N∑
( ) ( ) k N q k
= log
1 − p N 10
N × p N−k
106 k 1 − p (22) N
k = 1
The resulting expressions in (15) and (22) were numerically evaluated for several different
values of p with N ∈ [1; 10 6 ] (see Figure 44). As expected the two plots look very similar. Each of
the curves is asymptotically limited by two straight lines (on a log-log plot). For a small number
of delivered components the according curves are limited by the minimum number of ppm, while
for a large number of delivered components the limiting line is y = log 10 (q10 6 ) – dependent only
on the actual failure rate q of the respective production process.
The plots have a straightforward practical interpretation. For a sufficiently small number of
produced components in clear probabilistic terms the expected number of defective components
105

E[ppm|ppm>0][log]
1e+00 1e+02 1e+04 1e+06
p = 70%
y = log10(30% x 10^6)
p = 90%
y = log10(10% x 10^6)
p = 99%
y = log10(1% x 10^6)
p = 99.9%
y = log10(0.1% x 10^6)
1e+00 1e+02 1e+04 1e+06
Delivered Components N [log]
E[log(ppm)|ppm]
1 2 3 4 5 6
●
●
●
●
p = 70%
y = log10(30% x 10^6)
● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
●
●
●
p = 90%
●
●
●
●
●
●
●
●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
●
●
●
●
●
●
●
● ●
● ● p = 99%
●
●
●
● ● ●
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ● ● ● ● ● ● ● ●
●
●
y = log10(10% x 10^6)
y = log10(1% x 10^6)
p = 99.9%
y = log10(0.1% x 10^6)
1e+00 1e+02 1e+04 1e+06
Delivered Components N [log]
(a) Expectation of the event Y .
(b) Expectation of the event Z.
Figure 44: Numerical evaluation of the conditional expectations of the events Y and Z 1 , plotted
for different values of p. The dashed line in both cases represents the minimal number of ppm for
the according delivery amount N, which can be calculated using equation (6) for k = 1 2 .
1 Note that due to the complex form of (22), the according numerical evaluation of the equation was limited by the
floating point precision of the R software and therefore the respective simulation does not cover the entire range of the
number of delivered components. Nevertheless, the key characteristics of the plotted curves are still discernible.
2 Since both axises are on a logarithmic scale, the hyperbola described by (6) appears as a straight line on the plot
G(N) = log 10 (F (k = 1, N)) = log 10
( 1
N 106 )
= log 10 10 6 − log 10 N = 6 − log 10 N
remains below 1. For that reason, for small N it is expected that, if any defects occur at all,
they will be rather few and the resulting amount of ppm would be very close to the minimum
possible number. In other words for small number of components a supplier is ”lucky” most of
the time, i.e. has zero ppm. On the other hand, as the production volume increases and reaches a
particular critical value (N crit = 1/q), even for very small failure rates q in the production process
the expected number of defective components becomes larger than 1. Thus for a large number
of components the supplier cannot escape his ppm value by luck any more, since the probability
of defective components to occur is overwhelming due to the large number of produced parts.
These results are a possible explanation why in the two cases discussed above suppliers, which
lack quality related problems, tend to have smaller number of delivered components as compared
to suppliers with reported ppm.
106

Note that the critical amount of delivered components N crit for a given failure rate q can easily
be determined graphically – it is the x-coordinate of the intersection point of the two asymptotes
(see Figure 44). Note also that the larger the failure rate q of the process, the smaller the number of
produced components N crit has to be to get the first defective parts. For N larger than the critical
delivery amount N crit the number of ppm, which a particular supplier has, would be limited by the
reliability of its production process.
Automotive producers have particularly stringent quality requirements and expect their suppliers
to manage the failure rates of their production processes at levels of less than several hundred
ppm, meaning that in most of the cases the failure rate q is of the order of 0.01% and even less.
At such small failure rates, the probability to produce defective components becomes practically
significant for delivery amounts N crit = 1/q of the order of 10.000 or even more delivered components.
In order to verify whether these observations hold true also for other areas of the supply
chain, a two-sample Kolmogorov-Smirnov test was performed on a number of additional sets of
empirical data. The results of the test are presented in Figures 45 through 47 below.
Distribution of Records w/o ppm (MG 0068/Plant K)
Distribution of Records w/o ppm (MG 0068/Plant L)
Frequency
0 1 2 3 4 5 6
3 4 5 6
Distribution of Records with ppm (MG 0068/Plant K)
Frequency
0 2 4 6 8 10 12
Frequency
0 5 10 15
1 2 3 4 5 6 7
Distribution of Records with ppm (MG 0068/Plant L)
Frequency
0 2 4 6 8 10
3 4 5 6
1 2 3 4 5 6 7
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0068/Plant K)
and Records with ppm (MG 0068/Plant K)
Two−sample Kolmogorov−Smirnov test
D = 0.4493
p = 0.002056
n_1 = 29
n_2 = 35
Records w/o ppm (MG 0068/Plant K)
Records with ppm (MG 0068/Plant K)
3 4 5 6
Delivery Amount [log]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0068/Plant L)
and Records with ppm (MG 0068/Plant L)
Two−sample Kolmogorov−Smirnov test
D = 0.7058
p = 2.167e−10
n_1 = 61
n_2 = 37
Records w/o ppm (MG 0068/Plant L)
Records with ppm (MG 0068/Plant L)
1 2 3 4 5 6 7
Delivery Amount [log]
(a) Suppliers, which delivered parts from the material
group 0068 to Plant K.
(b) Suppliers, which delivered parts from the material
group 0068 to Plant L.
Figure 45: Comparison between the delivery amounts of suppliers with and without reported quality
related problems.
107

Distribution of Records w/o ppm (MG 0096/Plant F)
Distribution of Records w/o ppm (MG 0099/Plant L)
Frequency
0 1 2 3 4 5
2 3 4 5 6
Distribution of Records with ppm (MG 0096/Plant F)
Frequency
0 2 4 6 8
Frequency
0 1 2 3 4 5 6 7
1 2 3 4 5 6
Distribution of Records with ppm (MG 0099/Plant L)
Frequency
0 2 4 6 8 10
2 3 4 5 6
1 2 3 4 5 6
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0096/Plant F)
and Records with ppm (MG 0096/Plant F)
Two−sample Kolmogorov−Smirnov test
D = 0.7514
p = 7.25e−07
n_1 = 29
n_2 = 24
Records w/o ppm (MG 0096/Plant F)
Records with ppm (MG 0096/Plant F)
2 3 4 5 6
Delivery Amount [log]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0099/Plant L)
and Records with ppm (MG 0099/Plant L)
Two−sample Kolmogorov−Smirnov test
D = 0.4883
p = 0.002592
n_1 = 31
n_2 = 22
Records w/o ppm (MG 0099/Plant L)
Records with ppm (MG 0099/Plant L)
1 2 3 4 5 6
Delivery Amount [log]
(a) Suppliers, which delivered parts from the material
group 0096 to Plant F.
(b) Suppliers, which delivered parts from the material
group 0099 to Plant L.
Distribution of Records w/o ppm (MG 0105/Plant D)
Distribution of Records w/o ppm (MG 0302/Plant F)
Frequency
0 5 10 20 30
1 2 3 4 5 6
Distribution of Records with ppm (MG 0105/Plant D)
Frequency
0 2 4 6 8 10
Frequency
0 2 4 6 8
2 3 4 5 6
Distribution of Records with ppm (MG 0302/Plant F)
Frequency
0 1 2 3 4 5 6 7
1 2 3 4 5 6
2 3 4 5 6
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0105/Plant D)
and Records with ppm (MG 0105/Plant D)
Two−sample Kolmogorov−Smirnov test
D = 0.6792
p = 1.287e−10
n_1 = 167
n_2 = 30
Records w/o ppm (MG 0105/Plant D)
Records with ppm (MG 0105/Plant D)
1 2 3 4 5 6
Delivery Amount [log]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0302/Plant F)
and Records with ppm (MG 0302/Plant F)
Two−sample Kolmogorov−Smirnov test
D = 0.6387
p = 2.496e−05
n_1 = 25
n_2 = 31
Records w/o ppm (MG 0302/Plant F)
Records with ppm (MG 0302/Plant F)
2 3 4 5 6
Delivery Amount [log]
(c) Suppliers, which delivered parts from the material
group 0105 to Plant D.
(d) Suppliers, which delivered parts from the material
group 0302 to Plant F.
Figure 46: Comparison between the delivery amounts of suppliers with and without reported quality
related problems.
108

Distribution of Records w/o ppm (MG 0480/Plant F)
Distribution of Records w/o ppm (MG 0485/Plant C)
Frequency
0 1 2 3 4 5 6
1 2 3 4 5 6
Distribution of Records with ppm (MG 0480/Plant F)
Frequency
0 2 4 6 8 10
Frequency
0 1 2 3 4 5 6
3 4 5 6 7
Distribution of Records with ppm (MG 0485/Plant C)
Frequency
0 2 4 6 8
1 2 3 4 5 6
3 4 5 6 7
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0480/Plant F)
and Records with ppm (MG 0480/Plant F)
Two−sample Kolmogorov−Smirnov test
D = 0.6877
p = 2.603e−06
n_1 = 25
n_2 = 26
Records w/o ppm (MG 0480/Plant F)
Records with ppm (MG 0480/Plant F)
1 2 3 4 5 6
Delivery Amount [log]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Records w/o ppm (MG 0485/Plant C)
and Records with ppm (MG 0485/Plant C)
Two−sample Kolmogorov−Smirnov test
D = 0.7797
p = 2.597e−08
n_1 = 30
n_2 = 23
Records w/o ppm (MG 0485/Plant C)
Records with ppm (MG 0485/Plant C)
3 4 5 6 7
Delivery Amount [log]
(a) Suppliers, which delivered parts from the material
group 0480 to Plant F.
(b) Suppliers, which delivered parts from the material
group 0485 to Plant C.
Figure 47: Comparison between the delivery amounts of suppliers with and without reported quality
related problems.
In each of the above cases the p-values of the respective Kolmogorov-Smirnov test provide
strong evidence for statistical significance on the 5%-significance level. Based on the small p-
values the null-hypothesis of the test is rejected and in every single case it is concluded that the
distribution of the number of delivered components of suppliers with ppm is different from the distribution
of the number of delivered components of suppliers without ppm. Furthermore, the plots
with the cumulative distribution functions of each pair of empirical data systematically show that
suppliers which experience quality related problems deliver more components than suppliers without
any reported problems. These results are identical with the situation observed in the other two
cases discussed previously in this section as well as in accordance with the theoretical derivation.
The second part of the analysis presented in this section investigates the relation between the
amount of components a certain supplier delivered and the reported number of defective parts per
million. The theoretical derivation above revealed that the relation between the two quantities is
asymptotically limited by two straight lines on a log-log scale for a given failure rate. This is why
in each of the following cases a linear regression between the logarithm of the reported ppm and
109

