0

Designing a comprehensive framework to analyze

and improve engine MRO processes from an

integral perspective

A case study at KLM Engineering & Maintenance Engine Services

Amber J. C. Rozenberg

De

lft U

niv

ers

ity o

f T

ech

no

logy

TIL5060 – Master Thesis Project

MSc Transport, Infrastructure and Logistics

10 November 2016

i

ii

Designing a comprehensive framework to analyze

and improve engine MRO processes from an

integral perspective

A case study at KLM Engineering and Maintenance Engine Services

TIL5060 Master Thesis Project

For the degree of Master of Science in Transport, Infrastructure and Logistics

at the Delft University of Technology

by

Amber J. C. Rozenberg

4013549

Date: November 10, 2016

To be defended on November 30, 2016, 10:00 AM

Lecture room Daniel Bernoulli (C)

Faculty of Mechanical, Maritime and Materials Engineering

Report number: 2016.TIL.8077

Graduation Committee:

Prof. dr. ir. G. Lodewijks TU Delft, Faculty 3ME

dr. W. W. A. Beelaerts van Blokland TU Delft, Faculty 3ME

dr. ir. J. H. Baggen TU Delft, Faculty CiTG & TPM

G. Philips van Buren KLM Engineering & Maintenance

A. Gortenmulder KLM Engineering & Maintenance

Cover photo: Courtesy of KLM Engineering and Maintenance

iii

iv

Preface

Dear reader,

When it is not too hazy, one can see the large hangars of Schiphol-East from the Schiphol

airport terminal. For most passengers, this is the only behind-the-scenes look they will ever

get of the vastly complex operations that take place to support their business or leisure

flights. The past six months, I was able to explore this hidden world of aircraft Maintenance,

Repair and Overhaul during my graduation internship at KLM Engineering & Maintenance.

The work lying in front of you represents the result of my master thesis project to complete

my master studies Transport, Infrastructure and Logistics (TIL) at the Delft University of

Technology. The aim of this project was to develop a comprehensive framework to improve

aircraft engine Maintenance, Repair and Overhaul (MRO) processes, and to apply this

framework to decrease the turnaround time of CFM56-7B engine MRO at KLM Engineering

& Maintenance Engine Services.

Naturally, I could not have completed this master thesis project without the help of many

others. I would like to use this opportunity to express my gratitude to these people.

First of all, I want to thank Guus Philips van Buren and Alex Gortenmulder for the

opportunity to conduct my thesis project at KLM Engineering & Maintenance at the Lean

Six Sigma Office. Their enthusiasm, knowledge and feedback helped and challenged me to

get the most out of my internship period. Next, I want to thank all colleagues at Engine

Services for sharing their time, knowledge and data with me, even in tumultuous times.

Thirdly, I want to express my thanks to my graduation committee: Prof. Lodewijks, whom I

wish all the best on his Australian adventure; Dr. Beelaerts van Blokland, whose

enthusiasm and expertise elevated this research to a higher level than I could have

anticipated, and dr. Baggen, whose “mental coaching” helped me through periods of de-

motivation and helped me find structure in my thinking process.

And finally, I want to thank all my fellow interns, friends, family and especially Reinier for

their patience, support, input, distraction and food donations (you know who you are). Mom,

dad: It Is Done.

Thank you all and I wish you a pleasant read.

November 10, 2016

Amber Rozenberg

v

vi

Executive Summary

Around 20,000 commercial aircraft carry an estimated 3.5 billion passengers around the

world this year (2016). This number is expected to double over the next decades, to a total of

40,000 aircraft carrying 7 billion passengers. To guarantee the safety and airworthiness of

these aircraft, Maintenance, Repair and Overhaul (MRO) is a necessity. The current

estimated value of the global aircraft MRO market is $63.2 billion and growing, it is however

shared by many different players in different MRO domains: Airframe MRO, Components

MRO and Engine MRO.

This research focuses on the last category, responsible for 35%-40% of an airline’s

maintenance cost. The Engine MRO market is dominated by Original Equipment

Manufacturers (OEMs) that are increasingly strengthening their positions, by providing

more and more long-term service agreements and by using more complex technologies in

their new engines. To be competitive in this crowded market, maintenance providers need

to compete on MRO cost, turnaround time (TAT) and quality of the delivered services.

KLM Engineering & Maintenance Engine Services is an airline third party MRO provider

that needs to compete in the Engine MRO market. The focus for KLM E&M Engine Services

lies on competing on the turnaround time of Engine MRO, while guaranteeing sufficient

quality against competitive cost. However, the current TAT performance and the needed

performance are not aligned. This research aims to contribute to solving this problem by

creating a comprehensive framework to decrease the TAT of an engine MRO chain, and to

answer the following research question:

How can the output of aircraft engine Maintenance, Repair and

Overhaul processes be optimized from an integral perspective?

In order to answer this question, a comprehensive seven-step framework is designed, based

on process improvement methodologies, process modelling methodologies and solution

evaluation methodologies. First, the system and evaluation criteria need to be defined [I].

