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Eindhoven University of Technology

MASTER

Redesign of the control structure and inventory control at Vanderlande

Gubbels, P.M.

Award date:

2017

Link to publication

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Eindhoven, April 2017

Redesign of the control structure and inventory control at Vanderlande

by

P.M. Gubbels

BEng Industrial Engineering Student identity number 0658905

in partial fulfilment of the requirements for the degree of

Master of Science

In Operations Management and Logistics

Supervisors

Dr. M. Slikker, TU/e, OPAC

Dr. W.L. van Jaarsveld, TU/e, OPAC H. Bouwmans, Vanderlande J. van der Burg, Vanderlande

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TUE. School of Industrial Engineering and Innovation Sciences Series Master Theses Operations Management and Logistics

Subject headings: inventory control, control structure redesign, capital goods industry, 2- echelon supply chain, central decision making, lead-time uncertainty

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I ABSTRACT

This master thesis report addresses a three echelon supply chain, with one customer, four factories and their suppliers, and the goal is to increase the on time and complete performance of the Vanderlande factories and their suppliers. The current performance of three factories, a subcontractor and multiple suppliers is analysed, while focussing on a specific type of equipment. The analysis showed that there is a gap between the actual on time and complete delivery performance and the performance level the departments aim to meet, and that this gap is caused by low material availability. Therefore a conceptual control policy for local and central inventory was developed. During the analysis of the demand, required as input for the control policy, a different conclusion was drawn: Information about most purchase items is available at an earlier stage than what is currently known, therefore material should be ordered at an earlier, providing the suppliers with more time to produce and transport material.

A redesign of the control structure results in a solution that can increase material availability and consequently the factory performance. Moreover, it can increase control between departments, and increase workload control. Furthermore, an inventory control policy is developed to analyse the effect of lead-time uncertainty on the performance of the inventory model. Results show that lead-time uncertainty has a big negative impact on the performance of material availability.

The redesign contributes to a new structure for the supply chain of Vanderlande, and the inventory model provides insight in lead-time uncertainty. The redesign and new insight minimize the gap between the aimed and actual performance and lead to many future improvements.

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II

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III MANAGEMENT SUMMARY

This report presents the results of a master thesis project conducted at Vanderlande. The initial goal was to compare central and local inventory control policies for purchase items (PI) to improve material availability, and subsequently the performance of factories. However, results of the analysis of PI demand, required as input for the inventory model, show that most information required to define PI demand is available at an earlier stage in the supply chain than Vanderlande knows. Consequently, a redesigned control structure results in more performance improvements, and the focus shifted from designing an inventory control policy to redesigning the control structure.

Research context

The project is executed at supply chain centre Europe (SCCE), one of the three supply chain centres of Vanderlande. The project focusses on data concerning the Posisorter (SPO), a parcel and postal system, and includes three Vanderlande factories, located in The Netherlands (VIM), Spain (VIS), and America (VIA), and a single subcontractors.

Supply chain

Sales engineering composes a conceptual design, based on a customer order. Next a contract is signed, and within two months, engineering develops a detailed design. In the mean-time, SCCE uses the information of the conceptual design as input for a forecast to estimate future workload. After engineering is finished, an order is split into sub-components, and SCCE decides what sub-components are outsourced and what are produced internally, and releases orders to the factories and subcontractors. The requested lead-time for each order is eight weeks, meaning that within eight weeks the order is ought to be finished and the sub-component has to be delivered in the distribution centre.

Manufacturing engineering and production planning use the two first weeks to prepare work orders and define the routing, and the final two weeks are reserved to manufacture the sub-component. As a result, the factories give second tier suppliers a planned lead-time of four weeks to deliver raw material.

Research scope

Two Vanderlande factories are underperforming in delivering material on time and complete to SCCE.

The performance target is 95%, whereas the actual performance of VIM is 83%, and of VIS is 65%. The performance of VIA and the subcontractor cannot be measured. An analysis of the underperformance shows that 35% of the causes are due to overdue purchase orders, resulting in material unavailability, and 30% of the causes are due to excessive workload. Research on delivery on request date of second tier suppliers indicates that 80% of the order lines is delivered on time and complete. This underperformance can be caused by a difference between the standard lead-time given by a second tier supplier and the planned lead-time, or by a difference between the standard lead-time and the actual lead-time. Filtering the performance of the second tier suppliers on SPO suppliers shows that the performance of SPO suppliers is equal to the performance of all second tier suppliers.

Vanderlande factories and second tier suppliers are creating local inventory to increase on time and complete performance. However, decisions for inventory levels are made locally and are not proactively controlled, meaning that levels are not based on a mathematical model, and that inventory parameters are not proactively adjusted. This results in the following research question:

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IV

“What is the effect of proactive inventory control for the Posisorter equipment on the performance of the factories and suppliers?”

Conceptual inventory control

Three conceptual models are developed:

1. Local decision making and local inventory points;

2. Central decision making and local inventory points;

3. Central decision making and a single central inventory point.

A demand analysis is executed to define input for the inventory models. This demand analysis led to a new insight: the SPO Future demand contains information required to compute demand for most PIs, meaning that at the end of sales engineering, information about PIs demand is available, but unused.

In a purchase item analysis it is investigated for which PIs uncertainty is eliminated after sales engineering, and what the lead-times of the PIs is. It is concluded that out of 76 PIs within an SPO, 28 items have a standard lead-time longer than four weeks, with a maximum of ten weeks. However, only two of the 28 items require information that is not clarified at the end of the sales engineering phase.

If the forecast, referred to as SPO Future demand, is used to order PIs, suppliers are provided with two extra months to produce material. This induces a redesign of the control structure instead of an inventory model. As a result, the research question is extended in the following way:

“What is the effect of proactive inventory control for the Posisorter equipment on the performance of the factories and suppliers and how can Vanderlande better control its supply?”

Current control structure

An analysis of the current control structure resulted in the following improvement possibilities:

(1) Collecting input data required as input of the SPO Future demand is time intensive;

(2) Data from the SPO Future demand is not translated into purchase item demand data;

(3) The order acceptance function is based on historic data instead of workload;

(4) No proactive inventory control of purchase items, resulting in material unavailability;

(5) SCCE commits on a planning without having information about lead-times of purchase items;

(6) PIs have a standard lead-time of up to ten weeks, while planned lead-times are four weeks;

(7) Second tier suppliers keep local stock to increase performance, but still underperform in delivering material on the request date.