the logarithm of the respective delivery quantities was carried out. The corresponding results are
presented in Figures 48 through 50 below.
Cummulative Delivery Quantity vs Cummulative PPM
Cummulative Delivery Quantity vs Cummulative PPM
Cummulative PPM [log]
5 10 50 100 500 5000
●
●
●
●
●
●
●
●
●
●
●
●
●
Least Squares fit
Minimal number of ppms
MG 0068 | Plant K
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Cummulative PPM [log]
5 10 20 50 100 200 500 1000 2000 5000
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Least Squares fit
Minimal number of ppms
MG 0068 | Plant L
●
●
●
●
●
●
●
●
●
●
●
●
●
●
2e+04 5e+04 2e+05 5e+05 2e+06 5e+06
Cummulative Delivery Quantity [log]
(a) Suppliers, which delivered parts from the material
group 0068 to Plant K.
5e+03 2e+04 1e+05 5e+05 2e+06
Cummulative Delivery Quantity [log]
(b) Suppliers, which delivered parts from the material
group 0068 to Plant L.
Cummulative PPM [log]
1 10 100 1000 10000
Cummulative Delivery Quantity vs Cummulative PPM
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Least Squares fit
Minimal number of ppms
MG 0096 | Plant F
●
●
●
●
Cummulative PPM [log]
20 50 100 200
Cummulative Delivery Quantity vs Cummulative PPM
●
●
●
●
●
●
●
●
●
●
●
Least Squares fit
Minimal number of ppms
MG 0096 | Plant H
●
●
5e+03 2e+04 1e+05 5e+05 2e+06
Cummulative Delivery Quantity [log]
2e+04 5e+04 2e+05 5e+05 2e+06
Cummulative Delivery Quantity [log]
(c) Suppliers, which delivered parts from the material
group 0096 to Plant F.
(d) Suppliers, which delivered parts from the material
group 0096 to Plant H.
Figure 48: Linear regression between the delivery amount of suppliers with reported quality problems
and their ppm record. Please note that both axises are on a logarithmic scale.
110

●
Cummulative Delivery Quantity vs Cummulative PPM
Cummulative Delivery Quantity vs Cummulative PPM
Cummulative PPM [log]
1 10 100 1000 10000
●
●
●
●
●
Least Squares fit
Minimal number of ppms
MG 0099 | Plant L
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Cummulative PPM [log]
100 200 500 1000 2000 5000 10000 20000
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
Least Squares fit
Minimal number of ppms
MG 0105 | Plant D
●
●
●
●
●
●
●
●
●
●
●
●
●
●
2e+03 1e+04 5e+04 2e+05 1e+06
Cummulative Delivery Quantity [log]
(a) Suppliers, which delivered parts from the material
group 0099 to Plant L.
1e+04 5e+04 2e+05 5e+05 2e+06 5e+06
Cummulative Delivery Quantity [log]
(b) Suppliers, which delivered parts from the material
group 0105 to Plant D.
Cummulative Delivery Quantity vs Cummulative PPM
Cummulative Delivery Quantity vs Cummulative PPM
●
●
Cummulative PPM [log]
5 10 50 100 500 5000
●
●
●
●
●
Least Squares fit
Minimal number of ppms
MG 0302 | Plant F
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Cummulative PPM [log]
1 10 100 1000 10000
●
●
●
Least Squares fit
Minimal number of ppms
MG 0480 | Plant F
●
●
●
●
●
●
● ● ●
●
●
● ●
●
●
●
●
●
●
●
●
●
1e+02 1e+03 1e+04 1e+05 1e+06
Cummulative Delivery Quantity [log]
(c) Suppliers, which delivered parts from the material
group 0302 to Plant F.
1e+02 1e+03 1e+04 1e+05 1e+06
Cummulative Delivery Quantity [log]
(d) Suppliers, which delivered parts from the material
group 0480 to Plant F.
Figure 49: Linear regression between the delivery amount of suppliers with reported quality problems
and their ppm record. Please note that both axises are on a logarithmic scale.
111

●
Cummulative Delivery Quantity vs Cummulative PPM
Cummulative Delivery Quantity vs Cummulative PPM
●
●
●
●
Cummulative PPM [log]
1 100 10000
●
●
●
●
●
Least Squares fit
Minimal number of ppms
MG 0485 | Plant C
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Cummulative PPM [log]
1 10 100 1000 10000
●
Least Squares fit
Minimal number of ppms
MG 0337 | Plant C
●
●
●
●
●
●
●
●
●
●
●
1e+05 2e+05 5e+05 1e+06 2e+06 5e+06
Cummulative Delivery Quantity [log]
(a) Suppliers, which delivered parts from the material
group 0485 to Plant C.
5e+03 2e+04 5e+04 2e+05 5e+05 2e+06
Cummulative Delivery Quantity [log]
(b) Suppliers, which delivered parts from the material
group 0337 to Plant C.
Figure 50: Linear regression between the delivery amount of suppliers with reported quality problems
and their ppm record. Please note that both axises are on a logarithmic scale.
112

Table 4: Summary of the performed linear regression analysis between suppliers’ quality performance
and delivery quantity (both on a log scale).
Material
Group
Plant ID
Sample
Size N
Intersect Estimate β 0
(Standard Error)
Slope Estimate β 1
(Standard Error)
F-test p-value
0068 K 35 6.963 (1.625) −0.833 (0.287) 0.007
0068 L 37 2.830 (1.221) −0.089 (0.219) 0.686
0096 F 24 5.991 (1.129) −0.820 (0.200) 4.7 × 10 −4
0096 H 13 2.745 (0.940) −0.130 (0.170) 0.463
0099 L 22 7.198 (1.625) −0.911 (0.309) 0.008
0105 D 30 5.314 (0.716) −0.404 (0.138) 0.007
0302 F 31 4.613 (0.696) −0.537 (0.130) 2.7 × 10 −4
0337 C 18 2.339 (3.442) 0.114 (0.604) 0.852
0480 F 26 5.100 (0.604) −0.623 (0.114) 1.3 × 10 −5
0485 C 23 2.347 (3.996) 0.072 (0.656) 0.914
Theoretical values
vertical asymptote (N < N crit ) 6.000 −1.000
horizontal asymptote (N > N crit ) log 10 (q10 6 ) 0.000
Table 4 presents a summary of the estimated parameters of the linear fit in each of the above
ten cases. For each set of suppliers which deliver parts from a particular material group to a certain
Volkswagen production facility the table provides the sample size N as well as the estimated
intercept β 0 and accordingly slope β 1 of the least squares linear fit. The numbers included in
the brackets represent the standard error of each of the estimated parameters. These values were
obtained using the R function summary(). In addition, summary() provides the results of
an F-test, which evaluates the reliability of the slope estimate. The performed F-test in each of
the cases uses the null-hypothesis that the linear fit has a zero slope (β 1
= 0) and alternative
hypothesis of β 1 ≠ 0. The p-values of the performed F-test are listed in the last column in the table
above. In six of the cases the resulting p-values of the F-test are below the 5%-significance level
providing support to reject the null-hypothesis and accept that the slope of the linear fit is indeed
non-zero. In all of these cases the estimated slopes of the least squared fit are particularly high. In
the remaining four cases the p-values of the F-test are above the 5%-significance level, meaning
that the null-hypothesis of the F-test cannot be rejected and the according slope estimates, even
though in most of the cases negative, are not significant. In the latter cases the slope values are also
substantially smaller, as in the case of suppliers which deliver components from material groups
0337 and 0485 to Plant C the estimated slopes are even positive. To aid the comparison between
the parameter estimates in Table 4 they are presented also graphically in Figure 51.
113

Slope (β_1)
-1,5 -1,0 -0,5 0,0 0,5 1,0
10
8
0099; Plant L
0068; Plant K
Zero Slope
(N > N_crit)
Intercept (β_0)
6
4
2
Theoretical
value (min ppm)
0096; Plant F
0105; Plant D
0068; Plant L
0480; Plant F 0302; Plant F
0096; Plant H
0337; Plant C
0485; Plant C
0
-2
-4
Figure 51: Graphical representation of the estimated linear regression parameters presented in
Table 4. The error bars denote the standard error of the estimates. The label of each data point
consists of the respective Material group; Plant ID. A list of all material groups, which appear in
this paper, can be found in the Appendix.
For each of the performed linear regressions above the influence of the individual data points
on the estimated parameters was evaluated using Cook’s distance, which measures the change
of the estimated linear model parameters induced by the removal of the respective point from the
dataset (Cook, 1977, 1979). For every set of data a plot was generated, which displays the residuals
versus their leverage. Each of these plots includes also two dashed pairs of lines, representing a
Cook’s distance of 0.5 and 1.0 respectively. These plots were used to identify points, which have a
particularly strong influence on the estimated parameters, and thus could lead to serious offsets of
the linear fit. For three of the presented cases above these evaluations revealed indeed that there are
points, which have particularly strong influence on the parameter estimates, with Cook’s distance
of more than 0.5. The plots for the respective three cases are presented in Figure 52. Due to their
strong influence on the data parameters, the respective data points were consequently excluded
from the datasets and a new linear regression based on the remaining data points was carried out.
The new linear fits are presented in Figures 53 and 54. The resulting new plots of residuals versus
points’ leverage are also included alongside the linear regression plots.
114

Residuals vs Leverage
Residuals vs Leverage
Standardized residuals
−2 −1 0 1 2
● ●
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●
●
● ●
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●
●
●
● ●
●
●
●
●
3
Cook's distance
●
13
0.0 0.1 0.2 0.3 0.4 0.5
Leverage
lm(log10(ppm.1) ~ log10(liefermenge.1))
9
●
1
0.5
0.5
1
Standardized residuals
−2 −1 0 1 2
●
14
●
●
●
●
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●
●
●
●
●
●
●
●
●
●
●
●
●
4
Cook's distance 1
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Leverage
lm(log10(ppm.1) ~ log10(liefermenge.1))
●
8
●
1
0.5
0.5
(a) Suppliers, which delivered parts from the material
group 0096 to Plant F.
(b) Suppliers, which delivered parts from the material
group 0099 to Plant L.
Residuals vs Leverage
●
●
Standardized residuals
−2 −1 0 1
●
●
●
●
●
●
●
●
●
●
●
18
13
●
1
0.5
0.5
1
●
15
Cook's distance
0.0 0.2 0.4 0.6 0.8
Leverage
lm(log10(ppm.1) ~ log10(liefermenge.1))
(c) Suppliers, which delivered parts from the material
group 0337 to Plant C.
Figure 52: Datasets, which include data points with particularly large leverage. The leverage each
data point has on the estimated parameters of the linear regression was determined by computing
its Cook’s distance.
115