Next, the current state of the system is measured [II], and subsequently constraints in the

system are analyzed [III]. The fourth step is to create solution scenarios for the constraints,

by exploiting, elevating or creating the Ideal World [IV]. Next, the solution alternatives are

modelled [V], and evaluated in the sixth step [VI]. The seventh and last step consists of

implementing the optimal solution and controlling the process [VII], which is beyond the

scope of this research. This framework is subsequently applied to a case study at KLM

Engineering & Maintenance Engine Services, a sub-division of Air-France Industries KLM

Engineering & Maintenance.

Step I: Define the system & criteria

This case study considers the Engine MRO process of CFM56-7B engines – used for Boeing

737s - which consists of four main steps: (0) Work scope determination, (1) Disassembly of

the engine, (2) Repair and (3) Assembly of the engine. Five different evaluation criteria are

defined: MRO cost, Implementation cost, Product quality, Process quality and Turnaround

vii

Time. For the case study, Turnaround Time and Process Quality are measured on a

quantative basis, while the other criteria are assessed on a qualitative basis.

Step II: Measure the current state

The current state of the MRO process is measured on two levels: first on the level of the

integral chain, and subsequently on the Repair stage level. All measurements are based on

data retrieved from SAP, the enterprise resource planning system used by KLM E&M. The

current turnaround time (TAT) of the integral MRO chain is 62 days, with a large standard

deviation of 23 days. Currently, control is based on measurement of the different stages,

however, the norm agreements are unclear and inconsistent. The largest share in the total

TAT is realized by the repair stage, and more specifically outsourced repairs. The average

TAT of outsourced repairs is 31 days, including logistics.

Step III: Identify the constraints

The constraints are identified on two different levels: that of the MRO chain and on the

repair stage. In the overall MRO chain, no consistent agreements for control are in place and

control is based on stages instead of the value stream of an engine and its parts. When the

value stream is measured, outsourced work forms the largest constraint to the TAT output

of the chain. Within outsourced repairs, constraints are found in the logistical process and

at the vendors. For export logistics, this constraint is formed by fixed outgoing transport

times. At the vendor, the constraint is formed by lack of internal performance and the

varying contract agreements. For import logistics, the main constraint is formed by the

incoming goods inspection capacity.

Step IV: Create solutions for the constraints

Different solution alternatives are created based on exploiting the constraint, elevating the

constraint and creating the Ideal World solution – thus generating a wide spectrum of

alternatives. Five different alternatives are generated: the exploit alternative, elevate 28

days, elevate 21 days, elevate 14 days, and Ideal World.

Step V: Model the solutions

The effect of the different solution alternatives on the TAT is modelled using a static,

deterministic model. First, the effect of the detailed solution on the outsourced repair TAT

is modelled. This subsequently serves as a bottom-up input to the model to generate the

overall MRO TAT. The results are shown in the table below.

Solution

Average TAT

Outsourced Repair

St. dev. Of outsourced

repair

Average TAT

MRO

Current state 31 days 10 days 65 days

Exploit 29 days 7.2 days 57 days

Elevate 28 28 days 7 days 56 days

Elevate 21 25 days 6 days 54 days

Elevate 14 19 days 5.8 days 46 days

Ideal State 9.5 days 3.3 days 38 days

viii

Step VI: Evaluate the solutions against criteria

The solution alternatives are evaluated using the previously determined criteria. A

combination of quantitative and qualitative criteria is used, resulting in the application of

the Evaluation of Mixed Data method (Evamix), in combination with the Analytic Hierarchy

Process (AHP) for the determination of the criteria weights.

From the perspective of KLM E&M as a process owner, the optimal solution is the Exploit

solution, with Elevate 28 as a close second. For the Exploit solution, vendor management is

needed to maximally exploit the current contract agreements. Next to this, Import logistics

can be improved by implementing multi-skilled teams combining the DGO (Decentralized

Goods Receipt) and IIG (Inspection Incoming Goods) steps, and Export logistics can be

streamlined by introducing Pull based on the cutoff times for outgoing transport.

The Elevate 28 solution consists of vendor management in combination with a limit on the

repair contracts of maximum 28 days. Next to this, Export logistics can be improved by

introducing Pull and enabling direct dedicated transport to the Logistics Center. And finally,

Import logistics can be improved by increasing the IIG capacity from 5x2 to 7x2 shifts per

week and safeguarding the regular flow of incoming logistics by creating dedicated lanes for

priority packages, AOG (Aircraft on Ground) packages and other problem cases.

Implementing the Exploit solution will result in a total Turnaround Time of 57 days,

compared to a current modelled Turnaround Time of 65 days. The standard deviation of the

Outsourced Repair process will decrease from an average of 10 days to an average of 7.2

days. Implementing the Elevate 28 solution will result in a total Turnaround Time of 56

days, with an average standard deviation of 7 days for the Outsourced Repair process.

