Method of collecting and categorising performance ... - RESER

Method of collecting and categorising
performance indicators to measure the
productivity of modular services using an IT
tool
Mike Freitag 1 , Sabrina Lamberth 1 , Stephan Klingner 2 , Martin Böttcher 2
1
Fraunhofer IAO, 2 University of Leipzig
While the question of modularisation in the services sector has been
closely examined for a number of years already, it is only recently that the
productivity of services has been studied in more detail. Experts were
surveyed in order to incorporate their business needs into the process of
measuring service productivity. Subsequently, a collection and
categorisation of performance indicators were suggested based on
previous work. The performance indicators could be allocated to the
relevant modules, for instance, using the IT tools introduced here.
1. Introduction
Services are constantly gaining in importance within companies, and the proportion
of turnover generated by services is continually growing. It is therefore crucial to a
company that it systematically develops and provides services as the only way of
ensuring that customers are indeed offered high-quality services. In this respect, an
important role is played above all by the choice of optimum service architecture and
by the service productivity of the individual components of that architecture. In
particular, it has to be clarified to what extent the service architecture can be
modularised and how productively the chosen architecture can facilitate service
provision. While the question of modularisation in the services sector has been
closely examined for a number of years already, it is only recently that the
productivity of services has been studied in more detail. In this paper, we will take a
closer look at the following points:
• Defining service productivity
• Requirements for compiling productivity performance indicators
• Collecting and categorising productivity performance indicators
• Allocating performance indicators to individual services or service modules
using an IT tool
• Summary

2. Service productivity and controlling
2.1. Service productivity
There is a subcategory of economics dedicated to productivity, which generally
denotes the ratio of income (output) to expenditure (input) and expresses technical
profitability (Reinecke; Geis, 2006) or, in short, the “Profitability of the operations
factor combination” (Ergiebigkeit der betrieblichen Faktorkombination: Ganz;
Bienzeisler; Tombeil, 2006):
(Productivity = Output/Input)
Productivity is also considered to be a key economic goal for the services sector. In
Germany, services represent nearly 75% of companies’ entire revenue (DIN, 2009).
The growing importance of services to the total value added makes it necessary to
include them in the internal performance accounting and to systematically monitor
and influence the ratio of expenditure to income in the services sector. Based on a
comprehensive look at companies which provide services in Germany – of which
approximately 30 per cent were manufacturing companies and 70 per cent service
providers in 2008 – the percentage of revenue generated by new services is
expected to grow in comparison to the existing portfolio of services (Meiren, 2010).
Service productivity represents the efficiency of the processes for developing and
providing services and how effectively input resources can be transformed into added
value for the customer and economic success for the company (Grönroos; Ojasal,
2004). What sets service productivity apart is that both the provider’s performance
(input on the side of the provider) and the client’s input need to be accounted for
(Ganz; Bienzeisler; Tombeil, 2006). While the provider’s performance is easier to
quantify and is based on large-scale elements such as staff and the materials used, it
is more difficult to quantify the client’s performance, as this is often affected by soft
factors such as the client’s willingness and expertise. Quantifying and evaluating the
shared performance of the client and the service provider can only be achieved
through interaction (communication, coordination and collaboration), making it much
more difficult and also giving rise to the transaction expenditure associated with
these information and communication processes. In addition to integration of the
client as an external factor, other specific components influence the service
productivity concept. These include the inability to stock, which, due to its effects on
capacity and the commitment to providing services and increased flexibility and
communication, has an influence on result and process-related productivity. The fact
that services are intangible has a direct influence on the perception of quality on the
side of the client, who can only judge the service through experience and therefore
needs to believe in the quality from the outset. Heterogeneity represents the fourth
feature of services and complicates, on the one hand, the development of an allencompassing
measurement model for service productivity as services are only very
rarely standard products, and, on the other, the configuration of a standard services
package that is simple to manage and can be defined with precision. This in turn
impacts on potential, process and customer-related performance indicators.

