1

FLOW IMPROVEMENT IN OPERATIONS

MANAGEMENT FROM ROOT CAUSE IDENTIFICATION TO FLOW CONTROL

2

Master Thesis

DDM Operations management

Sjoerd Mulder

Nieuwe Boteringestraat 7

9712PE Groningen

Student number:

University of Groningen: S2227088

Newcastle University: 170709282

Supervisors:

University of Groningen: dr. M.J. Land

Newcastle University: Prof. C. Hicks

3

Abstract: Many improvements proposed by the field of Operations Management relate to the control

of flows. A new approach has been developed to diagnose the inhibitors of flow in production processes,

being different kinds of inventory. Inventory buffers are used to cope with different variability issues in

the production process. A better flow is created if inventory levels are reduced. First, this design science

research validates the new diagnosis tool by showing that it is capable of identifying different types of

inventory and that it generates consistent results. Second, it expands it by giving an overview of the

most common improvement measures in operations management and analyses which inventory types

they address. The analysis shows that some inventory types cannot be reduced with methods described

in literature. Furthermore, managers still need a method to test to ensure they are implementing the

appropriate measure. The new diagnosis tool appeared to be able to check if improvement measures are

a good fit with the issues they are aiming to resolve.

Keywords: Operations Management; Design Science; Flow Control; Improvement Measures

4

CONTENTS

Preface ..................................................................................................................................................... 6

1. Introduction ..................................................................................................................................... 7

2. Theoretical Framework ................................................................................................................... 9

2.1 Root cause identification framework ....................................................................................... 9

2.2 Framework analysis ............................................................................................................... 10

3. Methodology ................................................................................................................................. 11

4. Part one: Validating the new approach .......................................................................................... 14

4.1 Production process analysis ................................................................................................... 14

4.2 Approach analysis ................................................................................................................. 17

5. Part two, section one: Analysis of common improvement measures in operations management . 19

5.1 Common improvement measures .......................................................................................... 19

5.1.1 Small lot size ................................................................................................................. 19

5.1.2 Set up time reduction ..................................................................................................... 19

5.1.3 Pull Production .............................................................................................................. 20

5.1.4 Cell Layout .................................................................................................................... 21

5.1.5 Poka-Yoke ..................................................................................................................... 21

5.1.6 Just In Time delivery ..................................................................................................... 21

5.1.7 Total Preventive Maintenance ....................................................................................... 22

5.2 Theory of Swift, Even flow ................................................................................................... 22

5.3 Analysis of variability improvement measures in operations management literature ........... 23

6. Part two, section two: Improvement measures analysis ................................................................ 25

6.1 Basic end date spreading ....................................................................................................... 25

6.2 Order release rules ................................................................................................................. 26

6.3 SMED project ........................................................................................................................ 27

6.4 new approach as improvement measure analysis tool ........................................................... 28

7. Discussion ..................................................................................................................................... 29

8. Conclusion ..................................................................................................................................... 30

8.1 Limitations and future research ............................................................................................. 31

5

References ............................................................................................................................................. 32

Appendix ............................................................................................................................................... 34

Appendix A: interview protocol ........................................................................................................ 34

Appendix B: Translation interview protocol ..................................................................................... 36

Appendix C: Inventory label abbreviations ....................................................................................... 38

6

PREFACE

Before you lies the master thesis “Flow improvement in Operations Management - from root cause

identification to flow control”. A research conducted to provide better methods to control the flow in an

organisation. It has been written to complete the dual award in operations management from the

University of Groningen and Newcastle University. I conducted this research from August to December

2018.

This project was undertaken at the request of Broekema in Veendam. The research was difficult and

many changes were made along the way. Fortunately, people involved in the company, and particularly

Gerda Bos and Coen den Hertog, as well as my supervisors from both institutions, dr. Martin Land and

Prof. Chris Hicks, were always available to assist me and willing to answer my questions.

I would like to thank my supervisors for their excellent guidance and support during this process. I

would like to thank the company for giving me all the support and facilities I needed to conduct my

research. I also would like to thank all the foremen and other employees from Broekema for answering

my questions and their openness.

I would like to thank Jasper Dijkhoff and Ruben Meijer for their help during the process. They were

always prepared to give me feedback and were willing to debate the topics present in this thesis. I want

to thank Leah Hamilton for helping me finalizing the thesis.

Lastly I would like to thank all my friends and family. If I ever lost interest, you kept me motivated.

Special thanks to my parents for always supporting me throughout my entire studies.

I hope you enjoy your reading.

Sjoerd Mulder

Groningen, December 10, 2018

7

1. INTRODUCTION

A central concept in the field of operations management is flow. When materials are processed, they

will progress through a series of activities where they are transformed. Between activities products may

dwell for some time in inventories. These waiting times do not add value to the product. The materials

moving through the process, nor the assets performing the activities may be fully utilized (Slack,

Brandon-Jones and Johnston, 2016). When material waits in inventories to be used in the production,

the flow of the organisation does not reach its full potential. Flow has been a predominant factor of most

operations management theories but companies still struggle to realize flow improvements (Land et al.,

2018). To face issues created by flow control problems, the root cause of these problems needs to be

identified and addressed.

This thesis will build upon the work of Land et al. (2018). In their paper Land et al. (2018) proposed a

new framework that will help organisations identify the root causes of different types of inventory.

Inventory, being the accumulation of flow items, will hinder the flow. The purpose of this thesis is

twofold. The first part of this thesis aims to validate the new approach by Land et al. (2018). In this step

of the design science approach, it will evaluate if it is a good method to diagnose the root causes of

inventory and will test if it generates consistent results. If the new approach is validated, a method to

distinguish 28 different type of inventories can be added to the current flow improvement literature.

However, when an inventory point is diagnosed, its root cause in the form of the source of variability,

is not taken away yet. There are many studies that focused on variability issues, like the “theory of Swift

and Even Flow” created by Schmenner and Swink (1997) and lean (e.g. Shah and Ward, 2007). Although

these theories exist, a clear overview once problems are identified does not exist yet. Managers may

continue to struggle with flow control issues. This thesis is, besides validating the new approach, aiming

to improve the approach and make it more than a diagnose tool, to support managers to control the flow.

Therefore, the second part of this thesis will give an overview of the most common improvement

measures found in literature and analyse which root causes they address. Furthermore, it will test if the

approach, that has been developed for diagnosis, can also support the analysis of improvement measures

that are implemented or are proposed to be implemented.

8

This study will use design science as a methodology as part of a larger design science research since the

problem is raised by operation managers. Design science is driven by field problems, knowledge is

developed by engaging with real-life operations management problems (van Aken, Chandrasekaran and

Halman, 2016). In the past the company subject to this research implemented improvement measures

which were not successful. They were not a fit with the root cause they wanted to address. A tool would

help managers to check if the measure is a good fit to avoid implementing wrong and costly

improvement measures. The field driven problem in this thesis is therefore the need for a better method

to cope with flow control issues. The research questions this thesis answers is:

How can managers improve flow by addressing flow control issues?

To answer this question, three sub question will be studied successively:

(1) Is the research approach developed by Land et al (2018) a good method to identify the root

causes of inventory?

(2) Which improvement measures are currently known to reduce variability in the field of

operations management?

(3) How can the approach developed by Land et al (2018) be used to analyse flow improvement?

The theoretical base of this research comes from the research done by Land et al. (2018) and an overview

of their new approach will be given in section 2. Section 3 will outline the methodology and explain

how this study fits in a larger design science research cycle. Section 4 will redo the initial analyses done

by Land et al (2018) to validate the approach and evaluate whether it is capable of identifying different

root causes and generates consistent results. Section 5 will give an overview of the most common and

accepted flow improvement methods in literature that focuses on variability reduction. An analysis will

be made to find which root causes they affect. Section 6 will test if the new approach can be used to

analyse improvement measures. Section 7 will discuss the results gained in the previous sections and

section 8 will present the conclusions.