Answering the main research question: How can the output of engine Maintenance, Repair

and Overhaul processes be optimized from an integral perspective?

A comprehensive framework, consisting of seven steps, is created to develop, model and

evaluate solutions to optimize engine MRO processes. This seven-step model is successfully

applied to a case study at KLM E&M Engine services, wherein different solution alternatives

are created to decrease the turnaround time of the MRO process. The recommended solution

alternatives consist of either exploiting the constraints in the MRO chain, focusing on

Outsourced Repairs, or elevating the constraints with a cap of 28 days in the contract

agreement. The potential reduction in turnaround time in the integral MRO chain by

implementation of these solutions is 8 or 9 days.

The developed comprehensive seven-step framework has added value over existing

frameworks on two aspects: it, on one hand, forces researchers to create the widest possible

array of solution alternatives – from the current state to the ‘Ideal World’, and on the other

hand it forces researchers to evaluate their solutions against different criteria in the

evaluation step.

Recommendations, further research and limitations

This research has focused on improving the main constraint limiting the Turnaround Time

of the MRO chain of CFM56-7B engines at KLM E&M Engine Services: Outsourced Repairs.

For KLM E&M Engine Services, it is recommended to implement the solution Exploit or

Elevate 28. Next to this, it is essential that all stage agreements are consistent in the chain

– meaning that all stakeholders have the same view of the agreements - to effectively plan

and control the MRO chain. Subsequently, the method of measurement needs to be changed

from a stage approach to a Value Stream approach. Next to implementing these solutions, it

ix

is necessary to implement the previously developed solutions for in-house repairs by (Meijs,

2016) and (Mogendorff, 2016).

Implementation of the Outsourced Repair solutions can result in a decrease in Engine MRO

TAT of 8 or 9 days. More potential days can be found in the Disassembly and Assembly

stages, so it is recommended to apply the same framework to these stages to find more

optimization strategies. In this way, by continuous improvement, the Ideal World can be

achieved – with a potential engine MRO TAT of 38 or less days.

Furthermore, it is recommended to conduct research on the qualitative criteria used to be

able to measure the criteria on a quantitative, ratio scale. Next, it is recommended to develop

different models for different engine work scopes. And finally for further research it is useful

to apply the framework to other engine types, such as the CF6-80E1 engine.

From a scientific aspect, it is useful to fit previously developed frameworks for aircraft MRO

into the comprehensive seven-step framework developed in this research. Examples of these

frameworks are given by (Meijs, 2016), (Mogendorff, 2016) and (van Rijssel, 2016). And

finally, it is useful to apply the comprehensive framework to other processes in other

industries and subsequently compare and evaluate the used methods and tools within the

framework.

In each step of the framework applied to the case study at KLM E&M Engine Services,

limitations occur. First of all, the outcome of the case study is limited by the availability and

quality of data. For the case study, engine data of 2015 is used, however sometimes for

certain WBS assemblies the available data was limited.

Next, the research is limited by the focus on main constraints for the development of solution

alternatives. Many different smaller constraints were observed – which makes sense when

looking at the whole MRO chain – but only the main constraints were used to develop

solutions.

A third limitation is formed by the assumptions made when modelling the different

solutions, as described in the modelling chapter. And finally the evaluation of the different

solution alternatives is limited by the use of qualitative criteria and scores. Even though the

use of Evamix enabled the use of qualitative criteria, ideally one would have an objective,

quantitative basis to all criteria. Furthermore, the use of Evamix is not very straightforward

or immediately insightful, and it is not possible to easily add or remove different alternatives

as the dominance is determined relative to the whole set of solutions.

x

Contents

Executive Summary ---------------------------------------------------------------------------------------------- vi

List of abbreviations -------------------------------------------------------------------------------------------- xiv

List of figures and tables -------------------------------------------------------------------------------------- xvi

Part One: Define Phase --------------------------------------------------------------------------------------- 1

Introduction ---------------------------------------------------------------------------------------------------- 3

1.1. Research Context and Problem -------------------------------------------------------------------- 3

1.2. Research Scope and Objectives -------------------------------------------------------------------- 4

1.3. Research Questions ----------------------------------------------------------------------------------- 6

1.4. Research Approach ------------------------------------------------------------------------------------ 7

Literature Review: Process Improvement, Modelling and Evaluation ----------------------- 9

2.1. Process Improvement--------------------------------------------------------------------------------- 9

2.1.1. Lean ------------------------------------------------------------------------------------------------- 9

2.1.2. Six Sigma ---------------------------------------------------------------------------------------- 11

2.1.3. Lean Six Sigma -------------------------------------------------------------------------------- 12

2.1.4. Total Quality Management ---------------------------------------------------------------- 12

2.1.5. Theory of Constraints ------------------------------------------------------------------------ 13

2.1.6. Creative Problem Solving------------------------------------------------------------------- 13

2.1.7. Summary of process improvement methodologies ---------------------------------- 15

2.2. Process Modelling ----------------------------------------------------------------------------------- 16

2.3. Solution Evaluation --------------------------------------------------------------------------------- 17

2.4. Literature framework ------------------------------------------------------------------------------ 19

Definition of the Case Study at KLM E&M Engine Services ---------------------------------- 23

