Improvement of Material Flow in the Production and Supply Chain of ...

ROYAL INSTITUTE OF TECHNOLOGY
Improvement of Material Flow in the
Production and Supply Chain of ADB
Products
By
Jie Jiang & Yuran Zhu
Supervised by Daniel T. Semere
Stockholm, Sweden
March, 2008

Improvement of Material Flow in the Production and Supply
Chain of ADB Products
Jie Jiang & Yuran Zhu
Dept. of Industrial production, Royal Institute of Technology
SE-100 44 Stockholm, Sweden
ABSTRACT
With economic globalization and the advent of the knowledge-based economy era, as
well as the emergence of global manufacturing, supply chain management in the
manufacturing sector have universal application. Material flow throughout the supply
chain, the merits and demerits of the material flow in the supply chain is vital impact.
Based on the investigation in the factory there are two main problems when produce
the ADB (Air Disc Brake) in Haldex, AB. Because the rapid growth of market demand
the first challenge is production volume of the present assembly line can not satisfy
the market demand. Another problem is when the production volume of ADB
increased the inventory space is too small to store the productions.
The objectives of the research include improving the current production capacity and
readapting the relationships with suppliers and customers to achieve the increasing
market demand in the coming future. And discuss the inventory problem associate
with the suppliers and customers. The paper begins with the research inside the
production line. First of all gather data from the practical production line of ADB in
Landskrona. Then analyze the data by statistical method. Depending on the results of
data analysis, a model has been developed to simulate the production line. By
simulating and analyzing, a bunch of methods in improving the production volume
have been proposed and an appropriate proposal is concluded after comparisons
according to cost, reliability and lead time. As soon as the productivity is able to reach
the market, researches in supply chain is taking place from suppliers’ aspect, inventory
and delivery. Current suppliers are evaluated based on multiple criteria, such as quality,
delivery, cost, co-design capability, and so on. Considering the forecast of market and
integrated a number of factors advices are given for the supplier selections in the
future. At last, current inventory and delivery statues are stated and corresponding
improvement with the increasing production volume are calculated to reduce the
inventory and optimize the delivery. Meanwhile, theoretical advices are given to deal
with the package and delivery problem due to unpredictable situation of the future
market.
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Needs to be pointed out is that in this paper chapter 1 and chapter 6 are written by
Yuran Zhu. Chapter 2, chapter 3 and chapter 5 are written by Jie Jiang. Chapter 4 and
chapter 7 are written by the two co-operations.
Key words: Supply Chain, Material flow, Modeling, Simulation, Assembly line.
*The contents of table 4.1, table 5.1 and table 5.2 are hidden by black because these
are sensitive information. The detail information of them shows in appendix A but not
published.
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ACKNOWLEDGEMENTS
The report is the result of our thesis project at Royal Institute of Technology. It is the
final part in our study in the master’s program of Production Engineering and
Management. The initiator of the project was the Commercial Vehicle System
deviation of Haldex AB.
We would like to first and foremost like to express our gratitude to supervisors: Jonas
Warnander in Haldex AB, who provided us with valuable information and advices
throughout the project, as well as his kindly reception when we visited the factory;
Daniel T. Semere in Royal Institute of Technology particularly for his guidance on our
project and paper all the time. Thanks to their invaluable guidance and support we can
finish this paper smooth. Thank you for Haldex gave us the chance to perform our
thesis work at Haldex AB, arranged our visit to the factory in Landskrona and all
interviewees who have taken their time to answer our questions.
Special thanks are extended to Professor Mihai Nicolescu, for providing additional
opportunities to perform work relating to our thesis at Haldex AB.
Finally, we would like to express our appreciation to all of those at the factory in who
provided a friendly and inspirational environment.
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TABLE OF CONTENTS
ABSTRACT...................................................................................................................................... I
ACKNOWLEDGEMENTS........................................................................................................... III
TABLE OF CONTENTS................................................................................................................IV
LIST OF TABLES ..........................................................................................................................VI
LIST OF FIGURES...................................................................................................................... VII
CHAPTER 1 INTRODUCTION OF THE RESEARCH.................................................................1
1.1 BRIEF BACKGROUND OF THE RESEARCH......................................................................................1
1.2 PRESENT PROBLEMS HALDEX FACES IN THE ADB PRODUCTS.......................................................3
1.3 AIM AND OBJECTIVES.................................................................................................................3
CHAPTER 2 RESEARCH THEORY AND METHODOLOGY.....................................................5
2.1 RESEARCH FRAMEWORK............................................................................................................5
2.2 SCIENCE AND PRACTICAL SIGNIFICANCE OF THE STUDY ...............................................................8
2.3 RESEARCH METHODOLOGY ........................................................................................................8
CHAPTER 3 DATA ANALYSIS OF PRODUCTION LINE..........................................................10
3.1 INTRODUCE OF DATA ANALYSIS.................................................................................................10
3.2 PREASSEMBLY LINE DATA ANALYSIS..........................................................................................12
3.2.1 PRIMARY DATA ANALYSIS OF PREASSEMBLY LINE ..............................................................12
3.2.2 FURTHER DATA ANALYSIS FOR TTF OF PREASSEMBLY LINE................................................14
3.2.3 FURTHER DATA ANALYSIS FOR TTR OF PREASSEMBLY LINE................................................15
3.3 MAIN ASSEMBLY LINE DATA ANALYSIS ......................................................................................16
3.3.1 PRIMARY DATA ANALYSIS FOR MAIN ASSEMBLY LINE .........................................................16
3.3.2 FURTHER DATA ANALYSIS FOR TTF OF MAIN ASSEMBLY LINE.............................................17
3.3.3 FURTHER DATA ANALYSIS FOR TTR OF MAIN ASSEMBLY LINE ............................................17
3.4 RESULTS .................................................................................................................................18
CHAPTER 4 MODELING AND ANALYSIS OF PRODUCTION LINE .....................................20
4.1 MODELING..............................................................................................................................20
4.2 SIMULATION AND ANALYSIS .....................................................................................................22
4.3 SOLUTIONS..............................................................................................................................26
4.3.1 INVESTING NEW MACHINES IN THE BOTTLENECKS.............................................................26
4.3.2 CUTTING DOWN THE TIME TO REPAIR IN BOTTLENECKS ....................................................27
4.3.3 INCREASING THE OPERATION TIME OF BOTTLENECKS ........................................................29
4.3.4 SOLUTION FROM INTEGRATIVE ASPECTS IN BOTTLENECKS.................................................29
4.3.5 PROLONG THE PRODUCTION HOURS..................................................................................31
4.3.6 IMPROVE THE EMPLOYEES’ SKILLS ...................................................................................31
4.4 COMPARISONS AND RESULTS ....................................................................................................31
4.4.1 COST COMPARING ...........................................................................................................32
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4.4.2 RELIABILITY COMPARING ................................................................................................33
4.4.3 LEAD TIME COMPARING...................................................................................................34
4.5 RESULTS .................................................................................................................................35
CHAPTER 5 SELECTION AND ASSESSMENT OF SUPPLIERS..............................................37
5.1 RULES AND METHODS OF SELECTING SUPPLIERS........................................................................39
5.2 CURRENT SITUATION AND PROBLEMS OF SUPPLIERS...................................................................39
5.2.1 CURRENT DISTRIBUTION OF SUPPLIERS.............................................................................40
5.2.2 EVALUATION OF CURRENT SUPPLIERS ...............................................................................40
5.3 SELECTION OF FUTURE SUPPLIERS ............................................................................................44
5.3.1 MARKET DISTRIBUTION AND SUPPLIERS’ SELECTION.........................................................44
5.3.2 INVENTORY AND SUPPLIERS’ SELECTION...........................................................................46
5.4 INTEGRATIVE SELECTION OF SUPPLIERS ....................................................................................47
5.5 RESULT ...................................................................................................................................49
CHAPTER 6 INVENTORY REDUCTION AND DELIVERY OPTIMIZATION........................50
6.1 RULES AND METHODS TO BALANCE THE PROFIT BETWEEN FACTORY AND CUSTOMERS .................50
6.2 CURRENT STATUE ANALYSIS .....................................................................................................53
6.2.1 SITUATION OF INVENTORY ...............................................................................................53
6.2.2 DISTRIBUTION OF THE MARKET........................................................................................54
6.2.3 DELIVERY DETAILS..........................................................................................................55
6.3 ANALYSIS AND SUGGESTIONS...................................................................................................57
6.4 RESULTS .................................................................................................................................58
CHAPTER 7 CONCLUSION AND FUTURE WORK..................................................................60
7.1 CONCLUSION...........................................................................................................................60
7.2 FUTURE WORK.........................................................................................................................61
REFERENCE .................................................................................................................................62
APPENDIX A .................................................................................................................................66
APPENDIX B .................................................................................................................................70
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List of Tables
Table 3.1 Descriptive Statistic of TTF and TTR of Preassembly Line....................................13
Table 3.2 Results of One-Sample Kolmogorov-Smirnov Test for Preassembly Line’s TTF ..14
Table 3.3 Descriptive Statistic of TTF and TTR of Main Assembly Line...............................16
Table 3.4 Results of One-Sample Kolmogorov-Smirnov Test for Main Assembly Line’s TTF
.........................................................................................................................................17
Table 3.5 Values of Parameters of Different Distribution .......................................................18
Table 3.6 Values of Parameters of Triangular Distribution .....................................................19
Table 4.1 Working Parameters of Each Station.......................................................................21
Table 4.2 First Time Pass Rate of Station 5b, 6, 8, 10 and 11.................................................22
Table 4.3 Output Date Come From the Simulation.................................................................23
Table 4.4 Length of the Transient State (Time).......................................................................24
Table 4.5 Current Production Volumes per Year (Model set following table 3.5)...................25
Table 4.6 Current Production Volumes per Year (Model set following table 3.6)...................25
Table 4.7 Simulation Results with Different Machine Numbers.............................................26
Table 4.8 Simulation Results with Different TTR Values .......................................................28
Table 4.9 Simulation Results with Different TTR Values & Machine Number ......................30
Table 4.10 Comparison Results of Different Solutions...........................................................35
Table 5.1 Suppliers & Suppliers’ Location and Quality Criterion..........................................41
Table 5.2 Delivery Information of Current Suppliers .............................................................42
Table 5.3 Integrative Estimation of Selection Suppliers .........................................................48
Table 6.1 Capacity of Warehouse for the Finished ADBs ......................................................54
Table 6.2 Transportation Fee Paid by Haldex during Nov.13, 2007 and Jan. 31, 2008..........56
Table 6.3 Transportation Amount under Different Suggestions .............................................58
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List of Figures
Figure 3.1 Simplified Assembly Line .....................................................................................11
Figure 3.2 TTF & TTR of Preassembly Line..........................................................................12
Figure 3.3 TTF & TTR Trend of Preassembly Line................................................................13
Figure 3.4 Results of P-P Plots for Preassembly Line’s TTR .................................................15
Figure 3.5 TTF & TTR of Main Assembly Line.....................................................................16
Figure 3.6 Results of P-P Plots for Main Assembly Line’s TTR ............................................18
Figure 4.1 Simplified Assembly Line Model ..........................................................................20
Figure 4.2 Transient State and Steady State (Semere, 2007)...................................................22
Figure 4.3 Production Rate of the Assembly Line ..................................................................23
Figure 5.1 General Supply Chain Model ................................................................................37
Figure 5.2 Supply Chain in the Past and Present ....................................................................38
Figure 5.3 Current Suppliers Distribution...............................................................................40
Figure 5.4 Current Market Distribution of ADB.....................................................................44
Figure 5.5 Current Market Distributions of Commercial Vehicle Systems.............................45
Figure 5.6 Net Inventories in Supply Chain............................................................................46
Figure 6.1 Flows in Supply Chain Between Factory and Customers......................................51
Figure 6.2 Market Distributions during Nov.13,2007 and Jan.31, 2008.................................54
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CHAPTER 1
INTRODUCTION OF THE RESEARCH
1.1 Brief background of the research
The pioneer of logistics appeared in the United States of America in the earlier
twentieth Century. And it is improved and perfected with the development of society
and technology. There is more than one definition of logistics, but in general, the goal
of logistics is meeting the requirements of customers; it is the art of managing the
supply chain and science of managing and controlling the flow of goods, information
and other resources between the point of origin and the point of consumption. The
scope of logistics includes the integration of information, transportation, inventory,
warehousing, material handling, and packaging (Wikipedia, viewed 2007). According
to the different effects, logistics is classified into five varieties: Supply Chain Logistics,
Business Logistics, Production logistics, Reclamation Logistics, and Castoff Logistics
(Qin & Wang, 2001). In fact, logistics management is part of supply chain management
and also the genesis of supply chain management.
Supply chain management (SCM) is the activity of integrating the whole business, from
procurement to production (processing), storage and sale for the products and services
provided by a company (Takenaka, viewed 2007). It is the process of planning,
implementing, and controlling the operations of the supply chain with the purpose to
satisfy customer requirements as efficiently as possible. It spans all movement and
storage of raw materials, work-in-process inventory, and finished goods from
point-of-origin to point-of-consumption. (Wikipedia, viewed 2007). Generally
speaking, supply chain management is concerned with management of the material
flow, information flow, and fund flow (Luo, 2006). From this point, “its scope includes
timing and quantity of material flow, logistics, improving efficiencies in problems with
several decision makers, etc” (Sosic, 2002). Among the three flows, material flow is the
focus and keystone of the research of supply chain management.
“Material flow is a significant factor in the design of manufacturing systems. To design
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a material system, not only the specifications of individual system components but also
the overall objective of the manufacturing system should be considered” (Tanchoco,
1994). For a production enterprise material flow run through the production line, the
route ways between factory and suppliers and customers. Manage and control
material flow effectively is a method to increase profit. How to optimize the material
flow takes important part in production. In order to optimize production line and
supply chain, material flow analysis is necessary.
To analyze material flow, each point in it needs to be clearly presented. In the
manufacturing system, there are lots of steps and workstations. It is hard to keep all of
them with a high reliability. Material flow through all over the steps and can be
divided into several types. In real production there is usually at least one unreliable
component in different types of material flow system. This feature makes it particularly
important to design such a system carefully by taking into account the uncertainties
introduced by the component unreliability (Yu., S. & J., 2000). After found the most
critical stations or the bottleneck of system, modeling tools can be used to simulate and
help to analysis such problem to find out possible solutions. Then based on the analysis,
material flow system model can be improved and evaluated according to the expected
performance.
Modeling is a powerful tool deal with practical problems. It can be used to analyze,