9

2. THEORETICAL FRAMEWORK

This thesis will expand the research done by Land et al (2018) by validating and improving the approach.

In this section the background of the new approach will be discussed.

2.1 ROOT CAUSE IDENTIFICATION FRAMEWORK

Operations management involves in what way organizations design, improve and deliver products

(Slack, Brandon-Jones and Johnston, 2011). Many production companies have flow control problems

where the production process does not reach its full potential. To improve flow organisations should

know what the root causes are that inhibit the flow. For managers and researchers who want to improve

flow it is essential that they know why inventories exist (Land et al., 2018).

There are many studies done to reduce variability issues. However, none of them focussed on the

identification of the different types of inventory. The root cause of the problems needs to be known to

address it and have an effective measure in place. Land et al. (2018) solved this problem by developing

a comprehensive framework to understand why flow items wait in inventories. They found that each

type of inventory relates to a core source of variability. These root causes can be found by following a

systematic procedure where the following questions are asked:

1. For which process input (missing input) are the flow items in this inventory waiting?

2. Which input creates the main source of variability that causes missing inputs?

3. What form does this variability take?

The missing input could be the lack of demand (there is no production order for the next process step),

capacity (the machine or worker is not ready for the next items yet) or flow items (either parts to be

assembled with, or part needed in the same batch are missing). Next, the main source of variability and

the form of this variability need to be established. The main source of variability can be demand, supply

of flow items or capacity. The form they take can be uncertainty, predicted fluctuation or batched. Once

these three questions are answered the root cause of variability can be found. The different labels that

inventory can have by analysing the root causes can be found in figure 2.1.

10

2.2 FRAMEWORK ANALYSIS

The new approach from Land et al (2018) is designed as a diagnosis tool. With this new diagnosis tool

managers can find the root causes for certain inventory points. However, it is not clear what managers

can do to resolve these issues and how these root causes can be taken away. Common literatures focuses

on variability issues but it is not specified which root cause they address. It is not known if a manager

identifies, for example, an inventory being Capacity Anticipation Induced Congestion, which measures

he needs to take to resolve the problem. Even if there is a pretty good understanding on the root cause

of the problem, companies do not know if the measures created are a good fit for these specific problems.

This research aims to create a method which allows managers to create a better flow control. First, it

wants to validate the work done by Land et al (2018) and check if it is indeed a good diagnosis tool

which will produce consistent results. This will be important because managers need to know what the

root cause of the problem is in order to take it away effectively. For example, inventory can be

accumulated at a certain point. A simple solution would be to raise the capacity of the machine that has

to process the items that accumulate at this point. But if the source of the problem lies with demand or

flow item problems, a larger machine capacity will not resolve the problem.

Second, It will expand the approach by creating an overview of existing improvement measures that

addresses variability in the production process and analyse which root causes they are aiming to resolve.

This part of the research will enable managers to act on the problems identified. Literature is not clear

which specific variability issue it addresses yet. With this overview and the diagnosis tool managers can

find common improvement measures they can customize to improve their processes. Furthermore, the

customized measures and measures created by companies that are process specific, need to be analysed

with a simple tool to check if the measure could provide the improvement the company is hoping for.

Therefore, it will be tested if the new approach can be used to analyse in place and proposed

improvement measures to check if they are a good fit with the problem they are aiming to resolve.

11

3. METHODOLOGY

This thesis is part of a larger design science research cycle as described by e.g. van Aken (2004). The

connection between knowledge and practice in design science lies in the creation of useful things that

are established by scientific knowledge (Wieringa, 2009). Design science started with the work of Simon

(1996) who introduced the science of design. It aimed at improving actual situations. Later Hevner et

al. (2004) created a methodological framework for design science with the emphasis on creating new

and innovative artefacts. Design science tries to find solutions for problems faced by industry and

translates these solutions to more generic designs. These problems can be described as practical

problems. To tackle practical problems and evaluating these solutions, the goals of the stakeholders need

to be addressed (Wieringa, 2009).

Since the goals of the stakeholders change over time the design science method involves testing the

design through a largely iterative process. A design science cycle contains the following steps: Problem

investigation, treatment design, design validation, treatment implementation and implementation

evaluation (Wieringa, 2013). The design cycle is not a chronological framework. Designers move back

and forward through phases and they often redesign previous phases. Having a grounded argument for

the design is not enough, it needs to be evaluated repeatedly (Hevner, 2007). As a consequence, this

study needs to redo some phases done by Land et al. (2018) to evaluate the approach properly.

This research consists of two phases. The first phase will be used to validate the new approach to

contribute to the design science cycle. The second phase will aim to contribute to literature by expanding

the new approach and make it more than a diagnosis tool.

To validate the new approach, the first phase will consist of redoing earlier research done by Land et al.

(2018) at the same company where the first research was conducted. This company is a medium sized

capital goods manufacturer. The annual turnover is approximately €20 million and the company has

roughly 100 employees. It produces a wide range of conveyor belts used on crop harvesting machines.

It can alter the product to cater individual customer preferences. The shop floor is organized in a

functional layout. It has several departments where specific steps of the process are completed. Some

items may require only a subset of the production steps or have to be processed multiple times by the

same department. Due to the nature of this research, the main emphasis will be on the process of making

the rods and belts and the assembly of these parts. These steps are the processes where the company

faces the main issues regarding flow control. Multiple iterations of the diagnosis will be done to create

the most comprehensive result. Multiple questions will be asked until the missing input, main source of

variability and form of variability are clear. The root causes of the different inventory points will be

identified by answering the three questions stated in the new approach of Land et al. (2018) and

afterwards the different inventory points will be labelled according to the labels stated in figure 2.1. First

it will be analysed if the questions from the new approach are ample to identify the inventory points.

12

Second, it will be compared to the original research to check if the new approach generates consistent

results. This research is done at the same company as the initial research done by Land et al. (2018) to

determine if it generates consistent results when the analysis is redone by another researcher at another

point in time.

The second phase of this research will aim to expand the new approach created by Land et al. (2018) by

analysing the most common improvement measures in the field of operations management that focusses

on the reduction of variability in the production process. Furthermore, it will be tested if and how this

new approach can be used to check if the flow improvement measures are a good fit with the problem

they are aiming to resolve. First, an overview of the most common flow improvement literature that

focuses on variability issues will be given and it will be analysed which missing input, main source of

variability and which form of variability the measure addresses. This will be linked to the root causes of

inventory that can be identified by the new approach.

Second, improvement measures that are implemented or proposed by the company subject to this

research will be analysed using the new approach. It will be tested if and how the new diagnosis tool

can be used to analyse flow improvement measures. The data gathered by the validation of the new

approach will be used together with semi structured interviews performed with the production manager

and the planner of the company. The diagnostic analysis will be performed by analysing each step in the

production process where inventory could be aggregated together with the production manager. The

semi structured interviews are conducted to determine how these measures are aimed to reduce certain

inventory levels. It will be analysed if these measures are the right fit for the root causes that are aimed

to be reduced by checking if they are indeed addressing the right missing input, main source of

variability and form of variability, as identified during the validation of the new approach. These

interviews will be conducted in Dutch. The Dutch version of the interview guide of the semi structured

interview can be found in appendix A. The English translation can be found in appendix B. An overview

of the structure of this research is shown in table 3.1.