3.1. Technological Design ------------------------------------------------------------------------------- 23

3.1.1. Turbofan engines ------------------------------------------------------------------------------ 23

3.1.2. Turbofan engine Maintenance, Repair and Overhaul ------------------------------ 24

3.1.3. Serviced engine types at KLM E&M Engine Services ----------------------------- 24

3.2. The engine MRO Market -------------------------------------------------------------------------- 25

3.2.1. Competition landscape engine MRO ---------------------------------------------------- 26

3.2.2. Different strategies in the engine MRO landscape --------------------------------- 26

3.3. The Organization ------------------------------------------------------------------------------------ 27

3.4. Criteria to evaluate solution alternatives ---------------------------------------------------- 28

xi

Part Two: Measure Phase ---------------------------------------------------------------------------------- 31

Current state – Case study at KLM E&M Engine Services ----------------------------------- 33

4.1. Methods used for current state measurement ---------------------------------------------- 33

4.2. General current state engine MRO ------------------------------------------------------------ 34

4.2.1. SIPOC of the engine MRO chain --------------------------------------------------------- 34

4.2.2. Flow Chart engine MRO Process --------------------------------------------------------- 35

4.2.3. Current control of the engine MRO chain --------------------------------------------- 35

4.2.4. Currently measured output performance ---------------------------------------------- 36

4.2.5. General Observations engine MRO chain --------------------------------------------- 39

4.3. Research focus: Repair Stage -------------------------------------------------------------------- 39

4.3.1. Current state of In-House Repairs – Summary of previous research --------- 40

4.3.2. Current state of Outsourced Repairs --------------------------------------------------- 42

4.4. Conclusions current state ------------------------------------------------------------------------- 44

Part Three: Analyze Phase -------------------------------------------------------------------------------- 45

Identification of constraints in the engine MRO chain at KLM E&M Engine Services 47

5.1. Methods used for constraint identification -------------------------------------------------- 47

5.2. Analysis of the engine MRO Chain – Value Stream Based ----------------------------- 48

5.3. Constraints in the Outsourced Repair Stage ------------------------------------------------ 49

5.4. Conclusions of constraint identification ------------------------------------------------------ 55

Part Four: Improve Phase --------------------------------------------------------------------------------- 57

Creation of solution alternatives for KLM E&M Engine Services --------------------------- 59

6.1. Methods used to create solutions --------------------------------------------------------------- 59

6.2. Solutions for the MRO chain --------------------------------------------------------------------- 59

6.3. Solutions for the repair stage -------------------------------------------------------------------- 60

6.3.1. Summary of previously developed solutions for In-house Repair -------------- 60

6.3.2. Solutions for Outsourced Repair --------------------------------------------------------- 60

6.4. Conclusions and overview of solutions -------------------------------------------------------- 63

Modelling and results of solution alternatives for KLM E&M Engine Services --------- 65

7.1. Methods used for solution modelling ---------------------------------------------------------- 65

7.2. Modelling of the current state - engine MRO chain --------------------------------------- 66

7.2.1. Current state model specification and assumptions -------------------------------- 66

7.2.2. Current state model results --------------------------------------------------------------- 67

7.2.3. Model verification & face validation ---------------------------------------------------- 67

7.3. Modelling of Outsourced Repair Future State TAT and process quality ----------- 67

7.3.1. Future state Exploit-------------------------------------------------------------------------- 67

xii

7.3.2. Future state Elevate – 28 days ----------------------------------------------------------- 68

7.3.3. Future state Elevate – 21 days ----------------------------------------------------------- 69

7.3.4. Future state Elevate – 14 days ----------------------------------------------------------- 69

7.3.5. Future state Ideal World ------------------------------------------------------------------- 69

7.3.6. Probability plots Turnaround Time Outsourced Repairs ------------------------- 70

7.4. Modelling of the integral MRO chain Future State --------------------------------------- 71

7.4.1. MRO Chain – Future State Exploit ----------------------------------------------------- 71

7.4.2. Future State Elevate ------------------------------------------------------------------------- 71

7.4.3. Determining the Ideal World turnaround time of the MRO chain ------------- 71

7.5. Modelling results ------------------------------------------------------------------------------------ 72

Part Five: Validate & Control Phase ------------------------------------------------------------------ 73

Evaluation of the solutions for KLM E&M Engine Services ----------------------------------- 75

8.1. Method used for solution evaluation ----------------------------------------------------------- 75

8.2. Evaluation of solutions----------------------------------------------------------------------------- 75

8.2.1. Criteria ------------------------------------------------------------------------------------------- 76

8.2.2. Giving weights to the criteria using AHP --------------------------------------------- 76

8.2.3. Multi-Criteria Analysis scores and results using Evamix ------------------------ 76

8.2.4. Multi-Criteria Analysis sensitivity test ------------------------------------------------ 78

8.2.5. Chosen solution -------------------------------------------------------------------------------- 79

8.3. Control – Towards a new integral MRO chain control structure ---------------------- 81

Evaluation of the literature framework -------------------------------------------------------------- 83