ETO Characteristics

According to Bertrand and Muntslag (1993) there are three types of uncertainty and three types of complexity in an ETO control structure: uncertainty in product specifications, mix and volume uncertainty for future demand, process uncertainty, complexity in the structure of goods flow, complexity in the multi-project character, and complexity in the assembly structure of a product.

Redesigned control structure

The redesign considers the improvement opportunities and ETO characteristics, and involves three steps:

designing the logistic chain, developing the production control framework, defining the decision structure. The redesign is illustrated in Figure 1.

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V

Figure 1, Redesign of the control structure

Logistic chain

The logistic chain refers to the primary process involved in production planning control. SCCE has to add one activity to its current activities: the procurement of PIs after the SPO Future demand is translated into PI Demand. Furthermore, five production phases and ten production units (PUs) are defined:

1. Conceptual product design, executed by PU sales engineering;

2. Detailed conceptual design, executed by PU engineering;

3. Completion of detailed product specifications, executed by four PUs, consisting of engineering manufacturing and production planning of each factory;

4. Manufacturing of components, executed by four PUs, consisting of the factories;

5. Assembly of finished product, executed by the same four PUs that executed phase 4.

Control framework

The control framework involves Goods Flow Control (GFC) and Production Unit Control (PUC):

 GFC: high level coordination of processes and production units;

 PUC: lower level coordination of a specific independently operating PU.

The first aspect of GFC is aggregate production planning (APP). APP is a medium-term planning to control workload and create high-level forecasts, such as the SPO Future demand. The second aspect of GFC is operational production planning (OPP). OPP involves the coordination of material and capacity, and includes PI demand control, ordering items with a lead-time longer than four weeks, signalling demand for items with a lead-time equal to or shorter than four weeks, and central inventory control. The third aspect is the interface between production and sales, and concerns close cooperation between sales engineering, engineering, SCCE, the factories, and subcontractor.

PUC involves the control of manufacturing processes. The difference between the current control structure and the redesign is that in the redesign, the items with a lead-time longer than four weeks are requested by a PU, and all other items are ordered. Moreover, the factory should only order the exact quantity, otherwise other factories are negatively affected.

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VI Decision structure

The logistic chain incorporates four key decisions. The first one is the customer order acceptance function, part of GFC, defined to control the regulation of work flow, involving sales engineering, engineering and SCCE. The second function, part of GFC, assigns sub-orders to a factory or subcontractor.

A step-by-step allocation decision is defined that considers the capacity of the factories and uses VIM as a flexible location. The third function controls workload by work order (WO) release rules and is part of PUC. The fourth function is considers detailed PU aspects, and is therefore left out of scope.

Redesign results

The redesign is only feasible for SPO projects, as solely SPO data is analysed. Furthermore, the redesign only provides qualitative results. The redesign requires multiple adjustments if actual lead-times to America are considered. Nevertheless, the redesign does address key ETO characteristics and increases the performance of factories. Multiple employees are informed to validate the redesign, and it is concluded that the redesign can be implemented if small IT adjustments are made.

Effect of the redesign

The redesigned process and control structure ease workload control by improved communication, and consequently decrease product and process uncertainties. Moreover, GFC and OPP result in a more accurate planning, increase performance of second tier suppliers, material availability, performance of factories, and lead to more interaction. As a result, the complexity of the structure of goods flow and of the multi-project character decrease. Furthermore, the redesigned functions positively affect workload control. The redesign does ask for close coordination between SCCE and factories and factories have to order the exact amount of material. Otherwise, the performance of other factories is negatively affected.

Detailed inventory control

The effect of a possibility on an actual lead-time one week longer than the standard lead-time is investigated by a simulation in Visual Basic for Applications in Excel. Four types of inventory models are defined, and the goal of each model is to minimize costs, subject to a service level constraint.

The results of the inventory model

Two numerical examples are presented to illustrate features of the models: item 1 and item 2. Two models consider deterministic lead-time and either an adjusted or fixed replenishment level. For item 1, both models perform above the 98% service level constraint, but costs are lowest at the model with an adjusted level. For item 2, the model with an adjusted replenishment level performs above the 98%

constraint, whereas the model with a fixed replenishment level performs 0.28% under the constraint.

The two other models consider lead-time uncertainty, and results for item 1 are illustrated in Graph 1.

The effect of lead-time uncertainty is even bigger on performance of item 2. Both items never meet the constraint if the uncertainty is P(L(t) = L) ≤ 0.9, inducing that lead-time impacts the performance.

Conclusions and Recommendations

Due to a shift from inventory control to a redesign, no conclusions are given about the effect of inventory control policies on the performance. The redesigned process structure, control structure, and decision structure have a big impact on the performance of factories, as they decrease material unavailability and increase the flexibility of VIM, resulting in less workload issues.

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VII

Graph 1, Performance and costs of an inventory model with lead-time uncertainty

Furthermore, the redesign decreases product and process uncertainty, and complexity of structure of goods flow and the multi-characters of ETO organisations. Additionally, the results of the simulation show that little lead-time uncertainty has a big impact on the performance of an inventory model.

It is recommended to redesign the process, control, and decision structure as this affects two of the main causes that lead to underperformance of the factories. Vanderlande has to analyse PIs of other equipment, as the redesign might be applicable to other equipment. Moreover, it is advised to reduce lead-time uncertainty as it negatively impacts the performance of inventory control. Further research should elaborate on quantifying results, analyzing the true control structure of shipping items to America, and to relax assumptions of the inventory control policy on the standard lead-time.

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Average performance and costs, item 1

Costs Models with adjusted replenishment level

Costs Models with fixed replenishment level

Performance Models with adjusted replenishment level

Performance Model with fixed replenishment level

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VIII

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IX ACKNOWLEDGEMENT

This report is the result of a master thesis project executed at Vanderlande in Veghel and it marks the end of my master Operations Management and Logistics at Eindhoven University of Tehnology. Within seven months, I have executed a master thesis project and a lot of people have helped me. Therefore I would first like to express my gratitute towards these people.

Marco, thank you for the weekly support. We have had many meetings; meetings that could be finished within 30 minutes, but that could also take up to two hours. You always made time to discuss the progress and were patient whenever I had trouble understanding the material. Thank you for giving me the opportunity to conduct this thesis under your supervision. Also many thanks to my second supervisor Willem van Jaarsveld, for his useful feedback.