Table 3.1: Research structure

Part Subject

Part 1 (Chapter 4) Validation of diagnosis tool created by Land et al. (2018)

Part 2, section 1 (Chapter 5) Analysis of common improvement measures and linking them to the

inventory types of the new approach created by Land et al (2018)

Part 2, section 2 (Chapter 6) Testing the new approach created by Land et al. (2018) as an

improvement measure analysis tool

13

Based on the above study setup, this study can be positioned more precisely within the field of design

science. Gregor & Hevner (2013) created a design science research knowledge contribution framework.

This research would be classified as an “improvement” project. Many companies face flow control

issues for many years. This problem can thus be specified as known and mature. The goal of

improvement projects is to create better solutions to known problems (Gregor and Hevner, 2013). The

known problems are the following: The missing of a good diagnosis tool to find the root causes of

inventory and, finding and checking appropriate improvement measures once these root cause is known.

With the completion of this thesis managers and researchers are able to do both.

Van Aken, Chandrasekaran and Halman (2016) formulated two key criteria for design science research.

The first criterion is the pragmatic validity. It answers the question of how strong the evidence is that

the design created will produced the desired results. The second criterion addresses the practical

relevance. The design needs to contribute to addressing a significant field problem or exploit a promising

opportunity. The pragmatic validity of this project mostly lies in the first phase of this research. By

testing and validating the approach, it contributes to the original design proposition of Land et al. (2018);

“concerning the problem of achieving a continuous flow, a typology of inventory types is developed that

can be used to identify inhibitors to flow in any organisation. This provides the foundations for the

organisation to develop context-specific flow improvement solution.”. The practical relevance is more

present in the second phase of the research. Managers would like to have the ability to act on problems

that are identified. With the analysis of common improvement measures and the test if the new approach

can be used to check if new improvement measures are a fit with the problem that is aimed to be resolved,

managers are capable to act on the problems they face.

14

4. PART ONE: VALIDATING THE NEW APPROACH

This section aims to validate the new approach. The validation of the new approach will be conducted

at the same company as the first research was executed by Land et al. (2018). The new approach can be

considered validated if the approach is indeed capable of identifying the root causes of the inventory

present at a production process and if the new approach generates consistent results. The research is

conducted at the same company as the initial research to validate that the approach does indeed generates

consistent results. At the end of this section the results of both tests will be discussed.

To perform the analysis, a detailed flow chart of the process will be used. Together with the production

manager, the process steps are analysed and the reasons for the different types of inventory will be given

until it is clear what the missing input, main source of variability and form of variability of the different

inventory points are. When these are known, inventory points can be labelled according to the labels

shown in figure 2.1. A flowchart of the process researched is shown in figure 4.1.

Figure 4.1: Detailed flow chart

4.1 PRODUCTION PROCESS ANALYSIS

There are two customer order decoupling points present at this part of the production process, being the

inventory points a and i. Customer order decoupling points/order penetration points are stages in a

manufacturing value chain where products are linked to the order of the costumer (Olhager, 2003). At

these points, demand is determined to be the missing input. Raw material which is used to make rods

are stored (a) until the company receives an order before they are planned to be cut. At the inventory

point a, a large amount of material is waiting to be processed. This is due to demand uncertainty of the

raw material. There are several types of steel used in the production process and forecasting the demand

for each type of steel is complicated. Since the form of the variability is demand uncertainty, inventory

point a can be labelled as Demand Safety Stock.

15

Some of the steel rods can only be delivered by one supplier. The lead times for these steel rods are

uncertain due to the production process of the supplier. These inventory items can be labelled as Supply

Safety Stock.

There are two forms in which the steel arrives at the facility. It can either be delivered in rods or in coils.

Coils need to be cut completely into rods since changing coils in between orders is very time consuming

and is therefore avoided. Since it is preferred that steel coils need to be cut until the coil is completely

used, the company combines orders of the same type of steel. Some of the inventory at point a can thus

be classified as Batch Waiting Time.

The belts have a shorter lead time compared to the rods. The decoupling point before punching is not a

customer order decoupling point since the punching process is operating at full capacity the entire time.

The flow items will be coupled to customer orders after the belts are punched. Since the belts arrive in

large orders inventory point h is classified as Supply Cycle Stock.

The majority of the belts that the company receives are belts that need to be punched. There are 2 types

of machines present at the facility. One type can process belts that are not punched yet and the others

can only process pre-punched belts. Punching belts to feed the second type of machine is done by a

puncher that works at full capacity and cannot cope with fluctuations in demand. The customer order

decoupling point is at inventory point i because the items at this point are waiting to be coupled to an

order. Once the order is received these belts are cut and processed. There are many varieties in the types

of belt the company can deliver. Due to the fluctuation in orders for each type of belt the company uses

Demand Safety Stock at costumer order decoupling point i. Furthermore, since the punching process

works on full capacity and cannot cope with fluctuations in demand, additional inventory is kept at point

h. When demand is low, the punching machine will process more orders to cope with a peak in demand

which the machine is incapable to handle. This type of inventory is called Demand Anticipation Stock.

When cutting belts (process number 9), different orders are combined. This will reduce the setup time

of the process and is highly preferred by operators to changeovers in different kind of belts. Therefore

some of the inventory at point i is not waiting for demand but is waiting until they can be processed in

a batch. This inventory is identified as Batch Waiting Time.

For the other inventory points, demand is not the missing input. These items are assigned to a specific

order and are ready to be processed. The missing input for these points is capacity or they are waiting

for (other) flow items. The raw material for rods, as mentioned above, can be delivered in coils. When

the rods are made from these coils orders are combined to cut up an entire coil. This results in the arrival

of large batches of material in subsequent processes (2 and 3). These steps are not able to process the

material in batches so inventory points b and c are labelled as Batched Supply Induced Congestion.

16

However, Batched Supply Induced Congestion is not the primary cause of inventory at point c (prior to

pressing). Pressing is a process which is considered a large bottleneck due to its set up time. Capacity at

these machines is limited. Material will wait until it can be processed and can, therefore, be labelled as

Batch Transformation Induced Congestion. Sometimes rods need to be processed by multiple presses

and will have an even longer lead time.

Steps 4 and 5, hardening and discharging, are optional processes. First, the rods move into an oven in a

continuous flow. After receiving the heat treatment the rods will be discharged in another, large oven.

This process is time-consuming. The discharging oven has a large capacity and the rods are stacked

upon a large rack prior to the discharging treatment. The first oven has a large setup time. It takes a long

time to set the oven to the desired temperature, which is expensive. Due to these high set up costs in

combination with the overcapacity of the oven, it is decided to let it run for only a couple of days a week.

Therefore, inventory at point d is considered to be Batch Transformation Induced Congestion. As

mentioned before, the output of the pressing process is also batched. Since the hardening oven works in

a continuous flow and the supply of flow items is batched some of the inventory is identified as Batched

Supply Induced Congestion. The discharging oven will wait until it can process enough rods to reach

full capacity. Therefore, inventory at point e can be classified as Batch Waiting Time.

After pressing or discharging, rods will continue to the riveting department, be cast or a loader is added

to the rods. There are many different types of loaders of which a customer can choose from to be added

to the final product. Adding the loaders to the rods can take a lot of time. Demand for the different kind

of loaders is fluctuating and hard to predict. Inventory point g is therefore classified as Demand

Uncertainty Induced Congestion. Demand for a casted rod is also fluctuating. Inventory point f can

consequently be labelled as Demand Uncertainty Induced Congestion as well.