Conclusions and Recommendations --------------------------------------------------------------- 87

10.1. Answering the research questions ---------------------------------------------------------- 87

10.2. Recommendations and Further Research ------------------------------------------------- 89

10.3. Research limitations----------------------------------------------------------------------------- 90

Bibliography -------------------------------------------------------------------------------------------------------- 91

Appendix ---------------------------------------------------------------------------------------------------------- 95

A. Process Improvement Methodologies ------------------------------------------------------------- 97

A.1. Business Process Re-engineering (BPR) ------------------------------------------------------ 97

A.2. Business Process Management (BPM) -------------------------------------------------------- 98

B. Used datasets for current state measurement ------------------------------------------------- 99

B.1. Used Datasets for current state measurement MRO chain -------------------------------- 99

B.2. Used Datasets for current state measurement Repair stage------------------------------- 99

B.3. Used Datasets for other stages ---------------------------------------------------------------------- 99

xiii

B.4. Output performance current state - Quality -------------------------------------------------- 100

C. Constraint observations ---------------------------------------------------------------------------- 101

C.1. General MRO Chain constraints ----------------------------------------------------------------- 101

C.2. Constraints in Outsourced Repair --------------------------------------------------------------- 101

D. Modelling of the Outsourced Repair solutions ----------------------------------------------- 105

D.1. Current state average TAT per process step MRO ----------------------------------------- 105

D.2. Future state outsourced repair – assumptions & results --------------------------------- 106

D.3. Future state MRO chain – Results -------------------------------------------------------------- 110

E. Solution evaluation----------------------------------------------------------------------------------- 113

E.1. Criteria weight determination -------------------------------------------------------------------- 113

E.2. Evamix approach -------------------------------------------------------------------------------------- 114

E.3. Qualitative criteria scores per solution alternative & dominance matrices --------- 115

E.4. Sensitivity analysis matrices ---------------------------------------------------------------------- 117

xiv

List of abbreviations

Abbreviation Explanation

AFI KLM Air France Industries KLM

AOG Aircraft On Ground

APrep Assembly Preparation

BPM Business Process Management

BPR Business Process Reengineering

CBBSC Connected Business Balance Score Card

CPS Creative Problem Solving

DGO Decentralized Goods Receipt

DMAIC Define, Measure, Analyze, Implement and Control

E&M Engineering and Maintenance

EGT Exhaust Gas Temperature

ES Engine Services (KLM E&M)

HPO High Performance Organization

IIG Inspection Incoming Goods

KLM Koninklijke Luchtvaart Maatschappij

KPI Key Performance Indicator

LC Logistics Center (KLM E&M)

LSS Lean Six Sigma

LTSA Long Term Service Agreement

MRO Maintenance, Repair and Overhaul

OEM Original Equipment Manufacturer

OTP On Time Performance

P&D Parts and Disposition

PPI Process Performance Indicator

SIPOC Supplier Input Process Output Customer

TAT Turnaround Time

ToC Theory of Constraints

TQM Total Quality Management

xv

xvi

List of figures and tables

Figure 1-1: Engine MRO Process 5 Figure 1-2: DMAIC Cycle (Reid & Sanders, 2010, p. 196) 7 Figure 2-1: the TPS House based on (Stewart J. , 2011, p. 27) 10 Figure 2-2: Literature Framework 20

Figure 3-1: Chapter 3 23

Figure 3-2: Schematic view of a Turbofan engine (Ackert, 2011) 24

Figure 3-3: Global market shares for commercial engine production (Flightglobal, 2015) 25 Figure 3-4: Competition landscape Profitability versus Growth (2015 figures) 26 Figure 3-5: Competition landscape Turnover versus Product strategy (2015 figures) 26 Figure 3-6: Process output objectives 27

Figure 3-7: Goal tree KLM E&M Engine Services 28 Figure 4-1: Chapter 4 33 Figure 4-2: SIPOC diagram of the Engine MRO Chain 34 Figure 4-3: Flow chart of the overall Engine MRO process 35

Figure 4-4: current organizational (institutional) control of the MRO chain 36

Figure 4-5: CBBSC KLM E&M ES MRO 36

Figure 4-6: Actual TAT per engine type 37

Figure 4-7: Actual TAT stage 0 per engine type 37

Figure 4-8: Actual TAT stage 1 per engine type 38 Figure 4-9: Actual TAT stage 2 per engine type 38

Figure 4-10: Actual TAT stage 3 per engine type 39 Figure 4-11: On Time Performance CFM56-7B In-House Repairs (HS=28 days) – 2015 41

Figure 4-12: On time performance of CFM56-7B modules (HS=28 days) – 2015 41 Figure 4-13: SIPOC Diagram Outsourced Repair 42

