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Design of an integrated global warehouse and field stockplanning concept for spare partsCitation for published version (APA):van Aspert, M. (2015). Design of an integrated global warehouse and field stock planning concept for spareparts. Technische Universiteit Eindhoven.

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Designer programme Logistics Management Systems

3TU.School for Technological Design, Stan Ackermans Institute

School of Industrial Engineering

Technische Universiteit Eindhoven

Design of an Integrated Global Warehouseand Field Stock Planning Concept for

Spare Parts

Logistics Design Project at ASML

TU/e supervisors:prof.dr.ir. Geert-Jan van Houtum Design mentorir. Paul van der Linden Daily supervisor

ASML supervisors:ir. Ruud van Sommeren Company supervisordr. Mehmet Atan Company supervisor

LMS project designer:ir. Martijn van Aspert

Eindhoven, December 2014

i

A catalogue record is available from the Eindhoven University of Technology Library

ISBN: 978-90-444-1344-1

(Eindverslagen Stan Ackermans Instituut ; 2014/083)

ii

“You can’t connect the dots looking forward; you can only connect them looking backwards.

So you have to trust that the dots will somehow connect in your future.”

Steve Jobs

Preface

Somewhere in the spring of 2013 I got in contact with Fred about doing an LMS design project

at ASML. The passion and engagement of Fred about ASML made me sure that I wanted to do

a project at this company. When I heard about the possibility to do a project about spare parts,

it couldn’t get any better. But even then it got better: In February 2014 I was welcomed by

the team which I can now call my colleagues. The atmosphere in the team was great: everyone

always made time available to answer questions but there was also time for fun. I am looking

forward to continue my career with these people!

Special thanks go to Ruud and Mehmet. Ruud is a walking encyclopedia when it comes to

spare parts planning in practice. I have learned a lot of him about the practical challenges that

come with spare parts planning. Mehmet was always there for the critical academic notes with

a practical twist, he knows perfectly how to present your ideas and results. Of course I would

also like to thank all people at ASML who have somehow supported me during the project.

I would also like to thank Geert-Jan, with whom I already had the opportunity to work

with on my MSc project and now again on this project. I have learned a lot of him, not only

scientifically but also practically. I hope I can keep learning from him in the future! My thanks

also go to Paul who really is a motivator and has been a great discussion partner for basically

everything. Next I would like to thank Will, the one that first inspired me to do the LMS

program when he was redesigning the program and still inspires me with his views on logistics

design processes. I would also like to thank Erwin for his critical notes and wish him success

with the future research on topics related to topics in this project. And of course I would like

to thank my colleagues from LMS, it has been great fun working with you!

Many special thanks also go to my parents and brother for their unconditional support and

advice regarding other than work-related aspects.

Last I would like to thank Mirjam, for her support, advice and help but most of all for her

love!

Veldhoven, December 2014.

Martijn van Aspert

iii

Summary

ASML is a manufacturer of lithography systems. These systems can cost up to e100 million and

are of such an importance for the customers of ASML that one hour down-time can cost them

up to e72.000. Therefore ASML has very strict service level agreements with customers that

require down-time to be very low. This down-time includes waiting for parts to be transported

from a warehouse to the customer. To keep this waiting time as small as possible, ASML

operates several types of warehouses around the world: Firstly, local warehouses that are closely

located to customers and where spare parts are kept on stock such that these are quickly

available to customers. Secondly, emergency hubs where spare parts are stocked in case the

local warehouses are out of stock. Thirdly, continental warehouses which serve customers that

allow for longer down-times and have less strict service level agreements and fourthly, global

warehouses where parts are stocked to directly sell to customers and to replenish the local

warehouses, continental warehouses and emergency hubs.

Currently, for the local warehouses a planning tool is used to do the planning per region,

where it is possible to take into account that local warehouses within one region can support

each other (stock pooling). For the planning of the continental warehouses, emergency hubs

and global warehouses, separate planning tools are used.

In this project, we have developed a planning concept that enables planning the local ware-

houses, continental warehouses and emergency hubs (field stock planning) and global warehouses

integrally. To make this possible, we have first decomposed the network of warehouses into two

parts: a global warehouse planning part and a field stock planning part (see Figure 1). Then

we have connected the two parts using the replenishment leadtimes as coupling.

As can also be seen in Figure 1, which shows our decomposition, the two parts are indeed

coupled via the replenishment leadtime from the global warehouse to the warehouses in the

field. The replenishment leadtime can be split up in a delay part due to unavailability of spare

parts in the global warehouses and a fixed part consisting of transportation and administration

time. By setting an objective on delay in the global warehouse planning, we can plan spare

parts such that the replenishment leadtimes that ASML wants to achieve, can be met. This

differs from the current situation, where in the global warehouse planning objectives are set on

the customer service degree (CSD, fill rate in literature) which is not directly related to the

replenishment leadtime. Having control over the replenishment lead-time means that a better

iv

v

GW

LW LW

LW

LW

Asia

Emer.

LW

US

CW

LW

LW

LW

LW

LW LW

LW

Asia

CW

LW LW

US

Emer.

EU

Emer.

EU

CW

LW

LW LW

LW

Figure 1 – Network decomposition

assumption can be made about the replenishment leadtime in the field stock planning, for which

currently 14 days is assumed for all SKUs.

In the current planning model it is possible to take into account that customers with very

tight service level agreements can request parts at other local warehouses in their region in case

the nearest local warehouse is out of stock. Our model also features this possibility, but now

there is more freedom in choosing at which warehouses parts can be requested. This means that

it is also possible to take into account that parts are requested at the continental warehouses

and emergency hubs, which is not possible in using the current planning model. This also means

that we can determine the amount of stock needed in the emergency hubs based on expected

demand instead of using rules of thumb, that are currently used.

The planning concept that we have developed, has been integrated into a prototype software

tool. We used data from ASML to test our new planning concept and were especially interested

in the total expected yearly cost and inventory value when the delay objective was set differently

per SKU. We have shown that if one differentiates the delay objective based on price or based

on price and demand of SKUs, cost savings of about 10% can be achieved compared to the

situation where 14 days is assumed (and must be met in the global warehouse) for all SKUs.

We recommend ASML to further implement this planning concept and prototype software

tool since it gives, in addition to the better control over replenishment leadtimes, the possibility

to do the planning for spare parts integrally. For future research we recommend to develop a

model that can be used to set the delay objectives in such a way that it leads to minimized costs

for the total spare parts network, i.e. the global warehouse and field stock planning together.

Contents

Preface iii

Summary v

Contents vi

Introduction 1

1 ASML and its Service Supply Chain 2

1.1 Company Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 ASML Service Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Currently Used Planning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.4 Limitations Current Planning Models and Methods . . . . . . . . . . . . . . . . 14

2 Scoping 16

2.1 Project Deliverable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2 Project Assignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.3 Involved Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4 Functional Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.5 Design Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.6 Out of Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3 Conceptual Design 20

3.1 Multi-echelon Model or Multiple Single-echelon Models . . . . . . . . . . . . . . 20

3.2 Model Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.3 Service Differentiation Between Demand Streams and Customers . . . . . . . . . 26

3.4 System Approach, Item Approach or ABC Classification Approach . . . . . . . . 27

4 Detailed Design 28

4.1 Conceptual Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2 Setting Delay Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.3 Global Warehouse Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.4 Field Stock Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

vi

CONTENTS vii

5 Integration 45

5.1 Prototype Software Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.3 Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.4 Test Case for the New Planning Concept . . . . . . . . . . . . . . . . . . . . . . 52

5.5 Single Delay Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.6 Influence of Different Delay Objectives . . . . . . . . . . . . . . . . . . . . . . . 59

6 Further Implementation Aspects 64

7 Conclusions and Recommendations 66

7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

7.2 Recommendations for Future Research . . . . . . . . . . . . . . . . . . . . . . . 67

Bibliography 68

List of Figures 69

List of Tables 70

List of Abbreviations 72

List of Variables 74

A Forecast and RoP setting Global Warehouse Planning 75

A.1 Stable Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

A.2 Intermittent Demand and Erratic Demand . . . . . . . . . . . . . . . . . . . . . 75

A.3 Lumpy Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

B Recursive Formulas for Backorder Calculations 77

C Determining the DTWP 78

D Explanation on Evaluation Algorithm 79

E Explanation on Optimization Algorithm 81

F Tool Input and Output 84

F.1 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

F.2 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

G Verification and Validation Tables 89

H Results Base Case Scenario 95

Introduction

This report describes the steps that have been taken in the design of an integral basestock

policy planning concept for local, continental and global planning of spare parts at ASML.

The design process has been split up in four phases: a scoping phase, a conceptual design

phase, a detailed design phase and an integration phase as is proposed in Bertrand (2008).

In Chapter 1 we will give a brief company introduction of ASML. Then we will explain

how ASML has arranged its service supply chain, including the types of warehouses they

operate, the different types of contracts they offer and how demand is fulfilled. In this

chapter we will also explain which current planning methods are used.

In Chapter 2, the scoping phase, we will discuss the problem deliverable and prob-

lem assignment. Furthermore we will discuss the functional requirements and design

parameters that have been determined for the design of the new planning concept.

In Chapter 3, the conceptual design phase, decisions are taken on which models can

be used for the spare parts planning at ASML. This chapter concludes with a proposal

for the spare parts planning concept and models from literature that are used in this

planning concept.

In Chapter 4, the detailed design phase, we will explain how the models that are

proposed in the conceptual design, need to be adapted to make these applicable for the

spare parts planning at ASML. In this chapter we will elaborate on the mathematical

models that will be implemented in the prototype software.

In Chapter 5, the integration phase, we will explain how the mathematical models

from the detailed design are integrated into a prototype software tool. In this phase,

it will also be explained how the prototype software tool is verified. Furthermore, we

will show that our new planning concept fulfills the functional requirements and we will

perform some analyses using the prototype software. In Chapter 6, we will explain what

ASML has to do, to further implement the new planning concept and prototype software

that has been developed. We will give our conclusions and recommendations for future

research in Chapter 7.

1

1

ASML and its Service Supply Chain

This chapter gives an introduction to ASML. In Section 1.1 a short introduction to the

company is given. Section 1.2, describes what the service supply chain of ASML looks

like and what kind of service level agreements are made with customers, what types of

demand can be classified and how demand is fulfilled.

1.1 Company Introduction

ASML is one of the world’s leading manufacturers of chip-making equipment and a key

supplier to the chip industry. ASML designs, develops, integrates and services advanced

lithography systems to produce semiconductors. In short, it manufactures the machines

that ‘print’ the chips. The public company is founded in 1984 and headquartered in Veld-

hoven, the Netherlands and is traded on both Euronext Amsterdam and NASDAQ

stock exchanges. It employs more than 13,000 people on payroll and flexible contracts

(expressed in full time equivalents, as of Dec 31, 2013).1 In the lithography systems

sector, ASML remains the market leader with an 80% share.2 ASML competes primarily

with Nikon and to a lesser degree with Canon.3

Figure 1.1 – An NXT and NXE system

ASML is located in over 70 locations in 16 countries around the world, including:1http://www.asml.com/asml/show.do?lang=EN&ctx=2262http://www.asml.com/doclib/investor/misc/asml_20130904_Moody’s_

2013-09-04-PR-ASML.pdf3http://www.asml.com/asml/show.do?lang=en&ctx=48039&dfp_fragment=ifrs_riskfactors

2

http://www.asml.com/asml/show.do?lang=EN&ctx=226

http://www.asml.com/doclib/investor/misc/asml_20130904_Moody's_2013-09-04-PR-ASML.pdf

http://www.asml.com/doclib/investor/misc/asml_20130904_Moody's_2013-09-04-PR-ASML.pdf

http://www.asml.com/asml/show.do?lang=en&ctx=48039&dfp_fragment=ifrs_riskfactors

CHAPTER 1. ASML AND ITS SERVICE SUPPLY CHAIN 3

• R&D and manufacturing facilities in the Netherlands, the United States and Taiwan

• Sales and customer support offices in Belgium, China, France, Germany, Hong

Kong, Ireland, Israel, Italy, Japan, Korea, Malaysia, Singapore, Taiwan, United

Kingdom, United States

• Training facilities in Asia and Europe

Customers of ASML include among others the world’s largest memory company, Sam-

sung, the world’s largest microprocessor company, Intel, and the world’s largest foundry

(made-to-order chipmaker), TSMC.

Within the semiconductor industry, the technological developments are characterized

by Moore’s Law. Moore’s Law is named after one of the founders of Intel (currently one

of the bigger investors in ASML). Moore’s Law shows that, over the history of computing

hardware, approximately every two years the number of transistors on integrated circuits

doubles while the costs are halved. For many decades, lithography system manufacturers

such as ASML have been pursuing and realizing this law, creating new opportunities

for chip manufacturers, their customers and manufacturers of e.g. electronic devices to

develop new products for end-consumers.

1.2 ASML Service Supply Chain

Within ASML, the department Global Logistics Services is concerned with all logistics

aspects, from service planning of spare parts and tools to trade and infra. This project

is executed at the Customer Logistics (CL) Department, which is a sub-department of

Global Logistics Services (GLS).

The Customer Logistics Department is among others responsible for the spare parts

planning. The systems of ASML are of such an importance for customers that down-

time of systems can cost customers up to e72.000 per hour. Therefore ASML has strict

contracts with customers with respect to service level agreements for availability of their

systems and thus spare parts. To guarantee these service level agreements, ASML has a

global network of warehouses where spare parts are stocked. It should be noted that at

the factories, no spare parts are kept on stock. In the next sections, it is described what

the different types of warehouses are that ASML operates, which type of contracts are

offered to their customers, what types of demand can be classified and how demand is

fulfilled.


1.2.1 The Network

Figure 1.2 shows a geographical representation of all warehouses ASML globally operates

for its service supply chain. The (green) warehouses in Veldhoven (The Netherlands),

Wilton (USA), San Diego (USA) and Linkou (Taiwan) are global warehouses (GW) from

where parts are shipped directly to the local (blue) and continental (orange) warehouses.

The spare parts planning for the local warehouses is done per region. These regions are

indicated with red dotted circles. In the next sections the different types of warehouses

are discussed in more detail. A note should be made on the continental warehouse in

Asia, since in this warehouse also parts are kept on stock for emergencies only. Parts

that are in this so-called emergency hub, can be used to serve customers with the most

expensive service contracts in case they have a machine down.

Global warehouse

Local Warehouse

Continental Warehouse

Figure 1.2 – ASML Service Supply Chain locations around the world

Local Warehouses

As mentioned in the introduction, down-time can cost much for customers and should

therefore be kept to a minimum. One of the possibilities to keep down-time to a minimum

is by having spare parts close to the customer’s manufacturing site. These warehouses

are called local warehouses, of which there are 29. The most expensive service level

agreements ASML offers are the Downtime Waiting Parts (DTWP) contracts (these are

explained in more detail in Section 1.2.2). Since transportation times are also included

in this service measure, ASML operates these local warehouses nearby customers such

that transportation times are as short as possible.


Continental Warehouses

ASML operates three continental warehouses: one in the USA, one in Korea and one

in The Netherlands, to deliver parts to customers that accept a longer down-time for

their systems and therefore pay less for the service contracts. This results in the fact

that parts for these customers are not stocked locally (i.e. close to the customer) but

somewhere in the continent such that customers can be reached within 48 hours. In case

the continental warehouse is out of stock for a certain item, the item is sent to these

customers from a global warehouse. It should be noted that parts from the continental

warehouses can also be sent to customers with a DTWP contract. This will be explained

in more detail in Section 1.2.4.

Emergency Hub

The emergency hub, which is in the same warehouse as the continental warehouse of Asia,

is used to serve customers in case no stock is available in any of the local warehouses

(close to this customer) or in the continental warehouse (in the continent where this

customer is located) when a system is down. It should be noted that only customers

with DTWP contracts can make use of the stock in this emergency hub.

Global Warehouses

The global warehouses in Veldhoven (The Netherlands), Wilton (USA), San Diego (USA)

and Linkou (Taiwan) are the connection between the suppliers and the service network

of ASML. The stock in these warehouses is used to replenish the local and continental

warehouses and to serve customers with a contract with less strict service level agree-

ments. Furthermore, in the global warehouse also parts are stocked that are used for

upgrades and the return streams of failed parts go via these warehouses. Each of the

warehouses has a part of the assortment of spare parts. Together, these assortments form

the complete assortment of spare parts.

1.2.2 Types of Contracts

On a tactical planning level, there are three types of contracts. The type of contract

determines whether a customer is entitled to local parts availability (LPA), continen-

tal parts availability (CPA) or global parts availability (GPA). The type of availability

determines whether a customer is assigned to a local, continental or global warehouse.

Customers that have LPA, have a DTWP (downtime waiting parts) service level agree-

ment (contract) and are assigned to a local warehouse. Customers that have CPA or GPA

have a CSD (customer service degree) service level agreement (contract) and are assigned

to a continental warehouse or global warehouse respectively. These type of contracts and

service level agreements are discussed in the next sections.


Downtime Waiting Parts

The total downtime of a machine consists of several components, e.g., first an engineer

needs to determine why the machine has failed. The time that the machine is down due

to the unavailability of parts is only a part of the total downtime. This is graphically

depicted in Figure 1.3. The DTWP service level is based on the time from the moment the

engineer orders the part at the local warehouse until it is delivered at the customer. This

time mainly consists of the time to transport the part from the nearest local warehouses

to the customer or in case that part is not available at the nearest local warehouse, to

ship it from one of the other local warehouses or continental warehouse in that region to

the customer or even from the global warehouse. The realized DTWP service level can

therefore be calculated as follows:

DTWP =Total system(s) unavailability in 13 weeks due to transportation leadtime of parts (hours)

24 hours× 7 days× 13 weeks×# of Systems× 100%

As can be seen the DTWP is calculated per group of systems that belong to one plan

group (customers / plan groups can have more than one system of ASML installed) and

per 13 weeks. Therefore the denominator of the DTWP equation includes the number of

systems. A customer can e.g. have a DTWP service level of 1%, which means that the

total downtime in 13 weeks maximally can be 22 hours per system.

Available

Down

DTWP

Figure 1.3 – Downtime Waiting Parts concept

Continental Customer Service Degree

Customers who are entitled to continental parts availability, have a Customer Service

Degree (CSD) contract. This states that e.g. 90% of demand is delivered within 48 hours

from the continental warehouse.


The CSD is calculated as follows. Note that CSD in literature is known as aggregate

fill rate.

CSD =Demand supplied from agreed stock location

Total demand× 100%

Global Customer Service Degree

Customers that are entitled to global parts availability, also have a Customer Service

Degree (CSD) contract. This states that e.g. 90% of demand is delivered within 14 days

from a global warehouse. The service level is calculated in the same way as for continental

CSD.

1.2.3 Types of Demand

In the following list, the different types of demand are summarized. Between parentheses,

the percentage of total demand from February 2011 till February 2014 that belonged to

that category is shown.

• System Failures (84%)

– Local Parts Availability (57%)

– Continental Parts Availability (13%)

– Global Parts Availability (14%)

• System Enhancements (9%)

– Planned (8∼9%)

– Unplanned (<1%)

• Changing Installed Base (7%)

– Planned (6∼7%)

– Unplanned (<1%)

84% of total demand comes from system failures. We can divide this 84% over the

different types of parts availability contracts. The other 16% of demand consists of system

enhancements (performing upgrades or changed) and changing the install base. When

a system is installed, upgraded or changed, a set of parts that is needed during these

actions is sent to the customer where the installation, upgrade or change will take place.

The demand for these parts is known as planned demand. However, sometimes during the

action another part accidentally breaks down. Then a spare part is taken from the stock


point where the customer is assigned to. When this happens, this demand is registered as

unplanned demand. As can be seen, the unplanned demand during system enhancements

and changing the installed base is only a very small part of the total demand.