After cutting, belts can follow two paths. If they get a hinge joint they will first receive rivets and

afterwards the hinge is attached to the belt. If the customer requires one of the other joints (e.g. an

endless belt) the joint is made before rivets are inserted. At inventory point j, belts are waiting to be

processed by the machines that will insert the rivets. The units will stay relatively short at this inventory

point. Inserting rivets is a short process and can be done quickly but the belts will mostly arrive in

batches since multiple belts are cut at once. Inventory point j is labelled Batched Supply Induced

Congestion. It is hard to predict when a customer requires a certain joint. The joint department cannot

always cope with peaks in demand for certain types of joint (e.g. an endless joint) and inventory will

then be accumulated at inventory point k. Inventory at this point is therefore labelled as Demand

Uncertainty Induced Congestion.

The point where most of the inventory accumulates is before riveting at point l. It is here where the

different units will come together and are waiting to be assembled. Since there is little to no

communication between departments regarding the orders that are currently in production or orders

17

which are finished, several semi-finished products are waiting until they can be assembled. Each

department optimizes their own schedules and the delivery to the riveting department is not

synchronized. These inventory points can be classified as Supply Uncertainty and Batched Supply

Induced Assembly Waiting Time for the unplanned and planned schedules respectively.

4.2 APPROACH ANALYSIS

The aim of phase one is to validate if the new approach is able to identify different inventory points and

to check if the new approach generates consistent results. Using the three questions of the framework,

researchers and managers were able to label all the inventory points investigated. There was not a single

inventory point of which the root cause could not be identified. It can be concluded that the new approach

is able to identify ten different inventory points in this production process when the questions provided

are used.

The second validation concerned the results of the new approach. The labels of different inventory points

of the research done by Land et al (2018) compared to this research can be found in table 4.1 where the

inventory points Identified by Land et al (2018) are marked with an L and the inventory points identified

by this research are labelled with an M. Please note that the research of Land et al (2018) did not include

the inventory points g and k.

As is shown, for most points consistent results are generated. Inventory point e and l differ and point i

is extended. This difference has multiple explanations. First, the moment of measuring. The root causes

for some inventory points can change over time. It is therefore wise to conduct this diagnosis several

times at different time intervals to check if the root causes has changed and other measures may need to

be implemented. Second, by conducting this research multiple times the analysis can differ. Where the

root cause for an inventory point seem to be clear and straightforward further research can alter these

results. This can be seen at inventory point l. Where the first research found that it could be labelled

Supply Anticipated Induced Assembly Waiting Time this research found that it is actually Batched Supply

Induced Assembly Waiting Time by conducing multiple iterations of this research and therefore digging

deeper into the reasons why this specific inventory point exist. It can be concluded that this diagnosis

tool is able to generate consistent results. However, it will find the best results when the research is done

multiple times at different time intervals.

18

Table 4.2: Identified Inventory Points

19

5. PART TWO, SECTION ONE: ANALYSIS OF COMMON IMPROVEMENT

MEASURES IN OPERATIONS MANAGEMENT

This chapter consists of two parts. First the most common literature that proposes improvement

measures in the field of operations management that focuses on variability issues will be described.

Second, it will be analysed if these measures can be linked to the root causes of inventory they aim to

address.

5.1 COMMON IMPROVEMENT MEASURES

There are several solutions to improve the flow in an organization that addresses variability issues. The

majority of measures come from the principles that are linked to lean manufacturing. These solutions

often assume certain flow inhibitors and then apply methods to reduce or get rid of the negative effects

caused by the inhibitor. This section gives an overview of the most common and accepted flow

improvement methods. It will first give a short description of the measure and will than analyse how it

will reduce a certain kind of variability.

5.1.1 SMALL LOT SIZE

Many factors are influenced by the size of the batches in which a company produces its products. Cost,

quality, lead time and the flexibility of the operation are all influenced by lot sizing. Large lots were

favoured many years in industry to avoid large set up and order costs. The drawback of large lot size is

that the lead times are longer and the flexibility of the production process is reduced. To be more lean

and to create a better flow small lot sizes are favourable. Buffers, however, still need to be present to

cope with any variability present in the operations (Nicholas, 2011). With a smaller lot size, the transfer

batches (items that go from one department to another) can be reduced. The supply of items could be

more smooth and congestions are less likely to appear since there will be a reducing in the batch sizes

that arrive at a machine which reduces batched supply. However, it will probably create more set ups.

This could have an impact on the capacity of the machines and can generate a congestion at the inventory

point that is located before the production process which is going to produce in smaller lots.

5.1.2 SET UP TIME REDUCTION

To counter the congestion that could be created when a process produces in smaller batches, the

reduction of set up could be an effective measure. Set up time refers to the total amount of time that is

needed to make the process ready to produce. Set ups do not add value to the product but are a necessity,

otherwise the machines are not installed properly for the next series of products. When more time is

spent on set up activities, less products can be made. With large set up times companies tend to produce

more than they need to. This will increase the total amount of inventory and reduces the flexibility (Berk,

2010).

20

There are many ways to reduce set up times but the Single Minute Exchange of Dies (SMED)

methodology created by Shingo (1985) is the most common. This four step method is aimed to reduce

or eliminate the time and need for set ups. When Setup time reduction is realized the entire production

process will probably be less complex. Planning could become easier since the process gained flexibility.

It could reduce the batched supply of items through the organization and create more capacity for the

machines. Furthermore, due to the reduced time needed for set ups, batches probably have less waiting

time until they are processed. This could reduce the batched waiting time. Furthermore, since complexity

of the planning could be reduced, the assembly of different items can be synchronized more easily.

5.1.3 PULL PRODUCTION

Realising short throughput times could be achieved by optimizing material control. The flow of material

on the shop floor can be regulated by the authorisation of the start of a job, creating a priority list for

jobs, releasing new material to the shop floor and initiation succeeding activities (e.g. Transport)

(Riezebos, 2010). Pull production systems are types of material control systems. They control which

material is allowed to flow through different stages in the production process. A pull system is able to

react to changes and problems in the production process. When one of the production processes faces

problems the upstream processes do not receive any signals to produce (Nicholas, 2011).

In literature, there are two main pull production control methods. One is Kanban, which uses

authorization cards which states if products can be produced or moved and what kind of material is

needed, its destination and source (Nicholas, 2011). Another pull production method is POLCA (Paired-

cell Overlapping Loops of Cards with Authorization). POLCA aims to make the principals that work in

a Kanban system applicable to a make to order production facility (Riezebos, 2010). POLCA was first

described by Suri (1998) and is a quick response manufacturing technique to control the material flow.

This method is best to be used in the context of a cellular organisation with a high level of material

requirements planning. The systems tells a cell when that cell is authorised to work.

Pull production methods could reduce variability by creating demand for items which do not exceed the

capacity of the machines in subsequent stations. It could make a production process more even by

determining when a production order need to be created. This could reduce demand variability.

Furthermore, there could be a reduction in under capacity of machines. This could reduce the occurrence

of congestion. The supply of flow items could be regulated as well. It will be known when certain items

are planned to be produced. This could create a smoother production.

21

5.1.4 CELL LAYOUT

For operations that are considered being a relatively high variety production process, a change towards

a cell layout could result in an improved flow. A cell layout is a layout where the machines required to

produce different parts of the operation are clustered. A product can move through different cells until

it is considered a finished product. Cell layouts try to bring some order in complex flow operations

(Slack, Brandon-Jones and Johnston, 2016). In a cellular layout, autonomous groups (consisting of

workers and machines) work on a set of part types (Bokhorst, Slomp and Gaalman, 2004).

It is possible to create a fast throughput of items and create more flexibility with a cell layout. However

it can be costly to implement it could require investments in additional equipment since some machines

are used in multiple processes (Slack, Brandon-Jones and Johnston, 2016). Cellular manufacturing aims

to move a high variety of items through the process as quickly as possible while reducing waste. It could

be capable of a reduction in the transportation of supply items and the size of transfer batches.