design, and operate complex systems. Models are used to assess real-world processes
too complex to analyze via spreadsheets or flowcharts, testing hypotheses at a fraction
of the cost of undertaking the actual activities. As an efficient communication tool,
modeling shows how an operation works and stimulates creative thinking about how to
improve it. Models in industry, government, and educational institutions shorten design
cycles, reduce costs, and enhance knowledge (Farris, viewed 2007). There are many
Visual simulation tools explored to do the modeling job such as VisSim, SIMUL8,
Extend, SimCreator, etc. Although each of them is especially good at certain area, in
many fields performances of Extend are outstanding and general applications. Along
with the development of Extend, more and more cases use it to analyze, especially
about the supply chain problems.
Extend TM is designed from the ground up to be a flexible, extendable simulation tool. It
can be used to model every aspect of an organization at all levels of expertise - from
manager to engineer/scientist and from novice to professional modeler (Farris, viewed
2007). Building the model with variances of the most critical stations is a shortcut to
analyze problems of a production flow.
Subsequent to the analysis of the material flow, evaluation based on reliability helps to
filter the possible solutions. System reliability is the probability that the system will
perform its intended function under specified working condition for a specified period
of time. Analysis of system reliability together with feasibility and cost leads to the
optimal solution.
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1.2 Present problems Haldex faces in the ADB products
The Haldex Commercial Vehicle Systems division develops, manufactures and markets
brake systems for heavy trucks, trailers and buses. The product offering includes all
main components and subsystems included in a complete brake system. Disc brakes are
one of the main products in Commercial Vehicle Systems division in Landskrona.
There are two types of products: ABA (Automatic brake adjuster) and ADB (Air disc
brake). Each of the products has several types to satisfy special customer requirements.
The volume of ABA is 1.6 million per year and it shares more than sixty percent market.
While the volume of ADB is only 30,000 in 2006 and it got two percent of the market.
However, with the development of technology, production volume of ADB products
increased sharply. They will share more and more market and might even replace
ABA products in a few years. It is predicted that it will reach 200,000 in 2008.
Therefore, capacity of inventory will become a critical issue in the supply chain of
ADB products. Besides, both ABA and ADB products are packed with standard pallets,
which can pack 600 ABA items or 9 ADB items each. Along with the increasing
production volume of ADB products, more pallets will be needed. The products will
occupy much more space in the warehouse, especially the ADB, because of its big
volume and non-isochronous shape.
Beside of this, another problem the company faces at present is the unstable assembly
line of ADB products. The manufacturing system has difficulties in adapting to the
rapid growth of production volume. Bottlenecks appear in the assembly line, which
shut down the line frequently and slow down the system. In this project study, an
attempt is made to address the following issues:
1. Material flow along the assembly line;
2. Supplier selection based on multiple criteria, especially considering select
suppliers associate with the inventory and market;
3. Reduction of inventory of ADB items and more reliable delivery to customer.
1.3 Aim and objectives
As stated above, there are two main aims of the project. One is to optimize the material
flow of ADB from both internal and external material flows to meet the future demand.
Internal material flow indicates the material flow in the assembly line, mainly in the
main assembly line. Model of the current manufacturing line needs to be built up to
simulate the coming situation. By setting variances in the critical stations, check
whether the current line able to sustain the coming big demand. Then, give advises
about improving the internal material flow. While external material flows indicate the
flows from supplier to Haldex and from Haldex to customers. It can be treated as part of
finding a way to optimize the supply chain.
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The other aim is to reduce the inventory, as soon as the production line is able to
achieve the requirement of market after optimization. Based on investigation and
analysis of current situation, authors optimized the supply chain in order to find some
reasonable and feasible solutions to reduce the inventory. Cost, lead-time and reliability
are taken as parameters to compare the advantages and disadvantages of different
methods and approaches.
In a short word the aim of this paper is to improve the material flow of the ADB
production and reduce inventory to satisfy the future market and achieve most
economical supply chain.
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CHAPTER 2
RESEARCH THEORY AND METHODOLOGY
2.1 Research framework
According to Luo, the research of logistics begins from last century. There is a relative
entire system till present. The focus of the research is the managing and control of the
whole logistics and supply chain. The aim of supply chain management is decrease the
cost of logistics and inventory, at the same time, increase the efficiency of all kinds of
sources and information in order to satisfy the requirements of market (Luo, 2006).
There are many modes to describe the production material flow. Such as MRP (Material
Requirements Planning), JIT (Just In Time), Lean Production, AM (Agile
Manufacturing), TOC (Theory of Constraint), supply chain management and so on. It is
well-known that many challenges will appear when the production volume increased
sharply. In this case, all of the modes mentioned above can be use to analyze and
optimize manufacturing. But for the enterprises, they usually select one or two of the
method to analyze their own situation and then adopt the corresponding method to
solve problems and enhance productivities. In another word, each of the mode is
advantage at certain filed instead of outstanding all-around.
“MRP (Material Requirements Planning) is a set of algorithms designed to establish
material requirements based upon known sales orders (or forecast), bills of materials
and material supplier lead times.”(OSIRS, viewed 2008) The limitation with MRP
about the principle is that the algorithms are normally carried out in sequence. In a word,
the materials requirements are calculated advance, and then plan the capacity. In
practice, this would lead to a situation where capacity constraints mean that materials
could be delivered later or may be required earlier; hence re-planning of materials is
required. In this case, there would be more problems, for example the lead-time is long.
And all of this could impact the capacity planning and so the process goes on. (OSIRS,
viewed 2008) Except that, MRP plays a role in part of the production line and this is a
limitation of MRP.
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JIT was firstly occurred in Japan in the early 1970s. It decreases waste by supplying
parts only when the assembly process ask for them. And the heart of JIT is the kanban
card. (12MANAGE, viewed 2008) “In a JIT environment, both earliness and tardiness
must be discouraged since early finished jobs increase inventory cost while late jobs
lead to customers’ dissatisfaction and loss of business goodwill” (Wong, Kwong & etc.,
2006). The typical attention areas of Just-In-Time include: Inventory reduction;
Smaller production lots and batch sizes; Quality control; Complexity reduction and
transparency; Flat organization structure and delegation; and Waste minimization.
(12MANAGE, viewed 2008) Even though, there is a high risk of the reliability of
suppliers. And the production might be great influenced by the cost of material and
market. In this paper, when discuss the filed of optimize the supply chain management,
it would be considered to use the JIT method.
Lean production was raised by MIT (Massachusettes Institute of Technology). It is an
approach to improve a manufacturing performance. The key of lean production is the
minimization of the amount of all the resources used in the various activities of the
enterprise. At all levels of the organization lean production employ teams of
multi-skilled workers. Except that it use highly flexible, increasingly automated
machines to produce volumes of products in potentially enormous variety. It helps
identifying and eliminating no value adding actives in design, production, supply chain
management, and dealing with the customers. (12MANAGE, viewed 2008)
Agile Manufacturing is based on low cost and high quality, improve and change the
productions following the market as soon as possible. The keystone of AM is time.
During the whole cycle life of production, furthest satisfy the customers’ requirements
to enhance the enterprise ascendancy.
Theory of Constraint is used to continually improve and solve the constraint of
bottleneck resources during the production material flow. The application of TOC is
broad in various industries, for example Distribution, Supply Chain, Project
Management, etc. The rationale of TOC is to find out the bottleneck in the production
line, and try to solve the problems in order to make the production cycle short, also
reduce the inventory. The thinking processes of TOC give us a series of steps, which
combine cause-effect and our experience and intuition to gain knowledge. TOC can
lead people how should they think. By this way, the manager can better understand the
world around and better understand how to improve. (12MANAGE, viewed 2008)
Depend on the project researched in this paper, production line model to simulate
assembly line and improve supply chain management will be set up. Simulation is an
important topic in a wide scope, and it is a powerful and economical way to solve
problems. With the development of society and technology there are more and more
kinds of simulation software. Some of the simulation software have mentioned and
discussed in the first chapter. Here only gives a short abstract. Discrete event
simulation has traditionally been defined by items. This modeling paradigm has served
the simulation industry well, but fell far short for many industries in which the pieces
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mindset simply does not accurately portray their particular processes. During recent
years Simulation Dynamics has been working with industries where the item paradigm
falls short as a descriptive tool. This work has led to the development of a revolutionary
set of simulation tool built on the Extend simulation engine (Pheips, Parsons & Siprelle,
2002).
In order to set up a simulation model the first thing have to do is build up a basic
model and find out the parameters required. The original data have to be analyzed
firstly. In recent years, the software Statistical Program for Social Science (SPSS) is
used more and more in statistical analysis work. It is proved that SPSS is a great
method to analysis statistical data. Because of it outstanding performances, in this
paper the SPSS will be used to analyze the data in a further step. Before that a primary
data analysis will be given.
SPSS is a kind of professional statistical analysis software. It takes a very important
role both in social science and natural science. As Cai and Zhuang described in 2001
from the point of data statistical analysis, there are three categories.
1. The first is basic statistic. It includes Descriptive Statistic, Explore Statistic,
Crosstabs’ Analysis, Linear Compounding Measurement, T-Test, One-Way
ANOVA Analysis, Multiple Response Analysis, Linear Regression Analysis,
Correlativity Analysis, Nonparametric Tests, etc.
2. The second is professional statistic. The scope of this field includes Discriminatory
Analysis, Factor Analysis, Cluster Analysis, Range Analysis, Reliability Analysis,
and otherwise.
3. The third is advanced professional statistic. Such as Logistic Regression Analysis,
MANOVA Analysis, Repeated Measure Variance Analysis, Nonlinear Regression,
Probit Regression, Cox Regression, Curve Estimation and so on.
According to significances of different parameters, the functions used in this paper
cover all of the three categories. After all required parameters are collected, a
production line model will be set up by the Extend software.
Extend is able to model a wide range of systems. It is a general purpose graphically
oriented discrete event and continuous simulation application with an integrated
authoring environment and development system. The Extend family of simulation
packages includes Extend CP, Extend OR, Extend Industry, and Extend Suite. All levels
of modelers can efficiently create accurate, credible, and usable models in the Extend
simulation environment by the tools it provides. Extend Design facilitates every phase
of the simulation project, from creating, validating, and verifying the model, to the
construction of a user interface which allows others to analyze the system. For the
simulation tool developers, they can use Extend Built-in, compiled language, to create
reusable modeling components. All of this is done within a single, self-contained
software program. (Krahl, 2003)
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2.2 Science and practical significance of the study
As the annual report of 2006 said Haldex Company includes four parts, which are
Commercial Vehicle Systems, Hydraulis Systems, Garphyttan Wire, and Traction
Systems. From the annual report for the Commercial Vehicle Systems the operations
are divided into five business units: Actuators, Air Management, Brake Controls,
Foundation Brake and Friction Products. Aftermarket sales count for nearly half of
sales. Commercial Vehicle Systems shares sixty percent of group sales (SEK 4,765
Million to SEK 7,890 Million) (Haldex, 2007). So the Commercial Vehicle Systems
deviation acts an important role in the company. Disc brakes are one of the main
products in Commercial Vehicle System deviation. Moreover, ADB is the development
trend of disc break, which defines its important role. It is necessary to go deep into the
research of ADB production line.
Extend software is wildly applied in simulation. Till now Extend Simulation shows
positive effect around production logistics, supply chain management, business flow,
medical service, transports, quality control, etc (EdgeStone, viewed 2008). For the part
of analysis problems exist in assembly line, the Extend Simulation will be used in this
research. By optimizing the simulation model find out the methods to improve the
assembly line to achieve the requirements of market. The inventory problem will be
discussed depending on the data gathered currently and some theoretic proposals will
be given afterwards. With the suggestions, the company is able to find a feasible and
economical way to solve problems.
2.3 Research methodology
In this paper, for the problem of assembly line, the main method is modeling. At the
beginning, in order to make constrains of the production prominent, an ideal
simplified model is set up based on the current component of the assembly line. Then
according to the real situation, bottlenecks and constrains in critical stations are added
in to the model. There are various parameters for different distributions and the values
of parameters have to be gained. So the statistic data analysis is done before setting up
the real simplified model. For this step includes two phases: primary analysis will be
use the Microsoft Excel software and the further data analysis will be use the
professional statistic analysis software SPSS. After testing, modifications are applied
to smooth the model. Different models are set up to simulate from different
optimizing methods. Simulations results aimed at reaching the required production
volume come out afterwards. Then take cost, reliability, and lead time as parameters
to compare suggestions.
As soon as the production volume is able to increase and satisfied the requirement of
market, the inventory issue is brought to the front. For the inventory problem there are
two aspects to be discussed. One is from the point of suppliers and the other is from
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the customers. Both of them are primary roles in the supply chain. In this paper the
supplier selection will base on the general rules as well as associate with the inventory
problem. The inventory reduction methods of the finished products will be stated
together with the optimization of delivery to customers. In like wise, the proposals are
evaluated from multiple criteria.
Depending on the analysis and discussion, feasible and economic approaches for the
company to optimize assembly lines and reduce inventory will be found.
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Chapter 3
Data analysis of production line
Material flow is through all over the production line. From very begin the raw and
processed materials to each step of process even to finished product. So material flow
analysis is the base of optimize production line. An operator-paced line flow production
system produces products on equipment arranged in a line layout. The material flow in
an operator-paced line flow production system is regular mostly (John Miltenburg,
2005). In this chapter, data collected from factory will be analyzed by Microsoft Excel
and SPSS (Statistical Program for Social Science).
3.1 Introduce of data analysis
Data gathered is the first step in the research. According to the requirement in setting up
production line model, the data around the production line are gathered from the
workshop during the early period of research. The scope of the data includes processing
time, maximum queue length of each step, percentage of scraps, production cycle time,
stop time and so on. Time to Failure (TTF) (or Time between Failures (TBF)) and Time
to Repair (TTR) are acquired through statistical analysis in this chapter.
The whole production of ADBs includes several sub-lines, such as main production line,
sub-production line, preassembly line, main assembly line and so on. In this study,
analysis is emphasized in the key issues. The research is taken out on the ADB (Air
Disc Brake) assembly line, which is comprised of a preassembly line (assemble the
magnetism part) and the main assembly line (final assembly line). Meanwhile, variety
of the ADB products is ignored to simplify the analysis and simulation. Instead, the
company provides integrative data on assembly line. The preassembly contains four
stations and the main line has ten stations. A general layout sketch of the whole
assembly line of ADB is shown in Figure 3.1. The blue rectangles are the preassembly
line stations and the gray ones are the final assembly line stations. The yellow triangles
mean the buffers of material, which are used to store the components, will be used in
the corresponding stations. The preassembly line joins the main line at station 5 after
10

processing in station 5b. In the real production, preassembly line and final assembly
line operate independently. At the end of preassembly line there is an area used to
deposit the first finished production i.e. the magnetism part of Air Disc Brake. In this
sketch in order to show the relationship of the two assembly lines, a connection line is
used between station 4 of the preassembly and station 5b of the main line instead of the
deposited area. Among the stations PA2, MA4, MA7 and MA8 are auto processing
and worked by robots.
Figure 3.1 Simplified Assembly Line
Among the data of assembly line gathered from the workshop, processing time and
maximum queue length of each step can be used in “Extend v6” directly when set up
simulation model. While the data of TTF and TTR are statistic data, which has to
analyze before used in building model. In this paper, the data will be analyzed in
Microsoft Excel and SPSS. The two groups of data for different stations will be
analyzed separately. First data for station 2 in preassembly line will be analyzed and
then the final assembly line data for station 8. The analysis methods and threads are
similar.
11