●
Cummulative Delivery Quantity vs Cummulative PPM
Residuals vs Leverage
Cummulative PPM [log]
2 5 10 20 50 100 200 500
●
●
●
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●
●
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●
●
●
●
●
●
●
●
●
●
●
Least Squares fit
Minimal number of ppms
●
MG 0096 | Plant F / Excluded Points: 9
1e+05 2e+05 5e+05 1e+06 2e+06 5e+06
Cummulative Delivery Quantity [log]
Standardized residuals
−2 −1 0 1 2
●
●
●
Cook's distance
●
13
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
0.00 0.05 0.10 0.15 0.20
Leverage
lm(log10(ppm.1) ~ log10(liefermenge.1))
3
●
18
●
●
0.5
0.5
(a) Suppliers, which delivered parts from the material
group 0096 to Plant F.
Cummulative Delivery Quantity vs Cummulative PPM
(b) Leverage of the individual data points for suppliers,
which delivered parts from the material group 0096 to
Plant F.
Residuals vs Leverage
Cummulative PPM [log]
1 10 100 1000 10000
●
●
●
●
●
●
●
●
Least Squares fit
Minimal number of ppms
MG 0099 | Plant L / Excluded Points: 8
●
●
●
●
●
●
●
●
●
●
●
●
●
5e+03 2e+04 1e+05 5e+05 2e+06
Cummulative Delivery Quantity [log]
Standardized residuals
−2 −1 0 1 2
●
13
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Cook's distance
4
●
●
●
17
●
0.0 0.1 0.2 0.3 0.4
Leverage
lm(log10(ppm.1) ~ log10(liefermenge.1))
1
0.5
0.5
1
(c) Suppliers, which delivered parts from the material
group 0099 to Plant L.
(d) Leverage of the individual data points for suppliers,
which delivered parts from the material group 0099 to
Plant L.
Figure 53: Recomputed linear regressions, excluding the influencing data points.
116

●
Cummulative Delivery Quantity vs Cummulative PPM
Residuals vs Leverage
0.5
●
●
●
Cummulative PPM [log]
1 10 100 1000 10000
●
●
●
●
●
●
●
●
Least Squares fit
Minimal number of ppms
●
MG 0337 | Plant C / Excluded Points: 13
500000 1000000 1500000
Cummulative Delivery Quantity [log]
●
●
●
Standardized residuals
−2 −1 0 1
Cook's distance
●
●
●
●
●
●
●
●
●
●
14
10
3●
0.00 0.05 0.10 0.15 0.20 0.25
●
Leverage
lm(log10(ppm.1) ~ log10(liefermenge.1))
●
0.5
1
(a) Suppliers, which delivered parts from the material
group 0337 to Plant C.
(b) Leverage of the individual data points for suppliers,
which delivered parts from the material group 0337 to
Plant C.
Figure 54: Recomputed linear regressions, excluding the influencing data points.
117

Table 5: Summary of the performed linear regression analysis between suppliers’ quality performance
and delivery quantity (both on a log scale) for the corrected datasets.
Material
Group
Plant ID
Sample
Size N
Intersect Estimate β 0
(Standard Error)
Slope Estimate β 1
(Standard Error)
F-test p-value
0096 F 24 (old) 5.991 (1.129) −0.820 (0.200) 4.7 × 10 −4
0096 F 23 4.124 (1.516) −0.498 (0.265) 0.074
0099 L 22 (old) 7.198 (1.625) −0.911 (0.309) 0.008
0099 L 21 8.974 (1.894) −1.233 (0.355) 0.003
0337 C 18 (old) 2.339 (3.442) 0.114 (0.604) 0.852
0337 C 17 7.986 (7.681) −0.856 (1.325) 0.528
Theoretical values
vertical asymptote (N < N crit ) 6.000 −1.000
horizontal asymptote (N > N crit ) 1/q 0.000
The new plots show that there are no more data points with Cook’s distance greater than 0.5.
The resulting new estimates for the intercept and slope of the respective linear regressions are
presented in Table 5 and graphically in Figure 55. Removing the respective influential data points
from the three datasets brought significant changes of the estimates of the intercept and slope of
the least squares fit. The largest change is observed in the case of suppliers of products from the
material group 0337 to Plant C. Here the initially estimated positive slope with small absolute value
(β 1 = 0.114) changed to a strongly negative slope (β 1 = −0.856). Furthermore, the according
intercept shifted as well from β 0 = 2.339 to β 0 = 7.986 (Figure 55). The new parameters of the
linear fit are very close to the theoretical line representing a minimal number of ppm with intercept
β 0 = 6 and slope β 1 = −1. However, the results of the performed F-test indicate that the estimates
are not reliable. The very high p-value of the F-test (p=0.528) indicates that the null-hypothesis
of the test that β 1 = 0 cannot be rejected on the 5%-significance level. In the other two cases the
p-values of the F-test show that the estimated slopes of the respective linear fits are statistically
significant even after the removal of the influential points. In the case of suppliers, which deliver
components from the material group 0096 to Plant F, the removal of the influential data point lead
to a smaller slope β 1 = −0.498 (in contrast to β 1 = −0.820 previously). The intercept of the
fitting line β 0 = 5.991 dropped down to β 0 = 4.124. In the case of material group 0099 for Plant
L, the removal of the influential point has an opposite effect. The slope of the least squares fit
increased in absolute terms from β 1 = −0.911 to β 1 = −1.233. The according intercept shifted
from β 0 = 7.198 to β 0 = 8.974 (see Figure 55).
118

Slope (β_1)
-2,5 -2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0
16
14
12
Zero Slope
(N > N_crit)
Intercept (β_0)
10
8
6
0099; Plant L
0337; Plant C
4
Theoretical
value (min ppm)
0096; Plant F
2
0
-2
Figure 55: Graphical representation of the shift of the estimated linear regression parameters presented
in Table 5, after the influential points have been removed from the data sets. The error bars
denote the standard error of the estimates. The label of each data point consists of the respective
Material group; Plant. A list of all material groups and plant codes, which appear in this paper,
can be found in the Appendix.
In two of the cases above – namely for suppliers, which deliver products from the material
group 0068 to Plant K, and suppliers, which deliver components from material group 0099 to Plant
L respectively (in the latter case even after the removal of the influential data points) – the estimated
linear fits are particularly close to the theoretical line limiting the number of ppm, which has an
intersect β 0 = 6 and a slope β 1 = −1 (as already shown previously). These results suggest that the
respective groups of suppliers did not yet reach their critical delivery amount of N crit = 1/q, and
that even though they have quality related problems on their records, their production processes
seem to be particularly stable.
On the other hand, the datasets which reveal slopes of the linear fit close to zero, such as in
the case of suppliers delivering material group 0068 to Plant L – β 1 = −0.089, or suppliers of
parts from the material group 0485 to Plant C – β 1 = 0.072, for example, suggest that part of the
suppliers in the respective group have reached their critical delivery amount and their performance
record is now limited by the second asymptote derived in the theoretical part above. Such groups
of suppliers require closer attention, since it is very probable that their production processes are
not stable enough.
119