5.1.5 POKA-YOKE

Poka-yoke is a method which focuses on the design of products and operations. It is a concept of fail-

saving the process and trying to eliminate human error. Human mistakes are to some extent inevitable

and the poka-yoke method focusses on preventing human error becoming defects. Poka-yokes are simple

systems or devices that are implemented into a process to avoid mistakes (Slack, Brandon-Jones and

Johnston, 2016). Poka-yokes are important in pull production systems where, because of small

inventories, stoppages anywhere interrupt the entire process (Nicholas, 2011). Poka-yoke methods can

create a higher quality of products. Prevention of defects in the process before they appear is the best

way to reduce defects (Dudek-Burlikowska and Szewieczek, 2009). It is a measure that focusses on a

first-time right principle. Uncertainty whether products with the desired quality will be supplied by other

departments could be reduced. When there are less defects and less measures needed for quality checks

the flow could be improved. It could also be used to fail proof set ups. When it is used as a set up time

reduction measure it could affect the same root causes as the SMED methodology described in section

5.1.2.

5.1.6 JUST IN TIME DELIVERY

Just in time (JIT) is a manufacturing method of planning and control. It aims to meet demand

instantaneously with perfect quality and no waste (Slack, Brandon-Jones and Johnston, 2016). The

products need to arrive where they are needed, when they are needed. JIT is a philosophy with two main

objectives: improving quality and an on time production and shipment of products (Aghazadeh, 2004).

It aims to reduce cost by holding less inventory. If JIT is realized in the entire firm, unnecessary

inventories in the factory could be completely eliminated (Monden, 2011). Although JIT can reduce

22

inventories in the entire factory it is mainly focused on the arrival of products. With a good functioning

JIT method the variability in product arrival time will be reduced. When these parts are used in assembly,

the supply of flow items in assembly will be less uncertain. When a company uses the JIT method the

batches it receives will be the same size as the items that they need, eliminating batched inventory

problems.

5.1.7 TOTAL PREVENTIVE MAINTENANCE

Total Preventive/Productive Maintenance (TPM) is a system that focusses on the maintenance and

improvement of machines in an organisations. TPM can be defined as: “a system that is designed to

maximize equipment effectiveness (improving overall efficiency) by establishing a comprehensive

productive-maintenance system covering the entire life of the equipment, spanning all equipment-

related fields (planning, use, maintenance, etc.)” (Tsuchiya, 1992, p.4). TPM provides a company-wide

approach to maintenance management. It can be separated in short- and long-term elements. The short-

term elements will focus on maintenance programs for the production and maintenance department

while the long-term elements will focus on the design of new equipment and the elimination of

downtime. Long-term elements will involve many areas of the organisation (McKone, Schroeder and

Cue, 2001). TPM is designed to prevent stoppage, defect and speed losses as well as speed reduction

caused by several forms of failures by improved manufacturing methods and equipment maintenance

(Chan et al., 2005). With TPM in place it is less likely to have a breakdown or a failure. The machines

should have the ability to operate according to their capacity. This could, among other things, reduce

the need to buffer for the chance of a breakdown.

5.2 THEORY OF SWIFT, EVEN FLOW

As seen in the previous section, lean provides several improvement measures that focusses on reducing

variability. There are other theories that also focusses on certain type of variability issues. Most of the

measures linked to these theories could have the same effect as some of the lean principles. However,

these theories could be more applicable than lean and thus be a good alternative to improve the flow in

the organisation.

The theory of Swift, Even Flow is one of those theories and focusses on the swiftness and flow of

materials through the process. Productivity of the production process rises with the speed of the materials

that flow through the process, and it falls with increases in the variability of the flow. This theory divides

work that is done into value added and non-value added. Non-value added work are processes like

transporting goods and inspections. They do not alter the product. Value added processes are steps that

make changes to a product. Materials can move quicker, more swift, through the process if the non-

value added steps are reduced or eliminated. Bottlenecks in a process can hinder the throughput of

23

products since materials wait to be produced. The swiftness can be measured in throughput time of

materials (Schmenner and Swink, 1998).

To create a more even flow, the variability of the demand in production and the process operations steps

need to be reduced. It is better to produce with a level production plan than with production plans that

have irregular quantities or due dates. The greater the variability in the process, the less productive it is.

Quality management is important to the Swift, Even Flow theory. It helps to lower the variability in the

products and will avoid bottlenecks (Schmenner and Swink, 1998). This theory could be more applicable

if the problems are mainly caused by demand variability issues since that is the main focus of this theory.

However, most of the improvement measures will have the same effect as the improvement measures

described in section 5.1.

5.3 ANALYSIS OF VARIABILITY IMPROVEMENT MEASURES IN

OPERATIONS MANAGEMENT LITERATURE

Improvement measures described in section 5.1 are all created to contribute to a better flow in the

organisation. Each of these measures is aiming to reduce a different kind of variability in the production

process. An overview of the most common improvement measures and the relation to the new approach

can be found in table 5.1. This overview is made by analysing the different methods according to the

questions that need to be asked to identify the root causes of inventory. For example, Total preventive

maintenance focusses on the reduction of uncertainties in the production process by making the

processes and equipment more reliable. This influence the arrival of different Flow Items and Capacity

as missing inputs. The main source of variability is the supply of Flow Items and the Capacity. It makes

both more reliable. The form is Uncertainty so the inventory types the measure influences are: Supply

Uncertainty Induced Congestion, Supply Uncertainty Induced Assembly Waiting Time, Capacity

Uncertainty Induced Congestion and Capacity Uncertainty Induced Assembly Waiting Time. An

overview of all the different inventory types and their abbreviation can be found in appendix C.

In figure 5.1 the framework of Land et al. (2018) is used to give an overview of which inventory points

are addressed by the common flow improvement measures. The inventory points addressed by the

common literature are highlighted. The common improvement measures will give generic solutions for

companies facing one of these issues. It gives a clear overview of possibilities, but these measures need

to be customized in order to be implemented.

Although there are a lot of improvement measures, several inventory points have not yet been addressed.

As shown, most of these types relate to demand as being the missing input. Most of these inventory

points are probably the result of a strategic decision. For example, companies can have demand cycle

stock to reduce the throughput time.

24

Table 5.1: Overview of the common improvement measures in relation to the root causes they aim to address

Improvement

measure Missing Input

Main source

of variability Form of variability

Affected root

cause

Small lot size Capacity, Flow

Items

Supply of

Flow Items,

Capacity

Batched

BSIC, BSIAWT,

BTIC, BTIAWT,

BWT

Setup time

reduction

Capacity, Flow

Items

Supply of

Flow Items,

Capacity

Uncertainty (assembly

items), Predicted

Fluctuation (assembly

items), Batched

SUIAWT,

SAIAWT, BSIC,

BSIAWT, BTIC,

BTIAWT, BWT

Pull

production

Capacity, Flow

Items

Demand,

Supply of

Flow Items,

Capacity

Uncertainty, Predicted

Fluctuation,

DUIC, DUIAWT,

DAIC, DAIAWT,

BDIC, BDIAWT,

SUIC, SUIAWT,

SAIC, SAIAWT,

CUIC, CUIAWT,

CAIC, CAIAWT,

Cellular layout Flow Items

(different type)

Supply of

Flow Items,

Capacity

Uncertainty, Predicted

fluctuation

SUIC, SUIAWT,

SAIC, SAIAWT

Poka-Yoke Capacity, Flow

Items

Supply of

Flow Items,

Capacity

Uncertainty SUIC, SUIAWT,

CUIC, CUIAWT

JIT delivery Demand, Flow

Items

Supply of

Flow Items

Uncertainty, Predicted

Fluctuation, Batched

SSS, SUIAWT,

SAS, SAIAWT,

SCS, BSIAWT

Total

Preventive

Maintenance

Capacity, Flow

Items (different

type)

Supply of

Flow Items,

Capacity

Uncertainty SUIC, SUIAWT,

CUIC, CUIAWT

25

6. PART TWO, SECTION TWO: IMPROVEMENT MEASURES ANALYSIS

Tailor made solutions cannot be found in literature. Furthermore, as is seen in section 5, not all

inventories can be covered by the most common flow improvement measures. Some of the problems

also arise from the complexity of the specific production situation in a company. It would be preferable

for managers if they are able to analyse other options to improve the flow in the organization to avoid

the implementation of wrong and possibly expensive measures. This section aims to analyse if the new

diagnosis approach created by Land et al. (2018) described in section 2.1 can be used, next to the

diagnosis tool, to check if improvement measures proposed by the company are a good fit with the

problem that they are trying to resolve.