3.2 Preassembly line data analysis
According to the statistic data gathered from the workshop, the bottleneck of
preassembly line is station 2. Thus, use the operation data gathered from station 2 to
represent the whole preassembly line. The data is gathered from week twenty to week
twenty-six in 2007 (The data show in appendix A, Table 1).
3.2.1 Primary data analysis of preassembly line
Microsoft Excel is used in the phase of the primary data analysis. Curves shown in
Figure 3.2 are the variation tendency of TTF and TTR. The left one is the TTF of
preassembly line and the right one is the TTR of preassembly line. Comparing the two
curves the tendencies of them are similar. Both of them have some data increased
sharply, especially the data from week twenty-nine to week thirty-two. There is no
function in Excel can be used to describe the trend.
The data of time between failures equal to the sum of data of time to repair and
operation time. So either time to repair or operation time changes the time between
failures will change. Because the operation time depends on the work schedule of
factory and it is relative steady. Besides of this it is evident that the variety of
observation points on the two curves are synchronous. It is can be said that the data of
time between failures changes mainly because of the time to repair changes. There are
several factors can affect the time to repair. The reasons lead to the repair time increase
may be the machines are in a serious problem and hard to repair; lack of components to
repair the machine and the lead time is long for some certain components; or the
repairers are in a holiday and so on. In any case the data recorded is not a usual one. So
when discuss the distribution of the TTF and TTR data, the usual data should be
deleted.
TTF/sec.
1600.0
1400.0
1200.0
1000.0
800.0
600.0
400.0
200.0
0.0
20
22
TTF of Preassembly Line
24
26
28
30
week
32
34
36
38
40
TTR/sec.
1600.0
1400.0
1200.0
1000.0
800.0
600.0
400.0
200.0
TTR of Preassembly Line
Figure 3.2 TTF & TTR of Preassembly Line
0.0
20
22
24
26
28
30
Week
32
34
36
38
40
12

After deleting the un-normal data in the samples of TTF and TTR, the tendency of
each curve is shown in Figure 3.3. Try to add the tendency lines for the observational
lines by Excel, the tendency lines can be described by the function of moving average.
The moving averages are usually used in the financial field while the parameters
studied in this paper are around the production. So there is no real significance to add
the tendency lines for the observational data. The moving average here only shows a
trend of the real observational lines.
TTF/sec.
500.0
400.0
300.0
200.0
100.0
0.0
20
TTF of Preassembly Line
23
25
27
33
week
35
37
39
Figure 3.3 TTF & TTR Trend of Preassembly Line
In the primary analysis phase it can be given a descriptive statistic. The results are
shown in Table 3.1. Associating with the Extend there is a Triangular Distribution can
be used. The parameters are the maximum data, minimum data and the most likely
data. The maximum data and minimum data can get from the table directly. The most
likely data is the mode, if follow the original data there is no mode. But if deal the
data with the rounded method up to the tens digit then the modes of TTF and TTR are
gained. And they can be approximately seemed as the most likely data.
Table 3.1 Descriptive Statistic of TTF and TTR of Preassembly Line
TTF of Preassembly Line TTR of Preassembly Line
Mean 328.44 Mean 177.31
Standard Error 14.91 Standard Error 16.22
Median 310.95 Median 151.65
Mode 280 Mode 140
Standard Deviation 59.65 Standard Deviation 64.90
Sample Variance 3557.73 Sample Variance 4211.82
Kurtosis -0.56 Kurtosis -0.67
Skewness 0.82 Skewness 0.87
Range 193.40 Range 200.90
Minimum 256.90 Minimum 100.90
Maximum 450.30 Maximum 301.80
Sum 5255.10 Sum 2836.90
Count 16 Count 16
TTR/sec.
400.0
300.0
200.0
100.0
0.0
20
TTR of Preassembly Line
23
25
27
33
Week
35
37
39
13

From the curves analyzed in Microsoft Excel, it is hard to identify which distribution is
suitable to describe the trend although the trend lines follow the moving average. As
discussed in last section it does not mean any real significance for the production field.
Data has to be further analysis. It is obviously samples gathered in preassembly line are
stochastic. It can be discussed and analyzed by more professional statistical analysis
software. In the further data analysis the SPSS (Statistical Package for Social Science)
is used to study whether there is a certain mathematic model or statistic distribution
can used to describe the data of TTF and TTR.
3.2.2 Further data analysis for TTF of preassembly line
Among the SPSS functions One-Sample Kolmogorov-Smirnov Test belongs to
nonparametric tests, which can validate whether or not the samples follow Normal
Distribution, Uniform Distribution, Poisson Distribution or Exponential Distribution.
Since it is necessary to know whether the Time to Failure and Time to Repair follow
any distribution, it is considerable to identify by One-Sample Kolmogorov-Smirnov
Test. Based on the significations of TTF of preassembly line neither Normal
Distribution nor Poisson Distribution can describe the trend. Since the data is limited,
the statistic analysis may be not ideal. According to the primary analysis the
distribution of TTF data is not Uniform Distribution. So first assume the TTF data
follow the Exponential Distribution and then test the supposition by the One-Sample
Kolmogorov-Smirnov Test.
Table 3.2 Results of One-Sample Kolmogorov-Smirnov Test
for Preassembly Line’s TTF
N
One-Sample Kolmogorov-Smirnov Test
TTF
16
Exponential parameter.(a) Mean 328.444
Most Extreme Differences Absolute .543
Positive .254
Negative -.543
Kolmogorov-Smirnov Z 2.170
Asymp. Sig. (2-tailed) .000
a Test Distribution is Exponential.
b Calculated from data.
Generally speaking, the probability of two tailed test is the yardstick to estimate
whether or not variable accords with a certain distribution. According to the statistical
theory when the probability of two-tailed test value is smaller than 0.05 shows the
positive answer, i.e. the variable follows the selected distribution. Otherwise the answer
is negative. In another word there are some differences between variable and
distribution. The One-Sample Kolmogorov-Smirnov Test results of preassembly line’s
Time to Failure are shown in Table 3.2. The probability of two tailed test value of
Exponential distribution’s value smaller than 0.05. Consequently the Time to Failure
14

data of preassembly line accords with exponential distribution. And the exponential
parameter, the mean is 328.444 seconds.
3.2.3 Further data analysis for TTR of preassembly line
When analyzing the Time to Repair (TTR) data of the preassembly line, considering the
signification of TTR and associated with the primary analysis result, the One-Sample
Kolmogorov-Smirnov Test cannot be used to analyze the data. In this case, it is have to
try to use other functions of SPSS. There is a function named P-P Plots, which used to
describe the probability distribution trend. The test distribution includes Beta,
Chi-square, Exponential, Gamma, Half Normal, Laplace, Logistic, Lognormal, Normal,
Pareto, Student, Weibull and Uniform. Since as above discussed the data can not
follow the Normal Distribution, Uniform Distribution, Poisson Distribution,
Exponential Distribution, all of the four kinds of distributions can be ignored. Associate
with the distribution kinds supported by Extend software, and compare the
significations of different distributions, finally only Lognormal Distribution is selected
to analyze the data.
Figure 3.4 Results of P-P Plots for Preassembly Line’s TTR
The results are shown in Figure 3.4. Theoretically speaking, if the data trend follows
the distribution the points of observational various are near the diagonal line and the
closer the better. The observational point distribute around the diagonal line closely, so
the Lognormal Distribution can be used in the simulation model. The parameters of
Lognormal Distribution required in Extend model are mean and standard deviation.
The values of them can get directly from the analysis results by SPSS and they are
177.306 seconds and 64.899 seconds separately.
15

3.3 Main assembly line data analysis
Because the bottleneck of the main assembly line is station eight, data of Time to
Failure and Time to Repair are gathered from this station. Data gathered from week
thirty to week fifty in 2007. (See data show in Appendix A, Table 2) The analysis
methods are similar to the preassembly line.
3.3.1 Primary data analysis for main assembly line
The results of Excel are shown in Figure 3.5. The left one the time between failures data
and the right one is the time to repair data. It is evident the equations or functions
offered by Excel neither suit to describe the trend of TTF nor the trend of TTR. All of
the data are stochastic. Because the limited sample and the complex practical situations
it is hard to say which of them are normal data. But the data for week fifty can be
deleted because this is the vacation week for all of the employees and the production
line stopped.
TTF/sec.
450.0
400.0
350.0
300.0
250.0
200.0
150.0
100.0
50.0
0.0
30
32
TTF of Main Assembly Line
34
36
38
40
week
42
44
47
49
TTR of Main Assembly Line
Figure 3.5 TTF & TTR of Main Assembly Line
In order to get the parameters of Triangular Distribution the descriptive statistics used
to analysis the data. And the method has been introduced in the process of analyzing
the data of preassembly line. The results of descriptive statistics for TTF and TTR of
main assembly line are shown in Table 3.3. The parameters of Triangular Distribution
are maximum data, minimum data and most likely data. The values are maximum
data and minimum data are show directly and the most likely value, which is the
mode value, is still the estimation value.
Table 3.3 Descriptive Statistic of TTF and TTR of Main Assembly Line
TTF of Main Assembly Line TTR of Main Assembly Line
Mean 276.32 Mean 187.32
Standard Error 15.71 Standard Error 16.08
Median 278.07 Median 179.33
Mode 320 Mode 130
TTR/sec.
350.0
300.0
250.0
200.0
150.0
100.0
50.0
0.0
30
32
34
36
38
40
week
42
44
47
49
16

Standard Deviation 68.49 Standard Deviation 70.07
Sample Variance 4690.93 Sample Variance 4909.82
Kurtosis -0.41 Kurtosis -0.57
Skewness 0.54 Skewness 0.61
Range 236.87 Range 224.05
Minimum 186.65 Minimum 99.49
Maximum 423.52 Maximum 323.53
Sum 5250.15 Sum 3559.17
Count 19 Count 19
3.3.2 Further data analysis for TTF of main assembly line
As the method used to analysis the TTF of preassembly line, in this section also assume
the TTF data following the Exponential Distribution. And then test the hypothesis by
One-Sample Kolmogorov-Smirnov. The results of One-Sample Kolmogorov-Smirnov
Test for main assembly Line’s Time to Failure are shown in Table 3.4. The probability
of two-tailed test value of the Exponential Distribution is smaller than 0.05, so the
Exponential Distribution satisfy the data trend. And the Exponential parameter: mean is
276.3210 seconds.
Table 3.4 Results of One-Sample Kolmogorov-Smirnov Test
for Main Assembly Line’s TTF
N
One-Sample Kolmogorov-Smirnov Test
TTF
19
Exponential parameter.(a) Mean 276.3210
Most Extreme Differences Absolute .491
Positive .216
Negative -.491
Kolmogorov-Smirnov Z 2.141
Asymp. Sig. (2-tailed) .000
a Test Distribution is Exponential.
b Calculated from data.
3.3.3 Further data analysis for TTR of main assembly line
The data of Time to Repair of the final assembly is analyzed by the same method: P-P
Plots. Still select Lognormal Distribution to analyze the TTR data. The results of P-P
Plots of main assembly line’s Time to Repair are shown in Figure 3.6. The mean and
standard deviation are 187.3263 seconds and 70.0659 seconds separately.
17

3.4 Results
Figure 3.6 Results of P-P Plots for Main Assembly Line’s TTR
Based on the analysis above, the results are shown that for the data of Time to Failure,
both station 2 in preassembly line and station 8 in main assembly line can take
Exponential Distribution into the simulation model. And for the data of Time to Repair
both of them should take Lognormal Distribution into the model. And the value of each
parameter for different distribution can get in the Table 3.5 followed. Since station 2 of
preassembly line is the main bottleneck of this assembly line and station 8 of main
assembly line also is the main bottleneck, the TTF and TTR of the whole assembly line
depends on the two bottlenecks.
Table 3.5 Values of Parameters of Different Distribution
Distribution Parameters Preassembly Main
Line Assembly
TTF Exponential Distribution Mean 328.4 276.3
TTR Lognormal Distribution Mean 177.3 187.3
Standard Deviation 64.9 70.1
As discussed above the sample size is small. The reliability of statistic analysis and
results are not so high. In order to solve the problem and test the analysis results the
Triangular Distribution is used at the same time in the assembly line simulation model.
After comparing the simulation models with different parameter functions find out the
better one. And then use the simulation model with better function in the future
discussions. The parameters of Triangular are minimal data, maximum data and the
most likely data. The parameters have studied above and values are shown in the Table
3.6 followed.
18

Table 3.6 Values of Parameters of Triangular Distribution
Preassembly Line Main Assembly Line
TTF TTR TTF TTR
Maximum Data 450.3 301.8 423.5 323.5
Minimal Data 256.9 100.9 186.7 99.5
Most Likely Data 280 140 320 130
19