1.2.4 Demand Fulfillment

The different types of warehouses, types of contracts and types of demand have been

discussed. A last distinction that is made is how demand for parts is fulfilled for the

different types of contracts.

Local Parts Availability

For customers that are entitled to local parts availability, a request for a part (demand)

is fulfilled as follows:

1. First it is checked whether the part is available in the local warehouse that the

customer is assigned to.

2. If the part is not available there, it is checked whether parts are available in another

local warehouse in that region.

3. If none of the local warehouses in that region have stock, it is checked whether

the continental warehouse of the continent where the customer is located has parts

available.

4. If also the continental warehouse is out of stock, an emergency shipment is per-

formed from the emergency hub.

5. If the emergency hub is also out of stock, a request is made at the global warehouse

where the requested part is stocked.

6. If also the global warehouse is out of stock, the emergency shipment comes from

another location, e.g. the factory or supplier.

In case a customer can be served easier from the global warehouse, steps 3 and 4

are switched: first the part is requested at the global warehouses, and thereafter at the

emergency hub. If no parts are available, at one of those two, the part is requested at

e.g. the factory or supplier.

In the current planning method for LPA customers, only steps 1 and 2 are taken into

account. If the part cannot be found in the country (after step 2), it is assumed that a

part can always be delivered within 48 hours from another location (i.e. emergency hub,

global warehouse, factory or supplier: step 3, 4 and 5).


Continental Parts Availability

For customers that are entitled to continental parts availability, a request for a part is

fulfilled as follows:

1. First it is checked whether the part is available in the continental warehouse.

2. When the continental warehouse has no stock available for this part, the part is

requested at the global warehouse where that part is stocked.

3. If the part is not available in the global warehouse, the request is backordered at

the global warehouse and delivered as soon as a part becomes available.

Global Parts Availability

For customers that are entitled to global parts availability, a request for a part is fulfilled

as follows:

1. It is checked whether the part is available in the global warehouse where that part

is stocked.

2. If the part is not available in the global warehouse, the request is backordered and

delivered as soon as a part becomes available.

1.3 Currently Used Planning Models

In this section the planning models that are currently used by ASML to determine the

basestock levels for spare parts in the global, continental and local warehouses and the

emergency hub are discussed.

Currently the global warehouse and emergency hub spare parts planning and field

stock spare parts planning (local and continental warehouses) are performed by two

different departments (supply planning and demand planning respectively) within the

Customer Logistics department. Both departments use different demand forecasts and

models to do the planning and rules of thumb are used to determine the amount of stock

needed in the emergency hub. Furthermore, in the spare parts planning for customers that

are entitled to local parts availability, it is not taken into account that these customers

can request parts at the continental warehouses.

1.3.1 Local Warehouse Planning

For the planning of spare parts in the local warehouses, the model of Van Houtum

and Kranenburg (2014, Ch. 5) is used. The model is implemented in a software tool


called SPartAn (Spare Parts Analyzer). The model has been developed in cooperation

with ASML during the PhD project of Kranenburg and was inspired by the way ASML

operates its stock in local warehouses in the different regions. It features the possibility

to define regular and main local warehouses within a region. Main local warehouses

can send parts to all other local warehouses by means of lateral transshipments and full

pooling of stock between the main local warehouses is assumed. If there is no stock in

any of the warehouses, parts are sent to customers by means of emergency shipments

from a stock point for which it is assumed that this stock point can always supply.

Input to this model/tool are part demand forecasts, holding costs / cost prices of

parts, transportation costs and times (both lateral and emergency), replenishment lead-

times and service level agreements (DTWP service levels). It should be noted that the

replenishment leadtime that is assumed in the tool is 14 days for all parts, but that these

14 days is only met in % of the times a part is requested (February 2014).

In addition it should be identified which warehouses are main and regular local ware-

houses. For each regular local warehouse, one main local warehouse is assigned, where a

part is requested in case the regular local warehouse is out of stock. For all main local

warehouses a pre-specified order (sequence) is defined, which indicates in which order

parts are requested at the other main local warehouses. Note that due to the full pooling

assumption, each main local warehouse will occur in the pre-specified order of other main

local warehouses.

Plan groups (a group of one or more systems at a customer for which one service level

agreement is set) are defined and a system approach is applied in the planning. Sher-

brooke (1968) describes the system approach as an approach that “focuses management

attention on the entire system so that an appropriate combination of system effectiveness

and system cost can be selected.” Furthermore a basestock policy is used. Note that

with system approach, we mean plan group / customer approach in this context since a

service level is set per plan group / customer (which can have more than one system).

After all input has been collected, a “SPartAn” run is performed. This run is per-

formed twice a year. Then the regional planners review the outcome of SPartAn and

finally set the target stock levels in SAP.4 This process is graphically depicted in Fig-

ure 1.4.

A note should be made with respect to the model of Van Houtum and Kranenburg

(2014, Ch. 5), that is used in SPartAn. This model has specifically been designed to

do the spare parts planning per region. This means that it is not possible to model

that customers can request parts at continental warehouses (which however happens in

practice), since this would mean that customers entitled to continental parts availability

4SAP is enterprise resource planning software that is among others used for managing businessoperations and customer relations.


Parts demand

forecast

SPartAn

System

parameters

Planner reviews

stock levelsSAP

Proposed

stock

levels

Target

stock

levels

Figure 1.4 – SPartAn run process

can also request parts at the main local warehouses in a region due to the assumption

of full pooling. Furthermore, if more regions are planned at once, it means that e.g.

main local warehouses in Japan have to perform lateral transshipments to main local

warehouses in Taiwan. Therefore, the stock-levels are determined per region and the

continental warehouses are planned separately.

1.3.2 Continental Warehouse Planning

For the continental warehouses planning, also a system approach is used per group of

systems. The objective is an aggregate fill rate objective (CSD objective). The model

that is used to find the basestock levels such that costs are minimized is the same as

described in Van Houtum and Kranenburg (2014, Ch. 2) and is a lost sales model. In

this planning, demand of LPA customers that cannot be fulfilled by the warehouses in

the different regions is not taken into account.

1.3.3 Global Warehouses Planning

Whereas a system approach is applied for the planning at the local warehouses and con-

tinental warehouses, for the global warehouse an ABC classification is used (i.e. CSD

objectives are set per item, where expensive items have lower CSD objectives than cheap

items). Furthermore, parts are categorized along two axes: Average Inter-Demand Inter-

val (ADI) and Coefficient of Variation (CV) of demand during leadtime. These variables

are measured as follows:

ADI =12 months

# months with demand

CV =Standard Deviation of demand during leadtime

Average demand during leadtime

Both variables can be either low or high, the cut-off value for the average demand

interval (ADI) is 2 and the cut-off value for the coefficient of variation is 0.6. This results


in four categories and for each category a different way of determining the target stock

levels is applied. The four categories are graphically depicted in Figure 1.5 and between

parentheses the percentage of SKUs that belongs to that category is shown (February

2014).

Stable

(8.3%)

Intermittent

(1.3%)

Erratic

(2.1%)

Lumpy

(88.3%)

Low ADI High ADILow

CV

High

CV

Figure 1.5 – Forecast and RoP setting in global warehouse

The parts in the stable parts category are referred to as forecast parts. The other

three groups of parts are referred to as Reorder Point (RoP) parts. For both forecast and

RoP parts, the forecast and RoP setting are reviewed each month. For the RoP parts,

the actual ordering of parts / offering parts for repair is done twice per week if the order

plus on hand stock minus backorders are smaller than the RoP. For forecast parts, this is

once a month. A more detailed description of how the forecast and RoP are determined

can be found in Appendix A.

For both forecast and RoP parts, safety stock levels have to be determined. To

determine the value of the safety stock, CSD (fill rate in literature) objectives are set

per item. The value of the objective is determined based on characteristics as whether

the part is an NPI (New Product Introduction) part, a mature part and whether it is

only stored in the GW or also in the network (i.e. in local or continental warehouses).

These service levels are represented in a service level breakdown which can be found in

Figure 1.6.

As can be seen, XLD parts are specifically mentioned in Figure 1.6. XLD parts are so

called extreme long down parts. For these parts it means that, when such a part fails in

a system, the system is down for 12 hours or more since the maintenance action requires

much time. In local warehouses for XLD parts a minimum required stock level is set.

In the global warehouse, if an XLD part costs less than e10.000, it automatically gets a

CSD (fill rate) service level of 98%.


Figure 1.6 – Service level breakdown


1.3.4 Emergency Hub Planning

The emergency hub planning is performed together with the global warehouse planning.

The amount of stock that is stored in the emergency hub is determined using the safety

stock level and Table 1.1. If the total number of parts available in the Emergency Hub

is at or below this emergency stock level, the available remaining part(s) can only be

used for emergencies and not for replenishments, or to satisfy demand from CPA or GPA

customers. It should be noted that these rules are only used to determine the amount

of stock for the emergency hub in Asia. It is not taken into account that parts are also

requested with emergency at the global warehouses.

Table 1.1 – Rules for setting emergency stock level

1.4 Limitations Current Planning Models and Methods

What we learn from the demand fulfillment and currently used planning models is, that

the spare parts planning at ASML is quite complex and that the different models that are

used are not really aligned with each other. In Table 1.2 we summarize the limitations

of the currently used planning models and methods. In this project we will try to design

a planning concept in which these limitations are solved. We will further discuss this in

the next chapter.


Table 1.2 – Limitations of currently used planning models and methods

Current Planning Model for: Limitation:

Local Warehouses Only one region at a time can be planned

Continental Warehouses Requests of LPA customers are not taken into account

Emergency HubsOnly one emergency hub (in Asia) is taken into accountStock levels are determined using rules of thumb

Global WarehousesNo grip on replenishment leadtimeEmergency demand of LPA customers is not taken into account

General

Different planning models are used for each type of warehousePlanning is done by two different departmentsDifferent forecasts are usedDifferent planning frequencies are used

2

Scoping

In Section 2.1 the project deliverable will be presented, followed by the project assignment

in Section 2.2. Thereafter the stakeholders will be presented. The functional requirements

and design parameters are discussed in Section 2.4 and Section 2.5. In Section 2.6 it is

discussed what is out of scope.

2.1 Project Deliverable

In this section we will describe the As-Is situation and the To-Be situation. The ‘delta’

between these two situations is what will be designed in this project and will be discussed

in the project assignment.

2.1.1 As-Is situation

Currently the global warehouse (and emergency hub) spare parts planning and field stock

spare parts planning (local and continental warehouses) are performed by two different

departments within the customer logistics department (supply planning and demand

planning respectively). Both departments use different demand forecasts and models to

do the planning. In Table 1.2 in the previous chapter we have summarized the limitations

of the currently used planning models and methods. Our goal is to solve these limitations.

This brings us to the to-be situation of the project

2.1.2 To-Be situation

In the to-be situation ASML wants to use only one forecast for the planning of spare parts

in the global, continental and local warehouses, and the emergency hub. Furthermore in

this new planning model, ASML wants to be able to take into account that the continental

warehouses also deliver parts to local warehouses by means of a lateral transshipment in

case the local warehouses are out of stock. It should also be taken into account that the

amount of spare parts to be stocked in the emergency hubs are determined based on the

expected demand for emergencies instead of pre-defined rules.

16

CHAPTER 2. SCOPING 17

2.1.3 Motivation for Project

The three main reasons why ASML wants to have investigated an integral planning model

are the following:

1) Transparency: when spare parts are integrally planned, only one forecast is used

and there will be one clear demand stream from customer to supplier.

2) Control: when spare parts are integrally planned, there is a better control in the

supply chain since the planning of the local, continental and global warehouses and

emergency hub are aligned.

3) Costs: when spare parts are integrally planned, it is expected that customer ser-

vices levels can be achieved against lower costs (warehousing, duties and freight).

2.2 Project Assignment

The project assignment can be stated as follows:

1) Develop an integral planning model for the service supply chain of ASML that takes

into account the lateral transshipments and emergency shipments as these take

place in reality and that takes into account the different types of parts availability

that customers are entitled to. Furthermore it should take into account that the

global warehouses face both replenishment demand and demand from global parts

availability customers.

2) Develop a prototype software tool based on the integral planning model that is

developed, that can be used by ASML to support the decision-making regarding the

basestock level settings in the local warehouses, continental warehouses, emergency

hubs and global warehouses.

2.3 Involved Stakeholders

There are different stakeholders in this project. These stakeholders are mentioned in

Table 2.1.

2.4 Functional Requirements

A design always starts with specifying the requirements. In this project, the model and

the prototype software that are developed should adhere to some functional requirements.


Table 2.1 – Involved stakeholders

Name Department & Organization Notes

Niels ArendsenCustomer Logistics ASML CL Directors

Paul Lavrijssen

Yvonne Quirijnen Demand Planning ASML Manager Demand Planning

Mark Bergkotte Supply Planning ASML Manager Supply Planning

Ruud van Sommeren Business Support ASMLCompany supervisor &Subject Matter Expert

Wout OomenSupply Planning ASML Inventory Control

Mark Donners

We will state the functional requirements for the model and the prototype software

separately:

Functional requirements for the model:

1. With the model we should be able to find basestock levels such that expected service

level agreements of customers are met against as low as possible expected costs.

2. For some parts, it should be possible to set a minimum basestock level.

3. The model should consist of building blocks that can easily be replaced / adapted

in the future when more advanced models become available.

Functional requirements for the prototype software:

1. The computation time for the tool should be around 3 minutes for problem instances

• around 1000 parts

• around 35 warehouses

• around 45 plan groups

2. The tool should run on a computer with

• an Intel Core i5-3320M CPU @ 2.60GHz processor

• 8.00 GB RAM

3. With the software tool, one should be able to perform the same analyses as one

can perform with the software tools that are currently used.


2.5 Design Parameters

To satisfy the functional requirements, we can take several decisions in the design. The

decisions that have to be made can be related to design parameters. To narrow down

the design space, we state design parameters with respect to models from literature, such

that we can find and apply the right and good models. The design parameters are as

follows:

1. Is the network planned via one multi-echelon model or via multiple single-echelon

models?

2. If via multiple single-echelon models, how is the network decomposed?

3. Is service differentiation applied for different types of demand (demand from cus-

tomers entitled to LPA, CPA or GPA, emergency demand or replenishment de-

mand)?

4. Is an item approach, a system approach or ABC classification (items get different

objectives based on certain characteristics such as price and demand) applied?

In the next part, the conceptual design, we will discuss the decisions that have been

taken with respect to the design parameters.

2.6 Out of Scope

Together with ASML it has been decided that the following items will be out of scope

for this project:

• Next to Downtime Waiting Parts (DTWP), downtime can also occur due to other

reasons, e.g. due to the fact that tools are not available. In this project, we will

only focus on downtime waiting for parts (DTWP).

• Investigating other stocking policies than basestock policies will be out of scope.

• The demand/usage due to installs or system enhancements will not be taken into

account. ASML is trying to plan which parts are possibly used during installs

or system enhancements and send these parts in advance such that these are not

unexpectedly used anymore. Also demand from the factory is out of scope, since

it only occurs on a very rare basis that demand service parts are used as factory

parts.

• Only parts that are part of the service Bill Of Material are taken into account, i.e.

we will not take into account the planning of factory parts for factory usage.

3

Conceptual Design

In the scoping chapter four design parameters have been stated. In this chapter different

choices that can be made with respect to the design parameters are discussed. The deci-

sions that are taken with respect to the design parameters, are supported with qualitative

arguments and knowledge that is derived from models in literature. The decisions that

are made with respect to the design parameters lead to the conceptual design.

The design parameters that were determined in the scoping phase were the following:

1. Is the network planned via one multi-echelon model or via multiple single-echelon

models?

2. If via multiple single-echelon models, how is the network decomposed?

3. Is service differentiation applied for different types of demand (demand from cus-

tomers entitled to LPA, CPA or GPA, emergency demand or replenishment de-

mand)?

4. Is an item approach, a system approach or ABC classification (items get different

objectives based on certain characteristics such as price and demand) applied?

In the next sections these design parameter questions will be answered, which result

in design choices.

3.1 Multi-echelon Model or Multiple Single-echelon Models

Alfredsson and Verrijdt (1999) have developed a model that almost fits the network and

description of the demand fulfilling process as mentioned in Section 1.2, i.e. a two-echelon

planning model with one single global warehouse and multiple local warehouses that can

send parts via a lateral transshipment to other warehouses and where the global ware-

house, which also functions as emergency hub, can also send parts to local warehouses

via an emergency shipment. Basten and Van Houtum (2014, p. 30) mention that this

model “is accurate but not sufficiently fast ... since the method requires a numerical

20

CHAPTER 3. CONCEPTUAL DESIGN 21

solution of two-dimensional Markov processes, which requires relatively much computa-

tional effort.” Instead Basten and Van Houtum (2014, p. 30) mention the possibility

to decompose the problem into single-echelon models for the global warehouse and local

warehouses for which models exist in literature. We will also decompose our network and

our first design choice therefore is:

Design Choice 1: We will decompose the network into multiple single-echelons.

In the next section it is discussed how the network can be decomposed into multiple

single-echelons.

3.2 Model Decomposition

From literature we know that there exist single-echelon, multi-location models. The

model of Van Houtum and Kranenburg (2014, Ch. 5), which is currently applied, is also

a single-echelon multi-location model. In the model of Van Houtum and Kranenburg

(2014, Ch. 5) the network is decomposed in sets of local warehouses that belong to one

region. In that case, for global and continental warehouses, and emergency hub other

models need to be applied (as is currently done).

However, in literature there also exist models that are generalized models of that of

Van Houtum and Kranenburg (2014, Ch. 5). The first one is the model of Reijnen et al.

(2009). In this model one is free to determine per plan group at which warehouses, in

a pre-specified order, parts are requested. The overflow demand (i.e. the demand that

cannot be satisfied when a local warehouse is out of stock) is assumed to follow a Poisson

process. Another model, the model of Van Wijk et al. (2012) can be seen as an extension

to the model of Reijnen et al. (2009). In that model, the overflow demand processes

that occur due to stock-outs at local warehouses are modeled as interrupted Poisson

processes, i.e. the Poisson process is on or off and the time it is on or off is exponentially

distributed. This method leads to more accurate approximations of the overflow demand

streams. Furthermore they include the possibility for hold back levels in their model,

i.e. stock is reserved at a warehouse and will not be used for later transshipments. A

downside however of the model of Van Wijk et al. (2012) is that the computation time

becomes relatively high and that heuristic optimization procedures for large instances

are currently not available. Therefore we have decided to continue our discussion on the

decomposition based on the model of Reijnen et al. (2009), which gives us the freedom to

take into account overflow demand that is requested at the continental warehouses due

to stock-outs at the local warehouses.

We now have a model that can be used for both the local warehouses and continental

warehouses. Next we discuss how we can model the global warehouses and emergency


hubs.

As has been discussed in the demand fulfillment description, in case of an emergency

request, for some customers the part is first requested at the global warehouses instead

of the emergency hub, depending on which of the two locations can supply the part

faster.1 Therefore, one has to take into account that the global warehouses should be

able to serve customers with an emergency request immediately upon request, i.e. some

stock should be held back by means of e.g. a critical level, else the demand is ‘lost’.

For all other part requests (requests from GPA customers and replenishments), demand

is backordered in case the stock is at or below the critical level. In literature, there

exists a model that exactly fits this situation: the model of Enders et al. (2014). In the

model of Enders et al. (2014), two customer classes are defined. Critical level stock is

kept for emergency demand. If the demand is not satisfied immediately, the demand

is lost. For replenishment demand and demand for GPA customers, the demand is

backordered if it is not satisfied immediately. Replenishment demand and demand from

GPA customers is not satisfied when the on hand stock is at or below the critical level.