Due to the unique features of input interactions and the input/output relationship, a
comprehensive approach is essential. Grönroos and Ojasalo (2004) describe a
concept for measuring service productivity as follows:
Service productivity
= f (internal efficiency, external efficiency, capacity efficiency)
Here it becomes clear that not only internal efficiency is crucial for productivity, but
also external efficiency (i.e. on the side of the client). What is more, it is assumed that
capacity in terms of service provision must be accounted for in order to identify and
optimise service productivity.
2.2. Service controlling
Service productivity is measured using performance indicators and performance
measurement systems (organised collection of various performance indicators).
Commonly used performance measurement systems include the DuPont System of
Financial Control, the profitability and liquidity performance measurement system, the
ZVEI performance measurement system and the balanced scorecard. When it comes
to practical applications, which performance indicators are suitable for effectively
portraying service productivity, and how should they be compiled in order to create
an informative performance measurement system customised to meet a company’s
specific needs Clear indicators are necessary both to monitor the profitability of
individual services and the portfolio of services as a whole and to provide the input
necessary to plan and develop new services or reorganize the service portfolio
structure.
Performance indicators represent a common controlling instrument and reflect the
company or sector’s key success factors. Because success can basically be
determined by how these combine and interact, several of these various “success
indicators” are grouped into performance measurement systems. Ideally, individual
informative performance indicators are aggregated in company or sector-specific
performance indicator catalogues (Kütz, 2007). As such, it would also be plausible to
use specific performance measurement systems for service controlling or
supplement the “general” performance measurement system with special “service
performance indicators” in order to create both an isolated overview of the
productivity of the services offered and the company’s contribution to the total value
added.
One possible type of categorisation that can be used to develop service-specific
performance measurement systems focuses on the wide range of service attributes,
comprising results, potential, process and customer-related performance indicators.
To integrate service-related performance indicators under the classification of
“general” parameters (e.g. in the product sector), it is possible to integrate a service
component while still maintaining the “general” part. The performance indicator
component for services contains a profitability, liquidity and “guarantee of continued
existence” component, ideally integrates not only monetary but also qualitative

factors and features both operational and strategic significance. To operationalise the
quality of services, which has a reciprocal effect on productivity, results taken from
customer satisfaction surveys, which reflect the way in which clients perceive the
quality of services, are translated into client-specific performance indicators and
integrated into the performance measurement systems associated with service
controlling (Ganz; Bienzeisler; Tombeil, 2006). Due to the associative nature of
services, the assessment criteria represent a combination of quantitative and
qualitative components (Reichwald; Möslein, 1995).
3. Requirements for compiling productivity
performance indicators
3.1. Approach to expert interviews
When it came to selecting the company experts to be surveyed, interviews focused
primarily on suppliers of technical services who already have experience in
modulating or componentising the services they provide. These “technical services”
refer to services associated with technical products, including industrial services such
as maintenance and repair, and services associated with technical support, including
IT services. Other criteria such as company size were ignored in the selection of
companies. The management and/or those responsible for designing the range of
services formed the target group of focus within the company, making it possible to
acquire educated statements to current approaches and problems.
Conducted in the summer and autumn of 2010, the interviews focused on in-depth
discussions with representatives from a total of 13 companies (Böttcher; Meiren,
2011). Featuring a standardised framework, the interviews served their purpose well
as the predefined topics provided both an extensive overview into company activities
and the freedom to delve further into especially interesting issues. This allowed the
opportunity to take a closer look at companies’ innovative approaches and solutions.
3.2. Earlier use of performance indicators within companies
The four performance indicator categories mentioned above were addressed during
the expert surveys and supplemented with a financial performance indicator,
resulting in the following performance indicator categories:
• Market and client-related performance indicators (e.g. market shares,
customer satisfaction, complaints)
• Result-oriented performance indicators (e.g. adherence to schedules, service
level)
• Process-related performance indicators (e.g. throughput times, waiting
periods)