CHAPTER 4
MODELING AND ANALYSIS OF
PRODUCTION LINE
4.1 Modeling
Figure 4.1 Simplified Assembly Line Model
Depend on the sketch layout of Figure 3.1 production line model is set up by software
Extend v6 after data analysis. The simplified assembly line in the beginning is detailed
by adding variances in critical stations and bottlenecks. Figure 4.1 is a copy of the
simplified assembly line model setting in Extend v6. There are two stocks at the
beginning of the flow, one for the main assembly line and the other for the preassembly
20

line. In order to simplify the model and bring the keystones to the front, the two lines
are joined together and working simultaneously. The detail situation about the
preassembly line and main assembly line has described in chapter three.
During the process of setting up model, there is a problem when associating the
preassembly line to the main assembly line. As shown in Figure 3.1, there are five
stations before insert the preassembly line at station 5b. According to the data of
process time shown in Table 4.1 below, from the beginning to output the first product,
the working time of first five stations in main assembly line is 105s, which is shorter
than the working time of preassembly line that is 179s. As a result, for the early period
preassembly line is behind the main assembly line. So a buffer with the products of the
preassembly line is needed. By setting another “Stock” block between station 5b and
station 5, the two assembly lines join together at station 5 of the main production line to
smooth the simulation. This solution is not discrete form the reality since in the virtual
assembly process, there is a small holding area used to store the magnetism parts from
the preassembly line, which can satisfied the requirement of main assembly line. The
“Stock” block in the model has the same function as the holding area. There are several
reasons lead to the necessary of a holding area; one of them is that the cycle time of the
main assembly is longer than the cycle time of the preassembly line, which can be
confirmed by the data shown in Table 4.1. Calculated the delayed time, the model can
work smoothly as long as more than three items are held in the stock.
Table 4.1* Working Parameters of Each Station
Station/Main TID Queue Station/Pre. TID Queue
Capacity
Capacity
1&2 1
3 2
4 3
5b
5
6
4
7
*Because of the different
8
configurations, TID of station 10 in
9
10
11
main assembly line is variation. 7/30
of the products cost 55s at this station
and the rests cost 35s.
In the model, each buffer before the stations is to simulate the length of the queue. All
the stations indicate the actual working times in the assembly line. The detailed
information of processing time (TID) and queue length for each station are listed in
Table 4.1 above.
Generally speaking, scraps occur in station 5b, 6, 8, 10 and 11 of main assembly line.
Statistical analysis work focus on these five stations. First time pass rates of the five
stations are shown in Table 4.2 separately. “Select DE Output” blocks are added after
these stations to simulate the pass rates.
21

Table 4.2 First Time Pass Rate of Station 5b, 6, 8, 10 and 11
Station 5b 6 8 10 11
First Time Pass Rate 98~99% 99.2% 99.2% 99.2% 99.2%
Based on the result analyzed by Haldex, the bottlenecks of the whole assembly line
system are station 2 in the preassembly line and station 8 in the main assembly line. For
these two parts, extra “Downtime” blocks are added. Those blocks have set TTF (or TBF,
Time between Failures) i.e. the time to fail mentioned in data analysis section and TTR (Time to
Repair) value to shut down and restart the system as time flows by. In the end of the
model, a “Plotter” is there, plotting the output rate corresponding to the two “shutdown”.
It is easier to detect that where the simulation is stuck by checking the plot.
Based on the simplified model, some further modifications are added in the model due
to different requirements of the solution and the changing situations. Detailed reasons
and approaches are specifically presented in the following section.
4.2 Simulation and analysis
After the modeling of current situation is finished, simulation and analysis are taking
place. First of all, there is a Transient State before the system run into a steady state.
This situation only happens at the beginning of the production since the flow of
material is from the head of the assembly line. Besides, all the constraints are set in
disciplinary values, which will not cause big fluctuations during the steady state. For
example, the interrupts caused by failures will only make the production pause in a
disciplinary time interval all through the assembly period. Figure 4.2 below can
describe the working state of the assembly line. In the figure, x could represent the
production rate and t could represent time or certain stops, such the number of outputs.
Figure 4.2 Transient State and Steady State (Semere, 2007)
Simulate the current line for a period of time, until 50 items are outputted. Table 4.3
listed the time when each item is finished and the calculated production rate, output
numbers divided by the time.
22

Table 4.3 Output Date Come From the Simulation
Output Time (sec.) Production Output Time (sec.) Production
no.
rate (pcs/sec) no.
rate (pcs/sec)
0 0 0 26 3318.292037 0.007835356
1 466.6531527 0.002142919 27 3384.292037 0.007978035
2 526.6531527 0.003797566 28 3450.292037 0.008115255
3 598.6531527 0.005011249 29 3516.292037 0.008247324
4 658.6531527 0.006072999 30 3582.292037 0.008374527
5 718.6531527 0.006957459 31 3648.292037 0.008497127
6 778.6531527 0.007705613 32 3714.292037 0.00861537
7 939.2833014 0.007452491 33 3780.292037 0.008729484
8 1025.283301 0.007802721 34 3846.292037 0.008839682
9 1085.283301 0.008292766 35 3932.292037 0.008900661
10 1145.283301 0.008731464 36 3992.292037 0.009017376
11 1223.283301 0.008992193 37 4052.292037 0.009130635
12 1283.283301 0.009351014 38 4112.292037 0.009240589
13 1841.517721 0.007059394 39 4368.044535 0.00892848
14 1901.517721 0.00736254 40 4610.691915 0.008675487
15 1961.517721 0.00764714 41 4670.691915 0.008778143
16 2039.517721 0.007844992 42 4730.691915 0.008878194
17 2099.517721 0.008097098 43 4808.691915 0.008942141
18 2159.517721 0.008335194 44 4868.691915 0.009037335
19 2237.517721 0.008491553 45 5393.005303 0.008344142
20 2303.517721 0.008682373 46 5619.69396 0.008185499
21 2363.517721 0.008885061 47 5679.69396 0.008275094
22 2423.517721 0.009077714 48 5952.749388 0.008063501
23 2873.895077 0.008003076 49 6038.749388 0.008114263
24 2959.895077 0.008108396 50 6098.749388 0.008198402
25 3252.292037 0.007686887
Figure 4.3 Production Rate of the Assembly Line
23

Figure 4.3 is the production rate corresponding to the output number. It is clear from the
figure that the system enters into the steady state after approximately twelve items are
finished. Run the system ten times to get a mean value of time interval of the transient
state. The results of the simulations are similar to the result shown in Table 4.3 and
Figure 4.3. It is can be concluded that the corresponding point is when twelve items are
produced. Table 4.4 contains the results of the ten runs together with the mean value in
seconds. After transformation, the transient state lasts 32.5 minutes, about half hour.
Table 4.4 Length of the Transient State (Time)
No. 1 2 3 4 5
Time (Sec) 1598 2157 1557 1944 2360
No. 6 7 8 9 10
Time (Sec) 1711 2450 1532 1792 2422
Mean
1952
There are mainly three ways to deal with the transient data to avoid bias (Semere,
2007).
1. Delete the transient data;
2. Overwhelm it by running for sufficiently long time;
3. Initial data as close as the steady situation.
Considering the real production situation, the first method is preferred if the simulation
time period is not very long, such as one or two days. However, since the production
volume is count in pieces per year, take the whole year into simulation. The running
time is sufficiently long and the transient state can be ignored.
Checking the data from the company, there are two shifts per day for both the
preassembly line and the main assembly line, and each shift works 8 hours. At the
beginning, assume the assembly line is under the most ideal condition for the
production, which means there is no scrap and stop. If the parameters are set to achieve
this ideal situation, the simulation result of output came out to be 200721 items per year
(count 230 working days per year) without fluctuation. The output is more than the
predicted production value in 2008, which goal is 200,000 items (also is the forecast of
market demand). In a word, under the ideal circumstance the production capability can
satisfy the requirement of market. However, it is impossible to achieve this result since
there are some constraints all through the assembly line.
According to the information and experiences of the factory, the main bottlenecks of
the whole assembly line are station two of preassembly line and station eight of final
assembly line. It is necessary to consider this limit into simulation. According to the
result of data analysis, the assembly line shuts down frequently and the repair time is
really long, which is almost half of the TTF. In another word, the operation time and
repair time are almost the same. When insert the parameters and constraints into the
ideal model, the outputs through ten times’ simulation are shown in Table 4.5.
24

Table 4.5 Current Production Volumes per Year (Model set following table 3.5)
No. 1 2 3 4 5 Mean
Output 116769 115982 116771 116668 116882
No. 6 7 8 9 10
Output 116367 116407 116851 116697 116789 116799
This average output (116799 items/year) is far away from the ideal operation output
(200721 items/year), which means there are many pitfalls with the assembly line. And
there would be a huge potential to improve the assembly line.
The main disturbances are from the bottlenecks. In station eight of the main assembly
line and station two in the preassembly line, even though the data count into analysis
may not from pure technique aspect (contain coffee break, etc), the repair time of
assembly line are too long. From the Table 3.5, the mean value of TTF is 276.3 sec and
TTR is 187.3 sec, which indicates that in station eight of the main assembly line the
TTR is almost half of the TTF value. It is quite a critical phenomenon. The same
problem exists in station two of the preassembly line, which can be proofed by values
also shown in Table 3.5 (mean value of preassembly line, TTF and TTR are 328.4 sec
and 177.3 sec separately). Moreover, there are some scraps coming out during the
assembly line process, which consequentially reduce the productivity as well. If all the
disturbances are reduced to an optimal level, productivity will enhance by a big range.
Hence, optimization of the assembly line is extremely urgent in order to nip on ahead
the market demand.
As discussed at the end of chapter three because of the sample size is limited the
statistic analysis might not in a high reliability. In order to estimate the statistic results
the Triangular Distribution is used. The parameters of Triangular Distribution are
shown in Table 3.6. The simulation results are shown in Table 4.6. The model shows
in appendix B. It is obviously that the results of these two methods are different.
Because one of the Triangular Distribution parameters is most likely value and this
data in the model is not an exact most likely data, it is an approximate value based on
dealing data by around method to the tens digit. It is hard to say which of them is
better to simulate the real production line. It is said that the production volume is
about 3,000 per week. In the simulation the simulate time is set as 230 workdays per
year. Calculate the production volume of one year, which should be about 130,000
pieces. Compare the simulation results with the real production volume it can be
concluded that when set the simulation model the functions of bottlenecks should
select the Exponential Distribution for TTF and Lognormal Distribution for TTR.
So the following discuss will not consider the model setting Triangular Distribution.
Table 4.6 Current Production Volumes per Year (Model set following table 3.6)
No. 1 2 3 4 5
Output 89775 89892 89723 89771 89683
No. 6 7 8 9 10
Output 89679 89994 89662 89719 89735
Mean
89763
25

The methods and suggestions to improve the situation will be discussed in the next
section. Most of the methods and solutions proposed in this paper will be simulated by
the Extend model. The others will be discussed from the view of theory.
4.3 Solutions
Since the scrap rates of different stations are in a reasonable range, reduce the scrap
rates will not be considered as one of the methods to improve the productivity. In the
following discussion the focuses are the bottlenecks, TTF, TTR, employees,
management and so on. Following are some solution points against the shortcomings
together with simulation results in hypothetical situations.
4.3.1 Investing new machines in the bottlenecks
Since bottlenecks are the main sticking point of the assembly line, it is discussed prior
to other solutions. If do not consider the cost and space, one of the simplest and most
efficient methods to solve the bottleneck problem is investing new machine. Since
ABD is a kind of new product the robots in assembly line are almost with the
advanced technology. Therefore when invest new robots they will have the same
capability as existing ones. Theoretically, invest one new machine will produce double
of the original volume in the same period of time even the TTF and TTR are still the
same. Meanwhile, the frequent systematic shutdowns caused by the bottlenecks will be
reduced.
Table 4.7 Simulation Results with Different Machine Numbers
No. of MA 8 1 2 2
machines PA 2 1 1 2
1 116448 154973 210764
2 116714 154215 210659
3 117387 153907 210386
4 116981 155143 210698
5 117058 155092 210478
6 117284 154867 210648
7 117277 154980 210560
8 116539 154282 210559
9 117281 155102 210384
10 117099 154690 210646
11 116471 155032 210591
12 116756 155617 210442
13 116526 154334 210685
14 116656 154877 210546
15 116694 154158 210632
Average 116878 154751 210579
Simulation Results (items/year)
26

Table 4.7 listed the simulation results due to different number of machines that
participate in the production in station 8 of the main assembly line (MA 8) and station 2
in the preassembly line (PA 2). In order to get more precise results, each condition has
been taken fifteen times’ simulations. And at last calculate the average value. Take the
average value as the measurement of the solution.
When add more machines in the assembly line, a problem in the simulation process
occurred. As the production rate increased, blocks happened in station 10 of the main
assembly line. According to the animation around the block point, it is inferred that the
causes of the block are the stochastic split stream in station 10 and the small queue
length between station 10 and station 11.
The stochastic choice of the direction of each incoming item in station 10 may cause the
disorder and block in the system. In the reality, products in different operating time
(TID) in station 10 are in batches. Therefore, not each item chooses the different
streams stochastically. In order to investigate whether this block problem would happen
in the batch production or not, simulate the system with one stream only for the two
situations. The results came out to be no block for each single stream. So the block in
station 10 only happens in simulation model, while in the real production line it will not
happen. Thus, it is feasible to smooth the model by increasing queue length between
station 10 and station 11.
In order to find out the ideal queue length, several tests are operated. When the queue
length increased to three or four items, the block rate is reduced but not eliminated. If
the value increases to 5, most of the simulations can run well. So the queue length can
be set to 5 items between station 10 and station 11 in the model. However, since the
change of queue length is only in the model to keep simulation smooth, set the value to
a bigger one to get a stable simulation environment. Based on this point, the queue
length increased to 10 when run the simulation model.
As mentioned above, comparing station 2 in preassembly line and station 8 in main
assembly line, station 8 is the main bottleneck. So at first, the author tries to only insert
one machine at station 8 to improve the current situation. From the last raw of Table 4.7,
which shows the average output of the assembly line, it is can not satisfy the market
requirement. If increase machines both at station 8 and station 2, the production volume
will hit the market in 2008.
4.3.2 Cutting down the Time to Repair in bottlenecks
As mentioned earlier, current production is too far away to the ideal situation because
of the frequent shut down and long TTR. The TTF is greatly relating to the technical
parameter of the machine, thus it is not easy to change the value. Nevertheless, TTR can
be reduced by effort of the company.
27