Also other models exist however in these models it is not possible to combine lost sales

models and backorder models: In the model of Mollering and Thonemann (2008), two

customer classes are defined and critical level stock is kept for high priority customers

(emergency demand). For both high and low priority customers, if demand cannot be

satisfied immediately, the demand is backordered. Van Houtum and Kranenburg (2014,

Ch. 4) propose a model where multiple customer classes can be defined. When demand

cannot be satisfied immediately, it is assumed to be lost. Since we want to be able to

backorder replenishment demand and demand from GPA customers, but do not want to

backorder emergency demand, the model of Enders et al. (2014) could be an option for

modeling the global warehouses and emergency hubs.

Another option, instead of applying service differentiation at the global warehouses,

is to take service differentiation into account by modeling two separate stock points:

one for emergencies, where demand has to be fulfilled immediately upon request (and

demand is ‘lost’ in case this is not possible) and one stock point for replenishments and

demand of GPA customers, where demand is backordered in case it cannot be fulfilled

immediately upon request. In this latter situation, the emergency stock points have the

same function as the emergency hub in Asia currently has. In this case the emergency

hubs can be planned together with the local and continental warehouses and we apply

separate models for the global warehouses.

Based on the two options that are proposed here, we can decompose the network

in two ways. In the first decomposition we describe the option where we have separate

1Remember that currently it is assumed that emergency requests are always requested at the emer-gency hub and therefore only rules of thumb exist for the emergency hub, not for the global warehouses.


stock points for the emergency stock and the global warehouse stock. In the second

decomposition we describe the option where we apply the model of Enders et al. (2014).

3.2.1 Network Decomposition 1

In the first decomposition option, which is depicted in Figure 3.1, we take all local

warehouses, continental warehouses and the emergency hubs (emergency stock points)

in one single echelon. As one can see, not only a European emergency hub is added but

also a US emergency hub is introduced. This needs some explanation: in some cases

demand for a certain item mainly occurs in one continent, in that case it might be better

to stock the parts that are needed to satisfy emergency requests in that continent, but

with the possibility that also LPA customers from other continents can request parts at

this warehouse. This does not mean that ASML has to open another warehouse, the

emergency hub can physically be the same location as the continental warehouse (as is

already the case for the Asia emergency hub). It should be noted that in these emergency

hubs parts are stocked for LPA customers only. The blue arrows show how requests from

LPA customers flow through the supply chain. As one can see, these requests also go

to the continental warehouses and end up in one of the emergency hubs. The orange

and green arrows show how requests from CPA and GPA customers, respectively, flow

through the supply chain. The grey arrows show the replenishment streams.

GW

LW LW

LW

LW

Asia

Emer.

LW

US

CW

LW

LW

LW

LW

LW LW

LW

Asia

CW

LW LW

US

Emer.

EU

Emer.

EU

CW

LW

LW LW

LW

Figure 3.1 – Network decomposition 1

For this decomposition, we use the model of Reijnen et al. (2009) to calculate the

stock levels at the local warehouses, continental warehouses and emergency hubs at once.


In this decomposition, the global warehouse is only used to satisfy replenishment

demand, demand from GPA customers and demand of CPA customers that cannot be

satisfied by the continental warehouse. We can use the basestock policy model with

backorders that is described in Van Houtum and Kranenburg (2014, Ch. 2). Note that

only one global warehouse is shown in Figure 3.1. This is due to the fact that we can

model the four global warehouses as one global warehouse since there is no overlap in the

assortment of parts at the different global warehouses.

We will denote the lower echelon as the field stock planning model and the upper

echelon as the global warehouse planning model. The two echelons are coupled using

the replenishment leadtimes: in the global warehouse, stock levels are set such that

replenishment leadtime objectives are met. This will be explained in more detail in the

next chapter. In the field stock planning model, the expected replenishment leadtimes

that are the outcome of the global warehouse planning model, are used to determine the

stock levels in the local warehouses, continental warehouses and emergency hubs.

3.2.2 Network Decomposition 2

In the second decomposition option, which is depicted in Figure 3.2, we choose for the

option of service differentiation at the global warehouses. This means that if the amount

of stock is at or below this level, only requests for emergencies will be satisfied. These

emergency requests can come from plan groups in Europe directly or via the Asia or

US emergency hubs. The emergency stock in the global warehouse is indicated with the

small green shaded triangle. For the global warehouse with critical levels we can use the

model of Enders et al. (2014). For the other two emergency hubs, a basestock policy

model with lost sales as described in Van Houtum and Kranenburg (2014, Ch. 2) can

be applied. It should however be taken into account, that the model is more complex

than is shown in Figure 3.2 since there are four global warehouses. Figure 3.2 only shows

the network for that part of the assortment that is stocked in the global warehouse in

The Netherlands, however the other parts of the assortment are stocked in the global

warehouses in the US and Asia, which means that we get four decompositions for the

upper echelon: one for each part of the assortment. This means that the model for the

global warehouses and emergency hubs needs to be solved four times: once per part of

the assortment. Furthermore, this decomposition results in two emergency hubs in the

US since there are two global warehouses in the US.

In the lower echelon however, we can still apply the model of Reijnen et al. (2009) and

we use the expected overflow demand from the local warehouses as input for determining

the demand at the emergency hubs.

The two echelons are again coupled using the replenishment leadtimes.


GW

LW LW

LW

LW

Asia

Emer.

LW

US

LW

LW

LW

LW

LW

LW LW

LW

Asia

CW

LW LW

US

Emer.

EU

Emer.

EU

CW

LW

LW LW

LW

Figure 3.2 – Network decomposition 2

3.2.3 Pros and Cons

We have proposed two ways in which we can decompose the network. In both cases,

two single-echelon models need to be used. We will now state the pros and cons of both

decompositions.

Pros of network decomposition 1:

• More freedom in modeling the global warehouse in terms of stocking policy (not

necessarily basestock policy)

• More clear cut between the two echelons

• Computation time is expected to be relatively low

Cons of network decomposition 1:

• No stock pooling between emergency hubs and global warehouses

Pros of network decomposition 2:

• Stock pooling between stock in emergency hubs and global warehouses

Cons of network decomposition 2:


• Computation time of critical level policy model of Enders et al. (2014) is relatively

high: in earlier projects it turned out that calculation problems occurred for items

with high demand and/or high leadtimes due to Markov processes that have to be

evaluated. One of these projects was executed at ASML.

• Difficulty of modeling global warehouses (with critical level) and emergency hubs

in one echelon

After taking the pros and cons of both decompositions into account, we concluded that

decomposition 1 is preferred over decomposition 2. After a meeting with the stakeholders,

it turned out that especially the pros of decomposition 1 are very strong.

The design choice therefore will be as follows:

Design Choice 2: We will decompose the model into two single-echelon models,

one single-echelon (upper echelon or global warehouse planning) model where the global

warehouse is one echelon and one single-echelon (lower echelon or field stock planning)

model that comprises all local warehouses, continental warehouses and emergency hubs.

The coupling between the two echelons will be the replenishment leadtime.

3.3 Service Differentiation Between Demand Streams and Cus-

tomers

At the global warehouse, service differentiation can also be made between satisfying re-

plenishment demand and demand from GPA customers. Furthermore, in the continental

warehouse one can apply service differentiation between LPA customers and CPA cus-

tomers and even at local warehouses one can differentiate between customers. After

discussion with the stakeholders, it has been decided that no service differentiation will

be applied at any of the warehouses. The main reason for this is that ASML does not

want to apply (customer) differentiation, e.g. they do not want to say when a customer

with a ‘lower’ service level needs a part and there is only one part left that they will keep

the part for the ‘higher’ service levelcustomer, since the probability that this ‘higher’

service level customer needs the part is very small. This leads to the following design

choice:

Design Choice 3: In none of the warehouses priority is given to customers or de-

mand streams by means of critical or hold-back levels.


3.4 System Approach, Item Approach or ABC Classification Ap-

proach

The last decision that has to be taken is whether a system approach, item approach or

ABC classification approach is taken in the planning models. In a system approach an

aggregate service level is set per plan group, in an item approach a service level is set

per SKU. In an ABC classification approach, SKUs are classified based on e.g. average

demand and price and for each class a service level is set that has to be met for each

SKU in that class.

Currently, at the local and continental warehouses a system approach is applied per

plan group, i.e. for LPA customers, an aggregate DTWP objective is set and for CPA

customers an aggregate CSD constraint is set. Is has been decided that we will continue

to apply a system approach in the field stock planning.

For the global warehouse both an item approach, where each item gets the same delay

objective, and ABC classification approach where delay objectives are set e.g. based on

price and/or demand will be applied. Delay forms a part of the replenishment leadtime

to the field and we will use this delay as the coupling mechanism. By setting delay

objectives, we are able to make better assumptions with respect to the replenishment

leadtime in the field stock planning, i.e. we use the expected delays that result from the

global warehouse planning in the replenishment leadtime. Then, based on the results of

both the item approach and ABC classification approach, one of the two approaches can

be chosen by ASML. A note should be made: if, the solution based on the item approach

or ABC classification approach does not lead to a feasible solution for the GPA customers,

that also have an aggregate CSD service level, a system approach will be applied that

increases stock levels until a feasible solution for the GPA customers is found. This leads

to the following design choice:

Design Choice 4: In the field, a system approach will be applied where an aggregate

service level is set per plan group, i.e. an aggregate DTWP or CSD constraint is set

per plan group. In the global warehouse both an item approach and ABC classification

approach will be used to set delay objectives per SKU or class of SKUs. If needed, a

system approach will be applied to find a feasible solution for GPA customers, that have

an aggregated service level.

4

Detailed Design

4.1 Conceptual Design

In the previous chapter, we have decided that we decompose the planning and how we

decompose the planning (see Figure 4.1). Furthermore we have decided that we do not

apply service differentiation at warehouses and that we use a system approach in the field

stock planning model and test both an item approach and ABC classification approach

in the global warehouse model.

GW

LW LW

LW

LW

Asia

Emer.

LW

US

CW

LW

LW

LW

LW

LW LW

LW

Asia

CW

LW LW

US

Emer.

EU

Emer.

EU

CW

LW

LW LW

LW

Figure 4.1 – Network Decomposition

In this chapter, we will explain our solution procedure to find basestock levels. This

can briefly summarized as follows:

1. We start with setting delay objectives for the global warehouse planning. Delay

forms a part of the replenishment leadtime to the field.

28

CHAPTER 4. DETAILED DESIGN 29

2. We determine the basestock levels in the global warehouses such that the delay

objectives are met and we increase stock in the global warehouses in case we have

not yet found a feasible solution for the GPA customers.

3. We use the expected delay plus a fixed transportation and administration time

as replenishment leadtime in the field stock planning to determine the basestock

levels that are needed to satisfy the service level agreements for the LPA and CPA

customers.

In Section 4.2 it will be discussed how delay objectives can be set for the Global

Warehouse. In Section 4.3 it is explained how basestock levels for the global warehouse

can be determined such that the delay objectives and service level agreements for GPA

customers are met using models from Van Houtum and Kranenburg (2014, Ch. 2). Lastly,

in Section 4.4, it will be explained how basestock levels for the field stock can be deter-

mined such that service level agreements for LPA and CPA customers are met, using the

model of Reijnen et al. (2009). Both Section 4.3 and Section 4.4 consist of two parts: a

model description and evaluation and optimization procedures.

4.2 Setting Delay Objectives

We assume that the total expected replenishment leadtime consists of two parts: a trans-

portation and administration time part (which is fixed) and a delay part which is caused

by the fact that parts are not always directly available at the global warehouse.

Let I denote the set of SKUs which contains |I| SKUs and let index i ∈ I denote a

certain SKU. Let tfixedi,j,repl denote the total transportation and administration time that is

needed to get a part from the global warehouse to the field in case it can be delivered

immediately. As can be seen this transportation and administration time depends both on

i and j. Let j denote the warehouse in the field where the part is shipped to and depending

on where SKU i is produced and sourced (at one of the four factory locations, The

Netherlands, Wilton, San Diego or Linkou). This fixed transportation and administration

time can be different for each combination of source and warehouse in the field (and thus

i and j). Let tdelayi,repl denote the delay part of the replenishment leadtime. Note that

this delay only depends on the SKU. Let ttoti,j,repl denote the total expected replenishment

leadtime. Then the total replenishment leadtime can be calculated as follows:

ttoti,j,repl = tfixed

i,j,repl + tdelayi,repl

The question is: How can we calculate the expected delay? For that we will use

Little’s Law. Let mi denote the total mean demand per time unit at the Global Ware-

house for SKU i. This demand is the sum of the total expected demand from the field


(replenishment demand: demand from all LPA and CPA plan groups) and demand from

the GPA customers. Let EBOi(Si) denote the expected number of backorders for given

basestock level Si. Then, the delay (tdelayi,repl) in case a part cannot be delivered immediately

can be calculated based on Little’s Law1 as follows:

tdelayi,repl =

EBOi(Si)

mi

So if we determine a maximum delay (objective) for each SKU i, we can determine an

objective in terms of the expected number of backorders such that this maximum delay

is met:

EBOobji = mit

delay,obji,repl

4.3 Global Warehouse Planning

For the global warehouse, the model of Van Houtum and Kranenburg (2014, Ch. 2) will

be used. In this model we have two constraints: 1) The expected number of backorders

constraint which is based on the delay constraint per SKU, and 2) the service level

constraints for the GPA customers. First we will describe the model that we use and

then we explain how to determine the expected number of backorders for given basestock

levels. Lastly, we will explain how we make sure that the GPA customer service levels

are met.

4.3.1 Model

Van Houtum and Kranenburg (2014, Ch. 2) describe a single warehouse where multiple

SKUs are kept on stock to replace components in a certain system and where the failed

part is sent into repair (in case of a repairable) or a new part is ordered at the supplier

(in case of a consumable).

Let I = {1, 2, . . . , |I|} denote the set of SKUs for which a stocking decision has to

be taken. SKUs can either be consumables or repairables. Consumables are parts that

are scrapped and newly bought after failure and repairables are parts that can possibly

be repaired. Let SGW = (SGW1 , . . . , SGW

|I| ) denote the vector with basestock levels for

SKUs i ∈ I at the global warehouse. We assume that demand for SKUs i ∈ I arrive

according to a Poisson Process with rate mi. Let DIi(t) denote the number of parts on

order or in repair at time t. When SKUs are ordered or repaired, a new-buy or repair

leadtime has to be taken into account. This leadtime is determined as follows:

For the consumables, always the new-buy leadtime has to be taken into account. For

repairables, the leadtime depends on two factors. When a part has failed, it is called

1Little’s Law: L = λW where L denotes the average number of customers in the system, λ theaverage arrival rate and W the average waiting time.


a Field Stock Defect at ASML. At the customer it can already be decided to send the

part into repair or to scrap it. For parts that are sent into repair, it can happen that

during the repair job it turns out that the part is too damaged to make it as good as new

again. In that case the part is still scrapped. This is summarized in Figure 4.2. Between

parentheses we show which leadtime has to be taken into account.

Field Stock Defect

% Scrapped

(New-buy leadtime)

% Sent into repair

% Repairable

(Repair leadtime)

% Not repairable

(New-buy + repair leadtime)

Figure 4.2 – Field stock defects an their leadtimes

For convenience let us denote the leadtime by tsuppli . After repair or arrival, the parts

are sent to the stocking location. The number of parts that are on hand at time t is

denoted by OHi(t).

When a demand occurs, depending on whether there is stock available, there are two

options: When stock is available, the on hand stock at time t, OHi(t), is decreased by

one and the number of parts on order, DIi(t), is increased by one. When there is no

stock available, the number of backorders at time t, BOi(t), is increased by one and the

number of parts on order, DIi(t), is also increased by one. The number of parts on order

plus the number of parts on hand minus the number of backorders has to be equal to the

basestock level:

DIi(t) + OHi(t)− BOi(t) = SGWi

This can also be written as:

OHi = (SGWi −DIi)

+

BOi = (DIi − SGWi )+

The following assumptions are made (Van Houtum and Kranenburg, 2014, Ch. 2):

1. Demands for the different SKUs occur according to independent Poisson processes.

2. For each SKU, the demand rate is constant.

3. Repair / new-buy lead times for different SKUs are independent and repair / new-

buy lead times for parts of the same SKU are independent and identically dis-

tributed.


4. A one-for-one replenishment strategy is applied for all SKUs.

For our optimization we have a constraint on the expected number of backorders that

is determined based on the delay objective that we have set for each SKU i: EBOobji =

mitdelay,obji,repl . Then we have to solve the following problem for all SKUs i ∈ I.

min Ci(SGWi ) = ch

i SGWi

subject to EBOi(SGWi ) ≤ EBOobj

i ,

SGWi ∈ N0.

where chi denotes the holding cost rate for SKU i. The total yearly cost is then the sum

of the holding costs for all items:

C(SGW) =∑i∈I

chi S

GWi

4.3.2 Evaluation and Optimization

The goal is now to find an expression for the expected number of backorders. Following

the logic of Van Houtum and Kranenburg (2014, Ch. 2), let DIi, OHi and BOi be the

steady state variables for the parts on order / in repair, the parts on hand and the parts

backordered respectively. The on hand inventory is maximally SGWi (in case there are no

items in repair or on order) and minimally 0. The number of backorders can range from

0 to ∞.

Since demand occurs according to a Poisson Process with rate mi, and have an average

leadtime of tsuppli , this can be modeled as an M/G/∞ queuing system and Palm’s Theorem

(1938) can be applied. Palm’s Theorem states that:

If jobs arrive according to a Poisson process with rate λ at a service system

and if the times that the jobs remain in the service system are independent

and identically distributed according to a given general distribution with mean

EW , then the steady-state distribution for the total number of jobs in the

service system is Poisson distributed with mean λEW .

This implies that the average number of parts on order or in repair is equal to mitsuppli .

Therefore, the number of items on order or in repair, DIi, is Poisson distributed with mean

mitsuppli and therefore

P{DIi = x} =(mit

suppli )x

x!e−mit

suppli (Van Houtum and Kranenburg, 2014, Ch. 2). For the

implementation we will use the recursive formula for the Poisson Distribution to prevent

the use of the factorial function, which does not work for large numbers. This recursive

function can be found in Appendix B.


Now we know how to determine the number of parts on order or in repair, we can

determine the expected number of backorders, EBOi. Backorders only occur when DIi >

SGWi . Therefore, for a given SGW

i the expected number of backorders are denoted by:

EBOi(SGWi ) =

∞∑x=0

(x− SGWi )+ P{DIi = x} =

∞∑x=SGW

i +1

(x− SGWi )P{DIi = x}

In Appendix B we give a recursive function to calculate the expected number of

backorders.

The question now is, what basestock level do we need to satisfy the expected number

of backorders constraint? We know that the expected number of backorders, EBOi(SGWi ),

are decreasing and convex in SGWi and the costs, Ci(S

GWi ) = ch

i SGWi , are increasing and

linear in SGWi . Therefore, we will increase Si until we have achieved the expected number

of backorders constraint. As a result of the linearity of the cost function, we also know

that these are the minimum holding costs that are incurred.