These findings are particularly important for the supplier management process, since the analysis
presented in this section provides a good approach to identify the areas, in which the probability
of arising quality related problems is rather high. The results show that suppliers who supply a
large number of components with a confirmed zero slope need to be handled with high priority.
On the other hand, suppliers with a slope near the ”detectability” asymptote (see Figure 44) are
only second priority since the probability of them causing problems is low, since their processes
are either really stable in absolute terms, or are at least stable enough for the lower number of
delivered components to have a low probability for causing problems. Such type of information is
a particularly important input for the supplier management process and can be used to efficiently
distribute auditing resources down the supply chain.
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8 Conclusion
Second-party quality auditing is an essential part of the quality assurance process especially in
businesses with high levels of outsourcing. The goal of the presented project was to assess the
effectiveness of the practical implementation of this quality management tool in the automotive
industry and using a case study approach to answer the following two research questions:
Can quality performance of automotive suppliers in the mass production be anticipated based
on quality evaluation of the capability of their production processes?
What is the quality of the quality auditing process at Volkswagen Group and what is its
improvement potential?
Figure 56 shows once again the flow of the discussion and summarizes the contents of the individual
sections of this paper. Section 1 introduced the concept of quality and its importance for the
business environment. It also briefly addressed the quality-related challenges faced by companies,
which manufacture complex products in cooperation with external business partners. Section 2
presented the research questions and the motivation for this paper. One important aspect in this
regard was that automotive companies need a quality management tool, which allows them to effectively
assess the quality capability of their potential business partners before the contracts are
in place and thus strategically select the most suitable suppliers, which can guarantee the stability
of OEMs’ production processes. At the same time it is important that the selected suppliers
maintain their quality capability even after the start of production and any quality related issues
are addressed in a timely manner. At this point the generally accepted quality standards such as
ISO 9001 and ISO/TS16949 and the Total Quality Management principle upon which they build
were introduced as a possible answer to the needs of automotive OEMs. The discussion focused
on the ability of such standards to guarantee the quality of certified companies. The section reviewed
scientific literature, which describes potential deficiencies of the certification according to
these quality standards and explains why second-party quality auditing is its preferred counterpart
in the quality management practices in a lot of industries including the automotive. Pursuing
the presented research questions was additionally motivated by the fact that there is small scientific
base, which provides information whether this quality management tool is indeed effectively
implemented in practice in the automotive industry.
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1.
Introduction
2.
Research Question and Research Motivation
Research Questions:
• Can quality performance of automotive suppliers in the mass production be anticipated based
on quality evaluation of the capability of their production processes?
• What is the quality of the quality auditing process at Volkswagen Group and what is its
improvement potential?
3.
Research Approach
Framework for the analysis (case study):
• Similarities between quality auditing and sampling
• Two aspects, which are important for effective quality auditing:
− Frequency of audits in the context of the general audit planning (sampling frequency)
− Effectiveness of individual audits (accuracy of the samples)
General Auditing Strategy
(Sampling Frequency)
4. Specifics of the Automotive Industry
Fundamental Principles of the
5.
Quality Audit
• Specifics of the industry:
− Internationalization of the
automotive operations
− Rising development costs
− Increased complexity of the
automotive supply chain
• Summary of the quality challenges and
Implications for the quality auditing
strategy in the automotive sector
• Evaluation of Volkswagen’s quality
auditing process with respect to the
insights from the discussion
8.
Conclusion
• Summary of the analytical results
• Comments of the results with
respect to the research questions
of the paper
• Outlook
6.
Effectiveness of Individual
Audits (Accuracy of Samples)
Empirical Data
• Overview of the analyzed empirical data
and the evaluation methods relevant for
data collection:
− Quality performance records
− Quality capability records
• Technical aspects of the analysis
• Important recommendations regarding
Volkswagen’s data management
7. Results and Discussion
• Comprehensive evaluation of the available
empirical data w/r/t incomplete records
• Account for potential biasing factors
− Revisions of Volkswagen’s quality
auditing process
− Relevance of audit results with time
− Influences resulting from the
calibration of auditors and quality
inspectors
• Correlation of quality performance and
quality capability for two data sets
• Theoretical and empirical evaluation of the
delivery amount as a second predictor for
quality performance
Figure 56: Flow of the discussion presented in this paper.
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Section 3 presented the research approach. The analysis in this paper was based on a case study
carried out in cooperation with one of the largest automotive producers in the world – Volkswagen
Group. An important contribution of this section is the analytical framework it introduced, which
draws similarities between the quality auditing process and one of the fundamental techniques in
the field of Electrical Engineering – sampling. An important benefit of this particular analytic
approach is that it is independent of the specifics of the automotive industry and can be used to
assess the effectiveness of quality auditing also in other industry sectors. The two aspects of the assessment
framework are the frequency of auditing (analog to the sampling frequency in Electrical
Engineering) and the ability of the individual audits to evaluate adequately the quality capability
of suppliers’ production processes (analog to the accuracy of the individual measurements). As a
consequence of the defined evaluation framework the rest of the paper was divided into two conceptually
different but mutually complementing parts. The aspect of auditing frequency was dealt
with in Section 4, while the remaining sections (Sections 5 through 7) focused on the effectiveness
of the individual audits. This division is also reflected in the current section.
8.1 General Auditing Strategy (Sampling Frequency)
Section 4 examined the specifics of the automotive market and the associated quality management
challenges down the automotive supply chain. On the one hand, strategic moves such as the relocation
of production to cost-efficient regions, increasing the share of outsourced production, and
at the same time the introduction of new production approaches such as the use of modular assembly
components, offer substantial competitive advantages to the individual market players and are
essential for the competitiveness of automotive manufacturers. On the other hand, factors such as
personnel fluctuation in developing regions, large production volume increases and the resulting
lack of sustainability of quality of suppliers’ production processes, rapidly growing complexity of
the supplier pool, product portfolio integration and quality risk concentration in production modules,
which are extensively used in smaller varieties and increasing numbers and complexity, not
only pose serious challenges to quality management in the automotive sector, but also potentially
endanger OEMs’ overall business operations.
Based on the insights of the discussion a possible approach was suggested (see Figure 8 in
Section 4.4), which can be used to prioritize quality audits in the general quality auditing plan. To
respond to the challenges of the sector automotive players need to adjust accordingly their audit
planning and the frequency of audits in areas, which are subject to particularly strong influence of
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the factors mentioned above. Special attention should be paid to sub-supplier management as well
as securing the stability of the production processes of key partners on the newly opened markets.
Production concepts similar to Volkswagen’s modular strategy MQB must also be handled with
priority in the general audit planning, especially considering the significantly larger volumes of the
sourced components.
Already in this section followed the first important conclusion regarding the research questions.
The average age of audit evaluations of Volkswagen suppliers from a number of different
regions were compared to the recommended audit prioritizing (Figure 8). The results showed that
Volkswagen’s audit planning accounts for the specifics of the automotive industry. Thus suppliers
in developing regions such as China, India, or Russia were audited much more frequently than
suppliers in developed regions such as Germany or North America. Furthermore, the number of
the sub-supplier audits conducted by Volkswagen is constantly rising. One aspect, which needs
yet to be considered in Volkswagen’s general quality audit planning, is the start of production of
Volkswagen models based on the MQB platform planned for 2012.
However, this conclusion is based on a qualitative evaluation of the empirical data only. It is
difficult to determine whether the auditing frequency is optimal for the according regions, e.g. is
the average age of quality audits in the individual regions small enough to prevent quality problems
or should it be reduced even further? The answer to such a question is limited by the a vast number
of parameters. Nevertheless, this particular point represents an interesting topic for subsequent
research.
8.2 Effectiveness of Individual Audits (Accuracy of Samples)
Once the aspects regarding the general audit planning (the sampling frequency) were treated, the
remaining part of the paper concentrated on the ability of individual audits to adequately evaluate
the quality capability of suppliers’ production processes (accuracy of the samples). This part included
the bulk of statistical evaluations on the available empirical data. In the beginning of this
paper (Section 3) it was hypothesized, that if individual quality audits are conducted effectively, it
should be expected that suppliers, which receive a higher quality capability rating during an audit,
should also perform better in terms of quality performance indicators, such as ppm. This is the
reason why the ultimate purpose of the analysis in this part of the paper was to correlate quality
capability to quality performance information in order to search for hints, which can help to find an
answer to the research questions. Before the according correlations could be performed, however,
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several additional considerations had to be taken into account such as the theoretical aspects of
the quality audit, specifics and quality of the empirical data, as well as a number of factors, which
could potentially bias the analytical results and obscure important trends in the evaluated data.
Section 5 introduced the theoretical aspects of the quality audit. It focused on the specifics of
its implementation and presented the individual stages of a single quality audit. The purpose of this
chapter was to introduce the different auditing approaches and thus the possible diversity of implementation
of this quality management tool in terms of auditing techniques, team size, scoping,
technical content, etc. Alongside the presented literature review the quality audit implementation
specific to Volkswagen was roughly outlined. One particular insight of this section is the fact that
the effectiveness of the audit can be strongly influenced by auditors’ knowledge of the state-ofthe-art
of the audited production processes. In this regard Volkswagen pursues a good strategy
of maintaining a pool of auditing experts, each with profound process knowledge in a particular
strategic area.
Section 6 presented in detail the two major types of empirical data, which were analyzed
throughout the paper – quality capability and quality performance information. Along with the
processes, which generate the empirical data, the section discussed also particular technical aspects
of the analysis regarding the automated data processing. This discussion outlined one especially
important finding, which is relevant for the more subjective research question defined in the
beginning of the paper regarding improvment potential of Volkswagen’s internal processes. The
challenges faced during the data processing underscored the importance of data management in
large companies and showed once again how complex this task can be.
The challenge at Volkswagen in particular originates, on the one hand, in the different types of
categorizations used by the two Volkswagen departments, which provided the empirical data – material
groups (Volkswagen Purchasing) and product groups (Volkswagen Quality Assurance). This
problem is on the conceptual level and in the meantime has already been addressed by the according
departments in order to improve the information exchange in Volkswagen’s internal processes
such as the corporate sourcing process. Once sufficient amount of empirical data is generated
using the new information management approach, this improvement will also benefit any future
analyses similar to the one presented in this work.
On the other hand, one important aspect which still has to be addressed is the fact that valuable
detailed quality capability information collected during supplier evaluations is stored in a form,
which makes it practically unusable for analysis on a large scale (see Section 6.3). These are
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data regarding the complexity of the evaluated production processes (number of processing steps),
partial evaluation results for the individual blocks of the auditing questionnaire, etc. Fortunately,
the solution to this particular problem is relatively easy, since the data are generated in an electronic
form. Addressing these problems will allow Volkswagen to gain valuable additional knowledge
about its internal processes and accordingly use it to improve its operations even further.
Section 7 presented the results of the conducted evaluations. Section 7.1 evaluated the quality
of the available empirical data as the integrity of the operational records was of particular interest.
The results of the analysis revealed differences in the completeness of quality performance information
on the regional level. However, considering the specifics of the automotive sector such
differences were to be expected. Nevertheless, the quality of the generated quality performance
records improved over the evaluated time period as regions such as Region 5 (Figure 22) and Region
8 (Figure 25in Section 7.1) demonstrated particularly positive development. All incomplete
records were excluded from the analysis. Due to their small overall amount (less than 5%) it is
assumed that the remaining data set is still representative. Even though the incomplete records
were eliminated, the identified differences between the quality of the regional datasets were the
reason to differentiate between regional data also in the subsequent analysis.
Section 7.2 dealt with potential sources of data bias. Among the evaluated factors were the
influence of revisions of Volkswagen’s quality auditing process (Section 7.2.1), the period of relevance
of quality audit results (Section 7.2.2), as well as calibration of the people involved in
supplier quality evaluation (Section 7.2.3.1) and production quality assessment (Section 7.2.3.2).
Section 7.2.1 compared the quality evaluation results of suppliers carried out according to four
different versions of the Formel Q-Fähigkeit. The initial analysis divided the data based only on
industry type – chemical, electrical, and metal accordingly. The results of the evaluations showed
that quality evaluations according to the fifth and sixth edition of the Formel Q-Fähigkeit (FQF 5
and FQF 6) are not statistically different in all cases, while the conducted Kolmogorov-Smirnov
test showed statistically significant differences between the evaluations according to FQF 3 and
FQF 4 and between these datasets and the former two. Similar results were observed also in most
of the cases on the regional level. These observations showed that the changes of the quality auditing
evaluation criteria do have an influence on the empirical data and if they are not accounted
for could bias the analytical results.
However, there were also several datasets, which were found to be statistically different even
with respect to FQF 5 and FQF 6. Among these are the quality capability evaluations of chemical-
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part suppliers in Region A (Figure 57 in Section A in the Appendix) and Region G (Figure 63), and
metal-part suppliers in Region C (Figure 71) and Region H (Figure 76). In Region D statistically
significant difference between the datasets was observed for both types of suppliers (Figures 60
and 72).
These observations suggested the presence of another factor, which affects the quality capability
records. This is namely the period of relevance of the auditing results (Section 7.2.2). In
this case the influencing factor is time. Due to the fact that a quality audit is an assessment of
a momentary state of the constantly changing production process, audit’s validity over time also