Hugo and Judith, thank you for giving me the opportunity to conduct this project at Vanderlande and thank you for the freedom and support you offered me while I was doing my master thesis project.

Whenever I needed some extra help, you were there to help me. Even via skype or telephone. Dealing with the interests of both the TU/e and Vanderlande sometimes led to challenges for me, but in the end I managed to find interesting results for both parties and you trusted me during this process and gave me the ability to perform the thesis in my own way.

Maartje, I am going to thank you separately as well, as you lived with me during most of the time I executed the research project. You were the one that always had to listen to my struggles and provided jokes that made me realise that my life was more than inventory models. Ruben, also many thanks to you, for being there whenever I needed some extra support, for keeping me going, and for always finding a way to make me smile. Mom and Dad, Tijs and Wout, thank you for the support during my sometimes lonely process. Eline, Debby, and Jonne, thank you for the daily mental support at 08:00 in the morning and 17:30 in the evening, and finally, Suzan and Maurice thank you for all detailed advise.

This master thesis not only marks the end of a project; it marks the end of my years as a student.

Therefore I would like to thank all my friends for making my time as a student so much fun. Again, I would like to thank my parents for all the support in the past years. The road I walked was a bit unusual, but you always gave me the trust that it was okay to do it my way. Thinking back of these years made me realise that there is a slogan of Walt Disney that perfectly matches with the support everybody gave to me and with my personal ambitions :

“If you can dream it, you can do it!”

Thank you all for the support in the past years.

Neeltje Gubbels, Eindhoven, 2017

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X

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XI TABLE OF CONTENTS

ABSTRACT ... I MANAGEMENT SUMMARY ... III ACKNOWLEDGEMENT ... IX TABLE OF CONTENTS ... XI

1. INTRODUCTION ... 1

1.1 Goal of the project... 2

1.2 Outline of the report ... 2

2. RESEARCH CONTEXT ... 4

2.1 Company introduction ... 4

2.2 Supply chain ... 4

2.3 Product hierarchy ... 4

2.4 SCCE and Manufacturing ... 5

2.4.1 Processes ... 6

2.4.2 Control structure ... 6

2.4.3 Information ... 9

2.5 Performance measures ... 9

3. RESEARCH SCOPE ... 10

3.1 Factory performance ... 10

3.2 Cause-and-effect diagram ... 10

3.3 Posisorter ... 11

3.4 Second tier supplier performance ... 13

3.5 Conclusion ... 14

4. RESEARCH DESIGN ... 15

4.1 Problem statement ... 15

4.2 Research questions ... 15

4.2.1 Main research question ... 15

4.2.2 Sub-questions ... 15

4.3 Project scope ... 16

4.4 Project approach ... 16

5. CONCEPTUAL INVENTORY CONTROL MODEL ... 17

5.1 KPIs ... 17

5.2 Conceptual model ... 17

5.2.1 Local inventory and local control ... 17

5.2.2 Local inventory and central control ... 18

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5.2.3 Central inventory and central control ... 18

5.2.4 Assumptions and input parameters ... 19

5.2.5 Demand estimation procedure ... 19

5.3 Demand analysis ... 20

5.3.1 Variants ... 20

5.3.2 Ratios ... 23

5.4 Conclusion ... 23

6. PURCHASE ITEM ANALYSIS ... 24

6.1 Characteristics ... 24

6.2 Standard lead-times ... 24

6.3 Conclusion ... 25

6.4 Extended research question ... 26

6.4.1 Additional Sub-question ... 26

7. REDESIGN OF THE CONTROL STRUCTURE... 27

7.1 Current Control Structure ... 27

7.2 Improvement possibilities ... 28

7.3 Characteristics of an ETO control structure ... 28

7.4 Redesign ... 29

7.4.1 Logistic chain ... 29

7.4.2 Control framework ... 32

7.4.3 Decision structure ... 34

7.4.4 Information ... 36

7.5 Redesign results ... 38

7.5.1 Feasibility ... 38

7.5.2 Validation ... 38

7.5.3 Effect of the redesigned process structure ... 39

7.5.4 Effect of the redesigned control structure ... 39

7.5.5 Effect of redesigned decision structure ... 40

7.5.6 Negative effects ... 40

8. DETAILED INVENTORY CONTROL MODEL ... 41

8.1 Introduction ... 41

8.2 Assumptions ... 42

8.3 Notation... 43

8.4 Objective function and constraint ... 43

8.5 Optimal replenishment level ... 44

8.6 Order of events ... 45

8.7 Mathematical Model ... 45

8.8 Average performance and costs ... 46

8.9 Validation and verification ... 47

8.9.1 Conceptual model validation ... 48

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8.9.2 Computerized model verification ... 48

8.9.3 Operational validation ... 48

8.9.4 Data validity ... 48

8.10 Results ... 49

8.10.1 Input parameters ... 49

8.10.2 Output ... 49

9. CONCLUSIONS AND RECOMMENDATIONS ... 53

9.1 Conclusion ... 53

9.1.1 Measurable performance indicators for the factories and suppliers ... 53

9.1.2 Current performance with uncontrolled stock points ... 53

9.1.3 Five local stock points and local decisions ... 54

9.1.4 A single stock point at the second tier supplier and central decisions ... 54

9.1.5 Five local stock points and central decisions ... 54

9.1.6 Compare the performance of the proactively and non-proactively controlled stock points ... 54

9.1.7 Effect of a redesign on the performance of the factories and suppliers ... 54

9.1.8 Other effects ... 55

9.2 Recommendations... 55

9.2.1 Redesign the control structure ... 55

9.2.2 Analyse other equipment... 56

9.2.3 Reduce lead-time uncertainty... 56

9.3 Academic relevance ... 56

9.4 Suggestions for further research ... 56

BIBLIOGRAPHY ... 59

APPENDIX ... 61

Appendix A. List of Abbreviations ... 61

Appendix B. Figures, Tables and Graphs ... 62

Appendix C. Assumptions ... 64

Appendix D. Dash explanation of an SPO sub-component ... 66

Appendix E. Factory Processes ... 67

Appendix F. Pie diagram with causes ... 67

Appendix G. Performance SPO suppliers ... 68

Appendix H. Components and characteristics ... 69

Appendix I. Components and ratios ... 71

Appendix J. Conclusion purchase item analysis ... 74

Appendix K. Historic demand at sub-component level... 77

Appendix L. Occurrence of purchase items in the variants ... 78

Appendix M. Fitting procedure and validation of the simulation ... 80

Appendix N. Results theoretical model and simulation, item 2 ... 83

Appendix O. Poster ... 84

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1 1. INTRODUCTION

The capital goods industry is characterised by its complex, high value and customer specific systems and Engineer-to-Order (ETO) strategy (Hobday, 2000; Hicks, 2004). One of the difficulties of the capital goods industry is information unavailability. The bill-of-material (BOM) is often finalised in later stages of the order life cycle, while the information from the BOM might be required as input for production planning activities. On top of that, high service levels to customers characterise the capital goods industry. Those service levels can only be realised when a company is able to maintain high material availability (Gosling and Naim, 2009; Radke and Tseng, 2012).