4.3.3 Meeting GPA Contracts

Since we also want to meet the aggregate CSD contracts of Global Parts Availability

(GPA) customers, we need to check whether the current solution (i.e. the basestock

levels such that the expected number of backorder constraints are met) also satisfies

the aggregate CSD constraints for the GPA customers. When this is not the case, we

need to increase the basestock levels such that we meet these contracts. For this we

will apply a system approach. In our optimization, we follow the procedure as described

in e.g. Kranenburg and Van Houtum (2007), Kranenburg and Van Houtum (2009) and

Van Houtum and Kranenburg (2014, Ch. 5) where we look at the distance to the set of

feasible solutions versus the increase in costs. They have shown that this greedy approach

performs well (small optimality gap) compared to Dantzig-Wolfe Decomposition Column

Generation. Let the set NGPA denote the set of plan groups that have a CSD contract

and SGWi,EBO the basestock level that is at least needed to satisfy the delay constraint. Then

we solve the following problem:

min C(SGW) =∑i∈I

chi Si

subject to CSDn(SGW) ≥ CSDobjn , ∀n ∈ NGPA,

Si ≥ SGWi,EBO, ∀i ∈ I,

Si ∈ S, ∀i ∈ I.

Where S = {S = (Si)i∈I |Si ∈ N0,∀i ∈ I} denotes the set of all solutions and SGWi,EBO ∈ S

denotes the minimum basestock level for SKU i that is already calculated to meet the

expected number of backorder constraints.


Note that we solve only one problem although we have four global warehouses. We

are able to do this since at each global warehouse a part of the assortment of spare parts

is stocked and the assortments at the global warehouses together form the complete

assortment.

Evaluation

To obtain the aggregate CSD, CSDn(SGW), we first need to know the item CSD (fill rate

in literature) at the global warehouse. Let βi(SGWi ) denote the fill rate for SKU i for given

basestock level SGWi at the global warehouse. Parts can only be delivered immediately

if OHi > 0 and thus DIi < SGWi . Therefore the fill rate is equal to the sum of the

probabilities that DIi < SGWi :

βi(SGWi ) =

SGWi −1∑x=0

P{DIi = x}

The aggregate CSD, CSDn(SGW) can then be calculated as:

CSDn(SGW) =∑i∈I

mi,n

Mn

βi(SGWi ),

where Mn =∑

i∈I mi,n is the total demand from plan group n and mi,n is the demand

for SKU i from plan group n.

Optimization

To solve this optimization problem, we apply a greedy approach to find a solution for

the basestock levels at the global warehouse such that the CSD contracts are met.

Define ∆iC(SGW) as the change in cost for an SKU i when SGWi is increased by one

as follows:

∆iC(SGW) = C(SGW + ei)− C(SGW), i ∈ I.

where ei, i ∈ I is the row vector of size |I| with the ith element equal to 1 and all other

elements 0.

Furthermore, define S feas ⊆ S as a subset of all solutions:

S feas := {SGW ∈ S|CSDn(S) ≥ CSDobjn ∀n ∈ NGPA}

Then, for each solution SGW ∈ S we define the distance d(SGW) to the set S feas of

feasible solutions as:

d(SGW) :=∑

n∈NGPA

(−CSDn(SGW) + CSDobj

n

)+,


where x+ := max{0, x} ∀x ∈ R.

The decrease in distance can be calculated as follows:

∆id(SGW) = d(SGW)− d(S + eGWi )

=∑

n∈NGPA

[(−CSDn(SGW) + CSDobj

n

)+−(−CSDn(SGW + ei) + CSDobj

n

)+]

=∑

n∈NGPA

[(−∑i′∈I

mi′,n

MnCSDi′,n(SGW

i′ ) + CSDobjn

)+

−

− ∑i′∈I\{i}

mi′,n

MnCSDi′,n(SGW

i′ )− mi,n

MnCSDi,n(SGW

i + ei) + CSDobjn

+ ,where ei, i ∈ I is the row vector of size |I| with the ith element equal to 1 and all other

elements 0.

We are interested in the decrease in distance to the set of feasible solutions versus

the costs, when we increase the basestock level Si with 1. Therefore we define the greedy

ratio Γi, i ∈ I, as Γi := ∆id(SGW)/∆iC(SGW) and the following greedy algorithm is used

to determine the basestock levels such that a feasible solution is found.

Greedy Optimization Algorithm

Step 1: Initialization

1. Set SGW := SGWEBO, ∀i ∈ I.

Step 2: Do:

1. Calculate ∆iC(SGW), ∆id(SGW) and Γi, ∀i ∈ I.

2. While d(SGW) > 0:

a) Determine i such that Γi ≥ Γi, ∀i ∈ I.

b) Set SGWi

:= SGWi + 1.

c) Calculate ∆iC(SGW), ∆id(SGW) and Γi, ∀i ∈ I.

In the first step we set all basestock levels equal to the basestock level that we already

calculated to meet the expected number of backorder constraints. In the second step we

increase the basestock level for that SKU i that has the biggest greedy ratio. We repeat

this until we have met all CSD contracts (i.e. d(SGW) = 0).


4.4 Field Stock Planning

For the field stock planning, i.e. determining a feasible solution for the basestock levels

in the local and continental warehouses and the emergency hubs, the model of Reijnen

et al. (2009) will be used. Adaptations to the model have to be made to apply it to the

case of ASML. In this chapter we will discuss the model as it will be used in this project

and we will present the evaluation and optimization procedure in mathematical terms.2

4.4.1 Model

Let again I = {1, 2, . . . , |I|} denote the set of parts for which a decision on the basestock

levels has to be taken. Let J = {1, 2, . . . , |J |} be the set of local warehouses, continental

warehouses and emergency hubs. Let N = {1, 2, . . . , |N |} denote the set of plan groups.

We have two types of plan groups: The plan groups (customers) that are entitled to

Local Parts Availability (NLPA ⊆ N) and plan groups (customers) that are entitled

to Continental Parts Availability (NCPA ⊆ N). Each plan group is connected to one

warehouse j ∈ J . Each warehouse uses a basestock policy for the replenishment of SKUs

and the basestock level for SKU i at warehouse j is denoted by Si,j.

For each plan group n an array vn is defined that contains all warehouses where

that plan group can request a part. The elements of the array resemble a pre-specified

order (sequence) of warehouses where the part is requested. This order could e.g. be

determined based on how long it takes to send a part from a warehouse to a plan group

(customer). In this array vn, vn(k) denotes the kth warehouse (k = 1, 2, . . . , pn) where a

part is requested and pn denotes the length of vn. The minimum length for this array is

1. For LPA customers, the last four elements of the array are the continental warehouse

of the continent where the customer is located and the three emergency hubs. For CPA

customers, the array will only contain the continental warehouse where that customer is

assigned to. Furthermore, let Jn ⊆ J denote the subset of warehouses where plan group n

can request a part.

Let mi,n denote the total demand for SKU i from plan group n (i.e. sum of demand

for SKU i from all systems (machines) together in plan group n). The number of systems

in plan group n is denoted by MCn. Furthermore define αi,n,j as the fraction of demand

for SKU i from plan group n that is satisfied by warehouse j (note that for warehouses

j ∈ J that are not in vn(k) this value is zero) and define θi,n(Si) = 1−∑pn

q=1 αi,n,vn(q)(Si)

as the fraction of demand for SKU i by plan group n that is not satisfied by any of the

warehouses or emergency hubs (in array vn). Si = (Si,1, . . . , Si,|J |) is the vector with

basestock levels for SKU i in warehouses j ∈ J .

The following assumptions are made (Reijnen et al., 2009):

2For the original model we refer to the paper of Reijnen et al. (2009).


1. The demand streams for all SKUs are independent Poisson processes.

2. For each SKU, the demand rate is constant.

3. The replenishment leadtimes for SKUs are independent and identically distributed.

4. A one-for-one replenishment strategy is applied for all SKUs.

Costs

For all warehouses we have to take into account the holding costs per time unit for holding

one part of SKU i, denoted by chi . Furthermore for LPA customers we have to take into

account costs for an emergency shipment in case the part cannot be delivered by any

of the warehouses or emergency hubs, denoted by cem and we have to take into account

costs in case a lateral transshipment is needed (i.e. a shipment from a warehouse in array

vn that is not the first warehouse in the pre-specified order), denoted by cn,j (i.e. the

cost to ship a part from warehouse j to plan group (customer) n).

Then, the expected total cost for SKU i is as follows:

Ci(Si) =∑j∈J

chi Si,j +

∑n∈NLPA

mi,n

(cemθi,n(Si) +

∑j∈Jn

cn,jαi,n,j(Si)

),

In the evaluation algorithm, the θi,n(Si) ∀n ∈ NCPA will be set to 0 since for these

plan groups no emergency shipments are applied.

The total expected costs are denoted as C(S) =∑

i∈I Ci(Si), where the matrix S is

defined as follows

S =

S1,1 S1,2 · · · S1,|J |

S2,1 S2,2 · · · S2,|J |...

.... . .

...

S|I|,1 S|I|,2 · · · S|I|,|J |

Service Levels

As has been discussed in Section 1.2, for its LPA customers, ASML has DTWP service

levels and CSD service levels for its CPA customers. For the DTWP service levels we

need to know the expected time spent on transporting parts.

For one SKU i we can calculate this as follows: We assume that transportation times

are independent of the SKU. Let tn,j denote the transportation time from warehouse j to

plan group n. Let tem denote the emergency transportation time (i.e. the time it takes to

deliver a part when it cannot be delivered by any of the warehouses in vector vn of plan


group n). Then the expected waiting time per SKU i for customer n can be calculated

as follows:

Wi,n(Si) = temθi,n(Si) +∑j∈Jn

tn,jαi,n,j(Si)

If we multiply the expected waiting time per SKU i with the demand per time unit

and divide this by the number of machines in the plan group, we obtain the DTWPi,n(Si):

DTWPi,n(Si) =Wi,n(Si)×mi,n

MCn

In Appendix C we explain this derivation in a bit more detail.

For a group of systems n, the total DTWP can be calculated as follows:

DTWPn(S) =∑i∈I

DTWPi,n(Si)

For its CSD customers (plan groups), there are no waiting time constraints but fill

rate (CSD) constraints. These customers cannot receive parts by means of a lateral

transshipment (the vector vn contains only one element). The CSDi,n(Si) can therefore

be calculated as:

CSDi,n(Si) = αi,n,vn(1)(Si) = βi,vn(1)(Si,vn(1))

In this formula, βi,j(Si,j) is the fill rate (CSD) for SKU i at warehouse j.

For a group of systems n, the aggregate CSD can be calculated as follows:

CSDn(S) =∑i∈I

mi,n

Mn

CSDi,n(Si),

where Mn =∑

i∈I mi,n.

Optimization Problem

We now state our optimization problem in which the goal is to minimize costs but such

that the DTWP and CSD constraints for each of the plan groups are met. In our

optimization problem, the basestock level Si,j is the decision variable. Furthermore, let

DTWPobjn and CSDobj

n be the DTWP and CSD objective for plan group n respectively.

min C(S) =∑i∈I

Ci(Si)

subject to DTWPn(S) ≤ DTWPobjn , ∀n ∈ NLPA,

CSDn(S) ≥ CSDobjn , ∀n ∈ NCPA,

Si,j ≥ Sstarti,j , ∀i ∈ I, j ∈ J,

Si,j ∈ S, ∀i ∈ I, j ∈ J.

Where S = {S = (Si,j)i∈I,j∈J |Si,j ∈ N0,∀i ∈ I and j ∈ J} denotes the set of all solutions,

Sstarti,j ∈ S denotes minimum basestock level for SKU i at location j as required by ASML.


4.4.2 Evaluation and Optimization

To be able to find a feasible solution for the optimization problem, we first need to be able

to evaluate service levels for given basestock levels and then an optimization procedure.

Reijnen et al. (2009) provide both an exact and approximate procedure for evaluation and

optimization. However, they mention that the exact evaluation and optimization are not

suitable for large (real-life) instances, therefore we will use the approximate evaluation

method where we assume general distributed leadtimes with mean ttoti,j,repl. Furthermore,

we will not use the optimization procedure since in Reijnen et al. (2009), an item approach

is applied. Since we want to apply a system approach instead of an item approach, we will

propose an optimization procedure that is closely related to the optimization procedure

as described in Van Houtum and Kranenburg (2014, Ch. 5).

Evaluation

In the approximate evaluation method there are two approximate steps that have to be

made:

• Overflow demand streams that occur due to stock-outs at local warehouses are

assumed to be Poisson distributed.

• Stock levels at the warehouses are assumed to be independent of each other such

that the warehouses can be analyzed as separate stock points.

In the evaluation algorithm of Reijnen et al. (2009), αi,n,j(Si), θi,n(Si) and βi,j(Si)

are calculated for given basestock levels. βi,j(Si) denotes the CSD (fill rate in literature)

of SKU i at warehouse j. Before we state the evaluation algorithm, first we need to

introduce a few more variables. Let mi,n,j denote the demand rate for SKU i from plan

group n that is faced by warehouse j (remind that mi,n denotes the total demand for

SKU i from plan group n, i.e. demand for SKU i from all systems in plan group n).

Let Mi,j =∑

n∈N mi,n,j denote the total demand for SKU i at warehouse j from all plan

groups.3 In the evaluation we need the Erlang Loss probability which is denoted and

calculated as follows:

L(c, ρ) =ρc/c!∑cx=0 ρ

x/x!

This probability is a result from queuing theory (a M/G/c/c system) and denotes the

probability that all servers are occupied. In the formula, ρ denotes the offered load (in

our case, demand during replenishment leadtime) and c the number of servers (in our

case, the basestock level at the warehouse).

3We assume that this demand follows a Poisson Process. According to Reijnen et al. (2009, p. 11),“this assumption does not hold in general, but is reasonable for high fill rates.”


The algorithm to evaluate an instance with given basestock levels at all warehouses

j ∈ J for given SKU i is as follows:

Approximate Evaluation Algorithm for each SKU i ∈ IStep 1: Initialization:

1. ∀j ∈ J, βi,j := 1− L(Si,j, ttoti,j,repl

∑n∈N |vn(1)=jmi,n).

2. ∀n ∈ N, mi,n,vn(1) := mi,n.

3. ∀n ∈ N, j 6= vn(1), mi,n,j := 0.

Step 2: Repeat until Mi,j does not change more than ε for each j ∈ J:

1. ∀n ∈ NLPA and for 2 ≤ q ≤ pn, mi,n,vn(q) := (1− βi,vn(q−1)(Si,vn(q−1)))mi,n,vn(q−1).

2. ∀j ∈ J, Mi,j :=∑

n∈N mi,n,j,

3. ∀j ∈ J, βi,j(Si) := 1− L(Si,j, ttoti,j,replMi,j).

Step 3: Finalization:

1. ∀n ∈ N, j ∈ J, αi,n,j(Si) :=βi,j(Si)mi,n,j

mi,n.

2. ∀n ∈ NLPA, θi,n(Si) = 1−∑pn

q=1 αi,n,vn(q)(Si)

3. ∀n ∈ NCPA, θi,n(Si) = 0.

Note that the ttoti,j,repl = tfixed

i,j,repl +tdelayi,repl, i.e. we take the expected delay that results from

the global warehouse planning into account for the replenishment leadtime. A further

explanation on the algorithm can be found in Appendix D.

Optimization

Since the optimization procedure as given by Reijnen et al. (2009) is not possible in our

case (we have a multi-item problem and want to apply a system approach) we need to

find another way to do the optimization.

Since we have a similar optimization problem in this project as in e.g. Kranenburg and

Van Houtum (2009) and Van Houtum and Kranenburg (2014, Ch. 5), we have decided to

follow the same logic. Here we again look at the distance to the set of feasible solutions

versus the increase in costs.

Since we have two different objectives (DTWP and CSD) and cannot compare these

two easily in the greedy algorithm to determine the greedy ratio, we will split the opti-

mization for DTWP and CSD customers. (This will later be explained in more detail).


Therefore, we need to define two sets of feasible solutions, one for the LPA customers

and one for the CPA customers. Let S feasLPA ⊆ S be the subset of all solutions that are

feasible to meet the DTWP service levels for LPA customers:

S feasLPA := {S ∈ S|DTWPn(S) ≤ DTWPobj

n ∀n ∈ NLPA}

Let S feasCPA ⊆ S be the subset of all solutions that are feasible to meet the CSD service

levels of CPA customers:

S feasCPA := {S ∈ S|CSDn(S) ≥ CSDobj

n ∀n ∈ NCPA}

We also define two distances to the sets of feasible solutions for each solution S ∈ S:

dLPA(S) :=∑

n∈NLPA

(DTWPn(S)−DTWPobj

n

)+,

dCPA,j(S) :=∑

n∈NCPA,j

(−CSDn(S) + CSDobj

n

)+,

where x+ := max{0, x} ∀x ∈ R.

As one can see, at the distance for CPA customers, a j is added as index. This

j ∈ JCW refers to a continental warehouse where JCW denotes the set of continental

warehouses.

We are again interested in the decrease in distance to the set of feasible solutions

versus the costs when we increase the basestock level Si,j with 1. The increase in cost is:

∆i,jC(S) = ∆jCi(Si) = Ci(Si + ej)− Ci(Si), i ∈ I, j ∈ J.

where ej, j ∈ J is the row vector of size |J | with the jth element equal to 1 and all other

elements 0.

The decrease in distance for both DTWP and CSD service levels can be calculated

as follows:

∆i,jdLPA(S) = dLPA(S)− dLPA(S + Ei,j)

=∑

n∈NLPA

[(DTWPn(S)−DTWPobj

n

)+−(

DTWPn(S + Ei,j)−DTWPobjn

)+]

=∑

n∈NLPA

[(∑i′∈I

DTWPi′,n(S′i)−DTWPobjn

)+

−

∑i′∈I\{i}

DTWPi′,n(S′i) + DTWPi,n(Si + ej)−DTWPobjn

+ ,


∆i,jdCPA,j(S) = dCPA,j(S)− dCPA,j(S + Ei,j)

=∑

n∈NCPA,j

[(−CSDn(S) + CSDobj

n

)+−(−CSDn(S + Ei,j) + CSDobj

n

)+]

=∑

n∈NCPA,j

[(−∑i′∈I

mi′,n

MnCSDi′,n(S′i) + CSDobj

n

)+

−

− ∑i′∈I\{i}

mi′,n

MnCSDi′,n(S′i)−

mi,n

MnCSDi,n(Si + ej) + CSDobj

n

+ ,where Ei,j, i ∈ I, j ∈ J is the matrix of size |I| × |J | with the (ith, jth) element equal

to 1 and all other elements 0.

As was mentioned before, since we have two service levels, it is hard to find the ‘biggest

bang for the buck’ in the greedy optimization algorithm since we cannot easily compare

an increase in CSD with a decrease in DTWP. Therefore, in our optimization algorithm,

we will separate the optimization for CPA customers and LPA customers. We do this as

follows: We start with making sure that we satisfy the CSD service levels for the CPA

customers. Since CPA customers only look in one warehouse, a continental warehouse,

we apply a greedy approach per continental warehouse and increase basestock levels at

this warehouse until we have met the CSD constraints from all CPA customers that are

‘connected’ to this warehouse. (That is the reason why we added the index j in dCPA,j(S)).

We check whether a certain CPA customer is connected to a certain continental warehouse

as follows: n ∈ NCPA,j if vn(1) = j. After we have met our service levels for all CPA

customers we continue with increasing the basestock levels at all warehouses (including

continental warehouses) such that we meet the DTWP constraints for the LPA customers.

After increasing the basestock levels such that the DTWP constraints are met, we also

know the expected overflow demand that is faced by the continental warehouses due

to stock-outs at the local warehouses. Therefore, we need to check whether the CSD

constraints for the CPA customers are still met. If this is not the case, we again need

to increase basestock levels at the continental warehouses until the constraints are met.

After this step, an overall evaluation will be performed to calculate all service levels and

costs. In the final expected costs calculation we have to correct the costs for the fact

that during the delay time, no holding costs have to be incurred. Therefore in the overall

evaluation we use the following cost function:

Ci(Si) =∑j∈J

chi Si,j +

∑n∈NLPA

mi,n

(cemθi,n(Si) +

∑j∈Jn

cn,jαi,n,j(Si)

)− ch

iMitdelayi,repl,

where Mi =∑

n∈N mi,n.