• Resource-related performance indicators (e.g. utilisation, staff qualification
level)
• Financial performance indicators (e.g. revenue, costs, gross margins)
Half of the companies surveyed generally rely on market and client-related
performance indicators to measure customer satisfaction and the number of
complaints received. Finally, these are allocated to categories of varying levels of
urgency.
Result-oriented performance indicators are also used by a majority of the companies.
“Deadline compliance” in particular was listed frequently, while “service level”
appeared very little in company controlling.
Unlike the first two performance indicator categories, process-related performance
indicators are used by very few companies. Those that do use them often do so to
measure the throughput times.
Resource-oriented performance indicators too are used by few companies. However,
if companies do use them, they attempt to quantify them based on the utilisation of
their staff or the required qualification level. The required qualification level can then
be used to infer what percentage of the employees should be further qualified and
what percentage of the positions will have to be filled again in the future.
The last performance indicator category, the financial performance indicator, is
unusual in that the majority of the companies use it. Nearly all of them compile and
analyse the costs and revenue generated by the services. Very few of the companies
additionally calculate the gross margins of select services. Due to high costs, this is
only done for larger projects. For smaller, advice-intensive projects, only person
months are usually calculated.
3.3. Requirements for compiling productivity performance
indicators in the future
Productivity performance indicators should be systematically used, as they are
essential for service controlling. The development of new performance indicators, the
expansion of existing performance measurement systems and the compiling process
have to fulfil organisational and content requirements. From an organisational point
of view, the question of measurability and generating data from the various serviceproviding
company units must be addressed. In terms of productivity performance
indicator content, it needs to be determined which input and output values should be
measured. In other words, which measurement values are suitable for measuring
productivity As a rule, performance indicators should be developed and used in line
with the aims of the company’s service business and adequately cover the resource,
process, client and result sectors. As such, organisational and content requirements
must be compiled in these categories.
In the expert interviews, just over half of the companies answered this question.
Moreover, the requirements mentioned were extremely varied. For this reason, they

are displayed in Table 1 in the form on a non-prioritised list. The requirements
compiled are allocated to two categories: data-related and tool-related.
Category
Data-related
requirements
(compiling,
organisation and
evaluation)
Requirements
• Increase in transparency
• The necessity of explaining which additional KPIs are
essential to increasing the points of reference for the
quality of a provided service beyond the binary
question of whether a process runs seamlessly or not
• Compiling the gross margins for each project
• Calculating and evaluating the average daily rate of
subcontractors
• Calculating and evaluating the average expense ratio
of off-shore drilling
• Development of threshold specifications in order to
determine at which point productivity is threatened
• Provision of KPIs for the process, IT and financial
sectors
• Introduction of customer satisfaction KPIs for
comprehensive analysis of IT-based services
• Analysing service provision time as a central element
of productivity, as it plays a crucial role
• Approach to analysing processes carried out at the
client’s facilities and incorporating them into the
productivity framework
• Use of visualisation applications
• Enabling simple measurement of customer
satisfaction
Tool-related
requirements
• Use of visualisation applications
• Enabling simple measurement of customer
satisfaction
Table 1
Content requirements for developing productivity performance indicators in the future
In addition to content requirements, organisational requirements for compiling
productivity performance indicators in the future were also addressed. This question
was answered less frequently than that regarding content requirements. For this

eason, the process-related requirements are displayed in Table 2. The requirements
are allocated to two categories: process-related and tool-related.
Category
Process-related
requirements
(compiling,
organisation and
evaluation)
Requirements
• Timely evaluation of performance indicators and the
average expense ratio of off-shore and/or near-shore
drilling
• Potentially adapting the processes, organisational
structure, IT infrastructure
• Introduction of central control mechanisms with the
aim of optimising the compiling process
Tool-related
requirements
• Selection of potential performance indicators from a
single database
• Compilability of performance indicators as a central
issue: measurement standardisation, consideration of
context
Table 2
Organisational requirements for compiling productivity performance indicators in the future
4. Collection and categorisation of productivity
performance indicators
From a practical point of view, the issue of which success indicators are suitable for
portraying service productivity and transforming it into a form that can be evaluated
needs to be addressed. In addition to the expert interviews, a comprehensive Excelbased
catalogue consisting of 180 productivity performance indicators was created in
order to aid in the measurement of service productivity and integrate an IT tool.
Existing performance indicators were collected through wide-scale analysis of
controlling literature, analysed for suitability for measuring productivity in the services
sector and allocated to categories. The suitability analysis and categorisation was
carried out objectively, taking into account the wide range of service attributes as
identified by Kleinaltenkamp (2001). The resulting framework comprised potential,
process and result-oriented dimensions, as well as that of the external factor (client),
which introduces itself into the service process and therefore incorporates potential,
process, client and result-related productivity performance indicators. The sectors
measured by the individual performance indicator categories are listed in Table 3.