In Table 4.8 there are output rates per year after resetting the TTR value in the two
bottlenecks. From data analysis results in chapter three of this paper, the TTR values of
both station 2 in preassembly line and station 8 in main assembly line follow the
lognormal distribution. According to the technical reference from the company, the
TTR can be reduced at most to 20% of the cycle time, which means, TTR can be 20% of
the TTF value instead of more than 50%. The simulation will not continue after the
TTR value hit the limitation. Along with the reduction of TTR, TTF reduced. But
operation time is equal to TTF minus TTR and it is the constant value here. Set formula
in Ms Excel to calculate the parameters of both TTR (including Mean and Standard
Deviation) and TTF. Table 4.8 shows the simulation results when TTR values reduce to
20% of the TTF values in both station 2 of the preassembly line and station 8 in the
main assembly line.
Table 4.8 Simulation Results with Different TTR Values
MA 8
PA 2
Simulation Results (items/year)
TTF Mean 276.3 111.3
TTR Mean 187.3 22.3
Std Dev 70.1 8.3
TTF Mean 328.4 188.9
TTR Mean 177.3 37.8
Std Dev 64.9 13.8
1 117353 163087
2 116713 163269
3 116928 163310
4 117129 163321
5 117215 163177
6 116592 163108
7 116821 163103
8 117005 163369
9 116706 163275
10 116663 163301
11 116829 163142
12 117362 163299
13 116998 163310
14 117214 163257
15 116527 163040
Average 116937 163225
Obviously, concluding from Table 4.8, it is hard to achieve the goal (200,000 ADBs per
year), even decrease the TTR to a very low value, which is close to the limitation. These
solutions may not be used individually to achieve the required production volume in
2008.
28

4.3.3 Increasing the operation time of bottlenecks
Operation time is relative stable under a certain circumstance. It will be varied with the
condition changes. If try to maintain machines in a more suitable schedule, the
operation time can be increased. There are many good examples in both academic
fields and practices. It is necessary and important to optimal terms of maintenance
(Peschanskii, 2006).
As data analyzed in chapter three, the operation time of the whole assembly line is half
of the TTF at present. There is a huge potential to increase the operation time. One of
the methods has been discussed in last section 4.3.2, but in fact that method is only
increase the percentage of operation time in the cycle time between the failures, the
operation time still keeps the same. Because there is no exact information of
maintenance about Hadelx, this solution only discussed from the point of the theory.
The results show in Table 4.8 means if keep others in the same situation only cut down
the time to repair, it is impossible to achieve the goal. Better maintenance can lead the
machines in more efficient and effective state, and then the operation time would
increase. As a result, the volume of production will increase. Assume the operation
time could double, simulate this condition by the model. Compared with the yield show
in Table 4.8, it is only increase about twelve percent, which still can not meet the goal of
2008.
4.3.4 Solution from integrative aspects in bottlenecks
Considering the reality and economy factors, the solution of the assembly line came out
to be a combination of the solutions mentioned above. Associate reduce the TTR to
different value with invest machines in the bottlenecks.
Table 4.9 lists the outputs in different assembles of data. From the table, once each of
station 8 in the main assembly line and station 2 in preassembly line has one more
machine, as well as reduce the TTR to approximately 20% of the TTF value, the
production volume per year (210 622 items) will exceed the demand in 2008, which is
200 000 items.
In fact the production volume by adding one more machine in station 8 of the main
assembly line together with reduction of TTR to limited value is already close to
200,000 per year, only about 1330 pieces behind. And these 1330 pieces can be
achieved by over working 24.6 hours, which three or four more shifts under certain
productivity.
29

Table 4.9 Simulation Results with Different TTR Values & Machine Number
No. of MA 8 1 2 2
Machines PA 2 1 1 2
TTF 276.3 111.3 111.3
TTR Mean 187.3 22.3 22.3
MA 8
Std Dev 70.1 8.3 8.3
TTF 328.4 188.9 188.9
TTR Mean 177.3 37.8 37.8
PA 2
Std Dev 64.9 13.8 13.8
1 116584 198812 210646
2 117226 198607 210619
3 117011 198833 210571
4 116922 198529 210613
5 116553 198440 210687
6 116648 198769 210659
7 117097 198484 210621
8 116731 198789 210538
9 116839 198611 210682
10 117300 198597 210546
11 116662 198616 210626
12 117371 198662 210599
13 117196 198756 210683
14 116470 198849 210545
15 116708 198696 210690
Average 116888 198670 210622
Simulation Results (items/year)
The optional solution to optimize the material inside the assembly line can be:
1. Invest a new robot in station 8 of the main assembly line
2. Reduce the repair time both in the robot of station 8 in main assembly line and
the machine in station 2 of the preassembly line to the limited value, which is
20% of the TTF value. Thus, the TTR for robot will be lognormal distributed
with a mean value of 22.3 seconds and standard deviation value of 8.3 seconds.
And the TTR for the machine in station 2 of the preassembly line will also be
lognormal distributed but with a mean value of 37.8 seconds and standard
deviation of 13.8 seconds.
3. Over working about 24.6 hours per year to supplement the production volume
to 200,000 pieces.
Generally speaking, focus on the bottlenecks there are three main solutions. The first
one is investing machines in both of the two bottlenecks. Second, increase one machine
at station 8 of the main assembly line and at the same time cut down the repair time to a
low value which is about twenty percent of time between failures. The last is decrease
repair time of the assembly line; at the same time add one machine at each of the
30

ottlenecks. Obviously, from the point of economic the second solution is better. But
cost is not the only measure to compare different solutions. This will be discussed latter
in this chapter.
4.3.5 Prolong the production hours
Another way to enhance production volume is to work more. Currently, there are two
shifts for both preassembly line and the main line, each of which is eight hours. One
more shift could be added. Meanwhile, weekends can be counted into production. Since
there are actually two assembly lines, each of which has different cycle time, additional
working time may be added asynchronously to where extra work needed. Now the yield
is around 120,000. If the number of shifts increases to three, the volume will increase
60,000. The total yield is 180,000 which still can not satisfy the requirement of market.
But when add the shifts’ number and cut down the TTR as well, then the volume would
reach the market requirement. From Table 4.8 if cutting down the TTR to the ultimate
value, the yield will increase up to 163,286. In theory if add one shift then the yield will
be increase about 80,000. Then the output will achieve about 240,000, which is more
than the market requirement 200,000.
4.3.6 Improve the employees’ skills
Improve the employees includes two aspects. One is to employ skilled workers who can
work in an efficient way. Another is to train the present workers. Obviously the first
method is faster than the second but the cost would be higher. The advantage of training
employees is that the company’s values are already built in their mind, which gives
great conveniences in team working. Besides, the existed employees are familiar with
the situation that can help them find the solutions. A successful trainee plan also can
help increase the productivity indirectly. In a whole, this method requires much time
and the result is hard to estimate.
In fact, either prolonging the production time or improving the skills of employees can
be replaced by enhancing the effective work hour. At present, the average effective
work time of each shift is less than five hours. If it reached to seven hours, the volume
of productions would achieve the market requirement.
Based on the results offered above, in the following section different solutions will be
compared and discussed. And the measurement parameters are cost, reliability and lead
time.
4.4 Comparisons and results
This section will compare the methods and solutions based on different parameters,
such as the reliability, lead time, cost, etc. And then, find out the correspondingly
31

economical and feasible method or suggestion to improve the current situation and
satisfy the market requirements.
As described in last section, the feasible solutions include:
1. Invest new machines in bottlenecks, both at station 2 of preassembly line and station
8 of main assembly line;
2. Cutting down the time to repair to twenty percent of time to fail, in the same time add
a machine at station 8 of main assembly line, meanwhile over working for 3 more
shift per year;
3. Invest new machines in two main bottlenecks, and cutting down the time to repair;
4. Setting three shifts on the assembly line and reduce ten percentage of the time to
repair;
5. Setting three shifts and improving the employees’ work efficiency. Or increase
effective work time of each shift.
In order to find out the better solution, it is necessary to measure them based on
different parameters.
4.4.1 Cost comparing
Since one of the important aims of every enterprise is to gain margin, cost will be
discussed first. Both station 8 of main assembly line and station 2 of preassembly line
are advanced robot. Investment will be high. And more space will be required
especially at station 8. Then the layout of current assembly line will be rearranged. The
investment of a new robot in station 8 of the main assembly line costs 1.2 Million SEK
and a new machine in station 2 of the pre assembly line costs 0.8 Million SEK. For the
first solution, the cost invest machines is 2 Million SEK. This cost does not include the
fee of maintenance, rearranging assembly line, etc. In the whole, most of the cost of this
solution belongs to equity-type investment and it is one time investment follow several
years use.
One of the methods to reduce the system repair time is repair quickly and effectively.
By calculate the values of TTR, it is easily to know that to achieve the goal described in
the second solution, the repair time required to reduce to twenty percent of current
value. So the upkeep will increase sharply. And it is difficult to calculate the cost
because of the broken down point and frequency are stochastic. Besides, the cost is
varying when the operation time is increasing. Beyond a certain year’s operation the
machines would become unstable and ask for more and more repair and maintenance to
keep them work well. As pre-mentioned, the second solution asks for investing one
machine in station 8 which cost is 1.2 Million. So the situation is similar with the first
solution, which needs cost of maintenance, rearrangement, space and so on. But over
the long haul, the cost of investing new machines and reducing TTR as well as invest
one machine are pretty much the same thing. In a word, cost is nearly for solutions one
and two. But considering the upgrade of products the second solution should better than
32

the first one.
For the third solution, it obviously needs more investment comparing to the first two
solutions. However if the market requirement much more than the forecast, this is the
mothball plan. The cost of reduce repair time is also difficult to estimate in the forth
solution. But the cost to employ more workers can be calculated. In the Swedish law the
average salary is 110 SEK per hour. The shift added is the night work shift. So
supposing the average salary is 150 SEK per hour per person. The effective work time
for one shift is about five hours and one shift needs about fifteen workers. As a result
the cost is 2,587,500 per year. And this does not include the cost to reduce the repair
time. The cost of solution four is much more than the first two solutions. The last
solution goes the same situation.
The result is when take cost as parameter among the five solutions the first and second
are similar and stand head of others. The third one is better than the last two but worse
than the first two according to the cost.
4.4.2 Reliability comparing
It is well known that the ability to analyze overall performance is critical to any
organization. When it comes to maintenance, improving effectiveness normally
includes three means: reducing downtime, reducing maintenance costs, and extending
component and asset life (Lawson, viewed 2008). In real production, an organization
must focus on sustainable results rather than just focus on cutting costs.
“Results-oriented organizations focus first on the quality and volume of production
throughput, followed closely by the cost to produce the required quality and volume.
This approach will improve reliability performance, which will drive manufacturing
costs down.” (Idhammar, viewed 2007) Many companies focus more on cutting
maintenance costs, as a result, maintenance costs cuts down temporarily, than increase
much more than the initial savings. And besides of this, reliability goes down, pave the
way for losses that can be substantial. This phenomenon has been shown many times.
The root cause of this phenomenon is often shortsightedness. (Idhammar, viewed 2007)
Among the five solutions, three of them require decrease the repair time. One way to
achieve the aim is to increase maintenance frequency, which will help the system work
in a good state. In fact there are many cases shown that maintenance costs increased and
consequently also production throughput increased steadily. And the increased cost of
maintenance is much less than the margin gained from the improvement of high
reliability. So for the three solutions, if try to cut down the repair time by investing
maintenance, the reliability will increase steadily from a low percentage to a high value.
As pre-mentioned when compare the cost of different solutions, the way to improve the
repair time is repair the broken down point as soon as possible. Since repair is a part of
maintenance, solutions try to satisfy the requirement of market at the same time
increase the reliability. In another word, it is sure that the second, third and forth
solution will improve the reliability of the production system.
33

There are many methods to measure reliability. The purpose of this section is to
investigate and compare the assembly line reliability estimated from yield simulation
model. Generally speaking, yield is a measurement of reliability. Yield is defined as the
ratio of the number of usable devices after the completion of a production process to the
number of devices at the beginning of a process (Kyungmee & Kim, 2008). Yield is
related to the profitability of any production line. Predicting yield for new technologies
is important to determine the cost to modify layout for the yield improvement
(Kyungmee & Kim, 2008).
Reliability will be estimated from the yield simulation model set in the first part of this
chapter. Such reliability is more useful, than reliability estimated from field failure data
(such as failure rate of production), for determining whether a reliability requirement
will be met or not. The volume of production of the first three solutions has been
simulated in section 4.3. The results shown in Table 4.7 and Table 4.9 indicate the
reliability of the first and third solutions meet the requirement while the second solution
will satisfy the reliability requirement by employing workers overwork three or four
shifts. The reliability in the last two solutions is stochastic situation, because both of the
solutions depend on the employees and workers’ performance are hard to forecast.
So we can say that in the point of reliability the first and third methods are the best.
Followed is the second method. The last two are disadvantaged methods.
4.4.3 Lead Time comparing
“Lead time is the total time required to complete one unit of a product or service.”
(Elsmar, viewed 2008) Lead-time is typically a product of:
1. The frequency with which dealer orders are submitted
2. The relative timing of dealer order entry and OEM order review activities
3. The time lag between order entry/acceptance and production
4. Order confirmation delays due to limitations posed by supply constraints, and
whether the dealer has earned allocation of those constraints (Elsmar, viewed
2008).
Analyze the lead time will help people to learn that reducing lead time contributes
directly to improving Quality, Safety, Customer Satisfaction, etc. The consequences of
longer lead times will often (David, viewed 2007):
1. Less dependable forecasts as these have to be made earlier; the long lead times
make the manufacturer vulnerable to unforeseen changes and inaccurate
demand forecasts.
2. Reduced production flexibility; i.e. greater difficulties to adjust to order
changes
3. Higher levels of inventory; a manufacturer will account for the uncertainties
34

and unforeseen events by keeping safety stocks. The safety stocks assure the
necessary flexibility; or rather they act as buffers for the lacks of flexibility in
supply chain.
4. Miss some customers.
Since all of the solutions discussed in this paper will help achieve the market
requirement, the lead time will be reduced. It is evident that the third solution will
reduce the lead time most since two new machines are invested as well as the repair
time is reduced. For the first and third solutions, it is difficult to compare these two but
both of them decrease the lead time. And they are better than the last two methods.
Comparing the last two, it is easy to get the result that the forth one is better than the last
one from the aspect of reducing lead time.
So the order from the lead time view is that the third solution takes the first; then is the
first and second solutions; and next is the forth solution; the fifth solution stands the
fifth.
4.5 Results
Based on the discussion above, a table is used to shown the result clearly. In this table,
using the mark denote different levels. The number one gets five, number two gets four,
then the third one gets three, the forth gets two, and the last one get one. After calculate
the total mark of each solution, the solution with the maximal mark will be the best one.
From the result shown in Table 4.10, the solutions focus on the bottlenecks get similar
marks with higher records. So the core to improve current situation is managing to
solve the problems of bottlenecks.
Table 4.10 Comparison Results of Different Solutions
Invest
machines
at station
8 and
station 2
Invest
machine at
station8 &
cut down
TTR &
three shifts
overwork
Invest
machines at
station 8
and 2 & cut
down TTR
Three
shifts &
cut down
TTR
Three
shifts &
more
effective
work time
Cost 5 5 3 2 2
Reliability 5 3 5 2 2
Lead Time 4 4 5 2 1
Once the productivity is confirmed to reach the production volume demand in 2008,
investigation in material flow among suppliers, the company and customers is taking
place. In the next chapters, pitfalls in the supply chain will be investigated and
suggestions in optimization will be given.
35