Next to its complex, high value systems and high service level, the capital goods industry is characterised by its long lead-times (Radke and Tseng, 2012). This can be caused by the raw materials or intermediate products that are used in the systems. These lead-times might be longer than the lead-times requested by customers (Radke and Tseng, 2012) and if an organisation does not anticipate on this, other activities have to be postponed or accelerated to maintain a high service level.

One solution is to stock the items to guarantee a high service level, but stocking items comes at a cost and demand uncertainty within projects makes it difficult to forecast on raw materials required for the production. This leads to a trade-off between a high service level, inventory budgets and the delivery lead-time, captured by Radke and Tseng (2012) in Figure 2.

Figure 2, ETO trade-off by Radke and Tseng (2012)

Another solution is to create a production control framework that is compatible with capital goods industry characteristics. Bertrand and Muntslag (1993) stated that many ETO organisations have a Make- to-Order (MTO) control structure. The difference between an MTO and ETO organisation is that an MTO organisation starts with the manufacturing activities when customer demand arrives, but orders the material required for the production based on a forecast, whereas an ETO organisation also orders its material after customer demand has arrived. Most information about what to order or manufacture is already known before a customer orders a product in an MTO organisation, while in an ETO organisation a company knows little about what to order until all engineering specifications are finished. The customer order decoupling point (CODP) separates the standardised part from the customised part of the supply chain. In an MTO organisation, the customised part involves the manufacturing, assembly, and distribution activities and the standardised part involves the purchasing processes. However, in an ETO organisation, the purchasing processes are included in the customised part, which increases demand uncertainty for purchase parts, product complexity and lead-times (Chopra and Meindl, 2013).

A different control structure is required to hedge uncertainty and to be able to deal with product complexity. Bertrand and Muntslag (1993) created a production control framework that suits the characteristics of an ETO organisation. They defined uncertainty as “the difference between the amount

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2

of information required to perform a task and the amount of information available in the organisation”.

A dynamic control structure can cope with fluctuations in demand and supply and uncertainty.

Vanderlande is world leader in designing baggage handling systems, sorting systems leading supplier of warehouse automation systems. These complex systems are part of the capital goods industry and Vanderlande is facing the same difficulties as just discussed. The complexity and uncertainty make maintaining a high service level a challenging task.

1.1 Goal of the project

This thesis describes the results of a master thesis project executed at Vanderlande in Veghel, the Netherlands. Initially, the goal was to compare different inventory control policies for raw materials to improve the performance of the factories. However, during the analysis of the demand, required as input for the inventory model, a different conclusion arose: A redesign of the control structure can increase the performance even more. This new insight resulted in a shift from developing an inventory policy to redesigning a control structure and an inventory policy. Therefore, the master thesis consists of two research questions. The first one is defined after the analysis of the on time and complete delivery performance of the Vanderlande factories and their suppliers, while the goal of the project was to design an inventory control policy for the Vanderlande factories and its suppliers. The dataset that was used as input for the model merely contained information about a specific type of equipment, called the Posisorter. The following research question was defined:

“What is the effect of proactive inventory control for the Posisorter equipment on the performance of the factories and suppliers?”

After the demand analysis the research question was extended into the following question:

“What is the effect of proactive inventory control for the Posisorter equipment on the performance of the factories and suppliers and how can Vanderlande better control its supply?”

The extended question is answered by a research conducted in five months. Six sub-questions are derived as guidelines for the first part of the project and a seventh sub-question is defined to fully answer the extended research question. The sub-questions are discussed later on.

1.2 Outline of the report

The structure of the report is as follows: Chapter 2 provides an overview of the research context of the master thesis project. Chapter 3 analyses the initial problem: underperformance of the factories and suppliers. Furthermore, Chapter 3 introduces a specific type of equipment that is produced by Vanderlande factories and on which the research will focus. Chapter 4 elaborates on the initial research design, which focusses on inventory control. In Chapter 5, the sub-questions defined in Chapter 4 are translated into three conceptual inventory control policies. To goal was to analyse the performance of the control policies by a simulation and compare the results. Therefore, valuable input data about the demand in purchase items, lead-times, and costs was essential. However, Vanderlande only had information about systems, and this information had to be translated into demand in purchase items.

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Chapter 5 elaborates on this demand analysis. At the end of the analysis, it is concluded that most information required to define purchase item demand is available at an earlier stage than what is currently known. Consequently, a purchase item analysis is initialised in Chapter 6 to define what information is required and when this information is available. The purchase item analysis led to the conclusion that redesigning the control structure has a bigger positive impact on factory performance and as a result, Chapter 6 is finalised with a redefined research question. Chapter 7 answers the second research question by redesigning the control structure of Vanderlande. As the redesign is not applicable on all items, and as the lead-times provided by suppliers can be different from actual lead-times, Chapter 8 considers a central inventory control policy in which the effect of lead-time uncertainty is investigated.

Chapter 9 contains the conclusion of the redesign of the control structure and inventory control policy, and the recommendations for Vanderlande, and for future research. In Appendix A, all abbreviations and their definitions are given. Appendix B contains the list of figures, tables and graphs, and Appendix C explains the assumptions made for the research.

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4 2. RESEARCH CONTEXT

This chapter starts with a company introduction, followed by an explanation of the supply chain, and product hierarchy. Moreover, Chapter 2 elaborates on the processes, control, and information of supply chain and manufacturing and the chapter finalises with the performance measures used by Vanderlande.