Before we state the optimization algorithm, we need to define the greedy ratios:

Define Γi,j,LPA, i ∈ I, j ∈ J , as Γi,j,LPA := ∆i,jdLPA(S)/∆i,jC(S) and Γi,j,CPA, i ∈ I, j ∈ J ,

as Γi,j,CPA := ∆i,jdCPA,j(S)/chi Si,j.




1. Set Si,j := Sstarti,j , ∀i ∈ I, j ∈ J .

Step 2: Do for each warehouse j ∈ JCW:

1. Calculate ∆i,jdCPA,j(S) and Γi,j,CPA, ∀i ∈ I.

2. While dCPA,j(S) > 0:

a) Determine i such that Γi,j,CPA ≥ Γi,j,CPA, ∀i ∈ I, j ∈ JCW.

b) Set Si,j := Si,j + 1.

c) Calculate ∆i,jdCPA,j(S) and Γi,j,CPA, ∀i ∈ I.

Step 3: Do for each SKU i ∈ I:

1. Calculate ∆jCi(Si), ∀j ∈ J .

2. While minj∈J{∆jCi(Si)} ≤ 0:

a) Determine j such that ∆jCi(Si) ≤ ∆jCi(Si), ∀j ∈ j.


c) Calculate ∆jCi(Si), ∀j ∈ J .

Step 4: Do:

1. Calculate ∆i,jC(S), ∆i,jdDTWP(S) and Γi,j,DTWP, ∀i ∈ I, j ∈ J .

2. While dDTWP(S) > 0:

a) Determine i and j such that Γi,j,DTWP ≥ Γi,j,DTWP, ∀i ∈ I, j ∈ J .


c) Calculate ∆i,jC(S), ∆i,jdDTWP(S) and Γi,j,DTWP, ∀i ∈ I, j ∈ J .

Step 5: If dCPA,j(S) > 0, do for each warehouse j ∈ JCW:



a) Determine i such that Γi,j,CPA ≥ Γi,j,CPA, ∀i ∈ I, j ∈ JCW.




Step 6: Do:

1. Calculate αi,n,j(Si), θi,n(Si), βi,j(Si), DTWPn(S), CSDn(S) and Ci(Si) ∀i ∈ I, j ∈J, and n ∈ N .

In Appendix E we explain this optimization algorithm in more detail.

This concludes the detailed design. In the next part, the integration, we will among

others discuss how the model is implemented and we will show results of applying the

model.

5

Integration

In this chapter it is discussed how the new planning concept has been integrated into

a prototype software tool. This prototype software will be discussed in Section 5.1. In

Section 5.2 it is discussed which data has been collected and will be used in the verification

section and as input for the test cases. In Section 5.3 it is discussed how the model and

tool have been verified. In Section 5.4 we present results and insights for the test case. In

Section 5.6 we will vary the delay objective (the coupling between the global warehouse

and field stock planning) and see how this influences the total yearly cost and total

inventory value.

5.1 Prototype Software Tool

In this section we will discuss how the tool can be used. We have tried to deviate as less

as possible from the current tool, SPartAn, with respect to input files, user interface and

output files. We will first discuss the programming environment, then what input files

are used, then we will show how the tool works and last we will discuss which output

files are created by the tool.

5.1.1 Programming Environment

The new planning concept has been implemented in the Integrated Development Envi-

ronment (IDE) Embarcadero Delphi. The version that has been used is Delphi XE5. This

environment was preferred by ASML and chosen since the current tool that is used for the

field stock planning (per region), SPartAn, also has been developed in this environment.

Furthermore, in Delphi it is easy to develop a Graphical User Interface (GUI), which

increases the user friendliness of the software tool. For the global warehouse planning

the current tool is developed in Microsoft Access, however in this project, the new global

warehouse planning concept has also been integrated in Delphi.

45

CHAPTER 5. INTEGRATION 46

5.1.2 Tool Input

As input for the tool, .txt files are used. This type of file can easily be read and saved into

arrays in Delphi. The files contain all information that is needed to run the optimization

algorithms. A detailed description of the files that are loaded into the tool can be found

in Appendix F.

5.1.3 Running the Tool

When the software tool, which is a standalone executable, is opened, the start screen

appears. A screenshot of this screen is shown in Figure 5.1. As can be seen, one can

choose between global warehouse planning and field stock planning. If one chooses the

first option, the global warehouse planning input files are read. If the second option is

chosen, the field stock planning input files are read.

Figure 5.1 – Start screen of software tool

We will first show the tool in case the ‘Global Warehouse Planning’ mode is chosen.

After selecting this option and pressing ‘Start SPartAn’, the screen as shown in Figure 5.2

will appear.

As can be seen, there are two options for the Decision Support Mode: ‘Optimization’

and ‘Overall Evaluation’. In Figure 5.2 the option ‘Optimization’ has been selected.

When this option is selected, the target value for the CSD service level for the GPA

customers is automatically shown. Note that the delay objectives for each SKU are


Figure 5.2 – Global warehouse planning screen of software tool

already set in the input files. In case one wants to change the CSD objective value, this

can also be done here. When the button ‘Optimize’ is pressed, the tool starts calculating

the basestock levels such that costs are minimized and service levels (delay objectives

and CSD) are met. After a feasible solution has been found, the output files are created

and saved.

The other option, ‘Overall Evaluation’, means evaluating the performance using the

evaluation model for given basestock levels. If this option is chosen, the button that

now shows ‘Optimize’ changes in ‘Evaluate’. When the button is pressed, the costs and

service levels are calculated for the given basestock levels. The output files are again

created and saved. When the ‘Back’ button is pressed, one goes back to the start screen.

If the option ‘Field Stock Planning’ is chosen in the start screen, the screen as shown

in Figure 5.3 will appear.

As can be seen, there are again two options for the Decision Support Mode: ‘Op-

timization’ and ‘Overall Evaluation’. In Figure 5.3 we have again selected the option

‘Optimization’. When this option is selected the target values for the DTWP contracts

and CSD contracts are automatically shown. Again, one has the possibility to change

these values. When one presses the button ‘Optimize’, the tool starts calculating the

basestock levels such that costs are minimized and service levels are met. After feasible

solution has been found, the output files are created and saved.

If the option ‘Overall Evaluation’ is chosen, the button that shows ‘Optimize’ again


Figure 5.3 – Field stock planning screen of software tool

changes in ‘Evaluate’. When the button is pressed, the costs and service levels are

calculated for given basestock levels, using the evaluation model. The output files are

again created and saved.

There are also two options for the Replenishment Leadtimes. In the input files, the

replenishment leadtime can be determined per SKU by setting 1) transportation and

administration times and 2) the delay that one wants to take into account. These values

are taken into account when the option ‘From Input File’ is selected. If the other option,

‘Fixed’, is selected, the user can specify one replenishment leadtime for all SKUs. This

single value will overrule the values from the input file.

When the ‘Back’ button is pressed, one again goes back to the start screen.

5.1.4 Tool Output

The output that is created and saved by the tool are again .txt files. The two most

important files for both the global warehouse planning and field stock planning are:

• The files that contain information on the total yearly expected costs, total inventory

value and the expected service levels (Spartan OUTPUT gwp.txt and

Spartan OUTPUT fsp.txt).

• The files that contain per combination of warehouse and SKU the basestock level

(Spartan OUTPUT items gwp.txt and Spartan OUTPUT items fsp.txt).


Several more files are also created and saved by the tool. These are discussed in more

detail in Appendix F.

5.2 Data Collection

For the tool we need data with respect to the field stock planning and the global warehouse

planning.

For the field stock planning, the data from the input files that were used in the

SPartAn run of May 2014 are used. For each region1, these data contain the plan groups,

lateral transshipment times and costs, emergency shipment time and cost, demand for

SKUs per plan group, SKU prices and the minimum basestock levels required by ASML

for each SKU in each of the warehouses. Furthermore these data contain information

about which plan groups look at which warehouses and which pre-specified orders are

used when other warehouses are ‘visited’ in case of stock-outs. For the global warehouse

planning, more recent data (October 2014) has been used. Since we did not have the

percentages that are needed to calculate the expected repair leadtimes (see Figure 4.2)

we have used the new-buy leadtimes for all parts. For the demand we have taken the

sum of all demand in the field.

The SKUs that were selected for the test case consisted of the SKUs for which we

had both the data from the field stock planning and global warehouse planning available.

The two lists contained different SKUs e.g. due to the fact that SKU numbers (12NCs)

have been changed when successors have been introduced in the time between May 2014

and October 2014. However, for the test case we are mainly interested in the results of

applying the new planning concept that has been designed in this project and therefore

we will work with this reduced list.

5.3 Verification

In this section we will discuss how the software tool and model have been verified. To

test whether we have implemented our proposed evaluation right, we will perform some

manual calculations and compare these results with the outcomes of the tool and out-

comes in literature. Furthermore, for the field stock planning model, we will compare

evaluation results of our tool to the evaluation results of the currently used model. For

the optimization we will compare results of the currently used tool, SPartAn, and our

tool.

For comparing the evaluation algorithms, we used instances from Table 5.1, 5.4 and

5.5 from Van Houtum and Kranenburg (2014, Ch. 5). These instances were used to gain

1The regions are, USA, Europe, China, Singapore, Taiwan, Korea and Japan.


insights when the model that is currently used by ASML was developed by Van Houtum

and Kranenburg (2014, Ch. 5). These instances and the results of both the exact and

approximate evaluation algorithm of Van Houtum and Kranenburg (2014, Ch. 5) and the

approximate evaluation algorithm of Reijnen et al. (2009), which is used in this project,

are presented in Appendix G.

5.3.1 Manual Calculations

We first test whether we implemented the approximate evaluation algorithm of Reijnen

et al. (2009) right and thus whether the outcomes of the software tool are the same as

when these are manually calculated. We tested this for instances from Table G.1 (both

with two and four warehouses). For each of the instances that we tested the results of

the software tool were the same as when these were calculated manually.

To test whether we implemented the formulas for the global warehouse planning right,

we also performed some manual calculations and verified whether we obtained the same

expected number of backorders as given in Table 2.2 in Van Houtum and Kranenburg

(2014, Ch. 2). In addition to the manual calculations, we also obtained the same results

for expected number of backorders when we used our new tool.

5.3.2 Comparing Evaluation Algorithms

For specific cases, when no overflow demand streams have to be calculated, the results

of the evaluation algorithm of Van Houtum and Kranenburg (2014, Ch. 5) and the eval-

uation algorithm of Reijnen et al. (2009) should yield the same results. This holds for

instances where there is only one warehouse that can perform lateral transshipments,

which is the case in instances 1 to 20 in Table G.2. The results are shown in Table G.3.

See columns app.K. (approximation Van Houtum and Kranenburg (2014, Ch. 5)) and

app.R. (approximation Reijnen et al. (2009)) in Table G.3 for results of both approximate

evaluation algorithms. The exact results are taken from Van Houtum and Kranenburg

(2014, Ch. 5), the approximate results from app.K. are generated using the currently used

tool (SPartAn) and the results from app.R. are generated using our new tool. When we

compare the results of both evaluation algorithms, we indeed see that both evaluation

algorithms show the same results for instances 1 to 20.

For instances with more than one warehouse that can perform lateral transshipments

(main local warehouse in the terminology of Van Houtum and Kranenburg (2014, Ch. 5)),

we see that the model of Reijnen et al. (2009) is less accurate. Especially when the fill

rates of the first warehouse a customer requests a part from, is lower than 80%, the

model of Reijnen et al. (2009) is less accurate and overestimates the fraction of demand

that is satisfied by a lateral transshipment (the columns αi,n,vn(2)(Si), αi,n,vn(3)(Si) and


αi,n,vn(4)(Si) in Table G.1 and∑pn

q=2 αi,n,vn(q)(Si) (M) and∑pn

q=2 αi,n,vn(q)(Si) (R) in Ta-

ble G.3). This result was also found by Reijnen et al. (2009, pp. 16–17), who concluded

“the algorithm being too optimistic by assuming that overflow demand is Poisson dis-

tributed; specially in the case that the fill rate is approximately 80%.”

5.3.3 Comparing Optimization Results

To check whether we implemented the optimization algorithm right, we compared the

outcomes of the current tool (SPartAn) with the outcomes of the tool developed in this

project.2 For our checks we used the input data from the regions Singapore, Taiwan and

the US. Since we do not have CPA customers in this input, our optimization algorithm

is for these cases the same as in the currently used tool.

We started with comparing the results for Singapore. Singapore consists of only one

warehouse, and thus in this case, both tools should yield the same results since both

evaluation algorithms will return the same answers. After running the models, we can

conclude that both models indeed gave exactly the same results in terms of total yearly

costs, total inventory value and service levels (see Table G.4 and Table G.5).

Second, we compared the outcomes of Taiwan. In Taiwan three main local warehouses

and one regular local warehouse are defined. For Taiwan we saw differences in costs and

service levels (see Table G.6 and Table G.7). We saw that the inventory value was 3.33%

less than the inventory value that resulted from the current tool. For the total yearly

cost (holding costs, lateral transshipment costs and emergency shipment costs), we found

costs that are 2.87% lower.

Since we know that our evaluation method (the evaluation method from Reijnen et al.

(2009)) is less accurate than the evaluation method that is used in the current tool (the

evaluation method of Van Houtum and Kranenburg (2014, Ch. 5)), we evaluated the

solution obtained using our tool with the currently used tool (these results can also be

found in Table G.6 and Table G.7). From this evaluation we learned that our evaluation

method estimated the yearly costs 0.20% too low. For three out of the 21 plan groups, the

DTWP service level that we could expect is higher than the objective (0.007%, 0.004%

and 0.013%). For all other plan groups our solution still results in meeting the DTWP

objectives and thus a feasible solution.

For the US we performed a similar analysis. In the US there are five main local

warehouses and five regular local warehouses. We again see differences in costs and

service levels (see Table G.8 and Table G.9). The inventory value that results from using

our tool was 0.06% higher than when the currently used tool was used. However, for the

yearly total costs, we found a cost figure that is 0.46% lower.

2Here we make the assumption that the currently used tool is implemented right.


We again evaluated the solution from our tool with the currently used tool. From

this we learn that we underestimated the total yearly cost with 0.02%. We also learn

that, although the results differ, we still have found a feasible solution where the DTWP

objective for all customers is met after evaluating it with the currently used tool.

5.4 Test Case for the New Planning Concept

In this section our goal is to show results and insights from the new planning concept

that has been developed in this design project. We start with giving the input that we

used for the test case followed by the results and insights.

5.4.1 Input

For the input for our test case, we combined all data from the different regions from the

SPartAn run in May 2014, since we wanted to know whether the tool could handle this

amount of data. This led to 6950 SKUs for which both data from the global warehouse

and the field stock was available. After removing all SKUs for which there was no

expected demand, we remained with 3462 SKUs. The total number of warehouses is 35, of

which 29 local warehouses, 3 continental warehouses and 3 emergency hubs. Furthermore,

there are 95 plan groups in total for the field stock planning of which 92 LPA plan groups

and 3 CPA plan groups: one per continent. The holding cost rate was set to % of the

costs of parts. Since we want to make it unattractive to assume that parts can be found

elsewhere if even the emergency hubs are out of stock, we have set the ‘emergency’ time

to 14 days and related costs to e2400. For the times from the local warehouses to the

customers (plan groups) we used the lateral transshipment times from the SPartAn files.

For the times from the emergency hubs and continental warehouses to the customers, we

have used data that was provided by the Trade & Infra department. The replenishment

leadtime was assumed to be 14 days: 4 days expected delay and 10 days transportation

& administration time. For all LPA customers we set a DTWP service level of 1% for

all plan groups that do not have an NXE system. For the plan groups with an NXE

system, the DTWP service level was set to % and for the CPA customers we set a CSD

service level of 90%. For the global warehouse, to determine the expected replenishment

demand we summed the demand from the field.3 The computation time for this instance

was around 12 minutes.

3In the field we assume Poisson demand. Since the sum of independent Poisson Processes is also aPoisson Process, we can sum the demand from the field.


5.4.2 Results and Insights

Due to time constraints we decided to reduce the problem size, such that the computation

times are shorter and we can still learn from the new planning concept. To do this, we

only took the plan groups into account that have an NXT system. We will first discuss

what this means for the input. Then we are interested whether we are able to find a

feasible solution using our new planning concept. We will refer to this solution as the

base case. For this base case we will look at the costs and give insights in the solution.

Input

The reduced problem size consists of 923 SKUs. The prices for these SKUs range from

e to e . Total demand ranges from to parts per year. For

the plan groups we now had 44 LPA plan groups and we created 3 CPA plan groups by

randomly copying 3 LPA plan groups, which made 47 plan groups in total. We again had

35 warehouses, of which 29 local warehouses, 3 continental warehouses and 3 emergency

hubs. The holding cost rate was still % of the cost of an SKU and the emergency

leadtime and costs also remained the same. For all LPA customers we set a DTWP

service level of 1% and for the CPA customers we set a CSD service level of 90%. For

the global warehouse, to determine the expected replenishment demand we summed the

demand from the field. Furthermore we created a GPA customer by again randomly

copying one LPA plan group from the field stock data. The supplier leadtimes were

taken from the global warehouse data. For the GPA plan group, we set a CSD service

level of 90%. In both the field stock planning and global warehouse planning we started

with no stock (i.e. we did not set minimum basestock levels), such that we really could

see whether parts are e.g. stocked in the emergency hubs. The computation time for this

instance was for all cases in the test case around 3 minutes.

Results Base Case

Currently, ASML assumes a replenishment leadtime of 14 days for its field stock planning.

Based on shipping information from ASML, we set the transportation and administration

time to 10 days which results in a delay objective of 4 days to get a total replenishment

leadtime of 14 days. In order to satisfy this maximum delay of 4 days, we set this as

objective in our global warehouse model.

Setting 4 days delay as an objective for the global warehouse planning, means that

the expected delays (i.e. delays as calculated from the global warehouse planning model)

could be very close to these 4 days but could also be lower (e.g. when we have an SKU

with a demand rate of 0.35 per year and a supplier leadtime of 60 days, the expected

delay when one part of that SKU is stocked is only 1.69 days.) In our results we also see

that the expected delay is sometimes much lower than 4 days and the average expected


delay is 1.65 days. Therefore, in the field stock planning we take the expected delays

from the global warehouse into account.

Using the new planning concept and tool, we found a feasible solution. The expected

DTWP and CSD service levels for all LPA, CPA and GPA plan groups are met and can

be found in Table H.1 in Appendix H. For some plan groups the expected DTWP is

much lower than the objective (i.e. below 0.5%). This is due to the fact that these plan

groups have a relatively low demand compared to other plan groups that have a relatively

high demand and that are ‘connected’ to the same warehouse. The plan groups with low

demand benefit from the availability of stock for the high demand plan groups.

Furthermore we show the aggregate Direct CSD and aggregate Field CSD. The Direct

CSD is the CSD (fill rate in literature) of the first warehouse where a plan group requests

a part. The Field CSD indicates the fraction of demand that is satisfied by any of the

warehouses that are in the pre-specified order of that plan group. As can be seen, the

Field CSD is for all LPA plan groups above 99.9%. When we investigate the Field CSD

per SKU, we learn that for only two SKUs the fraction of demand that is not satisfied

by any of the warehouses is below 95% but still above 90%.

In our new planning concept we have also added emergency hubs to the network. We

are interested whether indeed parts are stocked in the emergency hubs, and if parts are

stocked in the emergency hubs, what the characteristics of these parts are (i.e. what

the characteristics are of these SKUs). In total, 18,802 parts are stocked in the local

warehouses, continental warehouses and emergency hubs. Of these 18,802 parts, 876

parts (4.7%) are stocked in the emergency hubs. The maximum number of parts that is

stocked in a single emergency hub for a single SKU is 2. The cost prices of the, in total

485 SKUs of the 923 SKUs for which parts are stocked in the emergency hubs, range

from e to e .