Performance
indicator
category
Potential-related
performance
indicators
Process-related
performance
indicators
Result-related
performance
indicators
Client-related
performance
indicators
Description (range of
attributes)
Service providers
possess the ability and
willingness to provide
services
Providing services is
characterised as a
process
Services can achieve
tangible or intangible
results
The service recipient
introduces itself or a
component into the
process (“external
factor”)
Measurement sector (services)
Resources
Process efficiency, performance
Monetary and qualitative effects (e.g.
benefits for the service provider)
Productivity increase or decrease as
a result of client influence in the
services sector, effects on
productivity on the side of the client
(e.g. customer satisfaction)
Table 3
Service measurement sectors and their productivity performance indicators (in-house portrayal
following Kleinaltenkamp, 2001)
The potential dimension represents the provider’s ability and willingness to provide a
service and is thus related to resources. As such, these performance indicators
should be allocated to the following standard categories: staff, raw materials,
consumables, supplies and IT infrastructure (Meiren, Barth, 2002). The process
dimension represents the actual provision of services, which is characterised as a
procedure and can therefore be subdivided into process steps (Meiren, Barth, 2002).
As a result, performance indicators have also been incorporated which make it
possible to evaluate the productivity of processes (input – activity – output), thus
forming a link to process efficiency, and which also make it possible to demonstrate
productivity values at points of intersection. This goes back to the fact that individual
process steps and points of intersection particularly in process design possess the
potential for productivity loss or optimisation (Meiren, Barth, 2002). Because service
processes have an effect on the recipient or an object (e.g. machines during
maintenance services), the client’s ability and characteristics as well as the degree of
integration into the process of providing the service must be taken into account. In
accordance with the wide range of service attributes, the effects of the services
provided must ultimately be analysed, as they can be tangible or intangible
(Kleinaltenkamp, 2001) and offer monetary or qualitative benefits. Analysis of these
effects is result-oriented and focuses on the financial contribution of services.
As with the practical requirements, the focus during collection and categorisation of
the productivity performance indicators was on increasing the transparency of the
management information and in particular developing a solution for evaluating
customer satisfaction, as well as simple classification and subdivision of the

performance indicators into departments of service-providing companies, such as IT
and financing. Due to the process-like nature of services, performance indicators
were also concentrated on which in particular take process-relevant factors such as
time, flexibility and capacity into account. For the sake of transparency and quality
optimisation in service controlling, the primary categories were also supplemented by
a subclassification into subcategories, such as quality, financing, quantity and
market-related performance indicators. The aim was to develop a classification that
was extremely straightforward and a framework for applying markers to areas of IT
support.
In addition to comprehensive descriptions, a definition and examples of applications
designed to inform the end user, the performance indicator catalogue also contains
the corresponding mathematical expression (formula) to aid in the development of
the IT tool. Table 4 represents a sample entry in the performance indicator catalogue.
Title of
performance
indicator
Description of
the performance
indicator
Formulas
Performance
indicator
application
Reference
Subclassification
I
Subclassification
II
Additional
performance
indicator
categories
Return on
Complaint
(RoC)
Return on
Complaint
represents the
benefits of
complaint
management
and/or complaintspecific
investments. The
“Benefits of
complaint
management”
counter can be
operationalised
(e.g. lifetime
increase,
revenue/periods,
etc.) through the
difference
between market
loss with and
without the
existence of
complaint
management
Basic
formula:
“return on …”
[in %] =
benefits
within in a
representativ
e
period/capital
investment x
100
The RoC allows
you to
determine the
profitability of
complaint
management
and/or
individual
measures for
further
developing
complaint
management
Witt, Frank-
Jürgen:
Dienstleistungs
controlling.
Munich 2003.
p. 125;
Brugger, Ralph:
Der IT
Business Case:
Kosten
erfassen und
analysieren -
Nutzen
erkennen und
quantifizieren.
Berlin 2009. p.
179 et seq.
Finances
Quality
Client-related
performance
indicator
Table 4
Extract from the productivity performance indicator catalogue, category: “result-related performance
indicators” with subcategory (in-house representation)
5. Allocating performance indicators to individual
services or service modules using an IT tool
As demonstrated by the results of the expert interviews, there is a continued need for
concrete proposals for solutions in the field of performance indicator integration for
services. Based on these requirements, an IT tool prototype has been developed to
effectively support a management of complex services which is both performanceindicator-based
and productivity-oriented.