2.1 Company introduction

Vanderlande is an Engineer-To-Order (ETO) organisation and market leader in designing and installing baggage handling systems in the major international hubs and regional airports, and sorting systems for the parcel and postal industry. Next to that, it is a leading supplier of warehouse automation systems. It distinguishes itself from its competitors with high-quality, reliable products, and fast deliveries.

Vanderlande is geographically divided into three supply chain centres: Northern America, Europe, and Asia Pacific. This thesis is executed at both supply chain centre Europe (SCCE) and the manufacturing department. Manufacturing originally only consisted of a factory in Veghel. Over a year ago, Vanderlande bought two factories: one in Spain and one in America. The factory in Spain, located in Santpedor, is already producing equipment, whereas the factory in America, located in Calhoun, is not.

Currently, several business cases are executed to find out what equipment can be produced in America and how much it will cost. When referring to the factories, the following abbreviations are used: the factory in Veghel is called VIM (Vanderlande Industries Manufacturing); the factory in Santpedor, Spain is called VIS (Vanderlande Industries Spain), and the factory in Calhoun, America is called VIA (Vanderlande Industries America). The headquarters of Vanderlande are located in Veghel. Next to the three factories, Vanderlande has a number of subcontractors producing Vanderlande equipment.

2.2 Supply chain

The processes in the supply chain can be non-physical and physical. The non-physical stage concerns sales engineering, engineering, design, supply chain, and planning activities activities and the physical stage concerns the manufacturing and assembly activities (Bertrand and Muntslag, 1993).

The non-physical stage starts with the activities of sales engineering, consisting of the conceptual design development. After the contract between Vanderlande and a customer has been signed, the planning department defines a schedule for the project and the engineering department starts designing the layout and functional requirements. Hence, engineers translate the high level characteristics into a detailed system. At the same time, high level information about a project is send to a supply chain centre and when engineering is finished, the supply chain centres decides upon what factory or subcontractor will produce what sub-component and sends orders to the factories and subcontractors. The factories and subcontractors are in charge of the production and make sure material is delivered to the warehouse within the lead-time given by the supply chain centre. Subsequently the supply chain centre arranges the distribution of the sub-components to site, and at site, the site department is in charge of the installation, testing and commissioning of the equipment.

2.3 Product hierarchy

Vanderlande uses different terms for the material of the systems in the different phases of the supply chain. This hierarchy is displayed in Figure 3. The highest level is the equipment level, representing the

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type of project that is sold. This project is split up into specifications, referred to as SPEC’s. One SPEC contains detailed information about the layout of a component, and consists of multiple orders, representing sub-components. The orders can be production orders (PO), if a sub-component is ordered from VIS, VIA, or a subcontractor. They can be orders from inventory (ON), if it involves sub-components picked from inventory, and they can be factory orders (OF), if they are produced by VIM. Vanderlande strives to use VIM as a flexible location, producing the most innovative equipment. VIS, VIA, and the subcontractor produce more standardized products, as those factories are not equipped for the production of advanced equipment. An OF is split up into different work kits (WK), depending on the routing through the factory and colours in which the assembly will be coated. One WK represents multiple work orders (WO) and within the WO, the Bill of Materials (BOM) is defined. A WO is created for a specific group within the factory, producing the assembly. The WO contains purchase item orders (PIO) involving sub-assemblies or raw materials. The suppliers from which sub-assemblies or raw materials are ordered are referred to as second tier suppliers. Hence, materials that enter a factory are intermediate products or raw materials, and materials that leave the factory are sub-components.

A sub-component can be engineered and manufactured in multiple ways, resulting in different types.

Each type has a master-item number assigned to it. Moreover, a type can also be manufactured in multiple ways, resulting in instances. An instance has a dash number assigned to it. The master-item number consists of 5 to 8 numbers or letters and a dash number is defined with 5 additional dash numbers. An example of a master-item and dash explanation is given in Appendix D.

2.4 SCCE and Manufacturing

This section elaborates on the processes, control and information flow between SCCE and manufacturing, as those are under investigation in this master thesis project.

Figure 3, Product hierarchy

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6 2.4.1 Processes

SCCE is responsible for the coordination of the movement of goods from suppliers and manufacturers to customers within Europe. This involves the production, purchasing, warehousing, distribution, and forecasting of material and projects. It gathers information about incoming projects from sales engineering, and adds the information to a forecast of that specific type of equipment to monitor the predicted demand. Meanwhile, engineering specifies the lay out and requirements of the project and splits it in SPECs. After an engineer has finished its activities, a supply chain coordinator divides the SPECs into ONs, OFs and POs, and sends these to the factories that produce the sub-component. Thereafter, the order is further specified by the manufacturing department. Order specification includes breaking the order up in WKs and WOs and defining the routing through the factory. The final action of manufacturing is to attach a BOM to a WO, and order the required intermediate products and raw materials. At this point, the physical stage starts. The material and invoice are received by a factory, which then starts its manufacturing activities. Appendix E illustrates the manufacturing activities.

Finished assemblies are send to the European distribution centre (EDC) for all European factories. At the EDC, the assemblies are allocated and send to site. The EDC is also the responsibility of SCCE. The invoice is extended with the costs for production and holding costs of the factory and send to SCCE. The material and information flow is illustrated in Figure 4. Purchase items are abbreviated to PI and finished goods are referred to as FG.

Figure 4, Material and information flow

2.4.2 Control structure

This sub-section elaborates on the control structure of SCCE and manufacturing.

Demand forecast

An estimation of the expected workload is created by SCCE. SCCE weekly gathers information about sold projects and developes a forecast per type of equipment that shows expected workload and the due date of projects.

Order release

SCCE releases the orders and sends them to a factory. Thus, order release includes order allocation.

Order allocation is based on historic production: for every sub-component, the information system knows whether the order is a make (OF or ON), switch (ON, PO or OF), or buy (PO) order. If the order contains a new sub-component, SCCE and manufacturing together decide who should produce it. This decision depends on the capacity of the factories and the newness of the equipment.

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7 Order acceptance

Before a factory starts with its activities, it first has to accept the OF. This decision is based on the capacity of the factory and the planning attached to an OF. If the production planning department concludes that the worload is too high or the planning too tight, it contacts SCCE and together they decide whether the planning is changed or the order is outsourced.