When we compared 12 SKUs with a yearly demand between x and x + 1 parts and

different cost prices (the most expensive part is also included) we clearly see that the

more expensive parts are stocked in the emergency hubs whereas the cheap parts are

stocked in the local and continental warehouses (see Table 5.1). In Table 5.1 we also

clearly see the result of the system approach: the basestock levels of the cheap parts are

higher than the basestock levels of the expensive parts, i.e. cheap parts contribute more

to the service level than expensive parts do. When we performed this analysis for parts

with other demand rates, we saw the same results.

Lastly, the cost results of the base case are presented in Table 5.2. As can be seen, we

both show the total yearly costs, the costs that are calculated in the optimization (note

that we distract the holding costs during the delay time) and the total inventory value,

which is the sum of the basestock levels multiplied by their prices.


Table 5.1 – Basestock levels in local warehouses, continental warehouses and emergency hubs

Cost Price Total Yearly Local Continental Emergency Total(Normalized) Demand Warehouses Warehouses Hubs

100.000% x.0980 0 0 2 26.222% x.8389 4 0 4 85.454% x.1163 4 0 4 81.489% x.1163 12 3 1 161.329% x.8389 16 3 1 200.242% x.7554 16 3 1 200.225% x.1163 24 4 1 290.130% x.3367 21 3 1 250.031% x.1126 28 4 1 330.014% x.8389 35 5 0 400.002% x.8389 41 6 0 470.002% x.1163 43 6 0 49

Table 5.2 – Total yearly cost (normalized) and total inventory value (normalized) for the base casescenario

Global Warehouse Field Total

Total yearly cost: 48.39 51.61 100.00Total Inventory value: 50.55 49.45 100.00

5.4.3 Reflection on Functional Requirements

Now we have found a feasible solution, we will reflect on the functional requirements that

we stated in Section 2.4:

1. In the results of the base case scenario we have shown that we have found a feasible

solution for our test instance.

2. It is possible to set starting basestock levels in the model. These basestock levels are

then already taken into account in the service level (DTWP or CSD) calculations

in the optimization.

3. Since we have decomposed the total network in a global warehouse planning part

and field stock planning part, it is possible to use other models, e.g. when the

situation at ASML asks for this or when more advanced models become available.

It should however be taken into account that the two models (global warehouse

planning model and field stock planning model) are coupled using the replenishment

leadtime.

4. We have been able to develop a tool that has a computation time of around three

minutes for an instance with 923 parts, 35 warehouses and 47 plan groups. For


the instance with 3,462 SKUs, 35 warehouses and 95 plan groups, the computation

time was around twelve minutes.

5. We have run the tool on the computer that was supplied by ASML which is a laptop

with:

• an Intel Core i5-3320M CPU @ 2.60GHz processor

• 8.00 GB RAM

6. The analyses that can be done using the tool that has been developed are almost the

same as the analyses that can be done with the current tool, SPartAn. What has not

yet been implemented due to time constraints and complexity is the functionality

to optimize service levels while a constraint is set on budget for expected yearly

costs. We will elaborate on this in the recommendations for future research.

5.4.4 Conclusions on Test Case

Having reflected on the functional requirements, we will also draw conclusions on the

limitations that were stated in Section 1.4. With the new planning concept and tool,

ASML is able to ...:

1. ... plan all local warehouses, continental warehouses and emergency hubs at once

2. ... take into account request of LPA customers that cannot be satisfied by the local

warehouse in the planning for the continental warehouses

3. ... take into account emergency hubs in Europe and USA in addition to Asia

4. ... determine basestock levels in the emergency hubs based on expected emergency

demand

5. ... determine basestock levels in the global warehouses such that replenishment

leadtimes are met

6. ... use one model and one forecast for the planning of spare parts

It should be noted that the input files can be created in such a way that one is free

to choose the number of local warehouses, continental warehouses and emergency hubs,

e.g. when warehouses are added or removed in the future.

In the next chapter, Chapter 6, we will discuss the organizational aspects that have

to be taken into account when the new planning concept and tool are implemented at

ASML. But first we will look at the influence of different delay objectives on the total

yearly costs and total inventory value.


5.5 Single Delay Objective

In this section we discuss the results when the delay objective is varied, but equal for all

SKUs. We start with a delay objective of 4 days (as in the base case scenario). Then we

set a delay objective of 7 days (1 week) and increase this delay objective with multiples

of 7 days (1 week). We will also set the delay objective to 319 days: the largest supplier

leadtime is 318 days and thus setting the delay objective to 319 days means that, for

meeting the delay objectives, no stock is needed and thus stock is only increased such

that the CSD constraints of the GPA customers are met.

5.5.1 Results

In Table 5.3 we show the delay objective that is set, the average expected delay and the

total expected yearly costs and inventory value. In Figure 5.4 and Figure 5.5 we show the

total expected yearly cost and inventory value and the expected yearly cost and inventory

value for the global warehouses and the field, for delay objectives from 7 to 119 days.

Table 5.3 – Results when the delay objective is set equal for all SKUs

TotalDelay Objective Average Delay Yearly Cost (normalized) Inventory Value (normalized)

4 1.65 100.00 100.00

7 2.65 96.29 96.5114 4.30 92.14 92.8021 5.56 89.43 90.7328 6.60 88.46 90.4235 7.26 87.73 90.0842 7.79 86.38 89.3249 8.22 86.06 89.2956 8.58 86.05 89.4463 9.05 85.93 89.4770 9.88 85.77 89.4077 10.70 85.44 89.2184 11.99 85.41 89.2291 12.77 85.35 89.2598 13.86 84.71 88.97105 14.08 84.57 88.84112 15.00 84.67 89.02119 16.18 84.38 88.75

319 21.76 83.62 88.25

The first thing that we see in Table 5.3 is that the higher the delay objective, the lower

the costs. In case the delay objective is set to 14 days (10 days more than currently) the

expected yearly cost savings are already 7.86% compared to the situation with 4 days

delay objective. The second thing that attracts attention is that the average delay is

much smaller than the delay objective, especially for the cases with high delay objectives.


7 21 35 49 63 77 91 105 1190

10

20

30

40

50

60

70

80

90

100

Delay objective (days)

Yea

rly

Cos

t

TotalField

Global Warehouse

Figure 5.4 – Expected yearly cost (normalized) for different delay objectives

7 21 35 49 63 77 91 105 1190

10

20

30

40

50

60

70

80

90

100

Delay objective (days)

Inve

nto

ryva

lue

TotalField

Global Warehouse

Figure 5.5 – Inventory value (normalized) for different delay objectives

One reason for this is that parts still need to be stocked to be able to meet the CSD

objectives for the GPA customers, which causes the expected delay to be smaller than

the delay objective. Another reason, which has also been discussed in the report, is that

sometimes stocking one part extra results in a large decrease in delay. We also see that,

as expected, when the delay objective is increased, the inventory value decreases in the

global warehouses and increases in the field.

Also interesting to see is that when the delay objective is 319 days, the total expected

yearly cost and inventory value are lowest. What happens here is that the stock in the

global warehouse is increased according to a system approach to meet the CSD objectives

for GPA customers. Therefore, cheap SKUs will get very short delays and expensive SKUs

get long delays. 418 SKUs have a delay smaller than 1 day, the remaining 505 SKUs have


a delay longer than 1 day and the average delay of these SKUs is 39.58 days.

From Figure 5.4 and Figure 5.5 we learn that for delay objectives larger than 42 days

the results for both yearly cost and inventory value does almost not change anymore. In

the case with 42 days delay objective, 370 SKUs have an expected delay smaller than 1.

For the remaining 552 SKUs, the average delay is 12.83 days.

When we compare the cases where the delay objective is set to 42 days and 319 days,

we learn that on average 0.01 parts less and maximally 2 parts less are stocked in the

global warehouse in case the delay objective is 319 days. The number of parts stocked in

the field, is on average 0.25 parts more and maximally 10 parts more in case the delay

objective is set to 319 days.

When we only look at the SKUs that have a cost price higher than e10,000 we

learn that the difference in the number of parts stocked is also very small: in the global

warehouse, on average 0.60 parts less and maximally 2 parts less are stocked in case the

delay objectives is 319 days. The number of parts stocked in the field, is on average 0.48

parts higher and maximally 3 parts higher when the delay objective is set to 319 days.

5.5.2 Conclusions on Single Delay Objective

From the results we learn that the total expected yearly costs and inventory values are

lowest in case the delay objectives are high. For delay objectives higher than 42 days the

outcomes however do not change much.

When the delay objective is set to 319 days, which is higher than the largest supplier

leadtime, the only aspect that determines the basestock levels in the global warehouse is

the (aggregate) CSD objective that is set for the GPA customers. This is almost the same

as in the current situation, where also CSD objectives are set. Note that in the current

situation CSD objectives are set per SKU whereas we set an aggregate CSD objective for

the GPA customers. We concluded however that with using a CSD objective one does

not have grip on the replenishment leadtime to the field. Therefore, we still recommend

to use delay objectives.

In the next section we will discuss the results when the delay objective is differentiated

based on SKU characteristics.

5.6 Influence of Different Delay Objectives

In the next sections we will discuss the results when different delay objectives are set per

SKU. First we will look at the results when SKUs get different delay objectives based on

their cost price. Then we will look at the results when SKUs get different delay objectives

based on their cost price and yearly demand.


5.6.1 Differentiating Based on Price

As we have seen in Section 1.3, in the global warehouse planning, currently different CSD

objectives are set based on among others the price of an SKU. However, by setting CSD

objectives, one does not have grip on the replenishment leadtime of that SKU. Therefore,

we will use the strategy of differentiating but we will set different delay objectives, instead

of different CSD objectives, based on the price of an SKU. We use the cut-off values as

shown in Table 5.4 and test four cases.4 As can be seen, in each case we increase the

delay objective for one price class. For the parts that cost less than e500, we set the

delay objective to 1 day. This basically means that we never want to wait for cheap parts.

We have chosen to use 7, 14, 21 and 28 days since these are basically just multiplications

of weeks. Remind that in the field stock planning the expected delays from the global

warehouse calculations are used. In the four cases, all other input is the same as in the

base case scenario.

Table 5.4 – Parts differentiation based on price: delay objectives (days) for cases 1 to 4

Price (p) Number of parts Delay objectives (days)Case 1 Case 2 Case 3 Case 4

p ≤ e500 402 1 1 1 1e500 < p ≤ e10.000 452 7 7 14 14

e10.000 < p 69 14 21 21 28

In Table 5.5, we show the average delays that result from setting the delay objectives

according to cases 1 to 4. The expected total yearly costs and total inventory value

results of the four cases are shown in Table 5.6 to Table 5.9. As can be seen, when we set

different delay objectives based on the price of a part, the expected total yearly costs and

total inventory value are lower compared to the situation in which we set delay objectives

of 4 days for all parts (the base case). The decrease in yearly cost and inventory value is

especially big for the global warehouses. This is due to the fact that we now have quite

high delay objectives for the expensive parts which results in the fact that we need less

stock for these parts in the global warehouses. However, higher delays also lead to longer

replenishment leadtimes which could result in the fact that in the field stock planning

extra parts are needed to cover the higher demand during the replenishment leadtime.

Although, what we learn from the results is that when we increase the delay objective,

the decrease in yearly costs and inventory value in the global warehouses is bigger than

the increase in yearly costs and inventory value in the field. For future research, it is

interesting to see if delay objectives can be set in such a way that the total costs (i.e.

cost for global planning and field stock planning) are minimized.

4We will not use different objectives for XLD parts


Table 5.5 – Average delays for cases 1 to 4

Case: Average delay (days):

1 2.332 2.633 3.904 4.11

Table 5.6 – Costs (normalizefor case 1 delay objectives



Table 5.7 – Costs (normalized) for case 2 delay objectives









5.6.2 Differentiating Based on Price and Demand

In addition to differentiating based on price, one could also differentiate based on e.g.

price and demand. It could e.g. be the case that ASML wants to have smaller delays for

parts that are more often requested (compared to other parts in the same price category).

For this we use an ABC classification based on price and yearly demand. We have tried to

determine the cut-off values for yearly demand such that the number of parts are equally

distributed over each class of demand. For the prices we again used the same cut-off

values as in the first four cases. Table 5.10 shows the cut-off values that we propose for

the ABC classification including the number of parts in each class:

The two cases for setting delay objectives based on price and yearly demand can be

found in Table 5.11 and Table 5.12. We now chose to set one day as delay objective for


Table 5.10 – Parts differentiation based on price and yearly demand (number of parts are shown)

Price (p) \Yearly demand (d) d ≤ 2 2 < d ≤ 5 5 < d Total

p ≤ e500 209 83 110 402e500 < p ≤ e10.000 165 143 144 452

e10.000 < p 17 14 38 69

Total 391 240 292 923

the parts that are requested more than 5 times per year and are cheaper than e500. For

the other classes we again used multiplications of weeks.

Table 5.11 – Delay objecives (days) for case 5

Price (p) \Yearly demand (d) d ≤ 2 2 < d ≤ 5 5 < d

p ≤ e500 14 7 1e500 < p ≤ e10.000 21 14 7

e10.000 < p 28 21 14

Table 5.12 – Delay objecives (days) for case 6

Price (p) \Yearly demand (d) d ≤ 2 2 < d ≤ 5 5 < d

p ≤ e500 14 7 1e500 < p ≤ e10.000 28 21 14

e10.000 < p 35 28 21

In Table 5.13, we show the average delays that result from setting the delay objectives

according to case 5 and case 6. The expected total yearly costs and inventory values of

the two cases where we set delay objectives based on price and yearly demand, can be

found in Table 5.14 and Table 5.15.

Table 5.13 – Average delays for cases 5 and 6

Case: Average delay (days):

5 4.616 5.52




When we compare case 4 (differentiation on price only, Table 5.9) and case 5, where

in both cases the maximum delay objective that is set to 28 days, we see that we have





lower costs in case 4 than in case 5. This is due to the fact that from the 69 SKUs that

have a cost price higher than e10,000, 38 SKUs have a yearly demand of more than 5

and 14 SKUs have a yearly demand between 2 and 5 and thus have a delay objective of

respectively 14 and 21 days instead of 28 that in case 4.

When we compare the results of case 5 and case 6, where we increased the delay

objectives with one week for all SKUs with a cost price higher than e500, we see that

the total yearly cost and total inventory value are further reduced. The major part (88%)

of the inventory value decrease in the global warehouse is caused by the SKUs that have

a cost price of more than e10,000.

From this we learn that it can also be interesting to take demand rates into account

when differentiating. However, also here the question can be raised: how to determine

the delay objectives if SKUs are categorized along price and demand rate? Trial and

error could be one option, however, it would be more beneficial if a method is found to

determine the cut-off values and delay objectives such that total costs (global warehouse

planning costs and field stock planning costs) are minimized. It is also interesting to

investigate what other variables can be used to differentiate on.

5.6.3 Conclusions on Differentiation

From the results of differentiating based on price and based on price and demand rate,

we learn that total yearly costs and total inventory value can be reduced with respect

to the case where all SKUs get a fixed delay of 4 days. For future research, it would be

interesting if a method can be found where delay objectives can be set in such a way that

total yearly costs (global warehouse and field stock) are minimized.

6

Further Implementation Aspects

To implement the new planning concept and prototype software tool that have been

developed, the following aspects have to be taken into account.

With respect to the organization there are a few challenges: Currently the tactical

planning for spare parts in the global warehouses and the field are done by two different

departments. Looking at forecasting, at the start of the project, these two departments

used different demand forecasts for their planning, however, during the course of the

project ASML has started implementing the use of only one source for demand forecasts.

Another challenge is, that on tactical planning level close collaboration between the

two departments is necessary, e.g. the frequencies of forecasting should be aligned since

changing stock levels in the global warehouse may impact stocking decisions in the field.

Furthermore, the global warehouse planning department (supply planning) has to know

the assumptions that are made in the field stock planning (demand planning) with respect

to replenishment leadtimes and in the field stock planning, it should be known what can

be expected from the global warehouse planning. To enable a closer collaboration, it is

important that an overview is kept on both the planning of the global warehouse and

field stock, e.g. by someone being involved in both planning decisions. The planning

concept and tool that have been developed in this project can be of support in enabling

this closer collaboration.

If the planning concept is implemented, it should be explained to the planners what

the differences are between the current planning methods and the new planning concept.

For the global warehouse, in the new planning concept, a delay objective instead of CSD

objective is set per SKU. It should be explained what this delay objective is and why

it is chosen. In addition, we have added emergency hubs (in Europe and the US) and

the planning for the stock in the emergency hubs is in the new planning concept part of

the field stock planning instead of the global warehouse planning and based on expected

emergency demand instead of rules of thumb. In the field stock planning, it should be

explained that now per customer a pre-specified order of warehouses for requesting parts

is defined and that this pre-specified order can also include continental warehouses and

emergency hubs.

64

CHAPTER 6. FURTHER IMPLEMENTATION ASPECTS 65

Since it is known that currently the 14 days replenishment leadtime is not always met

(in February 2014, the 14 days were only met in % of the times a part was requested),

it is also interesting to investigate why customer service levels in the field can still be

met with a wrong assumption on replenishment leadtimes. One of the reasons could e.g.

be that there are more emergency shipments, such that customer service levels are still

met or that the input parameters need to be adjusted.

Furthermore, it is important to have more detailed data on the leadtimes that have

to be taken into account in the global warehouse planning. For the new-buy leadtimes

this means more detailed information on the real leadtimes, since these deviate from the

leadtimes in SAP. For the repairables, information is needed on the percentage that is

immediately scrapped and scrapped during repair.

It could also be investigated what time and cost should be taken into account when a

request for a part cannot be satisfied by any of the warehouses in the pre-specified order

of a plan group. Now we have set this to 14 days and e2400. It could be interesting to

vary these parameters and see the influence.

Before the prototype software can be further implemented, users of the tool should

be involved in further improvements of the tool with respect to e.g. the user interface.

During the project, a few discussions with users of the tool already have taken place and

the suggestions during these discussions have already been taken into account (such as

the possibility to view the output full screen within the tool). Furthermore, in the next

planning round(s) it is interesting to also use the prototype software developed in this

project parallel to the current tool, SPartAn, and compare the results of both tools.

If the software is eventually implemented at ASML, a software company should be

hired to do the integration with SAP (which is currently ongoing for SPartAn) such that

the input can directly be read from and saved to SAP instead of via .txt files. We

expect that the benefits such as a more transparent planning concept and better control

on replenishment leadtimes outweigh the costs of this implementation.

7

Conclusions and Recommendations

7.1 Conclusions

In this design project, we have developed a planning concept in which the global ware-

house planning and field stock planning are aligned (integrated). In the as-is situation,

different planning models are used and there is no clear alignment between these planning

models. We identified several limitations of these planning models and the current way

of working. In this project we designed a new planning concept with which the limita-

tions can be solved. This new planning concept leads to a more transparent planning of

spare parts and there is more control on the planning since the planning of the global

warehouse and field are aligned. Next we will reflect on the limitations and our solutions

in more detail.

1. For the local warehouses, in the current planning concept, it is only possible to

plan one region at a time. With the new planning concept, it is possible to plan all

regions at once. Furthermore, with the new planning concept, requests for parts

that cannot be satisfied by the field and are requested at the continental warehouse,

are taken into account in the continental warehouse planning.

2. In the new planning concept, in addition to the emergency hub in Asia, we also

added emergency hubs in Europe and the US. The amount of stock that is needed

in the emergency hubs is not anymore determined using rules of thumb, but based

on expected emergency demand.