Figure 4-14: Current TAT Outsourced Repair 43

Figure 5-1: Chapter 5 47

Figure 5-2: Different internal drivers that can constrain a process 48 Figure 5-3: CFM56-7B Engine with assemblies 48

Figure 5-4: Current State Value Stream of WBS assemblies 49

Figure 5-5: OTP Outsourced Work (based on 35 day handshake Stage 2) 50 Figure 5-6: OTP Vendor TAT (based on 28 day handshake) 50 Figure 5-7: OTP vendors (based on contract agreements) 51 Figure 5-8: OTP contracts (based on handshake of 28 days) 51

Figure 5-9: Logistics average TAT per vendor 52

Figure 5-10: Value Stream Map import logistics Engine Services 53 Figure 5-11: Value Stream Map export logistics Engine Services 54 Figure 6-1: Chapter 6 59 Figure 7-1: Chapter 7 65

Figure 7-2: Current state model MRO chain 67

Figure 7-3: Probability plot Outsourced Repair - vendor TAT 70 Figure 7-4: Probability plot Outsourced Repair – Logistics 71 Figure 8-1: Chapter 8 75 Figure 9-1: Applied framework and tools at KLM E&M Engine Services 85

xvii

Table 1-1: Sub questions ....................................................................................................... 6 Table 2-1: Lean Methodology tools .................................................................................... 10 Table 2-2: Six Sigma tools (iSixSigma, n.d.) ..................................................................... 11

Table 2-3: Seven tools of Quality Control (Reid & Sanders, 2010, p. 153) ...................... 13

Table 2-4: Overview of process improvement methodologies ........................................... 15 Table 3-1: Engine types at KLM E&M Engine Shop ........................................................ 25 Table 3-2: Engine MRO performance indicators KLM E&M ........................................... 28

Table 4-1: TAT agreements per stage - TAT60 ................................................................. 36 Table 4-2: Handshake versus actual TAT – CFM56-7B engines ..................................... 39 Table 5-1: Contracts outside of handshake of 28 days (percentage below 100%) ........... 52 Table 8-1: Criteria Weights Process Owner ...................................................................... 76

Table 8-2: Unweighted scores per alternative .................................................................. 77

Table 8-3: Total Dominance score matrix ......................................................................... 78 Table 8-4: Resulting ranking from KLM E&M perspective ............................................. 78 Table 8-5: Resulting ranking from a Client's perspective ................................................ 79

Table 8-6: Resulting ranking with equal weights ............................................................. 79 Table 10-1: Results of the solution alternatives ............................................................... 88

1

Part One: Define Phase

Photo Courtesy of KLM Engineering and Maintenance

2

3

Introduction

In this chapter the context of the research and the problem are discussed. Next, the scope of

the research is given, research questions are shown and finally the research approach is

discussed.

1.1. Research Context and Problem

Research Context

This year, around 20,000 commercial aircraft are expected to carry an estimated 3.5 billion

travelers around the world (IATA, 2015). This number is expected to grow over the coming

decades, to a total of 7 billion passengers by 2034. In accordance to this growth, many new

aircraft are expected to be introduced to increase capacity and to replace less efficient older

models; This growth will lead to a total global fleet of 40,000 in 2032 (The Guardian, 2013).

For aviation to remain the safest mode of transport, maintenance of this large fleet is

essential. Maintenance, Repair and Overhaul (MRO) of aircraft is necessary to guarantee

the airworthiness and reliability of aircraft.

The market for aircraft MRO is estimated to have a total value of $63.2 billion in 2016 (Shay,

2015). With the previously mentioned expected growth in aircraft fleet numbers, this figure

can only be expected to grow even bigger.

The Aircraft MRO market is thus very significant, but it also contains a large number of

different competing players, ranging from Original Equipment Manufacturers (OEMs) to

airlines, such as Air France-KLM. The different players on the MRO market can be divided

into four categories: in-house engineering (airline performs its own maintenance),

independent third party, airline third party (airline performs its own and external party

maintenance) and OEM (Original Equipment Manufacturer) (CAPA Centre for Aviation,

n.d.).

MRO of aircraft can be categorized into three focus areas: Airframe MRO, Components MRO

and Engine MRO. The total maintenance cost represent roughly 10-15% of an airline’s

operating expenses, of which Airframe maintenance contributes to around 40-45% of this

number, Components around 40-45% and Engine around 35-40% (Ackert, 2011). This

research will focus on the last maintenance area: Engine MRO.