As mentioned in section 4 the main issue that the company subject to this research faces concerns Supply

Uncertainty and Batched Supply Induced Assembly Waiting Time. These problems are assembly

problems at inventory point l (figure 4.1), right before riveting. At this point most of the inventory is

accumulated. To reduce the inventory levels at this point, the company introduced a number of

improvement measures. First, these measures will be analysed by using the new approach. Second, it

will be discussed if the new approach provides is an appropriate tool to analyse improvement measures.

These improvement measures are proposed by the company based on the knowledge of the earlier

diagnosis done in Land et al. (2018). In this section, first, the improvement measures and the aim they

have according to the company are explained. Using the new approach, the measure will be analysed.

To be more precise, there will be an assessment to determine if it addresses the correct missing input,

source of variability and form of variability, as it has been identified as the root cause of the inventory

at the considered point. If there is a match it can be concluded that the measure fits with the problem the

company tries to resolve or reduce.

6.1 BASIC END DATE SPREADING

The improvement measure ‘basic end date spreading’ aims to realize a better synchronization. Originally

orders were planned to be finished in weekly time buckets. This can create large gaps in planned

production dates for the different parts from the different departments since the planning software does

not synchronize them. In the new situation orders are planned with daily deadlines instead of weekly

deadlines. The goal of the company is to reduce both the Supply Uncertainty and Batched Supply

Induced Assembly Waiting Time that causes inventory problems at inventory point i ( figure 4.1).

As for the missing input, this measure addresses the flow items of the production of materials and could

have an influence on the capacity of the production machines. By controlling the planned orders and by

distributing the orders more evenly throughout the week, the supply of flow items are addressed. Since

this planning could result in less possibilities for operators to combine orders, the production manager

26

expects to have more changeovers. This can result in a larger overall set up time and less capacity.

However, it could also result in smaller batches that are sent to the next department. The main source of

variability the company addressed is the supply of flow items. By making a smoother planning, the

supply of the items to the successive station is expected to be more even. The forms of variability that

are addressed are uncertain and batched supply. The uncertainty is expected to be less since the orders

will be produced on a daily basis and are planned more precise. The riveting department is expected to

have a better forecast of arriving items. The batched supply of flow items will be reduced since there

will be less orders combined and the production will be carried out closer to its predetermined schedule.

Currently orders are planned by using a backwards scheduling method starting from the assembly

process. This provides deadlines for the production of the individual items to be assembled. Individual

items are given an end date. This end date is the last possible date to get an item delivered on time to the

costumer, accounting for assembly lead time. The individual items are scheduled according to capacity

of the machines and the planned lead time of the individual production processes. Items are not

interchangeable due to the high variety of the products the company produces.

The different items to be assembled, e.g. rods and belts, are not synchronized except for the originally

planned end dates. Due to capacity restrictions items can be scheduled earlier than they are needed,

earlier than their end date, either within the rod department or the belt department. This can result in

long wait times until the items needed for assembly arrive at the riveting department. Furthermore,

operators are allowed to combine orders to reduce the total set up time, which is another issue at the

company. This results in a further desynchronization of the different assembly items and thus a large

amount of work in progress. Sometimes orders at one department are rushed to deliver them “on time”

when other items of the same order still need to start their production process. These issues are expected

to reduce with this new measure.

When the missing input, source of variability and form that this measure is addressing is analysed, it can

be determined that it could influence Supply uncertainty induced Congestion, Supply Uncertainty

Induces Assembly Waiting Time, Batched Supply Induced Congestion and/or Batched Supply Induced

Assembly Waiting Time where this measure is implemented. The aim was to reduce Supply Uncertainty

and Batched supply induced Assembly Waiting Time at inventory point l ( figure 4.1). Therefore, it can

be concluded that this measure will have an effect on the root causes of inventory point l.

6.2 ORDER RELEASE RULES

To gain an even better synchronization in the production and thus reducing the assembly waiting time,

a new order release method has been developed. The rods department is highly inflexible. This is partly

due to the fact that the set up times for the machines are very long. The operators at these machines

need to plan ahead and combine orders to gain maximum machine efficiency and to meet the planned

27

demand. The belt department however, is very flexible. The set up times are negligible and the lead

times are significantly shorter than the lead times for rods.

The operation of the rods department will be leading for the belts department in the new situation. The

end date for the items for both departments will not change but the order release to the operators will.

Belts will only be released for production when the first step of the production process for rods, cutting,

is finished. Only when the Rod Department signals that rods for an order are ready, operators can see

the corresponding belts appear in their work list. Since errors still exist in the production process and

operators are allowed to combine orders, not every order is produced according to schedule. This

measure is expected to create a better synchronization between the rods and belts. Items laying idle at

the riveting department ready to be assembled are expected to be reduced. This measure aims to reduce

the Supply Uncertainty Induced Assembly Waiting Time when items of different departments need to be

assembled and when it is difficult to produce according to schedule.

The missing input that this measure could address are the flow items since it focusses on an order release

for the different items. This measure concerns the supply of the different flow items to the riveting

department. It could reduce the time gap of the different items that are send to the riveting department.

This department could have less uncertainty of the arrival of the different items. The production of rods

and belts are linked to each other so when one of the items arrive the other one is expected soon to

follow. This could reduce the Supply Uncertainty Induced Assembly Waiting Time. It can be concluded

that this measure is a fit with the problem that is aimed to be resolved.

6.3 SMED PROJECT

A SMED analysis is being conducted for one of the presses. This project is still ongoing, but some small

easy fixes are in place. The SMED project will be continued by the company in the near future. Due to

the basic end date spreading, operators at the presses will have less room to combine orders. Without

setup time reduction, this could create problems for the pressing department since they need to set up

the presses more often. Inventory at point c (figure 4.1), Batch Transformation Induced Congestion,

could be increased due to this measure. The SMED project aims to reduce the negative side-effects of

the basic end date spreading and to create more flexibility for the pressing department. This SMED

project is seen as a necessity by the company when the organisation faces a peak in demand. The aim is

to reduce the Batch Transformation Induced Congestion at inventory point c as well as the Batched

Supply Induced Assembly Waiting Time at point i since the batches that arrive and leave this station will

be reduced in size.

As seen in section 5, Setup time reduction could reduce Batch Transformation Induced Congestion as

well as Batched supply induced Assembly Waiting Time. Therefore, it can be concluded that this measure

could be beneficial to reduce inventory at the points c and i and is thus a fit with the aim of the company.

28

6.4 NEW APPROACH AS IMPROVEMENT MEASURE ANALYSIS TOOL

As seen in analysis of the different improvement measures the new approach could be capable of

performing a check to see if the measure is a fit with the company. This test is however quite basic and

only shows if modifications to an common improvement measure and custom made improvement

measures addresses the correct type of inventory. It does not show what the effect (impact) will be and

what possible negative side effect are to processes outside of the scope of the measure.