3. For the global warehouses, currently there is no grip on the expected replenishment

leadtimes to the field since parts are planned using a CSD (Customer Service De-

gree, fill rate in literature) objective. In the new planning concept, we use a model

where an objective can be set on the delay, which is part of the replenishment lead-

time. This results in expected delays and thus expected replenishment leadtimes,

which are taken into account in the field stock planning. Furthermore, we increase

stock in a smart way such that we also meet the aggregate CSD objectives for

Global Parts Availability customers.

66

CHAPTER 7. CONCLUSIONS AND RECOMMENDATIONS 67

4. Instead of having multiple planning models and methods, with this new planning

concept, there is only one planning concept that is used to do the spare parts

planning. It consists of two models, but the two models are integrated due to the

fact that one has control over the replenishment leadtime, which is the coupling

between the two models.

5. The new planning concept has been integrated in a prototype software tool that

can be used by ASML.

6. We performed several analyses in our test case using the prototype software. In

these analyses we differentiated the delay objective based on the price of an SKU

and based on the price and yearly demand of an SKU. From these analyses we

learned that is interesting to apply differentiation since it leads to lower expected

total yearly costs and inventory value. The question however is, what delay ob-

jectives lead to minimum total yearly costs? This question brings us to our main

recommendation for future research.

7.2 Recommendations for Future Research

1. Our main recommendation for future research is to find a way in which delay objec-

tives can be set such that the total expected yearly costs (i.e. the expected yearly

costs for the field stock planning and global warehouse planning) are minimized.

We have already seen that setting delay objectives based on price differentiation

and based on price and demand differentiation results in lower expected costs than

having a single delay objective for all SKUs. Interesting questions are: What vari-

ables should be used to differentiate on, what cut-off values should be used and

what should be the objective in each class?

2. Another recommendation that we would like to give is with respect to the service

levels. In the field stock planning we had to take into account both DTWP and CSD

service levels. Now we have used a heuristic approach in which we first optimize for

CSD contracts, then DTWP contracts and then again for CSD contracts. It would

however be interesting if these two can be combined. This is especially interesting

if one also wants to be able to optimize service levels when a constraint is given on

budget.

3. In the test case we set a relatively high time and cost for a request that cannot be

satisfied by any of the warehouses in the pre-specified order of a plan group to make

this option unattractive. Another possibility could be to add this as constraint in

the model.

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68

List of Figures

1 Network decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

1.1 An NXT and NXE system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 ASML Service Supply Chain locations around the world . . . . . . . . . . . . . . . . 4

1.3 Downtime Waiting Parts concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 SPartAn run process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.5 Forecast and RoP setting in global warehouse . . . . . . . . . . . . . . . . . . . . . . 12

1.6 Service level breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1 Network decomposition 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2 Network decomposition 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.1 Network Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2 Field stock defects an their leadtimes . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.1 Start screen of software tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.2 Global warehouse planning screen of software tool . . . . . . . . . . . . . . . . . . . 47

5.3 Field stock planning screen of software tool . . . . . . . . . . . . . . . . . . . . . . . 48

5.4 Expected yearly cost (normalized) for different delay objectives . . . . . . . . . . . . 58

5.5 Inventory value (normalized) for different delay objectives . . . . . . . . . . . . . . . 58

F.1 Screenshot of input file for Field Stock Planning . . . . . . . . . . . . . . . . . . . . 86

F.2 Screenshot of input file for Field Stock Planning . . . . . . . . . . . . . . . . . . . . 87

69

List of Tables

1.1 Rules for setting emergency stock level . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.2 Limitations of currently used planning models and methods . . . . . . . . . . . . . . 15

2.1 Involved stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5.1 Basestock levels in local warehouses, continental warehouses and emergency hubs . . 55

5.2 Total yearly cost (normalized) and total inventory value (normalized) for the base

case scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

5.3 Results when the delay objective is set equal for all SKUs . . . . . . . . . . . . . . . 57

5.4 Parts differentiation based on price: delay objectives (days) for cases 1 to 4 . . . . . 60

5.5 Average delays for cases 1 to 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.6 Costs (normalizefor case 1 delay objectives . . . . . . . . . . . . . . . . . . . . . . . 61

5.7 Costs (normalized) for case 2 delay objectives . . . . . . . . . . . . . . . . . . . . . . 61



5.10 Parts differentiation based on price and yearly demand (number of parts are shown) 62

5.11 Delay objecives (days) for case 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.12 Delay objecives (days) for case 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.13 Average delays for cases 5 and 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62



G.1 Parameter settings and results for 2 mains and 4 mains . . . . . . . . . . . . . . . . 90

G.2 Parameter settings for 1 main and 1 regular, 1 main and 2 regulars and 2 mains and

2 regulars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

G.3 Results for 1 main and 1 regular, 1 main and 2 regulars and 2 mains and 2 regulars 92

G.4 Cost comparison (normalized) Singapore . . . . . . . . . . . . . . . . . . . . . . . . 93

G.5 Service level comparison Singapore . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

G.6 Cost comparison (normalized) Taiwan . . . . . . . . . . . . . . . . . . . . . . . . . . 93

G.7 Service level comparison Taiwan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

G.8 Cost comparison (normalized) US . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

G.9 Service level comparison US . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

70

List of Tables 71

H.1 Results base case scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

List of Abbreviations

12NC 12 Digit Numerical Code

ADI Average Demand Interval

CL Customer Logistics

CPA Continental Parts Availability

CSD Customer Service Degree (fill rate in literature)

CV Coefficient of Variation

CW Continental Warehouse

DTWP Downtime Waiting Parts

GLS Global Logistics Services

GPA Global Parts Availability

GW Global Warehouse

LMS Logistics Management Systems

LPA Local Parts Availability

LW Local Warehouse

NPI New Product Introduction

OPAC Operations Planning Accounting & Control

RoP Reorder Point

SKU Stock Keeping Unit

XLD Extreme Long Down

72

List of Variables

cem Cost of an emergency shipmentchi Holding cost for SKU iCi(Si) Total cost in the field for SKU i for given basestock levels SiCi(S

GWi ) Total cost at global warehouse for SKU i for given basestock level SGW

i

cn,j Cost of shipping a part from warehouse j to plan group nCSDn(S) Aggregate CSD (fill rate) for plan group n for given basestock levels in

the fieldCSDn(SGW) Aggregate CSD (fill rate) for plan group n for given basestock levels at

the global warehouseDTWPn(S) Total DTWP for plan group n for given basestock levels in the fieldEBOi(Si) Expected number of backorders for SKU i for given basestock level Sii Index for item / SKU / 12NCI Set of items / SKUs / 12NCsj Index for warehouseJ Set of warehousesmi Demand at the global warehouse for SKU imi,n Demand for SKU i from plan group nmi,n,j Demand for SKU i from plan group n requested at warehouse jMi Total demand in the field for SKU iMi,j Total demand for SKU i requested at warehouse jMn Total demand from plan group nMCn Number of machines / systems installed at plan group nn Plan group / customerN Set of plan groups / customerspn Length of vn of plan group nS Matrix with basestock levels for SKUs i ∈ I at warehouses j ∈ JSGW Vector with basestock levels for SKUs i ∈ I at the global warehouseSi basestock level for SKU iSi Vector with basestock levels for SKU i at warehouses j ∈ JSGWi basestock level for SKU i at the global warehouseSi,j basestock level for SKU i at local warehouse, continental warehouse or

emergency hub j

73

List of Tables 74

tem Emergency shipment time

tdelayi,repl Delay in replenishment leadtime due to unavailability of parts at the

global warehousetfixedi,j,repl Transportation and administration time needed to ship item i to ware-

house j

tsupplyi Supplier / order / repair leadtimettotali,j,repl Total replenishment leadtime needed to ship item i to warehouse j

(ttotali,j,repl = tdelay

i,repl + tfixedi,j,repl)

tn,j Time it takes to ship a part from warehouse j to plan group nvn Array with pre-specified order of warehouses where parts are requested

for plan group nαi,n,j(Si) Fraction of demand for SKU i from plan group n fulfilled by warehouse

j for given basestock levels at the field stock warehousesβi(S

GWi ) CSD (fill rate) for SKU i at the global warehouse for given basestock

level SGWi

βi,j(Si,j) CSD (fill rate) for SKU i at warehouse jθi,n(Si) Fraction of demand for SKU i from plan group n that cannot be fulfilled

by any of the warehouses in the pre-specified order vn

A

Forecast and RoP setting Global WarehousePlanning

In this appendix it is described how the forecast is made for forecast parts and how the

RoP (reorder point) is determined for RoP parts.

A.1 Stable Demand

Parts with stable demand are parts that have a low average demand interval (ADI) and

low coefficient of variation (CV). Due to these characteristics demand is predictable and

parts are forecasted. Every month a decision is taken on the number of parts that has to

be ordered. To cover uncertainty in demand, always a minimum amount of parts is kept

on stock which is referred to as the safety stock level. This safety stock is determined

using a Normal Distribution based on demand during the leadtime (DDLT).

A.2 Intermittent Demand and Erratic Demand

Parts with erratic demand are parts with a high CV and low ADI whereas parts with

an intermittent demand are parts with a high ADI and low CV. However, both type of

parts are planned the same way. Due to the characteristics of these parts, they are less

predictable. Therefore a reorder point (RoP) is used to determine when parts should be

ordered. To cover uncertainty in demand during the leadtime, a safety stock level (SSL)

is set. A Poisson Distribution is used to estimate the demand during the leadtime.

The Reorder Point is calculated as follows:

RoP = DDLT + SSL

A.3 Lumpy Demand

Parts with a lumpy demand pattern, are parts that have both a high ADI and high CV.

For these parts rules are set to determine the RoP:

75

APPENDIX A. FORECAST AND ROP SETTING GLOBAL WAREHOUSEPLANNING 76

1. Parts that have had usage the past year

• If RoP based on Poisson Distribution is smaller than , the RoP is set to .

• Else the proposed RoP is used.

2. Parts that have had no usage the past years

• If current RoP is , than leave it .

• Else set RoP to .

B

Recursive Formulas for Backorder Calculations

Recursive formula for the Poisson Distribution:

P{DIi = x} =mitix

P{DIi = x− 1}

P{DIi = 0} = e−miti

Recursive formula for expected number of backorders calculation:

EBOi(Si) =∞∑

x=Si+1

(x− Si)P{DIi − x}

= P{DIi − Si + 1}+∞∑

x=Si+2

(x− Si)P{DIi − x}

EBOi(Si + 1) =∞∑

x=Si+2

(x− Si − 1)P{DIi − x}

=∞∑

x=Si+2

(x− Si)P{DIi − x} −∞∑

x=Si+2

P{DIi − x}

= EBOi(Si)− P{DIi − Si + 1} −∞∑

x=Si+2

P{DIi − x}

= EBOi(Si)−∞∑

x=Si+1

P{DIi − x}

= EBOi(Si)−

(1−

Si∑x=0

P{DIi − x}

)

EBOi(Si) = EBO(Si − 1)−

(1−

Si−1∑x=0

P{DIi − x}

)EBOi(0) = miti

77

C

Determining the DTWP

The expected waiting time per SKU i for customer n can be calculated as follows:

Wi,n(Si) = temθi,n(Si) +∑j∈Jn

tn,jαi,n,j(Si)

If we multiply this expected waiting time (Wi,n) with the expected demand per time

unit for SKU i from all systems MCn in plan group n (mi,n) and a certain time window

t (e.g. thirteen weeks), then we obtain the percentage of time of this time window t that

SKU i is unavailable:

Wi,n(Si)×mi,n × t

If we divide this number with the total availability that we have in this time window:

MCn × t, we obtain the DTWPi,n(Si):

DTWPi,n(Si) =Wi,n(Si)×mi,n × t

MCn × t=Wi,n(Si)×mi,n

MCn

78

D

Explanation on Evaluation Algorithm ofReijnen et al. (2009)

In this appendix we will explain the steps of the evaluation algorithm that is used in

the field stock planning. For reading convenience, we also state the evaluation algorithm

here.

Approximate Evaluation Algorithm for each SKU i ∈ IStep 1: Initialization:

1. ∀j ∈ J, βi,j := 1− L(Si,j, trepli,j

∑n∈N |vn(1)=jmi,n).

2. ∀n ∈ N, mi,n,vn(1) := mi,n.

3. ∀n ∈ N, j 6= vn(1) ∈ Jn, mi,n,j := 0.

Step 2: Repeat until Mi,j does not change more than ε for each j ∈ J:

1. ∀n ∈ NLPA and for 2 ≤ q ≤ pn, mi,n,vn(q) := (1− βi,vn(q−1))mi,n,vn(q−1).

2. ∀j ∈ J, Mi,j :=∑

n∈N mi,n,j,

3. ∀j ∈ J, βi,j := 1− L(Si,j, trepli,j Mi,j).

Step 3: Finalization:

1. ∀n ∈ N, j ∈ J, αi,n,j :=βi,jmi,n,j

mi,n.

2. ∀n ∈ NLPA, θi,n = 1−∑pn

q=1 αi,n,vn(q)

3. ∀n ∈ NCPA, θi,n = 0.

In the first step of the algorithm (Step 1.1), the fill rates (CSDs) of the warehouses

are calculated based on the total demand for SKU i that is faced by warehouse j (the

total demand consists of the demand from all plan groups n ∈ N that have warehouse j

79

APPENDIX D. EXPLANATION ON EVALUATION ALGORITHM 80

as first in their array vn). In Step 1.2 the demand for SKU i at the warehouse where

plan group n will first request a part (warehouse vn(1)) is set to the demand for SKU i

from plan group n (mi,n). In Step 1.3 the demand for SKU i at all other warehouse (i.e.

the warehouses where plan group n will not at first request a part) is set to zero.

In Step 2.1 the demand for SKU i from plan group n at all other warehouses in

its array vn are calculated based on the fill rates (CSDs) of these warehouses using the

recursive formula that is given in Step 2.1. When this step is performed for all plan

groups, we can recalculate the total demand for SKU i that is faced by warehouse j and

the fill rate for SKU i by warehouse j. Step 2 is repeated until the demand for SKU i at

all warehouses j ∈ J do not change more than ε with ε small.

In Step 3.1 we calculate for each plan group n which fraction of demand for SKU i

that is satisfied by warehouse j. Last, in Step 3.2 we calculate the fraction of demand for

SKU i from plan group n that is not satisfied by any of the warehouses and thus satisfied

via a shipment from somewhere else (e.g. a warehouse that is not in the array vn1).

1It should however be noted that this probability should be very small, by e.g. setting a very highpenalty cost on this.

E

Explanation on Optimization Algorithm

In this appendix we will explain the steps of the optimization algorithm that is used in the

field stock planning. For reading convenience, we also state the optimization algorithm

here.



1. Set Si,j := Sstarti,j , ∀i ∈ I, j ∈ J .

Step 2: Do for each warehouse j ∈ J that is a continental warehouse:



a) Determine i such that Γi,j,CPA ≥ Γi,j,CPA, ∀i ∈ I, j ∈ J .



Step 3: Do for each SKU i ∈ I:

1. Calculate ∆jCi(Si), ∀j ∈ J .

2. While minj∈J{∆jCi(Si)} ≤ 0:

a) Determine j such that ∆jCi(Si) ≤ ∆jCi(Si), ∀j ∈ j.


c) Calculate ∆jCi(Si), ∀j ∈ J .

Step 4: Do:

1. Calculate ∆i,jC(S), ∆i,jdDTWP(S) and Γi,j,DTWP, ∀i ∈ I, j ∈ J .

81

APPENDIX E. EXPLANATION ON OPTIMIZATION ALGORITHM 82

2. While dDTWP(S) > 0:

a) Determine i and j such that Γi,j,DTWP ≥ Γi,j,DTWP, ∀i ∈ I, j ∈ J .


c) Calculate ∆i,jC(S), ∆i,jdDTWP(S) and Γi,j,DTWP, ∀i ∈ I, j ∈ J .

Step 5: If dCPA,j(S) > 0, do for each warehouse j ∈ JCW:



a) Determine i such that Γi,j,CPA ≥ Γi,j,CPA, ∀i ∈ I, j ∈ J .



Step 6: Do:

1. Calculate αi,n,j(Si), θi,n(Si), βi,j(Si), DTWPn(S), CSDn(S) and Ci(Si) ∀i ∈ I, j ∈J, and n ∈ N .

In Step 1.1 all basestock levels for SKUs i ∈ I at warehouses j ∈ J are set to basestock

levels that are at least required by ASML.

In Step 2, we look at the ratio distance to feasible set of solutions versus increase in

holding costs. As long as the CSD objectives for CPA customers that are connected to

that warehouse are not met (i.e. dCPA,j(S) > 0) we increase the basestock level for SKU i

at continental warehouse j for which the decrease in distance versus increase in holding

costs is largest (biggest bang for the buck). We stop doing this when all objectives for

all CSD plan groups n at that warehouse have been met (i.e. dCPA,j(S) = 0 and thus

Γi,j,CPA = 0,∀i ∈ I).

In Step 3 we increase basestock levels for each SKU i separately until an increase

in basestock level does not lead to a decrease in costs (the costs only depend on Si).

The reasoning for this is as follows: In case we have basestock levels of zero for all

SKUs i ∈ I at all warehouses j ∈ J , the only costs we have are emergency shipment costs

(cem). When we increase the basestock level for SKU i we have an increase in holding

costs but a decrease in transportation costs. We will check the decrease in costs for all

warehouses j ∈ J and at the warehouse j where the decrease is largest the basestock will

be increased by one.

In Step 4 we look at the ratio distance to a feasible set of solutions versus increase

costs. As long as the DTWP objectives are not met (i.e. dLPA(S) > 0) we increase the

APPENDIX E. EXPLANATION ON OPTIMIZATION ALGORITHM 83

basestock level for SKU i at warehouse j for which the decrease in distance versus increase

in costs is largest (biggest bang for the buck). We stop doing this when all objectives for

all plan groups n have been met (i.e. dLPA(S) = 0 and thus Γi,j,LPA = 0,∀i ∈ I, j ∈ J).

In Step 4, computation time can be saved as follows: DTWP and costs do only depend

on Si, therefore DTWPs and costs only have to be recalculated for the SKU for which the

stock was increased by one. This means that the first time Step 3.2 is performed, |I|×|J |evaluations have to be performed (for each SKU-warehouse combination determining

what the costs are for stocking one part of SKU i at warehouse j and zero parts for all

other SKU-warehouse combinations). Then, when we perform Step 3.2 for the second

time and thereafter, the costs and waiting times only have to be calculated for the SKU

that gave the biggest bang for the buck after the previous step. We do need to calculate

this for this all warehouses however. By doing this we save |I − 1| × |J | evaluations each

time we perform Step 3.2.

In Step 5, we repeat Step 2 in case the solution is not feasible anymore for the CPA

customers due to the overflow demand from LPA customers.

In Step 6, an overall evaluation is performed to calculate the cost and service levels.

F

Tool Input and Output

In this appendix we discuss the input and output files that are loaded and saved by the

tool. We discuss the input and output for the global warehouse and field stock separately.

F.1 Input

Global Warehouse Planning

For the global warehouse planning, we use five .txt files as input.

The first file, Spartan gwp.txt, contains the following information:

• The number of SKUs that are planned;

• The number of plan groups (this needs to be at least one: the plan group that

contains all replenishment demand);

• The holding cost rate;

• A matrix with information with respect to the plan groups: the type of contract

and the service level objective.

The second file, Spartan items gwp.txt, contains information about the SKUs. Per

SKU we have the following information:

• 12NC: The 12 digit numerical code that is used by ASML to identify the SKU;

• The cost price;

• The maximum allowed delay (in days);

• The supplier leadtime (in days) that is taken into account;

• A CSD objective (in case one wants to set CSD objectives instead of delay objec-

tives).