Production control

The given lead-time from OF or PO release until OF or PO finish is eight weeks, consequently the requested finish date for the factories is eight weeks later than the release date (Figure 5). The factory has two weeks to create WKs and WOs and before it places PIO at suppliers. Thus, material procurement happens after all uncertainty about the product and processes is eliminated. Within four weeks, Vanderlande expects to receive the materials from the second tier suppliers, resulting in two weeks for production and distribution to the EDC. The control structure is similar for all factories.

Figure 5, Lead-time from factory order release until finish date

Purchase item control

Three different definitions for the lead-time are mentioned in this thesis:

 The four weeks in which second tier suppliers are supposed to deliver their material are referred to as the planned lead-time;

 The lead-time given by second tier suppliers is referred to as the standard lead-time;

 The lead-time it took for material to arrive at the factory is referred to as the actual lead-time.

Different factories order material from the same second tier suppliers. Every factory orders its own material and ordering is done via an MRP-system. Subsequently the factories receive an order confirmation stating when they will receive their material. If the receipte date is later than the request date, the factory tries to expedite the order. The lead-time of PIs consists of two parts: a production and a transportation part. The production part is similar to each factory. However, the transportation part differs between the factories. All second tier suppliers are located in Europe, resulting in a transportation time of one week to a European factory. Shipping material to America takes at least three more weeks, resulting in a transportation time of more than four weeks. In this report, whenever there is referred to the standard lead-time given by second tier suppliers, it involves European transportation times.

Inventory control

PIS with a standard lead-time longer than four weeks are stocked in order to decrease the risk of material unavailability. The safety stocks are determined in different ways: the level can be equal to one or two weeks of historic demand, or the expected demand during a part of the standard lead-time. The computations for the expected demand are based on gut feeling and updated every 3 to 6 months.

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8

Second tier suppliers also stock material due to demand fluctuations. However, these inventory levels are unknown at the factories.

Project cost control

During the selling stage, the price of a project is determined. The price consists of direct and indirect costs and profit. The direct costs are the costs for material and hours of direct work allocated to a project.

Both the indirect costs and the profit are a percentage over the direct costs. The percentage of indirect costs includes a fixed part for indirect and overhead costs. Similarly, the percentage of profit consists of a fixed percentage of EBIT (Earnings Before Interest and Tax) computed over the direct costs. There is often an extra disclosure for penalty costs in the contract signed by the customer. The penalty costs are a weekly fine that should be paid whenever a project is delayed. This fine can vary per project and can either be a fixed number or a percentage of the total price.

FO or PO cost control

The costs are controlled differently for the three factories. VIM does not have to create actual invoices for its production, as it is part of the same private limited company. However, the invoice of material does arrive at the factory and this invoice, together with direct and indirect costs required for the production of a sub-component, is send to SCCE. VIS and VIA are not part of this private limited company and therefore they do have to create invoices that are send to SCCE. All three factories strive to have a 0% EBIT. Subcontractors do want to make a profit, resulting in a different invoice for similar orders.

PIO cost control

The purchase price for the material ordered from second tier suppliers is determined in cooperation with the suppliers and captured in a contract. The costs made by the second tier supplier include amongst others raw material ordered by the supplier, production and transportation. The purchase price may fluctuate, due to market and economic changes. A PIO consists of the daily demand of purchase items required for multiple projects. Small batches cost more than large batches, and expediting orders results in a higher purchase price.

Holding cost control

The fluctuating purchase price for purchase items is not considered when computing the holding cost.

The purchase price used to compute the holding costs is a fixed price, adjusted every six months. The holding cost, as computed by Vanderlande, is a fixed percentage of this fixed purchase price, and is time independent. This fixed percentage is determined at the end of a year by computing the total costs for utilities, rent, insurance, handling, and dividing it by the total costs for purchase items of that year. Only when a major increase or decrease occurs, the purchase price is recomputed. Currently, Vanderlande computes its holding costs in the following way:

ℎ𝑜𝑙𝑑𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠 𝑝𝑒𝑟 𝑖𝑡𝑒𝑚 = 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝑝𝑟𝑖𝑐𝑒 𝑝𝑒𝑟 𝑖𝑡𝑒𝑚 ∗ 0.049

As this holding costs computation is time independent, the computation is not used in the project.

Instead, a holding cost computation found in literature is used.

The control structure proves that Vanderlande has an ETO strategy, as PIO are based on customer orders, inducing a pull strategy.

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9 2.4.3 Information

There are a number of information systems, and information sharing techniques used by Vanderlande.

This report exclusively elaborates on the ones used by the SCCE and the factories, and that are interesting for the research.

ERP-system

J.D. Edwards, abbreviated JDE, is the Enterprise Resource Planning system that is used company-wide.

One can integrate other applications with it, and collect, store and manage data from all sorts of activities in this system. Departments can work in different J.D. Edwards work-branches. However, VIS uses a different ERP system to manage its data.

MRP-system

The factory daily uses two MRP-systems. The first one is used to compute the amount of assembly hours required in a WO and the second one is used to compute the time required for parts production, the amount of PIs required in WOs set that day. Per PI, the MRP-system generates a PIO is, which specifies the requested quantity and request date. This quantity is based on information about the lead-time, minimum order quantity, safety stock and multiple order quantity of the PI. The factory daily sends these PIO to its second tier suppliers and the confirmed date is saved in the second MRP-program. The second MRP-program also keeps track of the inventory position of each PI. The PIO send to the second tier supplier is thus based on the total amount of items required for the production activities that start four weeks later. The material is ordered anonymously, which means that the items within the PIO are not assigned to a specific project.

2.5 Performance measures

The performance of the factories is measured on either a monthly or weekly basis by a number of key performance measures (KPIs). A KPI is a performance measure that evaluates the success of an organisation. Together with SCCE, the factories have defined four KPI-groups that consist of a number of KPIs. These groups are: employees, process, customers, and financial. Within the customers-group, there are two critical KPIs: on time and complete performance of the factories and on time and complete performance of the suppliers:

𝑂𝑛 𝑡𝑖𝑚𝑒 𝑎𝑛𝑑 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒 =# 𝑜𝑟𝑑𝑒𝑟𝑙𝑖𝑛𝑒𝑠 𝑑𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝑜𝑛 𝑡𝑖𝑚𝑒 𝑎𝑛𝑑 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒

# 𝑜𝑟𝑑𝑒𝑟𝑙𝑖𝑛𝑒𝑠 ∗ 100%

SSCE expects the factories to have a 95% on time and complete performance and the factories expect their second tier suppliers to have a 98% on time and complete performance.