changes. Thus even effectively conducted audits do not correspond to the actual quality capability
of a supplier after a sufficiently long period of time. To account for this issue it is necessary to
define a suitable weighing function, which will give quality performance records collected around
the time of the audit more importance than the rest of supplier’s quality performance records (see
Section 7.2.2). The definition of such a weighing function is particularly important for regions with
very high fluctuations of the process quality capability such as developing regions. Suitable input
for the definition of a weighing function are supplier-specific process variation indicators such as
process cpk-values, company-specific KPIs, etc. However, the empirical data analyzed here does
not include any such information. This is the reason why, the comparisons between quality capability
and quality performance were restricted mainly to regions with relatively high stability of
suppliers’ production processes such as developed regions.
Section 7.2.3.1 assessed whether there are differences between the evaluations of audit teams in
the individual regions. The majority of the evaluated audit records showed very good consistency,
which implies that Volkswagen’s audit teams are well calibrated. In the few cases, in which statistically
significant difference between the compared datasets was observed, either the number of
observations was so low, that the small size of the datasets is the most probable reason for the statistical
significance of the Kolmogorov-Smirnov test, or the empirical data were subject to specific
regional influences and such differences had to be expected. Thus the observed differences do not
necessarily indicate different calibration of the audit teams. These results show that differences between
the evaluations of the auditors are possible, even when they are equally well calibrated. It is
therefore necessary in any subsequent analysis to also test for differences between the evaluations
of the individual audit teams.
The last section, which addressed a possible biasing factor was Section 7.2.3.2. It evaluated
how similar or how different the quality performance of suppliers is assessed by the individual
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Volkswagen production facilities. Most of the calculations revealed consistent evaluations of the
individual production plants. However, in several cases the evaluations of one or more production
plants showed systematic deviations from the evaluations of other plants, which received the same
components from the same material group (see Figures 34 through 36 in Section 7.2.3.2). In
these cases the differences are due rather to the heterogeneity of the components comprising a
particular product group (Section 7.2.3.2), than to lack of calibration between the assessment
of individual production facilities. Thus, these results reveal another factor, which could have a
negative influence on the correlation study of quality capability and quality performance of the
suppliers.
After the possible sources of bias in the empirical data were examined, for two datasets quality
performance information was tested for correlation against the quality capability records of the respective
suppliers (Section 7.3). The purpose of these calculations was to test for the initial hypothesis
of this study introduced in Section 3. The results of the linear regressions between the number
of ppm and the respective supplier evaluation scores in the two case studies presented in Figures 38
and 40 do not show any statistically significant evidence that supports the null-hypothesis. Furthermore,
the results of the two case studies did not show any statistically significant evidence also
for differences between the quality capability evaluation results of suppliers, which are reported
to have quality related problems (expressed in terms of ppm), and suppliers, which delivered with
excellent performance record (0 ppm) throughout the entire time period covered by the analysis.
The last section of the presented analysis describes one particularly important finding, which
was not anticipated in the beginning of this project. It turned out that suppliers with quality related
problems differ from suppliers without any problems, not only based on their ppm record
but also on the amount of delivered components. Section 7.4 presents a mathematical derivation
of the relation between the performance record expressed in ppm of suppliers with quality related
problems and the amount of components they delivered. These two quantities have a relatively
simple relationship. Expressed on a double logarithmic scale the mathematical expectation of the
ppm-value as a function of the delivery amount N is asymptotically bound by two straight lines
(see Figure 44 in Section 7.4). Using this information one can deduct valuable information about
the quality capability of a supplier’s production process.
This issue was further investigated for several sets of empirical data, as the relation between
the ppm and the delivered amount of components was tested with a linear regression. Cases, in
which the estimated linear fits are particularly close to the theoretical line limiting the number
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of ppm, which has an intersect β 0 = 6 and a slope β 1 = −1 (see Section 7.4), suggest that the
respective groups of suppliers did not yet reach their critical delivery amount of N crit , and that
even though they have quality related problems on their records, their production processes seem
to be particularly stable. By contrast, datasets, which reveal slopes of the linear fit close to zero,
require closer attention, since it is very probable that their production processes are not stable
enough. Such information might be a useful contribution for the supplier management process and
can provide assistance in distributing the auditing resources.
8.3 Answers of the Research Questions
Can quality performance of automotive suppliers in the mass production be anticipated based
on quality evaluation of the capability of their production processes?
A definitive answer to this question is not possible at this stage due to several important reasons.
First of all, due to the technical difficulties experienced during the analytical part of this
project (such as difficult access to empirical data) the analysis was seriously restricted in its scope.
The only two cases with relatively small datasets, which were evaluated in Section 7.3, are not representative
for the entire set of empirical data. Therefore, even though the results of the empirical
analysis of the two datasets does not provide sufficient evidence, that the quality performance of
suppliers in these two particular cases can be anticipated based on the evaluation on the capability
of their production processes, this does not definitely mean that such relation does not exist. On the
contrary, further analysis surely has the potential to provide evidence that support this hypothesis.
Furthermore, as already discussed above there are a number of factors, which could potentially
introduce bias. Through statistical evaluation it was shown that the following factors can surely
influence the character of empirical information:
Revisions of the Formel Q-Fähigkeit;
Period of relevance of the audit results;
Coherence of the evaluations of different auditors.
The conducted evaluations surely account for the potential biases mentioned above. On the
other hand, the evaluations are based on quality performance data, which are averaged over the
entire time period considered in the analysis. The data averages were computed without the use
of a weighing function. This approach might not be optimal, given that quality problems with
significantly large time offset from a particular supplier evaluation are given the same weight as
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problems arising in a point of time close to the actual quality audit. The analysis was also technically
limited given the fact that some of the available operational data was difficult to access.
For example, the analyzed supplier quality capability records do not provide information about
the structure of the evaluated processes. This could eventually result in handling relatively simple
processes the same as processes, which are considerably more complex. Further, the quality
performance information included in the analysis is clustered based on the material group classification,
which in many cases includes a large number of diverse components in the same material
group. These particular factors were not regarded during the analytical stages, and any influences
they might have on the conducted evaluations is not excluded. Such potential bias should definitely
be investigated in any subsequent studies.
Even though the current analysis could not provide a definitive answer to this research question,
it employs important analytical methods, which can serve for guidance of any future works on the
topic. The comprehensive evaluation of the empirical data derived valuable relations and identified
important biasing factors, which have to be definitely avoided in any subsequent analysis. One of
the major contributions of the current analysis was the identification of a second quality predictor
– the amount of delivered components. This paper presented a formal mathematical derivation of
the identified relationship as well as a set of empirical evaluations, which demonstrate its practical
significance. This finding definitely has a lot of potential for further research and can reveal further
valuable aspects of the quality management process.
What is the quality of the quality auditing process at Volkswagen Group and what is its
improvement potential?
The analysis presented in Section 4 showed that the general quality auditing strategy employed
by Volkswagen adequately responds to the quality challenges of the automotive sector. In this
regard the more frequent auditing in developing regions accounts for process instability resulting
from factors such as personnel fluctuations and rapid growth. Furthermore, the increased focus
on sub-supplier audits allows Volkswagen to counteract to another trend in the automotive sector
– the growing complexity of the automotive supply chain. However, as already mentioned previously,
these conclusions are based on qualitative evaluation of the empirical data, and therefore a
quantitative analysis of this aspect is a particularly interesting topic for further research.
Volkswagen’s quality auditing process is defined by good practices also on the level of individual
audits. As already mentioned in Section 5, the comprehensive know-how about the state-of-theart
of the audited processes gives auditors an advantage during the quality audit. Such knowledge
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allows them to identify potential process weaknesses more quickly and therefore to provide a more
adequate process evaluation in the limited amount of time. The strategy pursued by Volkswagen
to maintain strategic know-how in expert circles is particularly useful to this end. Volkswagen
auditors perform audits mainly in one particular area of specialization, which allows them to accumulate
a very comprehensive knowledge base in that particular area. This approach is much more
efficient than auditing in all possible areas.
Along with the positive aspects of the quality auditing process, based on the experiences gained
throughout this project several particularly important improvement potentials of Volkswagen’s information
management were identified. These can be summarized within the following:
Categorization of quality capability and quality performance information needs improvement
(mismatch between the material / product group classification, see Section 6.2); A
solution to this particular point has already been implemented by the responsible departments.
However, at the time this project was carried out, the generated empirical data using
the new information management approach was not sufficient for a comprehensive analysis.
The new database, however, is of particular interest for future studies.
Valuable data from audit reports is very difficult to access (stored as document attachment,
not suitable for analysis, see Section 6.3); however, as already mentioned in the previous
sections a solution is possible with little overhead.
Incomplete quality performance records are present (Section 7.1); nevertheless, the overall
amount of complete records amounts to more than 95% and the quality of empirical data
shows considerable improvement over time.
Addressing the identified improvement areas will contribute to the quality of the operational
data and will ultimately have positive effect on the learning cycles in the organization.
Even though the discussion presented in this paper focuses on topics from the automotive production,
the analytical approaches and methods used here are applicable in any other business
context. Several important conclusions stem from the presented analysis:
In order to properly assess the effectiveness of the implementation of second-party quality
auditing specific to a particular company or sector it is necessary to evaluate the auditing process
from two perspectives – strategic employment of the audit expressed in the general audit
plan and its technical implementation expressed in terms of the structuring and execution of
individual quality audits.
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Frequency of auditing is affected by factors specific to the industry of operation, as well as
the characteristics of the individual markets. To appropriately schedule the quality audits
it is important to understand the specific conditions of a particular business field, which
determine the direction of its development. Example of such factors are regional differences
as well as the economic challenges, which influence companies’ decisions and production
strategies.
It is important to consider factors, which could potentially influence the individual evaluations
such as calibration of the respective quality auditors.
One particularly important conclusion, which results from the presented case study is the fact
that a proper management of the quality auditing process is strongly influenced by company’s
approach for management of the operational data obtained during the supplier visits. For this
aspect it is very important to develop a suitable concept and provide the required technical resources,
which allow for seamless regular evaluation of data generated in different phases of
the overall business process. Special attention is required in matching the respective records
especially for data resulting from different departments of the organization. Data analysis is
a necessary step, which boils down the collected knowledge into specific recommendations
for management and continuous improvement of the quality auditing process.
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Team_A_Chemie_FQF5
Team_A_Chemie_FQF6
Team_A_Chemie_FQF5
Team_A_Chemie_FQF6
Appendices
A Results from the analysis presented in Section 7.2.1
Distribution of Team_A_Chemie_FQF5
Frequency
0 5 10 15 20 25
60 70 80 90 100
Shapiro-Wilk Normality test p-values
Distribution of Team_A_Chemie_FQF6
Frequency
0 5 10 15
60 70 80 90 100
p-values 0,002 2,9E-06
Samples 174 154
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_A_Chemie_FQF5
and Team_A_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.152
p = 0.04596
n_1 = 174
n_2 = 154
Team_A_Chemie_FQF5
Team_A_Chemie_FQF6
60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_A_Chemie_FQF5 1,000 0,046
Team_A_Chemie_FQF6 0,046 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 57: Distribution comparisons between the evaluation scores of chemical-part suppliers
located in Region A, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
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Distribution of Team_B_Chemie_FQF4
Distribution of Team_B_Chemie_FQF5
Frequency
0 2 4 6 8
50 60 70 80 90 100
Distribution of Team_B_Chemie_FQF5
Frequency
0 2 4 6 8 10 12
Frequency
0 2 4 6 8 10 12
60 70 80 90 100
Distribution of Team_B_Chemie_FQF6
Frequency
0 1 2 3 4 5
Team_B_Chemie_FQF4
Team_B_Chemie_FQF5
Team_B_Chemie_FQF6
Team_B_Chemie_FQF4
Team_B_Chemie_FQF5
Team_B_Chemie_FQF6
50 60 70 80 90 100
60 70 80 90 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_B_Chemie_FQF4
and Team_B_Chemie_FQF5
Two−sample Kolmogorov−Smirnov test
D = 0.09498
p = 0.8501
n_1 = 69
n_2 = 103
Team_B_Chemie_FQF4
Team_B_Chemie_FQF5
50 60 70 80 90 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_B_Chemie_FQF5
and Team_B_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.1068
p = 0.9154
n_1 = 103
n_2 = 37
Team_B_Chemie_FQF5
Team_B_Chemie_FQF6
60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 4.0 and FQF 5.0.
(b) Evaluations according to FQF 5.0 and FQF 6.0.
Shapiro-Wilk Normality test p-values
p-values 4,3E-09 2,3E-09 0,003
Samples 69 103 37
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Team_B_Chemie_FQF4 1,000 0,850 0,780
Team_B_Chemie_FQF5 0,850 1,000 0,915
Team_B_Chemie_FQF6 0,780 0,915 1,000
significant on the 10% significance level
significant on the 5% significance level
(c) Distribution comparisons summary
Figure 58: Distribution comparisons between the evaluation scores of chemical-part suppliers
located in Region B, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
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Team_C_Chemie_FQF5
Team_C_Chemie_FQF6
Team_C_Chemie_FQF5
Team_C_Chemie_FQF6
Distribution of Team_C_Chemie_FQF5
Frequency
0 5 10 15 20 25
50 60 70 80 90 100
Shapiro-Wilk Normality test p-values
Distribution of Team_C_Chemie_FQF6
Frequency
0 5 10 15 20
50 60 70 80 90 100
p-values 1,3E-12 0,003
Samples 202 120
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_C_Chemie_FQF5
and Team_C_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.03408
p = 1
n_1 = 202
n_2 = 120
Team_C_Chemie_FQF5
Team_C_Chemie_FQF6
50 60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_C_Chemie_FQF5 1,000 1,000
Team_C_Chemie_FQF6 1,000 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 59: Distribution comparisons between the evaluation scores of chemical-part suppliers
located in Region C, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
135