CHAPTER 5
SELECTION AND ASSESSMENT OF SUPPLIERS
A supply chain system consists of suppliers, manufacturers and retailers or customers
including raw material inventory, work-in-process inventory, and the inventory of the
finished goods. Supply Chain Management regards the various activities, functions,
and systems required to bring a product or service to market as a holistic perspective. It
advocates that the supply chain be strategically managed as a single entity or system
instead of optimizing fragmented segments or subsystems individually (Vickery,
Jayaram & etc, 2003). A supply chain system consists of suppliers, manufacturers and
retailers or customers. It includes raw material inventory, work-in-process inventory,
and the inventory of the finished goods (Biswas, 2007). The general model of general
supply chain is shown in Figure 5.1 (Li, 1990).
Figure 5.1 General Supply Chain Model
The sketch map shown in Figure 5.2 compared the differences between the past and
present of supply chain (Li, 1990). In the past the factors in supply chain are run
37

separately and the supply chain emphasizes rigor and tough bargaining. But in present,
the supply chain links up all the players in a horizontal supply chain and emphasizes
seamless delivery, optimization and integration (Li, 1990). An integrative supply chain
strategy recognized would create value for both the firms and customers. Recent years,
lots of persons study the topic of integrative supply chain and some of them get a
conclusion that higher the degree of integration across the supply chain, the better a
firm performs (Vickery, Jayaram & etc, 2003).
Supply chain is based on “Competition – Cooperation - Harmony” and harmony is the
foundation of a steady supply chain. There are two sides of supply chain harmony; one
is harmony inside of a firm and the other is the harmony among different cooperative
associates. The first one scope includes material flow, funds flow, and information flow
among different departments in one enterprise. While the second one means harmony
among suppliers, manufacturers, and venders. An effective and efficient harmony
among different cooperative associates could reduce cost, enhance manage supply
chain and improve the efficiency (Jiang, 2006).
Figure 5.2 Supply Chain in the Past and Present
In this research the goal is optimize the material flow and inventory, the follow section
will focus on them based on an integrative supply chain strategy. In this chapter, the
main research focuses on material flow between suppliers and factory.
38

5.1 Rules and methods of selecting suppliers
Supplier is one of the main roles in a supply chain and contributes to the comprehensive
performance of a supply chain. Poor supplier performance affects the whole chain’s
performance. As Araz and Ozkarahan (2007) said, selecting the wrong supplier could
be enough to deteriorate the whole supply chain’s financial and operational position.
The key to achieve the goal of get a supply chain with good performance is to develop a
healthy buyer–supplier relationship (Sarkar & Mohapatra, 2006). While the foundation
of healthy relationship is select suitable suppliers.
Supplier selection decisions are complicated by the fact that various criteria must be
considered in the decision-making process. Meanwhile, supplier selection and
evaluation is increasingly seen as a strategic issue for companies (Araz & Ozkarahan,
2007; Choy et al., 2002). During past several years, many scholars researched in the
filed and got the conclusion that cost, quality, and delivery performance were the three
most important criteria in supplier selection process (Sarkar & Mohapatra, 2006).
Among the three, quality is the most important selection criterion (Weber et al., 1991).
The quality is followed by delivery and cost. However, with the development of society,
especially the increasing significance of strategic sourcing and competition of global
environment, the criteria of supplier changed (Choy et al., 2005). Except the
traditional factors: quality, cost and delivery, supplier practices which includes
managerial, quality and financial, etc. and supplier capabilities that range over
co-design capabilities, cost reduction capabilities, technical skills, and so on have to
be considered (Sarkar & Mohapatra, 2006; Dowlatshahi, 2000).
There are lots of methods to select suppliers. And some mathematical models have
been developed for modeling the supplier selection problem (Kheljania et al., 2007).
Supplier selection problem typically consists of four phases as De Boer et al. reported
in 2001: first step is problem definition; second is formulation of criteria; third one is
the qualification of suitable supplier (or pre-qualification) and the last is final
selection. Following the steps the first thing to do is investigation current situation and
find out the problems.
5.2 Current situation and problems of suppliers
For the assembly line, the assignment is assembly. All of the components are prepared
before this step. Because of the globalization, competition becomes keener day by day.
Factories have to manufacture productions flexible, economical, effective and
efficient. According to the circumstance, most of the components Haldex uses in the
production of ADB are machining or produced by the suppliers.
39

5.2.1 Current distribution of suppliers
At present for the ADB assembly line there are about twenty-four suppliers. Among
them seventeen suppliers locate in Sweden, five lies in German, one from Denmark
and one from India. The pie chart in Figure 5.3 is the distribution of current suppliers.
Since the workshop of ADB products locates in Sweden, the current distribution of
suppliers is beneficial for the factory in some aspects, such as the delivery time is
short, the transport cost is low, and it is convenient for the company to communicate
with the suppliers, etc. In order to scale the current suppliers, it is necessary to assess
them according to different parameters.
21%
Distribution of Current Suppliers
4%
4%
71%
Figure 5.3 Current Suppliers Distribution
5.2.2 Evaluation of current suppliers
Sweden
Germany
Danmark
India
There are several criterions to measure suppliers, such as cost, quality, delivery,
supplier practices and capabilities. Since this is an assembly line example, supplier
capabilities are very important. For this point it could be evaluated by three factors:
co-design capabilities, cost reduction capabilities, and technical skills. De Toni and
Nassimbeni (2001) researched evaluation of supplier’s co-design. They suggest that
most of capabilities in co-design activities are concurrent engineering techniques,
offered by suppliers in the development stages as evaluation criteria, such as support
in product simplification, support in component selection, and support in design for
manufacturing/assembly activities, etc. These techniques lead to substantial
improvement in quality, cost and delivery performance. (Ceyhun Araz, Irem
Ozkarahan, 2007)
40

Table 5.1 * Suppliers & Suppliers’ Location and Quality Criterion
Name of Supplier Location Quality Certifying Environment Certifying Land
41

Table 5.2* Delivery Information of Current Suppliers
Name of Suppliers Delivery Condition Delivery Way Supplementary Item Posting Type Land
42

Quality is the most important factor to select supplier. Table 5.1 above indicates the
suppliers and their information about the location, quality certification, environment
certification, etc. All of the suppliers follow their standards, so at least the quality
should satisfy the requirements of the products. In a word, in normal situation the
suppliers can guarantee quality. In case a certain supplier produces products
imperfectly or the company asks for a higher quality, it is easy for the factory
communicates with supplier or even brings their own technologist to the supplier’s
factory since all of the suppliers except one locate in Europe. There is only one
supplier, who lies in India, far away from the company. If it is necessary, the
company may send delegates to India or hire some permanent delegates in India and
help the supplier to improve their technology. From the measurement of quality, all of
the current suppliers can satisfy the requirements.
In traditional theory, quality is the most important selection criterion and it is followed
by delivery and cost. The information of delivery is shown in Table 5.2. It is well
known that in a similar area, the shorter the distance is, the cheaper it cost on
transportation. Although the suppliers pay the transportation fee, the company should
consider it as well because the transportation cost is more or less counted into the price
of products. From Table 5.2, all of the suppliers except the supplier locate in Denmark
and India ask DHL deliver their productions. The cost could not have very huge
difference. For the Indian supplier the cost is 17,000 SEK per truck (transport by land).
If productions transported by the waterway the cost will much lower with a longer lead
time. But there is a benefit for the Indian supplier that the salary there is much cheaper
than in Europe. Considering this factor, the total cost may be similar or even lower than
suppliers locate in Europe. Beside of this, it can be as a support center to the Asian
market. Customers in Asia can get a better technology support in this way the. In fact
this is also one of the reasons that more and more global enterprises invest in Asia
during recently years.
As discussed in section 5.1, supplier capabilities are one of important measurement in
the global circumstance. This can be evaluated by three views co-design capabilities,
cost reduction capabilities, and technical skills. In respect that Dulmin and Mininno
(2003) define the co-design criteria as supplier’s effort within the project team (Araz
& Ozkarahan, 2007). First of all, the research focuses on the co-design capabilities.
The company does not have any plan to invest R & D department in India so far,
though there locates its furthest supplier. It plays a role only as a manufacturer. But it
cannot be concluded that this supplier’s co-design capability is worse than other
suppliers. German companies are always well known for their advanced technology,
high quality and the very positive attitude during work, as well as seeking for greater
perfection. Therefore, in the field of technology, the German suppliers hold an
advantage to some extent. Regarding the cost reduction capabilities, more detail
information is needed in order to discuss from this aspect. Based on the discussion
around the supplier capabilities from different views, German suppliers are in some
range better than the others. But this cannot be considered as a comparing result
43

ecause this only gets from experience and under normal situation.
In a whole, evaluating current suppliers by quality, delivery, cost as well as supplier
capabilities, all of them are able to satisfy requirements of Haldex in current situation.
But the dense distribution of suppliers has a problem with risks. Risks here includes
both natural factors and man-made factors, such as energy crisis, raw material
shortage, natural disasters, strike, enhanced social welfare or salary and so on. Some
of the risks could be avoid if suppliers distribute in a large scope. Furthermore, with
the change of market, it will be a good idea to select some suppliers as well as invest
new workshop close to the market to save transportation cost and reduce risks.
Certainly, in that situation, evaluations from many aspects are needed before
investment.
5.3 Selection of future suppliers
5.3.1 Market distribution and suppliers’ selection
Beside of the quality, cost and delivery, distribution of market is a pivotal factor to
decide the area to find suppliers. From the order quantity during the period of
November 2007 to January 2008, the main market of ADB locates in Europe. The
distribution is shown in Figure 5.4. Most of the suppliers locate in Europe and two
with smallest percentage locate in Asia. Comparing to the current suppliers
distribution, the two distributions generally match well with each other.
Korea
China
Austria
Spain
Belgium
Poland
Holland
Turkey
Sweden
Russia
France
Percentage of the whole market
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00%
Figure 5.4 Current Market Distribution of ADB
44

The market of the Commercial Vehicle Systems deviation in Haldx distribute in a
very different way. From the distribution shown in Figure 5.5, more than half of the
products are sold in North America and the second biggest market is in Europe. Since
ADB is a small branch and quite new product in the Commercial Vehicle System
deviation, the market is not ripe. With the development of technology in Haldex ADB
products and changing demand in the general automobile industry, Air Disc Brakes
stand a good chance to be the main production in the deviation. It can be forecasted
that the distribution will change a lot in the coming future. Assuming the market
distribution of ADB will be similar to Figure 5.5, the distribution of suppliers will no
longer corresponds to the market. Comparing the distribution of suppliers and the
distribution of the market, it seems that finding some suppliers located in North
America is a motivation for the development. But considering the long distance and
cost, constructing a sound technology-support system is much better for the
development of the factory. It will be propitious to reinforce the cooperation between
the suppliers, factory and markets as well.
South America
Asia
Europe
North America
Net Sales
0% 10% 20% 30% 40% 50% 60% 70%
Figure 5.5 Current Market Distributions of Commercial Vehicle Systems
With the increasing market, more suppliers will be required and it is an opportunity for
the factory to select suppliers in global scope. Of course the criterions discussed
before-mentioned in this paper, such as quality, cost, delivery, supplier practice,
supplier capabilities, etc. are still the basic measurements. Market distribution changing
trend should be considered in real-time. From the forecast of the market, Asia market
will increase sharply in a few years while the North America and Europe market would
keep steady. Nowadays the standards of manufacturing are perfect. So as long as
producing with an effective control system, quality will not be a problem for most
suppliers. Regards current suppliers distribution and the cost of suppliers in different
area, find some suppliers or copartners in Asia is a sane choose.
45

5.3.2 Inventory and suppliers’ selection
Under the circumstance of supply chain management, the ideal goal of inventory
management is managing to reduce the inventory with low cost as well as satisfy the
production and market requirement. Achieving an optimization is from both the area
of inventory control and supply chain management. Giannocaro et al. (2003) defined a
supply chain inventory management policy utilizing fuzzy set theory to model
uncertainty of demand and costs. Since inventory cost could up to seventy percentage
of the total cost of a production line, procedures that combine minimization of
inventories or inventory costs, and coverage of satisfactory service levels have
attracted significant attention (Skintzi et al., 2007).
Figure 5.6 Net Inventories in Supply Chain
One method to resolve the problem of inventory is sharing information among
cooperators. Traditionally, the management of information has been somewhat
neglected. Information management system is separate for different roles in the
supply chain. Now the global market develops increasingly. It is widely recognized
the importance of sharing information in the whole supply chain. More and more
cases prove that advancements of technologies in the fields of information,
manufacturing, and distribution systems have driven much change through the supply
chain. Especially, improving information technology enable follow instantaneous
global information sharing with more powerful information processing (Byrne &
Heavey 2006). Figure 5.6 (Machuca & Barajas, 2004) shows the inventory problem
clearly. This is also a performance of Bullwhip Effect, which is caused by lack of
information sharing.
For the factory, selecting a supplier who can share information effectively and
efficiently in the supply chain is necessary and important to solve the inventory
problem. Come to the situation of Haldex, the problem is that there is no enough space
to deposit the products. This problem can be solved from several points of view, for
example, reducing the warehouse occupancies from both raw material and finished
46

products. Selecting suitable suppliers is the first choice. For the current suppliers, the
factory could construct a benefit sharing system with them. Ask the suppliers increase
the delivery frequency in order to save the space used to store raw material for the
production of ADB. Furthermore, the factory can evaluate suppliers and discuss with
them and then trying to improve the assembly line work under an environment similar
with JIT (Just In Time) system. “Just In Time (JIT) is an inventory strategy
implemented to improve the return on investment of a business by reducing in-process
inventory and its associated carrying costs” (Wikipedia, viewed Feb.2008). This
method can lead to huge improvement in the areas of inventory, quality, investment,
efficiency and so on. The key of the JIT system is that, items should arrive when the
production system needs, neither earlier nor later. By this way the raw inventory level
can be reduce to very low. So it can help the factory improve the inventory problem.
However, the requirements towards the suppliers are higher. The factory has to pay
much more attention on this point. Under this situation, suppliers should manufacture
the products in high quality and low cost. Reliability and flexibility are also very
important factors when selecting suppliers because only reliably and flexible suppliers
can reduce the lost caused by unpredictable disaster furthest.
Considering the risk of applying JIT system in the workshop, the ordering quantity by
Haldex should lower than half of the production volume of the supplier in order to
garentee the timly supply. Otherwise in case there is anything wrong with the supplier,
the production in the workshop will be disastrous. There should be two to three
suppliers offer the same components. If there are too many suppliers, the cost will high.
The suppliers do not stand at the same level. They are divided into major and minor.
Then the factory can reduce the management cost while increase the management
effect and keep a stable source of raw materials. (Sun, 2005)
5.4 Integrative selection of suppliers
Based on the discussions above when select suppliers the factory should consider
several kinds of factors. Table 5.3 concluded most of the factors above-mentioned.
Because the distribution covered a huge scope and detail information is lack, this
paper only discuss on the level of continent. In this table the supplier will get a plus if
he takes an advantage on the factor, otherwise there is a minus. At the end an
integrative estimate will be received.
According to the total score in the future the selected suppliers should mainly locate
in Europe. The followed are Asia and North America. For the detail information about
which countries selected will depend on the practical information.
47