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10 3. RESEARCH SCOPE

In Chapter 2, the research context is discussed. The chapter finalises with performance measures of factories. This chapter analyses the actual performance of this performance measure. Moreover, performance of second tier suppliers is analysed and a specific type of equipment that is used as a business case for the research project is discussed.

3.1 Factory performance

The on time and complete performance target for the three factories is 95%, and is measured by SCCE.

Graph 2 illustrates the performance of VIM in 2016, and Graph 3 the performance of VIS in 2016. VIS did not have production before week 10, therefore there is no data concerning weeks 2-10. The records of VIA are not tracked yet, which is why the performance of this factory cannot be measured. The average percentage for on time and complete orders of VIM is 83%, with a minimum of 68% and a maximum of 94% over on average 64 orders per week. VIS finishes on average 65% of its orders on time and complete.

In week 35, VIS reached a minimum of 33% and in week 32 a maximum of 100%. It can be concluded that the average performance of both factories is a lot lower than the threshold of 95%. VIM is, based on data from this period, compared to the threshold underperforming with 12% and VIS with 30%.

Graph 2, Weekly performance VIM

Graph 3, Weekly performance VIS

3.2 Cause-and-effect diagram

If a factory does not deliver the material on time and complete in the requested week, the order is delayed and a reason for the delay is captured in the ERP-system. Every week, SCCE checks these reasons

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and there can be only one reason for one delay. The reasons are categorised into 5 main groups and illustrated in a cause-and-effect diagram displayed in Figure 6. A cause-and-effect diagram helps to trace the roots of a problem and organize them (Ishikawa, 1990).

Per branch, the proportion of the cause compared to other causes is computed and illustrated in a pie- diagram in Appendix F. 35% of the delays are due to overdue purchase parts, 30% due to excessive workload, and 13% due to human mistakes. Hence, the overdue purchase parts lead to most delays and therefore this research focusses on material availability to increase factory performance.

Radke and Tseng (2012) compared ETO and MTO organisations in terms of product structure and concluded that in ETO companies, nearly half of the purchase items are common parts, used in multiple projects. For those parts, the ordering of materials can be standardised and the CODP can be placed at the manufacturing process instead of at the purchase process. Standardisation of the ordering process can lead to higher material availability. Vanderlande has one material handling system that is considered to be standardized in terms of purchase parts and that is produced by all Vanderlande factories. This material handling system is the Posisorter.

3.3 Posisorter

Posisorter is abbreviated to SPO (SorterPosi), and it is a product suitable for the parcel and postal market, of which many projects are being sold. Vanderlande persists on producing a part of the equipment in the own factories, and in contrast to other equipment, Vanderlande finds it easier to predict future sales concerning these projects. Moreover, this product has not experienced many developments in the past years and the Research and Development department does not expect many changes in the near future.

The SPO is a line conveyor sorter that delivers products by means of sliding shoes (Figure 7). A SPO is split up into six components and the production of the components is executed by the following parties:

- Component (A) is produced by subcontractors;

- Component (B) is produced by VIM;

Figure 6, Ishikawa diagram of factory performance

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- Component (C) is produced by VIM, VIA and a subcontractor;

- Component (D) is produced by subcontractors;

- Component (E) is produced by subcontractors;

- Component (F) is produced by VIM, VIS, and VIA.

The components that are fully outsourced are left out of the scope of the research. Thus, the research project considers the production of component (B), (C), and (F). However, one single subcontractor is included as this subcontractor produces sub-components of component (F), a component that is also produced by the factories. The performance of the on time and complete delivery of the subcontractor is not known and therefore not added to the report. There are different ways to compute the amount of sub-components required for a system. Component (B) is measured in number of projects, as only one component is required for one project. Component (C) is measured in number of exits, and component (F) is measured in meters.

Figure 7, Layout of the Posisorter

SPO Future demand

SCCE collects information of sold SPO projects to determine future workload. There are two types of information: SPO forecast information, which concerns sold projects with an unfinished detailed design, and SPO ordered information, which concerns sold projects with a finished detailed design. Graph 4 illustrates the future demand. Information about the meters can be used to approximate workload and demand for SPO projects.

Graph 4, SPO Future demand in meters

There is a division between SPO meters that are assigned to VIM and to VIS, and this decision is based on the material of component (F). If component (F) is made out of steel, it is allocated to VIS, and if it is made out of plastic, it is allocated to VIM. Graph 3 also displays not assigned forecasted meters that still

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have to be assigned to a factory. VIA is not mentioned by the forecast, as the factory is not yet producing SPO equipment. It will start with its production in spring 2017. The horizontal line in the graph displays the 400 meter barrier representing the total maximum capacity of the factories, as VIM and VIS can each produce approximately 200 meter per week. In the future, VIA will be able to produce 100 meter SPO.

SPO purchase items

The planned lead-time for a second tier supplier to deliver material to a factory is always four weeks.

The list with purchase items required for the production of component (B), (C), and (F) consists of 76 items. 28 of these items have a standard lead-time longer than four weeks, 37 items have a standard lead-time of four weeks and eleven items have a standard lead-time that is shorter than four weeks.

37% of the orders delivered within Europe thus has a standard lead-time longer than the planned lead- time. Moreover, the actual lead-time of the supplier can deviate from the standard lead-time.

3.4 Second tier supplier performance

VIM and VIS keep track of the weekly performance of the second tier suppliers by measuring the Delivery to request performance of the suppliers. The definition is defined in Table 1.

Table 1, Performance measures second tier suppliers

KPI Definition

Delivery to request The KPI shows if the supplier is able to deliver according to the request date, set four weeks after the order date. The performance per order line is set as:

𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦 𝑡𝑜 𝑟𝑒𝑞𝑢𝑒𝑠𝑡 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘 =

# 𝑜𝑟𝑑𝑒𝑟 𝑙𝑖𝑛𝑒𝑠 𝑤𝑖𝑡ℎ 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑑𝑎𝑡𝑒 ≤ 𝑟𝑒𝑞𝑢𝑒𝑠𝑡 𝑑𝑎𝑡𝑒, 𝑖𝑛 𝑜𝑛𝑒 𝑤𝑒𝑒𝑘

# 𝑜𝑟𝑑𝑒𝑟 𝑙𝑖𝑛𝑒𝑠 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘

Graph 5 shows the Delivery to request performance of the second tier suppliers of VIM and Graph 6 the Delivery to request of the suppliers of VIS. Both factories request a 98% delivery performance. The suppliers of VIM have an average performance of 80%, with a minimum at 75% and a maximum of 87%.