However, to check if the right root cause is addressed is still useful. This could prevent the

implementation of incorrect measures, which could be a costly mistake since some measures are

expensive to implement. To do so, first the diagnosis need to be made. With this diagnosis the root cause

for the inventory at a certain point can be identified. Second, the questions to find the missing input,

main source of variability and form of variability need to be answered for the improvement measure. If

they are identified, it is known for which root causes the measure could possibly be used. If the

diagnosed inventory points are identified as one of the root causes the measure could address, the

measure is a fit for the problem.

29

7. DISCUSSION

This study focussed on flow improvement measures in Operation Management. This field was chosen

because flow control issues play a predominant role and managers continually struggle to achieve an

optimal flow. The researched validated the new approach of Land et al. (2018) as a good diagnosis tool.

This was done by repeating the research as done in the original paper at the same company. We answered

the three questions, which form the core of the diagnosis approach, for each of the inventory points

present at the company. Based on this, all root causes of inventories could be found. The repetition of

the diagnosis of the original paper was executed to check whether the method generates consistent

results and whether these results do not dependent on the researcher that executes the diagnosis.

Although there were some differences in the studies, the method still provides consistent results. These

differences were most probably the cause of a shifting root cause of the inventory points over time.

The initial method developed by Land et al. (2018) found the root causes by identifying the variability

issues faced at each of the inventory points. To determine which measures could reduce variability in

the field of operations management, common improvement measures were analysed. The majority of

these measures are linked to the theory of lean. These measures were chosen since lean management

provides a set of tools which reduces the variability in production processes. Other theories, like the

theory of Swift, Even Flow, will have measures that are similar to the measures linked to lean. The

measures analysed could therefore be determined as representative for the majority of flow improvement

measures. These topics appeared to be chosen well since they address the greater part of the inventory

points. However, other, less generic, methods could be studied as well. Especially for the root causes

created by (customer) demand as being the missing input. For example, forecasting could reduce

Demand Safety Stock since it could probably provide more demand certainty.

Generic designs still need to be customized to be a suitable fit with specific company problems.

Furthermore, solutions that are proposed by companies need to be checked to determine if there is a

good fit with the problems the company tries to resolve. Even with the overview of common

improvement measures present in literature, managers could still use an analysis tool for improvement

measures. With the questions provided by Land et al. (2018) companies are able to determine if the

proposed improvement measures are a good fit with the root cause they are aiming to address. However,

the impact and side effects cannot be determined. Other methods should be used or created to test these

effects.

30

8. CONCLUSION

This theses aimed to answer the question: “How can managers improve flow by addressing flow control

issues?”. This is done by 1) validating the approach developed by Land et al. (2018), 2) to improve it

by determining which improvement measures are most common to reduce variability in the field of

operations management and 3) to determine how the new approach could be used to analyse flow

improvement measures.

The first phase of the research validated the approach by Land et al. (2018) by redoing the initial

diagnosis to determine which variability issues were the root cause for specific inventory points at a

medium sized capital goods manufacturer. The approach is capable of identifying ten different kinds of

inventory types and it generated results consistent with an earlier diagnosis that used the same approach

in the same case. The approach generates the best results if there are more iterations of the research

performed at different time intervals.

The second phase of this study aimed at finding solutions for the problem that managers still may not

know what to do to improve flow, once the different types of inventory points are identified. First, the

most common improvement measures were analysed. The improvement measures analysed were: Small

lot size, Setup time reduction, Pull production, Cellular layout, Poka-Yoke, JIT delivery and Total

preventive maintenance. It was found that inventory of multiple types can be reduced by using one or

several of these methods. A table is provided that reveals which method may help to reduce each

inventory type. With the help of this table managers could determine which method they can costumize

and implement to create a better flow by adressing variability issues.

Not all inventory types can be improved with the methods existing in common literature, or are a suitable

option for company specific problems. Managers still need the ability to test if the measures they are

proposing are a good fit with the problems they are aiming to resolve. Based on the diagnosis provided,

the production manager in the case company, initiated a number of measures to improve flow. The

method created by Land et al. (2018) appeared to be able to determine if there is a fit with the proposed

improvement measure and the type of inventory which is aimed to be reduced. This could be done by

testing if the correct missing input, main source of variability and form of variability is addressed by the

measure.

By validating a new diagnosis tool, creating an overview of common improvement measures and

determining a new method to analyse improvement measures, this thesis answered how managers can

improve flow by addressing flow control issues.

31

8.1 LIMITATIONS AND FUTURE RESEARCH

This research focussed on variability reduction in operations management. Operation management is

not the only sector which faces difficulties with flow control. This diagnosis could probably also be used

in a service or health care environment, if flow control is an issue. This research did not include these

fields and new research could be conducted to analyse if this new approach is also a good diagnosis tool

for these fields. If the new approach appears to be a good diagnosis tool as well, common improvement

measures in these field could be analysed to determine which measures could improve each type of

inventory.

As shown, not all inventory types could be addressed with a common improvement measure yet. More

research need to be conducted to these inventory types and how these can be addressed. Studies could

focus on these types to create new generic measures or methods which are able to reduce the levels of

these inventories.

There were only ten different types of inventory points found at the company used for this research.

Although it can be reasoned that the new approach is able to find all 28 inventories since the method

identify them is appropriate, it cannot be concluded that all these inventories exist and/or can be found

with this new approach. The diagnosis should be conducted at multiple companies to determine if these

inventories exist and can be found.

32

REFERENCES

Aghazadeh, S. M. (2004) ‘Does manufacturing need to make JIT delivery work?’, Management

Research News, 27(1–2), pp. 27–42.

van Aken, J. E. (2004) ‘Management research on the basis of the design paradigm: The quest for field-

tested and grounded technological rules’, Journal of Management Studies, 41(2),

van Aken, J., Chandrasekaran, A. and Halman, J. (2016) ‘Conducting and publishing design science

research: Inaugural essay of the design science department of the Journal of Operations Management’,

Journal of Operations Management. Elsevier B.V., 47–48, pp. 1–8.

Berk, J. (2010) Cost Reduction and Optimization for Manufacturing and Industrial Companies, Cost

Reduction and Optimization for Manufacturing and Industrial Companies. Wiley.

Bokhorst, J. A. C., Slomp, J. and Gaalman, G. J. C. (2004) ‘Assignment Flexibility in a Cellular

Manufacturing System - Machine Pooling versus Labor Chaining’, in, pp. 1050–1057.

Chan, F. T. S., Lau, H. C. W., Ip, R. W. L., Chan, H. K. and Kong, S. (2005) ‘Implementation of total

productive maintenance: A case study’, International Journal of Production Economics, 95(1), pp. 71–

94.

Dudek-Burlikowska, M. and Szewieczek, D. (2009) ‘The Poka-Yoke method as an improving quality

tool of operations in the process’, Journal of Achievements in Materials and Manufacturing

Engineering, 36(1), pp. 95–102.

Gregor, S. and Hevner, A. R. (2013) ‘Positioning and presenting design science research for maximum

impact’, MIS Quarterly, 37(2), pp. 337–355.

Hevner, A. R. (2007) ‘A Three Cycle View of Design Science Research’, Scandinavian Journal of

Information Systems, 19(2), pp. 87–92.

Land, M., Thürer, M., Stevenson, M. and Fredendall, L. (2018) Flow improvement - A Design Science

Approach.

McKone, K. E., Schroeder, R. G. and Cue, K. O. (2001) ‘The impact of total productive maintenance

practices on manufacturing performance’, Journal of Operations Management, 19, pp. 39–58.

Monden, Y. (2011) Toyota production system, An integrated approach to Just-In-Time, Journal of

Experimental Psychology: General. Taylor & Francis Group.