The Engine MRO market is dominated by OEM MRO providers, such as General Electric

and Honeywell (CAPA Centre for Aviation, n.d.). This is caused by the fact that engines are

increasingly sold to airlines accompanied by long term OEM support programs, also known

as Long Term Service Agreements (LTSA) (Chellappa, 2015). Other providers, such as

airline third party providers, have to compete with these OEMs. In this market, which may

be worth $34 billion by 2022, the OEMs are strengthening their positions. On one hand by

providing more LTSAs – for example 85–90% of Rolls-Royce Trent engines have LTSAs of

over 10 years in duration – and on the other hand by incorporating more complex

technologies in their new engines, which makes engine MRO a lot more complicated for third

parties (Chellappa, 2015). This results in increasing market shares for OEMs – and a more

competitive market for other engine MRO providers. Depending on the engine type and age,

4

maintenance providers have to compete on cost, throughput time or quality of delivered

services (Ayeni, Baines, Lightfoot, & Ball, 2011, p. 2122).

Air France Industries KLM Engineering &Maintenance - Engine Services

Air-France Industries KLM Engineering & Maintenance (AFI KLM E&M) is such an airline

third party MRO provider, which has to compete with OEMs and other providers in the

increasingly competitive Engine MRO market. Air France Industries KLM Engineering and

Maintenance is a division of the Air-France KLM holding. It provides MRO for airframes,

components and engines, as an airline third party MRO provider – it provides MRO to both

AF-KLM aircraft and other airlines.

For Engine MRO, AFI KLM E&M aims to deliver total engine care – from engine availability

to on-site support, to material services and MRO (Air France Industries KLM Engineering

& Maintenance, n.d.).

This research is conducted at the Dutch branch of AFI KLM E&M, at the Engine Shop

(providing engine MRO) of KLM Engineering and Maintenance, located at Schiphol Airport.

The next section will discuss the problem that KLM E&M Engine MRO faces.

Research Problem

As KLM E&M is located in a high-wage, western country, it is difficult for this player to

compete on cost in the MRO market. Therefore, the focus lies on the turnaround time and

quality aspect. In the year 2015, the average on time performance of KLM E&M Engine

MRO was a mere 43%, meaning that 57% of the engines was delivered post contract due

date. Next to this metric, 50% of the engines delivered in this period passed the EGT

(Exhaust Gas Temperature) quality test. However, to remain competitive in the aircraft

engine MRO market, the performance of KLM Engineering & Maintenance Engine Services

has to be improved significantly. Previous researches (Meijs, 2016), (Mogendorff, 2016) at

KLM E&M have shown that significant improvements can be achieved in parts of the MRO

process, but many other areas within the MRO process remain unexplored.

An example of a current project at KLM E&M to reduce the turnaround time is the TAT45

project, aiming to reduce the goal turnaround time of a certain engine type from 60 to 45

days (Mattijssen, Boerrigter, & Klokkers, 2016). However, the way to reach this goal of 45

days remains unclear and no insight exists in how fast an engine can go through the MRO

process in theory, if the process went perfectly and without disturbances.

Considering the current performance of KLM E&M Engine MRO and the problem context,

the following problem statement is defined:

There is a clear gap between the current performance and the needed performance in the

future for KLM E&M to remain competitive in the aircraft engine MRO market. However,

the approach to locate the value drivers that influence this gap, how the drivers can be

improved, and what the theoretical optimal performance could be in terms of throughput

time is still unknown.

1.2. Research Scope and Objectives

Research Scope – case study

This section will define the scope of the research that aims to contribute to a solution to the

previously stated problem. First KLM Engineering and Maintenance is described. Next, the

research will zoom in on Engine Services (ES), the department within KLM E&M

5

responsible for engine maintenance. Within ES, the research focuses on engine shop visits

(full shop visits). This section will conclude with the scope of this research.

KLM Engineering & Maintenance

In 2004, Air France and KLM merged to become the largest European airline group,

transporting a combined 77 million passengers per year and having a combined fleet of 573

aircraft. AF-KLM has, as a group, three main divisions: Passenger Business, Cargo and

Engineering & Maintenance (KLM, 2015a).

KLM Engineering & Maintenance (E&M) is the KLM branch of this third AF-KLM division.

It employs over 5,000 people – a company in its own – and is one of the largest aircraft

maintenance companies in the world (KLM, 2013). Together with its French counterpart Air

France Industries (AFI), KLM E&M is responsible for third-party revenues totaling 1.2

billion euros, serving 150 customer airlines and handling 1500 aircraft in 2014 (Air France

KLM, n.d.).

KLM Engineering & Maintenance Engine Services

The aim of KLM Engineering & Maintenance Engine Services is threefold: To organize

Engine Availability using an exchange pool, to provide Engine MRO and thirdly to provide

parts repair and engine accessories MRO. This research will focus on the second goal – to

provide Engine MRO.

Engine MRO chain

The Engine MRO chain that is considered in this research at KLM Engineering &

Maintenance consists of four separate stages (Figure 1-1). Stage 0 consists of determining

the Work Scope of the engine repair through the execution of the incoming inspection. The

next stage, Stage 1, consists of the disassembly of the engine into smaller modules and

components and these disassembled parts are cleaned. The parts are inspected to assess

whether the part is serviceable or unserviceable. Within Stage 2, unserviceable engine

modules or components are repaired (either in-house or by outsourcing to a third party) or

replaced when needed. When all modules and components that needed repair or replacement

are ready, the engine is assembled and tested in Stage 3 (KLM, 2008).