84

APPENDIX F. TOOL INPUT AND OUTPUT 85

The third file, Spartan demand gwp.txt, contains per combination of SKU (12NC)

and plan group the expected daily demand. The first plan group contains the sum of the

demand from the plan groups that are planned in the field.

The last two files: Spartan BaseStockLevels gwp.txt and

Spartan BaseStockLevels gwp start.txt contain basestock levels per SKU (12NC).

The former file is read in case one only wants to evaluate the performance for given

basestock levels. The latter file is used in case one wants to optimize.

Field Stock Planning

For the field stock planning, we also use five .txt files as input. The input files are based

on the files that are currently used.

The first file, Spartan fsp.txt, contains the following information:

• The number of SKUs that is planned for;

• The total number of plan groups, the number of LPA plan groups and the number

of CPA plan groups;

• The total number of warehouses, the number of local warehouse, the number of

continental warehouses and the number of emergency hubs;

• The holding cost rate;

• The number of days it takes to deliver a part when it cannot be delivered from the

field;

• The costs that are made when a part cannot be delivered from the field;

• A matrix that contains in each row the name of the warehouse and whether it is a

local or continental warehouse or emergency hub (indicated by L, C or E);

• A matrix that contains in each row the name of the plan group, the type of contract,

the service level objective, the number of machines, the cut-off value for a long down

(in this project not used), and the length of the vector with the pre-specified order;

• A matrix with in each row the pre-specified order for a certain plan group;

• A matrix with the lateral transshipment times (in hours) from each warehouse to

each plan group;

• A matrix with the lateral transshipment costs from each warehouse to each plan

group.


Two screenshots of this input file can be found in Figure F.1 and Figure F.2.

The second file, Spartan items fsp.txt, contains information on the SKUs. Per

SKU we have the following information:

• 12NC: The 12 digit numerical code that is used by ASML to identify the SKU;

• The cost price;

• The delay (in days) that is taken into account;

• The transportation and administration leadtime (in days) that is taken into ac-

count.

As can be seen, one can differentiate between the time taken into account for delay

and for transportation and administration. The sum of these two values is used in the

software tool as replenishment leadtime.

The third file, Spartan demand fsp.txt, contains per combination of SKU (12NC)

and plan group the expected daily demand.

The last two files: Spartan BaseStockLevels fsp.txt and

Spartan BaseStockLevels fsp start.txt contain basestock levels per combination of

SKU (12NC) and warehouse. The former file is read in case one only wants to evaluate

the performance for given basestock levels. The latter file is used in case one wants to

optimize and wants to have some minimum stock levels.

Figure F.1 – Screenshot of input file for Field Stock Planning


Figure F.2 – Screenshot of input file for Field Stock Planning

F.2 Output

Global Warehouse Planning

After running the tool for the global warehouse planning, three output files are created.

The first output file, Spartan OUTPUT gwp.txt contains among others information

about the total expected yearly cost and the expected inventory value. Furthermore, the

expected value for the CSD of GPA customers is shown.

The second output file, Spartan OUTPUT items gwp.txt contains per SKU (12NC)

the basestock level that has been determined and the expected delay (in days).

The third output file, Spartan OUTPUT betas gwp.txt contains the expected CSDs

per SKU (12NC).

Field Stock Planning

After running the tool for the field stock planning, five output files are created.

The first output file, Spartan OUTPUT fsp.txt contains among others information on

the total expected yearly cost and the expected inventory value. The expected yearly

costs also include emergency shipment and lateral transshipment costs. Furthermore, it

is shown per plan group (customer) what the expected DTWP and CSD service levels

are, what aggregate fraction of demand can be delivered by the first warehouse in the

pre-specified order and what aggregate fraction of demand can be delivered by the field.

The second output file, Spartan OUTPUT items fsp.txt contains per combination of

SKU (12NC) and warehouse the basestock level that has been determined.


The third output file, Spartan OUTPUT betas fsp.txt contains per combination of

SKU (12NC) and warehouse the expected CSDs.

The fourth output file, Spartan OUTPUT thetas fsp.txt, contains per combination

of SKU (12NC) and plan group (customer) the expected fraction of demand that cannot

be satisfied by any of the warehouses in the pre-specified order.

The fifth output file, Spartan OUTPUT alphas fsp.txt, containes per combination

of SKU (12NC), plan group (customer) and warehouse what fraction of demand for a

certain SKU from a certain plan group is delivered by a certain warehouse.

G

Verification and Validation Tables

In Table G.1, Table G.2 and Table G.3, we find the results for different instances that we

evaluated using the currently used tool and the tool developed in this project. Both tools

use approximate evaluation algorithms. The exact results are taken from the tables in

Van Houtum and Kranenburg (2014, Ch. 5), the approximation results of Van Houtum

and Kranenburg (2014, Ch. 5) (app.K.) are calculated using the currently used tool

and checked with the tables in Van Houtum and Kranenburg (2014, Ch. 5) and the

approximation results of Reijnen et al. (2009) (app.R.) are calculated using the new tool.

In Table G.1 we see 16 instances. The first eight instances consist of two warehouses

with at each warehouse one customer for which the yearly demand is given by mi,n. At

both warehouses, Si parts are stocked. The replenishment leadtime that is taken into

account is 0.04 years. In case of a stock-out at warehouse 1, this customer will request

the part at warehouse 2 and in case of a stock-out at warehouse 2, this customer will

request the part at warehouse 1 (v1 = (1, 2) and v2 = (2, 1)). For the last eight instances,

we have four warehouses with at each warehouse one customer. Again the yearly demand

is denoted by mi,n and the replenishment leadtime is 0.04 years. The pre-specified orders

are as follows: v1 = (1, 2, 3, 4), v2 = (2, 3, 4, 1), v3 = (3, 4, 1, 2) and v4 = (4, 1, 2, 3).

In Table G.2 and Table G.3 we see 30 instances, which can be split in three sets of

10 instances. In the first set, we have two customers and two warehouses where one

warehouse can deliver parts to the other. We indicate this warehouse with an M (Main).

The other warehouse is indicated with an R (Regular). The pre-specified order for the

customer assigned to the main warehouse is v1 = (1) and for the customer assigned to the

regular: v2 = (2, 1). For the second set, we have three customers, one main warehouse

and two regular warehouses. The pre-specified order for the customer assigned to the

main warehouse is v1 = (1) and for the customers assigned to the regular warehouses:

v2 = (2, 1) and v3 = (3, 1). The last set consists of four customers, two main warehouses

and two regular warehouses. The pre-specified orders for the customers assigned to the

main warehouses are v1 = (1, 2), v2 = (2, 1), v3 = (3, 1, 2) and v4 = (4, 2, 1).

In Table G.4 to Table G.9 we show the results of the cost comparison and service

level comparison for Singapore, Taiwan and the US.

89

APPENDIX

G.VERIFIC

ATIO

NAND

VALID

ATIO

NTABLES

90

Table G.1 – Parameter settings and results for 2 mains and 4 mains

|J | & |N | mi,n Si αi,n,vn(1)(Si) αi,n,vn(2)(Si) αi,n,vn(3)(Si) αi,n,vn(4)(Si) θi,n(Si)exact app.K. app.R. exact app.K app.R. exact app.K app.R. exact app.K app.R. exact app.K app.R.

1 2 0.5 1 0.980 0.980 0.980 0.019 0.019 0.020 0.001 0.001 0.0002 2 1 1 0.960 0.960 0.960 0.037 0.037 0.038 0.003 0.003 0.0023 2 5 1 0.811 0.811 0.807 0.135 0.135 0.155 0.054 0.054 0.0384 2 10 1 0.660 0.660 0.649 0.189 0.189 0.228 0.151 0.151 0.1235 2 50 1 0.231 0.231 0.219 0.154 0.154 0.171 0.615 0.615 0.6106 2 5 2 0.983 0.983 0.983 0.016 0.016 0.017 0.001 0.001 0.0007 2 10 2 0.941 0.941 0.941 0.052 0.051 0.056 0.008 0.008 0.0038 2 50 2 0.489 0.492 0.463 0.201 0.197 0.249 0.311 0.311 0.288

9 4 0.5 1 0.980 0.980 0.980 0.019 0.020 0.020 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00010 4 1 1 0.960 0.960 0.960 0.038 0.038 0.038 0.002 0.002 0.002 0.000 0.000 0.000 0.000 0.000 0.00011 4 5 1 0.802 0.802 0.800 0.145 0.154 0.160 0.036 0.031 0.032 0.010 0.006 0.006 0.008 0.008 0.00212 4 10 1 0.623 0.623 0.609 0.203 0.211 0.238 0.082 0.080 0.093 0.035 0.030 0.036 0.056 0.056 0.02413 4 50 1 0.149 0.149 0.133 0.114 0.107 0.115 0.090 0.091 0.100 0.072 0.078 0.087 0.575 0.575 0.56614 4 5 2 0.983 0.983 0.983 0.016 0.017 0.017 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00015 4 10 2 0.940 0.940 0.940 0.054 0.056 0.056 0.005 0.003 0.003 0.001 0.000 0.000 0.000 0.000 0.00016 4 50 2 0.386 0.391 0.335 0.195 0.189 0.223 0.114 0.115 0.148 0.069 0.070 0.099 0.236 0.236 0.195

APPENDIX G. VERIFICATION AND VALIDATION TABLES 91

Table G.2 – Parameter settings for 1 main and 1 regular, 1 main and 2 regulars and 2 mains and2 regulars

Instance mi,n mi,n Si SiM R M R

1 0.5 0.5 1 12 1 1 1 13 5 5 1 14 10 10 1 15 50 50 1 16 5 10 1 17 10 5 1 18 5 10 1 29 5 10 2 1

10 10 5 2 1

11 0.5 0.5 1 112 1 1 1 113 5 5 1 114 10 10 1 115 50 50 1 116 5 10 1 117 10 5 1 118 5 10 1 219 5 10 2 120 10 5 2 1

21 0.5 0.5 1 122 1 1 1 123 5 5 1 124 10 10 1 125 50 50 1 126 5 10 1 127 10 5 1 128 5 10 1 229 5 10 2 130 10 5 2 1

APPENDIX

G.VERIFIC

ATIO

NAND

VALID

ATIO

NTABLES

92

Table G.3 – Results for 1 main and 1 regular, 1 main and 2 regulars and 2 mains and 2 regulars

Instance αi,n,vn(1)(Si) (M) αi,n,vn(1)(Si) (R)∑pn

q=2 αi,n,vn(q)(Si) (M)∑pn

q=2 αi,n,vn(q)(Si) (R) θi,n(Si) (M) θi,n(Si) (R)exact app.K. app.R. exact app.K. app.R. exact app.K. app.R. exact app.K. app.R. exact app.K. app.R. exact app.K. app.R.

1 0.980 0.980 0.980 0.980 0.980 0.980 0.019 0.019 0.019 0.020 0.020 0.020 0.001 0.000 0.0002 0.960 0.960 0.960 0.962 0.962 0.962 0.036 0.037 0.037 0.040 0.040 0.040 0.002 0.002 0.0023 0.812 0.811 0.811 0.833 0.833 0.833 0.127 0.135 0.135 0.188 0.189 0.189 0.000 0.032 0.0324 0.665 0.660 0.660 0.714 0.714 0.714 0.172 0.189 0.189 0.335 0.340 0.340 0.113 0.097 0.0975 0.238 0.231 0.231 0.333 0.333 0.333 0.143 0.154 0.154 0.762 0.769 0.769 0.524 0.513 0.5136 0.767 0.761 0.761 0.714 0.714 0.714 0.198 0.217 0.217 0.233 0.239 0.239 0.088 0.068 0.0687 0.699 0.698 0.698 0.833 0.833 0.833 0.109 0.116 0.116 0.301 0.302 0.302 0.057 0.050 0.0508 0.820 0.819 0.819 0.946 0.946 0.946 0.040 0.044 0.044 0.180 0.181 0.181 0.015 0.010 0.0109 0.962 0.964 0.964 0.714 0.714 0.714 0.267 0.275 0.275 0.038 0.036 0.036 0.019 0.010 0.010

10 0.938 0.939 0.939 0.833 0.833 0.833 0.153 0.156 0.156 0.062 0.061 0.061 0.014 0.010 0.010

11 0.980 0.980 0.980 0.980 0.980 0.980 0.019 0.019 0.019 0.020 0.020 0.020 0.001 0.000 0.00012 0.959 0.959 0.959 0.962 0.962 0.962 0.036 0.037 0.037 0.041 0.041 0.041 0.002 0.002 0.00213 0.792 0.789 0.789 0.833 0.833 0.833 0.124 0.132 0.132 0.208 0.211 0.211 0.043 0.035 0.03514 0.622 0.614 0.614 0.714 0.714 0.714 0.162 0.175 0.175 0.378 0.386 0.386 0.124 0.110 0.11015 0.184 0.176 0.176 0.333 0.333 0.333 0.112 0.118 0.118 0.816 0.824 0.824 0.555 0.549 0.54916 0.711 0.700 0.700 0.714 0.714 0.714 0.184 0.200 0.200 0.289 0.300 0.300 0.102 0.086 0.08617 0.684 0.682 0.682 0.833 0.833 0.833 0.107 0.114 0.114 0.316 0.318 0.318 0.060 0.053 0.05318 0.807 0.804 0.804 0.946 0.946 0.946 0.039 0.043 0.043 0.193 0.196 0.196 0.015 0.011 0.01119 0.937 0.940 0.940 0.714 0.714 0.714 0.259 0.268 0.268 0.063 0.060 0.060 0.027 0.017 0.01720 0.931 0.931 0.931 0.833 0.833 0.833 0.152 0.155 0.155 0.069 0.069 0.069 0.015 0.012 0.012

21 0.980 0.980 0.980 0.980 0.980 0.980 0.020 0.020 0.020 0.020 0.020 0.020 0.001 0.001 0.000 0.000 0.000 0.00022 0.959 0.959 0.959 0.962 0.962 0.962 0.038 0.038 0.040 0.038 0.038 0.038 0.003 0.003 0.002 0.000 0.000 0.00023 0.784 0.783 0.778 0.833 0.833 0.833 0.147 0.148 0.173 0.152 0.155 0.158 0.069 0.069 0.049 0.015 0.012 0.00824 0.595 0.592 0.578 0.714 0.714 0.714 0.199 0.201 0.244 0.217 0.227 0.235 0.206 0.207 0.178 0.069 0.059 0.05125 0.149 0.145 0.139 0.333 0.333 0.333 0.113 0.112 0.120 0.164 0.171 0.172 0.738 0.743 0.742 0.503 0.496 0.49426 0.724 0.720 0.712 0.714 0.714 0.714 0.168 0.172 0.205 0.245 0.255 0.262 0.109 0.108 0.083 0.041 0.031 0.02427 0.640 0.639 0.627 0.833 0.833 0.833 0.193 0.193 0.234 0.135 0.139 0.143 0.167 0.167 0.139 0.032 0.028 0.02328 0.793 0.793 0.788 0.946 0.946 0.946 0.143 0.144 0.167 0.049 0.051 0.052 0.064 0.064 0.045 0.005 0.003 0.00229 0.958 0.962 0.961 0.714 0.714 0.714 0.037 0.035 0.037 0.284 0.285 0.285 0.004 0.003 0.001 0.002 0.001 0.00030 0.931 0.932 0.932 0.833 0.833 0.833 0.059 0.058 0.064 0.164 0.165 0.166 0.010 0.010 0.005 0.002 0.002 0.001


Table G.4 – Cost comparison (normalized) Singapore

Current tool New tool

Total yearly costs: 100.00 100.00Total Inventory value: 100.00 100.00

Table G.5 – Service level comparison Singapore

Table G.6 – Cost comparison (normalized) Taiwan

EvaluationCurrent tool (CT) New tool (NT) solution NT with CT

Total yearly costs: 100 97.13 97.32Total Inventory value: 100 96.67 96.67

Table G.7 – Service level comparison Taiwan


Table G.8 – Cost comparison (normalized) US

EvaluationCurrent tool (CT) New tool (NT) solution NT with CT

Total yearly costs: 100 99.54 99.56Total Inventory value: 100 100.06 100.06

Table G.9 – Service level comparison US

H

Results Base Case Scenario

On the next page, the results of the base case scenario are shown.

95

APPENDIX H. RESULTS BASE CASE SCENARIO 96

Table H.1 – Results base case scenario

Group Name # of Total Objective Target Expected Direct FieldMachines Demand/Year Value (%) Value (%) CSD CSD

LPA1 DTWP 1 0.9699 0.8180 0.9999LPA2 DTWP 1 0.9796 0.8224 0.9999LPA3 DTWP 1 0.9838 0.7386 0.9998LPA4 DTWP 1 0.9704 0.7665 0.9999LPA5 DTWP 1 0.9473 0.8459 0.9998LPA6 DTWP 1 0.9666 0.8509 0.9998LPA7 DTWP 1 0.9543 0.8517 0.9998LPA8 DTWP 1 0.9838 0.8883 0.9998LPA9 DTWP 1 0.4780 0.7642 1.0000LPA10 DTWP 1 0.4237 0.8614 1.0000LPA11 DTWP 1 0.3717 0.8885 1.0000LPA12 DTWP 1 0.9105 0.7318 0.9998LPA13 DTWP 1 0.7059 0.8851 0.9998LPA14 DTWP 1 0.7207 0.8758 0.9998LPA15 DTWP 1 0.9738 0.7120 0.9998LPA16 DTWP 1 0.8825 0.6976 0.9999LPA17 DTWP 1 0.9477 0.8142 0.9998LPA18 DTWP 1 0.9377 0.8106 0.9999LPA19 DTWP 1 0.9377 0.8106 0.9999LPA20 DTWP 1 0.3754 0.7720 0.9999LPA21 DTWP 1 0.3039 0.8987 0.9999LPA22 DTWP 1 0.4400 0.6810 0.9999LPA23 DTWP 1 0.7950 0.7947 0.9997LPA24 DTWP 1 0.9390 0.6463 0.9997LPA25 DTWP 1 0.9400 0.6939 0.9996LPA26 DTWP 1 0.3525 0.8150 0.9998LPA27 DTWP 1 0.4451 0.6303 0.9998LPA28 DTWP 1 0.4476 0.6743 0.9998LPA29 DTWP 1 0.9410 0.8925 0.9998LPA30 DTWP 1 0.9709 0.7816 0.9999LPA31 DTWP 1 0.3665 0.9113 0.9999LPA32 DTWP 1 0.5016 0.7460 0.9999LPA33 DTWP 1 0.9305 0.8856 0.9997LPA34 DTWP 1 0.9751 0.8813 0.9997LPA35 DTWP 1 0.4175 0.8984 0.9998LPA36 DTWP 1 0.9740 0.9264 0.9999LPA37 DTWP 1 0.9864 0.9192 0.9999LPA38 DTWP 1 0.9859 0.9556 0.9998LPA39 DTWP 1 0.3849 0.9677 1.0000LPA40 DTWP 1 0.9881 0.9782 0.9999LPA41 DTWP 1 0.8269 0.8275 0.9998LPA42 DTWP 1 0.8796 0.9076 0.9999LPA43 DTWP 1 1.0000 0.9176 0.9999LPA44 DTWP 1 0.9406 0.9113 0.9999CPA1 CSD 90 90.0000 0.9000 -CPA2 CSD 90 90.0905 0.9009 -CPA3 CSD 90 90.3819 0.9038 -GPA1 CSD 90 90.0000 0.9000 -

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Design of an integrated global warehouse and field stock ......Design of an integrated global warehouse and field stock planning concept for spare parts Citation for published version