Team_D_Chemie_FQF5
Team_D_Chemie_FQF6
Team_D_Chemie_FQF5
Team_D_Chemie_FQF6
Distribution of Team_D_Chemie_FQF5
Frequency
0 5 10 15 20
60 70 80 90 100
Shapiro-Wilk Normality test p-values
Distribution of Team_D_Chemie_FQF6
Frequency
0 5 10 20 30
60 70 80 90 100
p-values
5,7E-06 7,4E-06
Samples 79 155
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_D_Chemie_FQF5
and Team_D_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.294
p = 0.0002357
n_1 = 79
n_2 = 155
Team_D_Chemie_FQF5
Team_D_Chemie_FQF6
60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_D_Chemie_FQF5 1,000 2,4E-04
Team_D_Chemie_FQF6 2,4E-04 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 60: Distribution comparisons between the evaluation scores of chemical-part suppliers
located in Region D, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
136

Team_E_Chemie_FQF5
Team_E_Chemie_FQF6
Team_E_Chemie_FQF5
Team_E_Chemie_FQF6
Distribution of Team_E_Chemie_FQF5
Frequency
0 5 10 15
60 70 80 90 100
Shapiro-Wilk Normality test p-values
Distribution of Team_E_Chemie_FQF6
Frequency
0 2 4 6 8 10
60 70 80 90 100
p-values
3,0E-08 2,3E-07
Samples 154 119
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_E_Chemie_FQF5
and Team_E_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.1971
p = 0.01086
n_1 = 154
n_2 = 119
Team_E_Chemie_FQF5
Team_E_Chemie_FQF6
60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_E_Chemie_FQF5 1,000 0,011
Team_E_Chemie_FQF6 0,011 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 61: Distribution comparisons between the evaluation scores of chemical-part suppliers
located in Region E, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
137

Distribution of Team_F_Chemie_FQF4
Distribution of Team_F_Chemie_FQF5
Frequency
0 10 20 30 40
65 70 75 80 85 90 95 100
Distribution of Team_F_Chemie_FQF5
Frequency
0 5 10 15 20 25 30
Frequency
0 5 10 15 20 25 30
70 75 80 85 90 95 100
Distribution of Team_F_Chemie_FQF6
Frequency
0 5 10 15 20
Team_F_Chemie_FQF4
Team_F_Chemie_FQF5
Team_F_Chemie_FQF6
Team_F_Chemie_FQF4
Team_F_Chemie_FQF5
Team_F_Chemie_FQF6
65 70 75 80 85 90 95 100
70 75 80 85 90 95 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_F_Chemie_FQF4
and Team_F_Chemie_FQF5
Two−sample Kolmogorov−Smirnov test
D = 0.1747
p = 0.005529
n_1 = 177
n_2 = 212
Team_F_Chemie_FQF4
Team_F_Chemie_FQF5
65 70 75 80 85 90 95 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_F_Chemie_FQF5
and Team_F_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.08201
p = 0.6845
n_1 = 212
n_2 = 119
Team_F_Chemie_FQF5
Team_F_Chemie_FQF6
70 75 80 85 90 95 100
Evaluation Scores [%]
(a) Evaluations according to FQF 4.0 and FQF 5.0.
(b) Evaluations according to FQF 5.0 and FQF 6.0.
Shapiro-Wilk Normality test p-values
p-values
3,3E-11 5,5E-10 4,5E-08
Samples 177 212 119
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Team_F_Chemie_FQF4 1,000 0,006 1,7E-04
Team_F_Chemie_FQF5 0,006 1,000 0,684
Team_F_Chemie_FQF6 1,7E-04 0,684 1,000
significant on the 10% significance level
significant on the 5% significance level
(c) Distribution comparisons summary
Figure 62: Distribution comparisons between the evaluation scores of chemical-part suppliers
located in Region F, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
138

Team_G_Chemie_FQF5
Team_G_Chemie_FQF6
Team_G_Chemie_FQF5
Team_G_Chemie_FQF6
Distribution of Team_G_Chemie_FQF5
Frequency
0 5 10 15
75 80 85 90 95 100
Shapiro-Wilk Normality test p-values
Distribution of Team_G_Chemie_FQF6
Frequency
0 10 20 30 40
75 80 85 90 95 100
p-values
9,2E-06 9,6E-09
Samples 102 125
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_G_Chemie_FQF5
and Team_G_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.2264
p = 0.006305
n_1 = 102
n_2 = 125
Team_G_Chemie_FQF5
Team_G_Chemie_FQF6
75 80 85 90 95 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_G_Chemie_FQF5 1,000 0,006
Team_G_Chemie_FQF6 0,006 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 63: Distribution comparisons between the evaluation scores of chemical-part suppliers
located in Region G, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
139

Team_H_Chemie_FQF5
Team_H_Chemie_FQF6
Team_H_Chemie_FQF5
Team_H_Chemie_FQF6
Distribution of Team_H_Chemie_FQF5
Frequency
0 5 10 15
40 50 60 70 80 90 100
Shapiro-Wilk Normality test p-values
Distribution of Team_H_Chemie_FQF6
Frequency
0 5 10 15 20 25
40 50 60 70 80 90 100
p-values
2,1E-04 1,5E-11
Samples 139 147
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_H_Chemie_FQF5
and Team_H_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.09382
p = 0.5556
n_1 = 139
n_2 = 147
Team_H_Chemie_FQF5
Team_H_Chemie_FQF6
40 50 60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_H_Chemie_FQF5 1,000 0,556
Team_H_Chemie_FQF6 0,556 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 64: Distribution comparisons between the evaluation scores of chemical-part suppliers
located in Region H, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
140

Team_I_Chemie_FQF5
Team_I_Chemie_FQF6
Team_I_Chemie_FQF5
Team_I_Chemie_FQF6
Distribution of Team_I_Chemie_FQF5
Frequency
0 2 4 6 8 10
80 85 90 95 100
Shapiro-Wilk Normality test p-values
Frequency
0 1 2 3 4 5 6 7
Distribution of Team_I_Chemie_FQF6
80 85 90 95 100
p-values 0,020 0,087
Samples 41 42
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_I_Chemie_FQF5
and Team_I_Chemie_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.1603
p = 0.6608
n_1 = 41
n_2 = 42
Team_I_Chemie_FQF5
Team_I_Chemie_FQF6
80 85 90 95 100
Evaluation Scores [%]
Team_I_Chemie_FQF5 1,000 0,661
Team_I_Chemie_FQF6 0,661 1,000
significant on the 10% significance level
significant on the 5% significance level
(a) Evaluations according to FQF 5.0 and FQF 6.0.
(b) Distribution comparisons summary
Figure 65: Distribution comparisons between the evaluation scores of chemical-part suppliers
located in Region I, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
141

Team_C_Elektrik_FQF5
Team_C_Elektrik_FQF6
Team_C_Elektrik_FQF5
Team_C_Elektrik_FQF6
Distribution of Team_C_Elektrik_FQF5
Frequency
0 1 2 3 4 5 6
65 70 75 80 85 90 95 100
Shapiro-Wilk Normality test p-values
Distribution of Team_C_Elektrik_FQF6
Frequency
0 1 2 3 4 5 6 7
65 70 75 80 85 90 95 100
p-values 0,017 0,001
Samples 50 55
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_C_Elektrik_FQF5
and Team_C_Elektrik_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.1673
p = 0.4562
n_1 = 50
n_2 = 55
Team_C_Elektrik_FQF5
Team_C_Elektrik_FQF6
65 70 75 80 85 90 95 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_C_Elektrik_FQF5 1,000 0,456
Team_C_Elektrik_FQF6 0,456 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 66: Distribution comparisons between the evaluation scores of electrical-part suppliers
located in Region C, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
142

Team_E_Elektrik_FQF5
Team_E_Elektrik_FQF6
Team_E_Elektrik_FQF5
Team_E_Elektrik_FQF6
Distribution of Team_E_Elektrik_FQF5
Frequency
0 2 4 6 8 10
75 80 85 90 95 100
Shapiro-Wilk Normality test p-values
Distribution of Team_E_Elektrik_FQF6
Frequency
0 1 2 3 4 5 6 7
75 80 85 90 95 100
p-values 0,005 0,017
Samples 52 23
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_E_Elektrik_FQF5
and Team_E_Elektrik_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.3403
p = 0.04977
n_1 = 52
n_2 = 23
Team_E_Elektrik_FQF5
Team_E_Elektrik_FQF6
75 80 85 90 95 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_E_Elektrik_FQF5 1,000 0,050
Team_E_Elektrik_FQF6 0,050 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 67: Distribution comparisons between the evaluation scores of electrical-part suppliers
located in Region E, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
143

Distribution of Team_F_Elektrik_FQF4
Distribution of Team_F_Elektrik_FQF5
Frequency
0 5 10 15 20
70 80 90 100
Distribution of Team_F_Elektrik_FQF5
Frequency
0 5 10 15
Frequency
0 5 10 15
70 80 90 100
Distribution of Team_F_Elektrik_FQF6
Frequency
0 2 4 6 8 10
Team_F_Elektrik_FQF4
Team_F_Elektrik_FQF5
Team_F_Elektrik_FQF6
Team_F_Elektrik_FQF4
Team_F_Elektrik_FQF5
Team_F_Elektrik_FQF6
70 80 90 100
70 80 90 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_F_Elektrik_FQF4
and Team_F_Elektrik_FQF5
Two−sample Kolmogorov−Smirnov test
D = 0.2556
p = 0.009894
n_1 = 126
n_2 = 60
Team_F_Elektrik_FQF4
Team_F_Elektrik_FQF5
70 80 90 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_F_Elektrik_FQF5
and Team_F_Elektrik_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.2358
p = 0.1333
n_1 = 60
n_2 = 41
Team_F_Elektrik_FQF5
Team_F_Elektrik_FQF6
70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 4.0 and FQF 5.0.
(b) Evaluations according to FQF 5.0 and FQF 6.0.
Shapiro-Wilk Normality test p-values
p-values 1,7E-08 1,3E-08 0,001
Samples 126 60 41
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Team_F_Elektrik_FQF4 1,000 0,010 0,024
Team_F_Elektrik_FQF5 0,010 1,000 0,133
Team_F_Elektrik_FQF6 0,024 0,133 1,000
significant on the 10% significance level
significant on the 5% significance level
(c) Distribution comparisons summary
Figure 68: Distribution comparisons between the evaluation scores of electrical-part suppliers
located in Region F, performed according to different versions of Formel Q-Fähigkeit by the local
audit team.
144