Nicholas, J. M. (2011) Lean production for competitive advantage, a Comprehensive Gui De To Lean

Methodologies and Management Practices.

33

Olhager, J. (2003) ‘Strategic positioning of the order penetration point’, International Journal of

Production Economics, 85, pp. 319–329.

Riezebos, J. (2010) ‘Design of POLCA material control systems’, International Journal of Production

Research, 48(5), pp. 1455–1477.

Schmenner, R. W. and Swink, M. L. (1998) ‘On theory in operations management’, Journal of

Operations Management, 17(1), pp. 97–113.

Shah, R. and Ward, P. T. (2007) ‘Defining and developing measures of lean production’, Journal of

Operations Management, 25(4), pp. 785–805.

Shingo, S. (1985) A Revolution in Manufacturing: The SMED System. Productivity Press Cambridge.

Simon, H. A. (1996) The sciences of the artificial. MIT Press.

Slack, N., Brandon-Jones, A. and Johnston, R. (2011) Essentials of Operations Management. Pearson.

Slack, N., Brandon-Jones, A. and Johnston, R. (2016) Operations Management. eight edit. Pearson.

Suri, R. (1998) Quick response manufacturing: a companywide approach to reducing leadtimes.

Portland, OR: Productivity Press.

Tsuchiya, S. (1992) Quality Maintenance: Zero Defects Through Equipment Maintenance. Productivity

Press.

Wieringa, R. (2009) ‘Design science as nested problem solving’, in Proceedings of the 4th International

Conference on Design Science Research in Information Systems and Technology - DESRIST ’09, p. 1.

Wieringa, R. (2013) ‘Introduction to design science methodology’, in 2013 Doctoral Symposium.

34

APPENDIX

APPENDIX A: INTERVIEW PROTOCOL

INTERVIEUW PROTOCOL

Intervieuw Broekema

Sjoerd Mulder – Rijksuniversiteit Groningen – Newcastle University

Introductie

Flow in een organisatie is een belangrijk onderwerp voor operations managers. Dit onderzoek richt zich

op problemen die spelen met betrekking tot de flow in het productie proces. Door middel van een nieuwe

methode kunnen de oorzaken van verschillende voorraden geïdentificeerd worden. Het doel van dit

interview is om te kijken wat de impact is van de verschillende verbeter maatregelen aan de hand van

de nieuw ontwikkelde methode.

Aan de hand van interviews willen we er achter komen wat het doel van de verbeter maatregel is en hoe

die bewerkstelligd denkt te worden. Dit zullen we doen door het productieproces door te lopen met de

geïnterviewde voor elke maatregel apart. De verschillende voorraden waarop de maatregel impact op

zal hebben willen we zo in kaart brengen. Hierbij wordt de focus gelegd op de productie en assemblage

van riemen en spijlen die gebruikt worden voor de producten van Broekema.

Zijn er tot nu toe nog vragen?

Ik heb een aantal vragen voorbereid maar voel je vooral vrij om, los van de vragen, dingen toe te voegen

als je die belangrijk vindt. Is het goed als dit interview opgenomen wordt voor onderzoeksdoeleinden?

(Bij een positief antwoord zal het interview vanaf hier opgenomen worden)

Bij elk blok zal het volgende flow chart doorgenomen worden:

35

Hierbij zal er gekeken worden op welke voorraadpunten en welke dimensies (missende input,

hoofdoorzaak voor de variabiliteit of de vorm van variabiliteit) de maatregel impact heeft. De vragen

per blok zullen hetzelfde zijn.

Blok 1: Basic end date spreiding

Wat is het doel van deze maatregel?

Welke voorraden denken jullie hiermee te verminderen?

Hoe denken jullie dat de voorraden hierdoor verminderd worden?

Zijn er neveneffecten van deze maatregel te verwachten in andere delen van het proces?

Blok 2: Order release rules

Wat is het doel van deze maatregel?

Welke voorraden denken jullie hiermee te verminderen?

Hoe denken jullie dat de voorraden hierdoor verminderd worden?

Zijn er neveneffecten van deze maatregel te verwachten in andere delen van het proces?

Blok 3: SMED project

Wat is het doel van deze maatregel?

Welke voorraden denken jullie hiermee te verminderen?

Hoe denken jullie dat de voorraden hierdoor verminderd worden?

Zijn er neveneffecten van deze maatregel te verwachten in andere delen van het proces?

36

APPENDIX B: TRANSLATION INTERVIEW PROTOCOL

INTERVIEWPROTOCOL

Interview Broekema

Sjoerd Mulder – Rijksuniversiteit Groningen – Newcastle University

Introduction

Flow is an important issue for operations managers. This research focuses on problems regarding flow

in the production process. By means of a new method the root causes of different inventories can be

identified. The aim of this interview is to analyze the impact of the various improvement measures with

the newly developed method.

Based on interviews, we want to find out what the goal of the improvement measure is and how you

think it can be achieved. This will be done by analyzing the production process step by step with the

interviewee for each measure individually. The different inventory points on which the measure is

expected to have an impact on will be identified. The focus of this interview will be on the production

of belts and rods that are used for the conveyor belts of Broekema.

Are there any questions so far?

I have prepared a number of questions but feel free to add things, if you think it is important, apart from

the questions. Do you mind if this interview is recorded for research purposes? (With a positive answer

the interview will be recorded from here)

At each block the following flow chart will be discussed:

It will be analyzed which dimensions (missing input, main cause for the variability or the form of

variability) the measure is expected to have an impact on. The questions per block will be the same.

37

Blok 1: Basic end date spreiding

What is the goal of this measure?

Which inventory points do you think the measure can reduce?

How do you think the measure can influence the inventory points?

Are side effects of this measure to be expected in other parts of the process?

Blok 2: Order release rules

What is the goal of this measure?

Which inventory points do you think the measure can reduce?

How do you think the measure can influence the inventory points?

Are side effects of this measure to be expected in other parts of the process?

Blok 3: SMED project

What is the goal of this measure?

Which inventory points do you think the measure can reduce?

How do you think the measure can influence the inventory points?

Are side effects of this measure to be expected in other parts of the process?

38

APPENDIX C: INVENTORY LABEL ABBREVIATIONS

Demand Safety Stock: DSS

Demand Uncertainty Induced Congestion: DUIC

Demand Uncertainty Induced Assembly Waiting Time: DUIAWT

Demand Anticipation Stock: DAS

Demand Anticipation Induced Congestion: DAIC

Demand Anticipation Induced Assembly Waiting Time: DAIAWT

Demand Cycle Stock: DCS

Batched Demand Induced Congestion: BDIC

Batched Demand Induced Assembly Waiting Time: BDIAWT

Supply Safety Stock: SSS

Supply Uncertainty Induced Congestion: SUIC

Supply Uncertainty Induced Assembly Waiting Time: SUIAWT

Supply Anticipation Stock: SAS

Supply Anticipation Induced Congestion: SAIC

Supply Anticipation Induced Assembly Waiting Time: SAIAWT

Supply Cycle Stock: SCS

Batched Supply Induced Congestion: BSIC

Batched Supply Induced Assembly Waiting Time: BSIAWT

Capacity Safety Stock: CSS

Capacity Uncertainty Induced Congestion: CUIC

Capacity Uncertainty Induced Assembly Waiting Time: CUIAWT

Capacity Anticipation Stock: CAS

Capacity Anticipation Induced Congestion: CAIC

Capacity Anticipation Induced Assembly Waiting Time: CAIAWT

Capacity Cycle Stock: CCS

Batched Capacity Induced Congestion: BCIC

Batched Capacity Induced Assembly Waiting Time: BCIAWT

Batched Waiting Time: BWT