Table 5.3 Integrative Estimation of Selection Suppliers
Supplier’s
Cost Supplier Capabilities
Location
Delivery
Total
Technology Labors Transport
Co-design Cost Technical
Quality Performance
Score
Support
To To
Capabilities Reduction Skills
Factory Market
Capabilities
Europe + - + + + + + - + 5
Asia - + - - + + - + + 1
North
- - - + + + - + - -1
America
South
- + - - + + - - - -3
America
Forecast the Market of ADB
3%
5%
North America
Europe
Asia
South America
58%
34%
Note: A presupposition is the suppliers locate in different area;
Assume the market distribution in the table following the
figure show on the right;
All the factors are estimated and compared based on the
current situation;
For the delivery performance all the suppliers get a plus
because the transport often hand professional delivery
companies to do;
For the quality since all suppliers test by similar standard
criterion, all of them receive a plus in the table.
48

5.5 Result
There are many rules and methods to select a supplier. In a real case it is necessary to
evaluate and select suppliers according with the practical situation. The standards for
each supplier-buyer are also different. But for all of them the buyers and suppliers
should construct a long period partnership based on trust and cooperation. For Haldex
the ideal suppliers should cooperate with them to solve inventory problem in current
situation. In the future suppliers selection should be in Europe first and then in Asia
and North America. The suppliers should ensure satisfy the basic requirements quality,
cost and delivery at first. Besides of this, factory and supplies should construct
information sharing system together and keep a long steady partner-ship.
49

CHAPTER 6
INVENTORY REDUCTION AND
DELIVERY OPTIMIZATION
Supply chain management is a cross-functional approach to managing the movement of
raw materials into an organization, certain aspects of the internal processing of
materials into finished goods, and then the movement of finished goods out of the
organization toward the end-consumer. (Wikipedia, viewed 2007). So far, this paper
has contained the optimizations of the first two segments in supply chain management.
Relationships between the company and the customers are complicated and the ability
to effectively match demand and supply is fundamental to nearly all supply chain
management processes. As the production volume increases, the balance between the
company and its customer may be destroyed and affect the company’s reputation if the
management of supply chain is not updated together the fluctuation. Besides, the
inventory is another consideration in operation management, which is changing along
with the production volume also and needs to be optimized. In this chapter, authors did
the research based on theories and experiences from successful cases of other
enterprises. Threads and opportunities will be analyzed and then, feasible solutions will
be proposed and evaluated.
6.1 Rules and methods to balance the profit between factory
and customers
The supply chain is the total flow of materials, information and cash, from the
suppliers' suppliers, right through an enterprise to the customers' customers. (Ahmad &
Benson, 1999). Figure 6.1 shows the flows between the supplier and customer. It is
clear that materials flow from the supplier’s supplier to the supplier, which can be the
role of the company here, and then to the company’s customer and to the customer’s
customer. Cash flows in the reverse direction, from the customer to the supplier.
50

Information flows both way through the system and meanwhile gives the visibility to
the whole system. Some of the problems can be predicted, while others are occasional.
The company always has to optimize the supply chain, no matter in production or
delivery or some other aspects, to deal with the problems may happen according to
prediction.
Since there are so many flows through out the supply chain, problems are easy to
happen. Sometimes products are wrong or late delivered. In some cases, incorrect
invoices are sent out. From time to time, inventory in the warehouse is counted wrong
against the data in the computer. In the warehouse, it happens now and then that too
much inventory or not enough of what is wanted. When changes happen in the supply
chain, no matter the demand or the production technology, the system is interrupted and
need to be rebalanced ahead of time. To those problems cannot be predicted by time,
preventive measures are necessary to deal with the unexpected situation.
Figure 6.1 Flows in Supply Chain Between Factory and Customers
(Clermiston Consulting Pty Ltd, viewed 2008)
Generally, the goal to optimize the supply chain is on one hand to reduce the cost of
materials, handling and processing, and on the other hand to establish a steady and
potential system that is able to deal with the similar situation in the future. Prerequisite
of the optimizations is the processes inside the chain must work, which must be
confirmed ahead of time. Another goal to optimize the supply chain is to achieve a
critical information system that matches the physical and transactional processes in real
time. The information is through out the supply chain in both directions, which means
suppliers and customers must have close relationships that contain synchronized
communications.
51

How to get the best value from a supply chain? First of all, one should understand the
processes. Investigations are necessary to get first a general ideal and then the details of
the whole supply chain or the special area that need to be optimized. After the
investigation, improve and reorganize the processes to match the best-in-class practice.
Then, match the information to the processes and measure the processes and
information performance. Sometimes if provided that circumstances permit, simulation
of the optimized supply chain can take place before the proposal is carried out.
(Clermiston Consulting Pty Ltd, viewed 2008).
There are many benefits of general supply chain optimization in the situation that the
optimization is not aim at a certain purpose. Sustained improvement in sales and profit,
problem can be fixed without necessarily major capital or big IT spends. Reduction in
warehouse and total inventory will be achieved if the approach is effective. After
optimization, a supply chain performance can be improved in variety areas (Larry
Lapide, viewed 2008):
• Reduced supply chain costs
• Improved product margins
• Lower inventories
• Increased manufacturing throughput
• Better return on assets
• Reduced lead times
By optimizing the supply chain, the goal of balance the benefit of factory and
customer will be achieved. Many theories can be used in optimizing the supply chain,
such as operation research and Six Sigma. Operation research has been applied in
Haier Co., Ltd’s products delivery process in 2004 and reduced the delivery time by
nearly 50%. Associate with the reduction of delivery time, inventory was reduced and
cash turnover was speeded up. (Haier, viewed 2008) Six Sigma is widely used in supply
chain optimization. Ford Motor Company has achieved significant success in applying
Six Sigma to its supply chain processes. (Marx, viewed 2008). Some other
methodologies can also be used in the supply chain optimization according to specific
requirement of different systems. In the supply chain of ADB products, optimization
also can be a method to balance the benefit between customers and the company and
make the products more competitive in the future market. Apropos of how to improve
the supply chain, the authors referred to selecting suitable suppliers in last chapter.
Beside of that, many other methods are suitable to be applied to improve the supply
chain of the ADB products. Detailed proposals will be discusses later in this chapter.
The main purpose of optimize the supply chain in this case is not only achieve better
performance of the current line, but also create a strategy to deal with the increasing
production volume.
52

6.2 Current statue analysis
The current sales part of the supply chain works in an appropriate way. However, it is
not stable and problems are popping up as the market demand increase day by day. The
urgent task at present is to solve the increasing inventory problem, which is a
significant element of the supply chain.
Before optimization, current situation needs to be investigated clearly. In this section,
the authors start with the capability of the warehouse and the transportation from the
end of the production line to the warehouse. Afterwards, distribution of the market is
investigated and related data are analyzed. And then, a forecast of future market will be
given. Related delivery detail between the company and its customers are stated
followed. And at last, suggestions to optimize the sales part of this supply chain will be
proposed.
6.2.1 Situation of inventory
Inventory is a keystone in the supply chain. It is held not only as a stock of goods, but
also in order to manage and hide from the customer the fact that manufacture/supply
delay is longer than delivery delay. Besides, inventory is held to ease the effect of
imperfections in the manufacturing process that lower production efficiencies if
production capacity stands idle for lack of materials. (Wikipedia, viewed 2008)
In Haldex, the production is according to order because each customer has particular
requirements of the products and the order is placed long ahead of delivery. Hence, the
inventory is only carrying the goods for the period of time between they are finished
and sent to the customers, not the backup to respond the urgent orders. It simplified the
inventory in a sense.
In the workshop, Haldex uses powered pallet jacks to do the transportation between the
production line and the warehouse. One powered pallets jack is able to carry two pallets
of the finished products. The distance between warehouse and the end of the assembly
line is about 200 meters. It takes 30 seconds to transport a batch of pallets including
pick up and drop times. Hence, it takes around one minute to do a round transportation,
which means 120 pallets can be transported by one jack in one hour. However, a down
time of 50% should be considered here. It reduces the capacity to 60 pallets per hour.
There are two powered pallets jacks working in the workshop, however, the ADB
organization is only planned to utilize 20% of this capacity. As a result, only 0.4 jack is
working on the ADBs during the working time. That is, 24 pallets of finished ADBs are
transported in one hour. The warehouse operates from 06:30 in the morning until 12:00
in the evening during the workdays. And during weekends, it works during the daytime,
which is around 8 hours. In workdays, the efficient working hour is 17.5 hours. After
53

calculation, 420 pallets can be successfully transported. In a similar way, 272 pallets of
ADBs can be transported during a weekend day.
In Chapter three, the working days are calculated as 230 days per year, which is only
the sum of 46 weeks’ weekdays. Since the warehouse is working during the weekend,
the same weeks are count in to calculation. Therefore, the warehouse works 322 days
per year, which includes 230 workdays and 92 weekend days. Altogether, the capacity
of the inventory can reach 114,264 pallets per year.
Ignoring the variety of products, one pallet is able to carry 10 ADBs in average. Thus,
the powered pallets jacks are able to transfer 1,142,640 ADBs in a year. This value is
far exceeding the production volume at the present and also much bigger than the
forecasted value in 2008, which is 200, 000 items. Table 6.1 listed out the step-by-step
result of the calculations above.
Table 6.1 Capacity of Warehouse for the Finished ADBs
Work Pallets/day (24 No.of Pallets/year ADBs/year (10
hours pallets/hour) days/year ADBs/pallets)
Weekdays 17.5 420 230 96600 966000
Weeend 8 192 92 17664 176640
Total 4761 hours/ year 114264 1142640
From the result, the increasing volume of production in the recent years will not affect
the warehousing for its big capacity. However, the idle time for powered pallets jacks is
two long, which gives an inspiration in reconsidering the demand of the warehouse to
achieve a more economic way of operation.
6.2.2 Distribution of the market
Currently, the market of the Haldex ADB is mainly distributed in Europe and Asia. In
order to estimate the product distribution, take the customer’s location in a period of
time (from November 13, 2007 to January 31, 2008) into analysis. Market distribution
is shown in Figure 6.2 followed. The order list is shown in appendix A, table 3.
So far ADB products are only produced in Landskrona, Sweden, which means all the
products are shipping from Sweden. Although the distribution above is not precisely
the entire market distribution of all the time, the market is mainly in Europe. This
distribution is close to the location of the manufacturer, which won’t transportation
problem theoretically. However, the market of the Haldex ADB is expanding and will
spread to the other side of the world in the coming future.
The current market leader of Air Disc Brakes is Knorr-Bremse, who occupies 80% of
the market in Europe. Up to the beginning of 2007, Knorr-Bremse already has 12
million ADBs on Europe roads and its subsidiary Bendix Spicer Foundation Brake has
110, 000 ADB on North American highways. (Bendix ® Air Disc Brake -- Fact Sheet,
54

viewed 2008). Another competitor of Haldex is WEBCO, who has big market share in
North America. Along with the development of technology and new features of the
ADBs, Haldex is by big chance becoming very competitive in the future and possible to
share a big part in North America, Asia or other states. In that case, new sub-factories
are necessary to be built according to the distribution of market. Or else, cooperating
with a third company is also a good way to solve the inventory and transportation
problems.
Figure 6.2 Market Distributions during Nov.13, 2007 and Jan. 31, 2008
6.2.3 Delivery details
Goods delivery is the last step of material flow in a supply chain. Many problems may
happen in this segment such as late delivery, wrong quantity, wrong location, etc. When
the production volume increases, the delivery is usually becoming more frequently.
At present, there is a truck leaving with ADB daily. However, customers are different
55

each day. The maximum load of one truck is 24 000kg, and the average weight of ADB
is 47kg. Thus, a truck can carry maximum 51 pallets full of ADBs. Nevertheless, the
utilization of the truck is often less than 100%. The manager believes that a utilization
of around 75% is the best average possible today. So instead of 51 pallets per truck the
average at the moment is around 38 pallets per truck.
Taking the same working day as the warehouse per year, there are 322 days in a year
that the truck is transporting the goods from the warehouse in Landskrona. Multiplying
322 by 38, there are 12 236 pallet sent out from the warehouse. As mentions above,
each pallet contains 10 ADBs, therefore, the truck is able to transfer 122 360 ADB
items every year. Comparing to the simulation result from Chapter 3, the delivery is
able to deal with the current production volume, which is 120 127 per year. However, it
cannot satisfy the delivery requirement if the optimized production volume.
Transportation costs of ADBs from Haldex and customers are in big percents paid by
the customers. Taking the same sample from November 13, 2007 to January 31, 2008 in
to analysis, which has been used to analyze the market distribution above,
transportation paid by Haldex is sorted out and compared to the total amount in Table
6.2.
Table 6.2 Transportation Fee Paid by Haldex during Nov.13, 2007 and Jan. 31,
2008
Customers’ Location Order
France (Max 25ton) 108
Holland 240
France (Max 25ton) 3984
Russian (Max 21,5 ton) 1752
France (Max 25ton) 6
Total 6090
Percentage in all the orders 33.61%
The result shows that 33.61% of the products’ transportation is paid by Haldex. Since
the cost varied from different destinations and those paid by customers are not
available, it is hard to calculate by how many percent in cash are paid by the company.
According to the manager of Haldex, customers pay around 80% of the transportation
costs while Haldex pays the rests.
A very important factor to mention is that the company has its own standard pallets to
carry all the products and most of the original materials. The pallets are also the
package of the products, thus they are sent to customers together with the products.
Some of the customers will send the empty pallets back after the goods arrived, but the
others won’t. As the result the company has to procure new pallets frequently. The
pallets are big and made of wood and one pallet with two layers can only carry 10
pieces of ADBs. Cost of packages will increase rapidly along with the production
56