Team_A_Metal_FQF5
Team_A_Metal_FQF6
Team_A_Metal_FQF5
Team_A_Metal_FQF6
Distribution of Team_A_Metal_FQF5
Frequency
0 5 10 20 30
50 60 70 80 90 100
Shapiro-Wilk Normality test p-values
Distribution of Team_A_Metal_FQF6
Frequency
0 5 10 20 30
50 60 70 80 90 100
p-values
1,5E-12 1,1E-09
Samples 209 303
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_A_Metal_FQF5
and Team_A_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.1094
p = 0.1037
n_1 = 209
n_2 = 303
Team_A_Metal_FQF5
Team_A_Metal_FQF6
50 60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_A_Metal_FQF5 1,000 0,104
Team_A_Metal_FQF6 0,104 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 69: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region A, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
145

Distribution of Team_B_Metal_FQF4
Distribution of Team_B_Metal_FQF5
Frequency
0 5 10 15
60 70 80 90 100
Distribution of Team_B_Metal_FQF5
Frequency
0 2 4 6 8 10 14
Frequency
0 2 4 6 8 10 14
80 85 90 95 100
Distribution of Team_B_Metal_FQF6
Frequency
0 2 4 6 8 10
Team_B_Metal_FQF4
Team_B_Metal_FQF5
Team_B_Metal_FQF6
Team_B_Metal_FQF4
Team_B_Metal_FQF5
Team_B_Metal_FQF6
60 70 80 90 100
80 85 90 95 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_B_Metal_FQF4
and Team_B_Metal_FQF5
Two−sample Kolmogorov−Smirnov test
D = 0.2226
p = 0.04086
n_1 = 126
n_2 = 57
Team_B_Metal_FQF4
Team_B_Metal_FQF5
60 70 80 90 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_B_Metal_FQF5
and Team_B_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.1602
p = 0.5309
n_1 = 57
n_2 = 46
Team_B_Metal_FQF5
Team_B_Metal_FQF6
80 85 90 95 100
Evaluation Scores [%]
(a) Evaluations according to FQF 4.0 and FQF 5.0.
(b) Evaluations according to FQF 5.0 and FQF 6.0.
Shapiro-Wilk Normality test p-values
p-values 7,0E-10 0,002 0,002
Samples 126 57 46
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Team_B_Metal_FQF4 1,000 0,041 0,479
Team_B_Metal_FQF5 0,041 1,000 0,531
Team_B_Metal_FQF6 0,479 0,531 1,000
significant on the 10% significance level
significant on the 5% significance level
(c) Distribution comparisons summary
Figure 70: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region B, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
146

Team_C_Metal_FQF5
Team_C_Metal_FQF6
Team_C_Metal_FQF5
Team_C_Metal_FQF6
Distribution of Team_C_Metal_FQF5
Frequency
0 5 10 20 30
70 75 80 85 90 95 100
Shapiro-Wilk Normality test p-values
Distribution of Team_C_Metal_FQF6
Frequency
0 5 10 15 20 25
70 75 80 85 90 95 100
p-values
4,6E-06 6,4E-07
Samples 171 157
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_C_Metal_FQF5
and Team_C_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.2306
p = 0.0003314
n_1 = 171
n_2 = 157
Team_C_Metal_FQF5
Team_C_Metal_FQF6
70 75 80 85 90 95 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_C_Metal_FQF5 1,000 3,3E-04
Team_C_Metal_FQF6 3,3E-04 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 71: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region C, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
147

Team_D_Metal_FQF5
Team_D_Metal_FQF6
Team_D_Metal_FQF5
Team_D_Metal_FQF6
Distribution of Team_D_Metal_FQF5
Frequency
0 2 4 6 8 10
50 60 70 80 90 100
Shapiro-Wilk Normality test p-values
Distribution of Team_D_Metal_FQF6
Frequency
0 5 10 20 30
50 60 70 80 90 100
p-values
4,8E-07 5,8E-06
Samples 88 116
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_D_Metal_FQF5
and Team_D_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.3433
p = 1.513e−05
n_1 = 88
n_2 = 116
Team_D_Metal_FQF5
Team_D_Metal_FQF6
50 60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_D_Metal_FQF5 1,000 1,5E-05
Team_D_Metal_FQF6 1,5E-05 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 72: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region D, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
148

Team_E_Metal_FQF5
Team_E_Metal_FQF6
Team_E_Metal_FQF5
Team_E_Metal_FQF6
Distribution of Team_E_Metal_FQF5
Frequency
0 2 4 6 8 10 14
70 80 90 100
Shapiro-Wilk Normality test p-values
Distribution of Team_E_Metal_FQF6
Frequency
0 5 10 15
70 80 90 100
p-values 1,5E-04 0,006
Samples 111 125
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_E_Metal_FQF5
and Team_E_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.1103
p = 0.4722
n_1 = 111
n_2 = 125
Team_E_Metal_FQF5
Team_E_Metal_FQF6
70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_E_Metal_FQF5 1,000 0,472
Team_E_Metal_FQF6 0,472 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 73: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region E, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
149

Distribution of Team_F_Metal_FQF4
Distribution of Team_F_Metal_FQF5
Frequency
0 10 20 30 40 50 60
70 80 90 100
Distribution of Team_F_Metal_FQF5
Frequency
0 10 20 30 40
Frequency
0 10 20 30 40
70 80 90 100
Distribution of Team_F_Metal_FQF6
Frequency
0 5 10 20 30
Team_F_Metal_FQF4
Team_F_Metal_FQF5
Team_F_Metal_FQF6
Team_F_Metal_FQF4
Team_F_Metal_FQF5
Team_F_Metal_FQF6
70 80 90 100
70 80 90 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_F_Metal_FQF4
and Team_F_Metal_FQF5
Two−sample Kolmogorov−Smirnov test
D = 0.2195
p = 5.419e−06
n_1 = 310
n_2 = 233
Team_F_Metal_FQF4
Team_F_Metal_FQF5
70 80 90 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_F_Metal_FQF5
and Team_F_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.08616
p = 0.4483
n_1 = 233
n_2 = 175
Team_F_Metal_FQF5
Team_F_Metal_FQF6
70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 4.0 and FQF 5.0.
(b) Evaluations according to FQF 5.0 and FQF 6.0.
Shapiro-Wilk Normality test p-values
p-values
1,9E-14 4,8E-09 1,2E-13
Samples 310 233 175
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Team_F_Metal_FQF4 1,000 5,4E-06 3,7E-04
Team_F_Metal_FQF5 5,4E-06 1,000 0,448
Team_F_Metal_FQF6 3,7E-04 0,448 1,000
significant on the 10% significance level
significant on the 5% significance level
(c) Distribution comparisons summary
Figure 74: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region F, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
150

Team_G_Metal_FQF5
Team_G_Metal_FQF6
Team_G_Metal_FQF5
Team_G_Metal_FQF6
Distribution of Team_G_Metal_FQF5
Frequency
0 5 10 15 20 25
75 80 85 90 95 100
Shapiro-Wilk Normality test p-values
Distribution of Team_G_Metal_FQF6
Frequency
0 10 20 30
75 80 85 90 95 100
p-values
8,4E-07 3,0E-10
Samples 85 89
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_G_Metal_FQF5
and Team_G_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.1309
p = 0.4459
n_1 = 85
n_2 = 89
Team_G_Metal_FQF5
Team_G_Metal_FQF6
75 80 85 90 95 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_G_Metal_FQF5 1,000 0,446
Team_G_Metal_FQF6 0,446 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 75: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region G, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
151

Team_H_Metal_FQF5
Team_H_Metal_FQF6
Team_H_Metal_FQF5
Team_H_Metal_FQF6
Distribution of Team_H_Metal_FQF5
Frequency
0 5 10 20 30
50 60 70 80 90 100
Shapiro-Wilk Normality test p-values
Distribution of Team_H_Metal_FQF6
Frequency
0 5 15 25 35
50 60 70 80 90 100
p-values
4,2E-14 2,1E-13
Samples 160 185
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_H_Metal_FQF5
and Team_H_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.3093
p = 1.487e−07
n_1 = 160
n_2 = 185
Team_H_Metal_FQF5
Team_H_Metal_FQF6
50 60 70 80 90 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_H_Metal_FQF5 1,000 1,5E-07
Team_H_Metal_FQF6 1,5E-07 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 76: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region H, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
152

Distribution of Team_I_Metal_FQF4
Distribution of Team_I_Metal_FQF5
Frequency
0.0 1.0 2.0 3.0
80 85 90 95 100
Frequency
0 1 2 3 4 5
65 70 75 80 85 90 95 100
Distribution of Team_I_Metal_FQF5
Distribution of Team_I_Metal_FQF6
Frequency
0 1 2 3 4 5
Frequency
0 2 4 6 8 10
Team_I_Metal_FQF4
Team_I_Metal_FQF5
Team_I_Metal_FQF6
Team_I_Metal_FQF4
Team_I_Metal_FQF5
Team_I_Metal_FQF6
80 85 90 95 100
65 70 75 80 85 90 95 100
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_I_Metal_FQF4
and Team_I_Metal_FQF5
Two−sample Kolmogorov−Smirnov test
D = 0.3271
p = 0.1774
n_1 = 19
n_2 = 28
Team_I_Metal_FQF4
Team_I_Metal_FQF5
80 85 90 95 100
Evaluation Scores [%]
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_I_Metal_FQF5
and Team_I_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.09921
p = 0.9934
n_1 = 28
n_2 = 54
Team_I_Metal_FQF5
Team_I_Metal_FQF6
65 70 75 80 85 90 95 100
Evaluation Scores [%]
(a) Evaluations according to FQF 4.0 and FQF 5.0.
(b) Evaluations according to FQF 5.0 and FQF 6.0.
Shapiro-Wilk Normality test p-values
p-values 0,694 0,647 7,7E-08
Samples 19 28 54
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Team_I_Metal_FQF4 1,000 0,177 0,063
Team_I_Metal_FQF5 0,177 1,000 0,993
Team_I_Metal_FQF6 0,063 0,993 1,000
significant on the 10% significance level
significant on the 5% significance level
(c) Distribution comparisons summary
Figure 77: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region I, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
153