The Delivery to request of the suppliers of VIS is on average 83.3%, with a minimum of 72.1% and a maximum of 93.5%. It can be concluded that the second tier suppliers of VIS perform better than those of VIM, but that the performance of suppliers to both factories is more than 10% lower than strived for.

Graph 5, Delivery to request, second tier suppliers VIM

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Graph 6, Delivery to request, second tier suppliers VIS

It is not possible to filter the factory performance on specific equipment, as the data used to measure performance does not contain information about the type of equipment. Nevertheless, the performance of second tier suppliers can be filtered on SPO data. Appendix G, Graph 8 illustrates the SPO performance of the SPO suppliers in VIM. On average, the SPO suppliers of VIM have a performance increase of 5%

compared to the total set (Table 2). The delivery performance of the SPO suppliers of VIS is based on values from the trial-period in which only a small amount of item numbers were ordered. The average performance of 25 order lines placed in eight weeks is 96% (Appendix G, Graph 9). Due to a limited amount of order lines, a similar table with the results of the comparison is excluded from the research.

Table 2, Comparison of suppliers VIM Factory Veghel

Delivery to request Delivery to request SPO Difference

Average 80% 85% 5%

Minimum 75% 70% -5%

Maximum 88% 100% 12%

The SPO supplier performance is not completely reliable, as for some SPO materials the operational buyers of VIM and VIS place orders beforehand. This results in not proactively controlled stock, meaning that no inventory model is used to determine inventory parameters and that parameters are not proactively updated. The inventory levels are not documented, but it does suggest that the measured performance is higher than the actual performance.

3.5 Conclusion

This research project concentrates on increasing on time and complete performance of factories, as the analysis of section 3.1 showed that they are underperforming in delivering material on time and complete. In section 3.2, it is concluded that factory performance is mostly affected by overdue PIs. The research focusses on SPO data produced by three Vanderlande factories and a single subcontractor. The analysis of the second tier suppliers, executed in section 3.4, revealed that there is a gap between the target level aimed by factories and the actual Delivery to request performance of suppliers. Filtering second tier suppliers on only those who supply SPO material gave similar results. Vanderlande factories and second tier suppliers are using local stock points to increase performance, but this stock is not proactively controlled. Proactive inventory control can increase the material availability and thus performance. Chapter 4 elaborates on research questions defined to create proactive inventory control.

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15 4. RESEARCH DESIGN

In Chapter 3 it is concluded that factories are underperforming in delivering material on time and complete. A cause of underperformance are PIs delivered later than requested. Analysis of second tier suppliers showed that they are underperforming in delivering PIs at the request date. Local stock points are supposed to increase performance, however target levels are not met. Chapter 4 shows the problem statement, research question, scope and project approach set in order to increase factory performance.

4.1 Problem statement

The research project analyses a two-echelon supply chain involving second tier suppliers, factories and a subcontractor. Vanderlande factories and subcontractors have to receive PIs within four weeks, due to order placement six weeks before order finish date, and two weeks required for production. VIM and VIS are creating stock as a precaution against material unavailability. Decisions for the stock levels are made locally and are not proactively controlled. Furthermore, second tier SPO suppliers are keeping stock to deal with demand uncertainties. Still, actual on time and complete performance is lower than the target level. Based on this information, the main problem for this research project is defined as:

Vanderlande factories and their suppliers are underperforming in delivering material on time and complete for Posisorter projects, whilst keeping local inventory levels that are not proactively controlled.

4.2 Research questions

To reach the preferred service level, second tier suppliers, factories, and subcontractor have to improve control of their inventory levels. Furthermore, considering the system as a whole while using central decision making might increase performance even more.

4.2.1 Main research question

The following research question is determined:

What is the effect of proactive inventory control for the Posisorter equipment on performance of the factories and suppliers?

4.2.2 Sub-questions

This question is be divided into six sub-questions, answered during the research project:

1. What are measurable performance indicators for the factories and suppliers?

2. What is the performance of the current approach for uncontrolled stock points in the factories and at Vanderlande suppliers?

3. How do the factories and suppliers perform if they use local stock points and make local decisions?

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4. How do the factories and suppliers perform if they use a central stock point at the supplier, no stock points at factories and make central decisions?

5. How do the factories and suppliers perform if they use central and local stock points and make central decisions?

6. What is the difference in performance between proactively and non-proactively controlled stock points?

Sub-question 1 and 2 are answered in Chapter 3, sub-question 3 to 5 are discussed in Chapter 5, and sub-question 7 is answered in Chapter 8.

4.3 Project scope

Shrivastava (1987) stated that a more detailed project increases feasibility, but simultaneously decreases scientific relevance. Consequently, this research project focusses on a two-echelon supply chain incorporation four factories (VIM, VIS, VIA, and a subcontractor) and second tier suppliers.

Furthermore, solely data concerning the SPO projects is used for the analysis. The goal is to design an inventory model for all PIs required in SPO projects. This scope results in a project detailed enough to ensure a certain level of feasibility, and scientifically relevant as literature concerning local and central inventory control policies can be used.

4.4 Project approach

The regulative cycle of Van Strien (1986) is used during the master thesis project. According to Van Aken et al. (1997), the regulative cycle consists of five process steps: problem definition, analysis and diagnosis, plan of action, intervention, evaluation. Figure 8 illustrates the steps and the activities executed for the research project. The master thesis project only concerns the first three steps, though the conclusion and recommendations of the master thesis report can discuss useful insights for the intervention step.

Figure 8, Regulative cycle by Van Strien (1975)

During the research, it has been important to find the right balance between rigour and relevance. In explanatory research the aim is to generate objective generic knowledge based on theories, whereas Field Problem Solving (FPS) has the aim to generate specific knowledge, and solve a specific business performance problem. The goal of the thesis is to make a contribution to both: improve the performance of the factories at Vanderlande and add something to existing literature. The model of Van Strien (1986) provided guidelines during the project, to ensure a both rigour and relevant research is conducted.

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