volume.
6.3 Analysis and Suggestions
Generally speaking, the main cause of all the threats in supply chain is the rapid
increasing production volume. Because of the increasing, many difficulties will pop up
very soon, or even some have already happened and affect the production so far.
Optimization is of great urgency.
In conclusion, the threats Haldex faces are:
• Big inventory occupancy, the warehouse’s capacity is not enough to carry the
increased products.
• Pallets supply is not enough or not in time, which might delay the delivery of
products.
• In the future, market both inside and outside of Europe probably will grow fast.
Headquarter of ADBs in Landskrona will not capable to carry the big amount of
manufacturing according to the demand.
One thing before solving the threats above is that the powered pallets jacks have too
much idle time. From Table 6.1, even work only in weekdays, the truck is able to
transfer 966 000 ADBs every year, which is nearly five time of the predicted production
volume in 2008. To save the workforce and enhance the efficiency of the jacks, it is
feasible to use only on powered pallets jack and working only during the weekdays.
Even in this way, only 10% of the jack’s capability is needed in 2008.
To deal with the increasing inventory, method, which can satisfy customer’s demand, is
of higher priority. It has been stated earlier that the truck that leave the warehouse
everyday will not able to satisfy the demand soon. Once the production volume exceeds
the delivery volume, goods will start accumulating. Meanwhile, customers’ demand
will not be satisfied. Simply there are two methods to solve this problem: deliver more
frequently and deliver more each time.
Currently, one truck is usually carrying 38 pallets. Therefore, 200 000 ADB items need
the truck to run 527 times, which is much more than the working days per year. Since
the utilization of truck is only 75% at present, it is possible to enhance to its capacity,
even to its maximum value 100%. Thus, 200 000 ADB items need the truck, who can
carry 51 pallets under full load, to run 393 times. As mentioned in Chapter 3 and
suggested above, both the production line and the pallets works only during weekdays,
the truck is also possible working in the same day. Table 6.3 lists transportation
amounts under different suggestions.
Once the capacity of truck reach 90% and there are two truck working every weekday,
all of the goods will delivered. Moreover, since the truck can achieve 100% capacity,
57

and can also overworking during weekend, some urgent deliveries are also achievable.
There are two ways to arrange the two trucks. One is both of them leaving together, the
other is one leaves in the morning, the left one leaves in the afternoon. The later
arrangement is better in reducing the inventory.
Suggestions
Table 6.3 Transportation Amount under Different Suggestions
No. of
trucks/day Working Capacity of the
days/year truck
Current 1 322 75%(38
Statue
pallets/truck)
1 2 230 75%(38
pallets/truck)
2 2 230 85%(43
pallets/truck)
3 2 230 90%(46
pallets/truck)
4 2 230 100%(51
pallets/truck)
Transportation
Amount (Items)
122360
174800
197800
211600
234600
The exhaustion threat of pallets is urgent to be eliminated. Procurement department is
working on finding the suppliers of pallets. However, more works can be done to save
the space. One suggestion is to design a new kind of packages of the ADBs, either for
individual or for collective, using lighter and easier materials. This is more favorable in
those orders to customers who will not send the pallets back. Another way is finding a
pooling company, who has warehouse close to Haldex workshop, to deal with all the
delivery and packaging. Haldex only need to send the goods to the inter-warehouse, and
take the pallets back. These suggestions need further researches, which will not carry
out in this paper. Considering the similar distribution of suppliers and customer, which
is mentioned in chapter 5, and the same standard of pallets, Haldex could also share
information with its suppliers and customers who are in the same area to arrange a
better utilization of the pallets. Nevertheless, this method is only effective in those
common areas, not for countries like Russia at present.
Facing the future market, Haldex had better get ready to start manufacturing ADB in its
branches or new factories. Because of the limitation of workshop and warehouse,
headquarter in Landskrona might be not capable to sustain the possible big production
in the future. Expansion is imperative. Nevertheless, location of the new factory is
according to the market and investment environment. The company has to pay close
attention to the market and environment globally, to balance the profit of itself and its
customers.
6.4 Results
In conclusion, suggestions to optimize the current statue and deal with coming threats
are:
• Reduce the powered pallets jack to one and away from duty during the weekend.
58

• Adding one more delivery to customers in the afternoon and enhancing the
utilization of the truck’s capacity at least to 90% in average. Delivery truck away
from duty during the weekend.
• Redesigning the packaging of the ADB products with easier and one-off materials.
• Cooperating with pooling company to do with the delivery and packaging.
• Sharing information of pallets with suppliers and customers in the same area.
• Invest new factories close to the market in the future to share manufacturing and
save transportation cost.
Because of information shortage and time limitation in the research, detailed
evaluations of the last four suggestions are incomplete. Further works based on the
in-time information are needed if either of them is in need to be evaluated. The work
includes investigation of the market and analysis of cost and feasibility.
59

CHAPTER 7
CONCLUSION AND FUTURE WORK
7.1 Conclusion
Based on the investigation in Haldex workshop located in Landskrona, the current
problems are the production volume cannot satisfy the requirement of forecast market
and the space for inventory is limited. After analyzing the data gathered from factory
the simulation model was constructed in Extend v6. Depending on the simplified
assembly line model, several methods and suggestions were proposed to improve the
current situation of the assembly line. After comparing these proposals by cost, lead
time and reliability, three best solutions are selected to solve the problem of
production volume:
1. Invest one machine at station 8 of main assembly line and cut down the TTR of
both station 8 of main assembly line and station 2 of preassembly line;
2. Invest one machine at station 8 of main assembly line and at one machine at station
2 of preassembly line as well;
3. Integrate the two solutions above. I.e. invest two machines one for station 8 in
main assembly line and the other one for the station 2 in preassembly line. At the
same time reduce the repair time of bottlenecks.
The inventory problem can be considered from two aspects. One is from the material
flow from suppliers. Selecting appropriate suppliers is very important and useful to
solve. After comparing suppliers based on multiple criteria, the suppliers selected in
the future should mainly locate in Europe and Asia, where are close to the market. If it
is necessary, suppliers locate in North America also can be considered but not
recommended. Haldex should cooperate with suppliers in many fields like technology
assistances. Associate with the location, suppliers with high quality and reliability with
reasonable cost should be selection first. In order to reduce the inventory, produce in a
similar JIT model is a good selection but in this option, reliability of supplies should
be estimated at first. Constructing a steady and long period partner-ship with suppliers
60

is good for both of factory and supplier. Because of its benefits, an information
sharing system, which takes an important role in reducing the inventory, is strongly
recommended.
The other way to solve the inventory problem is in the material flow from the
company to customers. Increasing delivery frequency to customers can help to reduce
the inventory. It is also feasible by building new warehouse to store the products and
find cooperators close to the workshop to take care of the storage of goods.
The inventory problem is coming along with the rapid increasing of the production
volume. Nevertheless, the current productivity even cannot satisfy the market
requirements. The core assignment for the company at present is improving the
production line in order to increase the production volume.
7.2 Future work
In this paper some solutions are discussed only from the view of theory. In order to
evaluate solutions and suggestions more scientific and acquire most feasible solutions,
more detailed information is needed. For example, when assessing current suppliers by
the evaluation criterions, it is necessary to know the information about operation and
management system of each supplier. Then a more veracious evaluation can be gained.
As regards building new factories, it is necessary to investigate the local environment
and market carefully, which is time consuming and can be another big project. As
described in chapter 6, for the package problem there are many things can be
researched in the future, such as the material of package, the style and size of package
box, and so on.
61

Reference
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65

Appendix A
Table 1 TTF & TTR Data of Preassembly Line
Week Cycle Time / sec. Stop Time / sec. Total Stop Quantity
20 110636 39341 390
21 360014 277367 630
22 140117 62860 440
23 150038 82800 445
24 221853 143892 544
25 203116 136633 492
26 137130 69546 492
27 151036 66546 588
28 151704 84853 567
29 414627 375982 352
30 945970 869390 640
31 724348 657305 577
32 341025 286588 427
33 91267 37233 310
34 110459 49682 364
35 143046 65406 409
36 140084 66361 432
37 108839 75288 388
38 218633 145526 544
39 187309 125563 416
40 137826 62931 495
Table 2 TTF & TTR Data of Main Assembly Line
Week Cycle Time / sec. Stop Time / sec. Total Stop Quantity
30 250217 188252 775
31 288729 233204 771
32 235535 176726 820
33 355117 285262 932
34 218188 130859 1008
35 364177 244152 1275
36 208917 124030 961
37 240848 161272 905
38 289967 204795 975
39 316794 242004 748
40 265803 167308 876
41 267763 162837 977
42 454758 328093 1413
43 263150 132508 1261
44 311439 200849 1120
46 360123 182959 1839
47 216705 149649 1161
48 260296 144848 1296
49 182101 100935 873
50 0 0 0
66

Table 3 Orders and Transportation Cost during Nov.13, 2007 and Jan. 31, 2008
Customers’ Location Order Transport cost, 1/2 Full and Full load
Landskrona, Sweden. 24 - (Internal)
France (Max 25ton) 563 Customer
France (Max 25ton) 108 9000SEK/ 17000SEK
Holland 240 9000SEK/ 17000SEK
Belgium 6 Customer
Poland 220 Customer
Korea 50 Customer
Austria 10 Customer
Turkey 88 Customer
China 120 Customer
France (Max 25ton) 3984 9000SEK/ 17000SEK
France 2058 Customer
Russian (Max 21,5 ton) 1752 50000 SEK (Always pay for full truck)
France 7423 Customer
Spain 100 Customer
France 198 Customer
Turkey 340 Customer
France (Max 25ton) 6 9000SEK/ 17000SEK
Sweden 816 Customer
Sweden 16 Customer
Total 18122
Table 4.1 Working Parameters of Each Station
Station/Main TID Queue Station/Pre. TID Queue
Capacity
Capacity
1&2 50s 6 1 52s 10
3 35s 2 2 53s 10
4 20s 4 3 38s 10
5b 60s 12 4 36s 10
5 55s 3
6 55s 4
7 18s 3 *Because of the different
8
9
10
11
66s
50s
55s/35s*
60s
5
3
2
2
configurations, TID of station 10 in
main assembly line is variation. 7/30
of the products cost 55s at this station
and the rests cost 35s.
67

Table 5.1 Suppliers & Suppliers’ Location and Quality Criterion
Name of Supplier Location Quality Certifying Environment Certifying Land
AB Indoma Sweden ISO 9002 SE
AB Linde Maskiner Sweden ISO 9001:2000 ISO 14001 SE
Freudenberg Simrit AB Sweden ISO 9001, QS 9000 ISO 14001 SE
Schaeffler Sverige AB Sweden TS 16949:2002 ISO 14001 SE
Nolato Sunne AB Sweden TS 16949 ISO 14001 SE
Finnveden Powertrain AB Sweden TS 16949 ISO 14001 SE
Svenska Statoil AB Sweden ISO 14001 SE
Henkel Norden AB Sweden TS 16949 ISO 14001 SE
Haldex Garphyttan Wire Sweden TS 16949 ISO 14001 SE
Stece AB Sweden ISO 9001, QS 9000 ISO 14001 SE
Bufab Sweden AB Sweden ISO 9002 SE
Precomp Solutions AB Sweden TS16949 ISO 14001 SE
Tilka Trading AB Sweden ISO 9001 SE
Lesjöfors Industrifjädrar AB Sweden ISO 9001:2000 ISO 14001 SE
Consafe Logistics Data Capture AB Sweden SE
Nermans Märksystem AB Sweden SE
Nomo Kullager AB Sweden ISO 9001:2000 ISO 14000:2004 SE
Honeywell Bremsbelag GmbH Germany ISO TS 16949 ISO 14001 DE
IMS Gear GmbH Germany TS 16949 Åtgärdsplan DE
Kamax-W. R Kellerman GmbH & Co Germany 9001, QS 9000, VDA 6,1 ISO 14001 DE
Hay Speed Umformtecknik GmbH Germany TS 16949 ISO 14001 DE
Walter Hundhausen GmbH & Co KG Germany ISO/TS 16949 DIN EN ISO 14001:2005 DE
Scan Press A/S Denmark ISO9001:2000 DK
Sakthi Auto Component Ltd USD India TS16949 ISO 14001 IN
68

Table 5.2 Delivery Information of Current Suppliers
Name of Suppliers Delivery Condition Delivery Way Supplementary Item Posting Type Land
AB Indoma DHL nat Out Delivery SE
AB Linde Maskiner DHL nat Out Delivery SE
Freudenberg Simrit AB EXW DHL nat Out Delivery SE
Schaeffler Sverige AB DHL nat Out Delivery SE
Nolato Sunne AB EXW DHL nat Out Delivery SE
Finnveden Powertrain AB DHL nat Out Delivery SE
Svenska Statoil AB DHL nat Out Delivery SE
Henkel Norden AB DHL nat Out Delivery SE
Haldex Garphyttan Wire DHL nat Out Delivery SE
Stece AB DHL nat Out Delivery SE
Bufab Sweden AB DDP Out Delivery SE
Precomp Solutions AB DHL nat Out Delivery SE
Nomo Kullager AB DHL nat Out Delivery SE
Tilka Trading AB DHL nat Out Delivery SE
Lesjöfors Industrifjädrar AB DHL nat Out Delivery SE
Consafe Logistics Data Capture AB FCA 002 DHL nat Out Delivery SE
Nermans Märksystem AB DDP Out Delivery SE
Walter Hundhausen GmbH & Co KG DHL int Out Delivery DE
Honeywell Bremsbelag GmbH DHL int Out Delivery DE
IMS Gear GmbH DHL int Out Delivery DE
Kamax-W. R Kellerman GmbH & Co DHL int Out Delivery DE
Hay Speed Umformtecknik GmbH Out Delivery DE
Scan Press A/S Truck/Swed Out Delivery DK
Sakthi Auto Component Ltd USD Out Delivery IN
69

Appendix B
Figure 1 Assembly line Model in Ideal Situation
70

Figure 2 Simplified Current Assembly Line Model
71

Figure 3 Current Assembly Line Model with Data under Triangular analysis
72

Figure 4 Assembly Line Model in Solution 1
73

Figure 5 Assembly Line Model in Solution 2
74

Figure 6 Assembly Line Model in Solution 3
75