Team_J_Metal_FQF5
Team_J_Metal_FQF6
Team_J_Metal_FQF5
Team_J_Metal_FQF6
Distribution of Team_J_Metal_FQF5
Frequency
0 1 2 3 4 5
80 85 90 95 100
Shapiro-Wilk Normality test p-values
Distribution of Team_J_Metal_FQF6
Frequency
0 2 4 6 8 10
80 85 90 95 100
p-values 0,036 0,146
Samples 31 72
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
CDFs for Team_J_Metal_FQF5
and Team_J_Metal_FQF6
Two−sample Kolmogorov−Smirnov test
D = 0.1962
p = 0.3744
n_1 = 31
n_2 = 72
Team_J_Metal_FQF5
Team_J_Metal_FQF6
80 85 90 95 100
Evaluation Scores [%]
(a) Evaluations according to FQF 5.0 and FQF 6.0.
Team_J_Metal_FQF5 1,000 0,374
Team_J_Metal_FQF6 0,374 1,000
significant on the 10% significance level
significant on the 5% significance level
(b) Distribution comparisons summary
Figure 78: Distribution comparisons between the evaluation scores of metal-part suppliers located
in Region J, performed according to different versions of Formel Q-Fähigkeit by the local audit
team.
154

B Results from the analysis presented in Section 7.2.3.2
Distribution of E_G_WSGR_0058_Plant_E_ppm
Frequency
0 1 2 3 4 5 6
Frequency
0 1 2 3 4 5 6 7
Plant: E
WSGR: 0058
Plant: G
WSGR: 0058
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
Plant: E
WSGR: 0058
Plant: G
WSGR: 0058
1 2 3 4
Distribution of E_G_WSGR_0058_Plant_G_ppm
1 2 3 4
CDFs for E_G_WSGR_0058_Plant_E_ppm
and E_G_WSGR_0058_Plant_G_ppm
Two−sample Kolmogorov−Smirnov test
D = 0.2667
p = 0.6781
n_1 = 15
n_2 = 15
E_G_WSGR_0058_Plant_E_ppm
E_G_WSGR_0058_Plant_G_ppm
1 2 3 4
ppm Scores [log]
Shapiro-Wilk Normality test p-values
p-values 0,015 0,028
Samples 15 15
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Plant: E
WSGR: 0058
Plant: G
WSGR: 0058
1,000 0,678
0,678 1,000
significant on the 10% significance level
significant on the 5% significance level
(a) Performance records comparison based on ppm
(b) Distribution comparison summary
Figure 79: Comparison between the quality performance records of the same 15 suppliers reported
by two different plants – Plant E and Plant G. The reported defective parts per million (ppm) refer
to quality problems of components from the material group 0058 (”Moulded parts < DIN A4 for
body”).
155

Distribution of H_I_WSGR_0058_Plant_H_ppm
Frequency
0.0 1.0 2.0 3.0
Frequency
0 1 2 3 4
Plant: H
WSGR: 0058
Plant: I
WSGR: 0058
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
Plant: H
WSGR: 0058
Plant: I
WSGR: 0058
0 1 2 3
Distribution of H_I_WSGR_0058_Plant_I_ppm
0 1 2 3
CDFs for H_I_WSGR_0058_Plant_H_ppm
and H_I_WSGR_0058_Plant_I_ppm
Two−sample Kolmogorov−Smirnov test
D = 0.3
p = 0.7869
n_1 = 10
n_2 = 10
H_I_WSGR_0058_Plant_H_ppm
H_I_WSGR_0058_Plant_I_ppm
0 1 2 3
ppm Scores [log]
Shapiro-Wilk Normality test p-values
p-values 0,601 0,044
Samples 10 10
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Plant: H
WSGR: 0058
Plant: I
WSGR: 0058
1,000 0,787
0,787 1,000
significant on the 10% significance level
significant on the 5% significance level
(a) Performance records comparison based on ppm
(b) Distribution comparison summary
Figure 80: Comparison between the quality performance records of the same 10 suppliers reported
by two different plants – Plant H and Plant I. The reported defective parts per million (ppm) refer
to quality problems of components from the material group 0058 (”Moulded parts < DIN A4 for
body”).
156

Distribution of O_P_WSGR_0058_Plant_O_ppm
Frequency
0 1 2 3 4 5 6 7
Frequency
0 1 2 3 4 5 6
Plant: O
WSGR: 0058
Plant: P
WSGR: 0058
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
Plant: O
WSGR: 0058
Plant: P
WSGR: 0058
0 1 2 3 4
Distribution of O_P_WSGR_0058_Plant_P_ppm
0 1 2 3 4
CDFs for O_P_WSGR_0058_Plant_O_ppm
and O_P_WSGR_0058_Plant_P_ppm
Two−sample Kolmogorov−Smirnov test
D = 0.4667
p = 0.07626
n_1 = 15
n_2 = 15
O_P_WSGR_0058_Plant_O_ppm
O_P_WSGR_0058_Plant_P_ppm
0 1 2 3 4
ppm Scores [log]
Shapiro-Wilk Normality test p-values
p-values 0,002 0,009
Samples 15 15
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Plant: O
WSGR: 0058
Plant: P
WSGR: 0058
1,000 0,076
0,076 1,000
significant on the 10% significance level
significant on the 5% significance level
(a) Performance records comparison based on ppm
(b) Distribution comparison summary
Figure 81: Comparison between the quality performance records of the same 15 suppliers reported
by two different plants – Plant O and Plant P. The reported defective parts per million (ppm) refer
to quality problems of components from the material group 0058 (”Moulded parts < DIN A4 for
body”).
157

Distribution of B_K_WSGR_0068_Plant_B_ppm
Frequency
0.0 1.0 2.0 3.0
Frequency
0 1 2 3 4 5 6
Plant: B
WSGR: 0068
Plant: K
WSGR: 0068
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
Plant: B
WSGR: 0068
Plant: K
WSGR: 0068
0 1 2 3 4
Distribution of B_K_WSGR_0068_Plant_K_ppm
0 1 2 3 4
CDFs for B_K_WSGR_0068_Plant_B_ppm
and B_K_WSGR_0068_Plant_K_ppm
Two−sample Kolmogorov−Smirnov test
D = 0.2667
p = 0.6604
n_1 = 15
n_2 = 15
B_K_WSGR_0068_Plant_B_ppm
B_K_WSGR_0068_Plant_K_ppm
0 1 2 3 4
ppm Scores [log]
Shapiro-Wilk Normality test p-values
p-values 0,720 0,085
Samples 15 15
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Plant: B
WSGR: 0068
Plant: K
WSGR: 0068
1,000 0,660
0,660 1,000
significant on the 10% significance level
significant on the 5% significance level
(a) Performance records comparison based on ppm
(b) Distribution comparison summary
Figure 82: Comparison between the quality performance records of the same 15 suppliers reported
by two different plants – Plant B and Plant K. The reported defective parts per million (ppm) refer
to quality problems of components from the material group 0068 (”Moulded parts > DIN A4 for
body”).
158

Distribution of H_I_WSGR_0068_Plant_H_ppm
Frequency
0 1 2 3 4
Frequency
0.0 1.0 2.0 3.0
Plant: H
WSGR: 0068
Plant: I
WSGR: 0068
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
Plant: H
WSGR: 0068
Plant: I
WSGR: 0068
0 1 2 3
Distribution of H_I_WSGR_0068_Plant_I_ppm
0 1 2 3
CDFs for H_I_WSGR_0068_Plant_H_ppm
and H_I_WSGR_0068_Plant_I_ppm
Two−sample Kolmogorov−Smirnov test
D = 0.3571
p = 0.3338
n_1 = 14
n_2 = 14
H_I_WSGR_0068_Plant_H_ppm
H_I_WSGR_0068_Plant_I_ppm
0 1 2 3
ppm Scores [log]
Shapiro-Wilk Normality test p-values
p-values 0,219 0,851
Samples 14 14
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Plant: H
WSGR: 0068
Plant: I
WSGR: 0068
1,000 0,334
0,334 1,000
significant on the 10% significance level
significant on the 5% significance level
(a) Performance records comparison based on ppm
(b) Distribution comparison summary
Figure 83: Comparison between the quality performance records of the same 14 suppliers reported
by two different plants – Plant H and Plant I. The reported defective parts per million (ppm) refer
to quality problems of components from the material group 0068 (”Moulded parts > DIN A4 for
body”).
159

Distribution of N_O_WSGR_0068_Plant_N_ppm
Frequency
0 1 2 3 4 5
Frequency
0 1 2 3 4 5
Plant: N
WSGR: 0068
Plant: O
WSGR: 0068
f(x)
0.0 0.2 0.4 0.6 0.8 1.0
Plant: N
WSGR: 0068
Plant: O
WSGR: 0068
0 1 2 3 4
Distribution of N_O_WSGR_0068_Plant_O_ppm
0 1 2 3 4
CDFs for N_O_WSGR_0068_Plant_N_ppm
and N_O_WSGR_0068_Plant_O_ppm
Two−sample Kolmogorov−Smirnov test
D = 0.1875
p = 0.9412
n_1 = 16
n_2 = 16
N_O_WSGR_0068_Plant_N_ppm
N_O_WSGR_0068_Plant_O_ppm
0 1 2 3 4
ppm Scores [log]
Shapiro-Wilk Normality test p-values
p-values 0,269 0,124
Samples 16 16
Kolmogorov-Smirnov Distribution Comparison test
p-values matrix
Plant: N
WSGR: 0068
Plant: O
WSGR: 0068
1,000 0,941
0,941 1,000
significant on the 10% significance level
significant on the 5% significance level
(a) Performance records comparison based on ppm
(b) Distribution comparison summary
Figure 84: Comparison between the quality performance records of the same 16 suppliers reported
by two different plants – Plant N and Plant O. The reported defective parts per million (ppm) refer
to quality problems of components from the material group 0068 (”Moulded parts > DIN A4 for
body”).
160

B.1 List of Material Groups
Table 6 includes the codes of all material groups which appear in the analysis presented in this
paper and their names. Note that the complete definition of the Volkswagen material groups comprises
of three levels of detail. However, here only the second level is listed, since this is the only
material group level available in suppliers’ quality performance records analyzed here.
Table 6: List of product groups and their names. (Source: Volkswagen AG, 2010)
Material Group Number Description Number of Subgroups
0058 Moulded parts < DIN A4 for body 2
0068 Moulded parts > DIN A4 for body 4
0096 Reservoirs, covers, pipes, wires 15
0099 Column trim, sill plates 4
0105 Bumper, Spoiler 2
0302 Light metal cast engine parts 1
0337 Light metal cast gearbox parts 4
0480 Pipes, pipe parts per drawing 13
0485 Swivel plates 1
161

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