The productivity observation is especially important with regard to comprehensive
service portfolios. The tool incorporates the modularisation paradigm to create a
manageable design of the performance indicator, despite its quantitative and
qualitative complexity (Böttcher, Klingner 2011). Therefore, the first step in using the
tool is the decomposition of the monolithic service portfolio into individual modules,
which are then arranged in a hierarchical structure. The “components” represent the
individual service modules, while logical and quantitative interdependencies between
the modules are depicted by the connectors within the hierarchy. For example,
Figure 1 shows a detailed view of a portfolio structured in this way, whereby the
boxes with angular corners represent the components and those with rounded
corners the connectors.
Figure 1: Modular service portfolio
After the hierarchical composition of the service portfolio has been modelled, the
performance of the individual modules can be quantified using productivity
performance indicators. An unlimited number of productivity performance indicators
can be allocated to each component during this stage. A productivity performance
indicator is calculated using:
• A fixed numeric value
• A calculation rule made up of fixed numeric and referenced values from other
components
• Referenced values from external programs

Additional functions, such as calculating the minimum value, maximum value or
mean, are also available to this end. Figure 2 shows an example of various
performance indicators for a component.
Figure 2: Key performance indicators
To facilitate the selection of the concrete performance indicators, the tool is
integrated into the collection of productivity performance indicators described in
Section 4 . This performance indicator catalogue can be referenced to assist in the
creation of new performance indicators and definition of the calculation rules for
these indicators. The performance indicator catalogue, which contains all
performance indicators divided into categories, can be accessed by clicking on the
“Add Characteristic”, (see Figure 3). Because the performance indicator can be
filtered based on the service type of the corresponding component, categorisation
makes it easier to identify the desired performance indicator. Each performance
indicator includes a brief description and list of references. If this performance
indicator is now added to the component, the corresponding formula, whose values
must be determined according to the options listed above, will be provided.
Figure 3: KPI-Library
A service portfolio fully modelled using these methods supports the identification of
components which could be improved while taking productivity factors into
consideration. Furthermore, it can also be used as the basis for a productivity-

oriented design of individual service configurations. During the composition of service
modules, the productivity performance indicators can be monitored and alternative
configurations can, if necessary, be recommended.
6. Summary
The growing importance of services to total value added and the need to guarantee
high service quality make it necessary to include service-related data in internal
performance accounting and systematically to monitor and/or influence the ratio of
expenditure to income. This militates in favour of service-specific indicators and
company-specific performance measurement systems being used. This approach
was adopted because it is an established and user-friendly mix of control instruments
and because the service-control component can easily be integrated into the
“general” part of the performance measurement system (e.g. for material assets). On
the basis of the segmentation of the services sector into “result”, “potential”,
“process” and customer” dimensions carried out in the preparatory phase, the project
broke down data into “result-related”, “potential-related”, “process-related” and
“customer-related” indicators, to which was added the category “financial indicators”
established on the basis of interviews. Experts were asked in interviews about these
categories with regard to their importance, acceptance and possibilities of further
development; and the categories were then divided on the basis of an analysis of
their content into 180 indicators and aggregated in an indicator catalogue. To
optimise the use of performance indicators in the services sector, requirements
relating to content and organisation were identified.
The results of expert interviews reveal the need for solutions to be proposed in this
area. In order to give effective support to the productivity-geared management of
complex services, an IT tool was developed in the next stage on the basis of those
requirements. This enables large service portfolios to be structured into individual
modules and the performance of those modules to be quantified using productivity
indicators. Various means of visualisation are used to make even complex service
portfolios with a multitude of modules and indicators manageable.
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Authors:
Mike Freitag
Fraunhofer IAO
New Service Development
Nobelstr. 12, 70569 Stuttgart, Germany
Mike.Freitag@iao.fraunhofer.de
Sabrina Lamberth
Fraunhofer IAO
New Service Development
Nobelstr. 12, 70569 Stuttgart, Germany
Sabrina.Lamberth@iao.fraunhofer.de
Stephan Klingner
University of Leipzig
Department of Computer Science
Johannisgasse 26
04103 Leipzig
Germany
klingner@informatik.uni-leipzig.de
Dr. Martin Böttcher
University of Leipzig
Department of Computer Science
Johannisgasse 26
04103 Leipzig
Germany
boettcher@informatik.uni-leipzig.de
»Increases in productivity via component-based services« (»Produktivitätssteigerung
durch komponentenbasierte Dienstleistungen«), the joint research project used as
the basis for this publication, is supported by funds from the Federal Ministry of
Education and Research (Bundesministeriums für Bildung und Forschung BMBF)
and listed under support codes 01FL09004 and 01FL09005. Additional information
on this project can be found online at: http://koproserv.uni-leipzig.de.