Figure 1-1: Engine MRO Process

Objectives and Deliverables

The research objective is derived from the problem stated in section 1.1 and the research

scope defined in section 1.2. The following objective is formulated:

Propose a comprehensive framework to optimize the processes of an integral aircraft

engine MRO chain and subsequently apply the framework to decrease the turnaround

time (TAT) of engine MRO at KLM Engineering & Maintenance Engine Services.

From this objective follows a number of deliverables:

A comprehensive framework to optimize the processes of an integral engine aircraft

MRO chain

Stage 0:

Work Scope

Stage 1: Disassembly

Stage 2: Repair

Stage 3: Assembly &

Test

6

Recommendations for process improvements within the main constraints in the

MRO chain at KLM E&M Engine Services

Model to assess impact on changes on the total MRO chain turnaround time at KLM

E&M Engine Services

1.3. Research Questions

Based on the previously stated research objective, the main research question can be defined:

How can the output of aircraft engine Maintenance, Repair and Overhaul processes be

optimized from an integral perspective?

To answer this main research question, sub-questions are derived. These sub-questions are

shown in Table 1-1.

Table 1-1: Sub questions

Sub question

1 What framework can be built from literature with the aim of finding and evaluating

solutions to improve the output of an aircraft engine MRO process?

2 What criteria can be used to assess the different solution alternatives for KLM E&M

Engine Services?

3 What is the current state of the Engine MRO process at KLM E&M Engine

Services?

4 What constraints are limiting the turnaround time of the Engine MRO process at

KLM E&M Engine Services?

5 What are solution alternatives to optimally reduce the turnaround time at KLM

E&M Engine Services from the current towards 45 days?

6 What is the effect of these improvements on the turnaround time of the integral

MRO chain at KLM E&M Engine Services?

7 What is/are the optimal solution alternatives to be implemented for KLM E&M

Engine Services?

8 What is the theoretical performance of the whole Engine MRO chain at KLM E&M

Engine Services?

9 What are new focus areas to further improve the MRO chain performance at KLM

E&M Engine Services?

7

1.4. Research Approach

The approach used to answer the main and sub research questions is DMAIC – Define

Measure Analyze Improve Control (Reid & Sanders, 2010, p. 196). This approach is taken

from the Six Sigma methodology, and consists of a study part (DMA) and an improve part

(IC). Each step of the DMAIC cycle (see Figure 1-2), in relation to this research, is explained

below.

Figure 1-2: DMAIC Cycle (Reid & Sanders, 2010, p. 196)

Define

In the Define phase, the problem is defined by giving the research context, the scope of the

research, research questions and the research approach. Next to this, the literature

framework is defined to give the steps to be applied to the case study at KLM E&M Engine

Services. This case study at KLM E&M Engine Services is further introduced in the define

phase.

Measure

In the Measure phase, the current state of the engine MRO process at KLM E&M Engine

Services is investigated, as part of the case study.

Analyze

Using data and observations from the Measure phase, constraints in the current MRO

process at KLM E&M Engine Services are identified.

Improve

The Improve step of the cycle aims to eliminate the constraints at KLM E&M Engine

Services, as found in the Analyze phase. Solution alternatives are created in a systematic

way, using tools and methods from the literature framework. Next, the effect of the different

solutions on the MRO chain output is modelled.

Validate-Control

In the last stage, the solution alternatives are evaluated using Multi-Criteria Analysis. Next

to this, the created comprehensive framework is evaluated and subsequently conclusions

and recommendations are given.

The next chapter will develop the framework to decrease the turnaround time of Engine

MRO, to be subsequently applied to a case study at KLM Engineering & Maintenance

Engine Services.

8

9

Literature Review: Process Improvement, Modelling

and Evaluation

This chapter aims to answer the first research sub-question as defined in section 1.3. This is

the following question:

“What framework can be built from literature with the aim of finding and evaluating

solutions to improve the output of an aircraft engine MRO process?”

The comprehensive framework is based on three different methodology groups. First of all

Process Improvement methodologies, discussed in section 2.1. These methodologies are used

to find different solution alternatives to improve engine MRO processes. Next to this, process

modelling methodologies are explored in section 2.2, with the aim of modelling the solution

alternatives. In section 2.3, methodologies for the evaluation of different solution

alternatives are discussed. Section 2.4 will provide the final comprehensive framework,

integrating these three methodology groups, along with the conclusions to this chapter.

2.1. Process Improvement

This section aims to explore different methods to create solution alternatives that can lead

to improvements in the engine MRO process, and identify the main elements used in these

methodologies. Before literature is reviewed to improve, and later on, model the processes,

it is necessary to define what a process is. In essence the definition of a process is the

following: “Processes are relationships between inputs and outputs, where inputs are

transformed into outputs using a series of activities, which add value to the inputs (Aguilar-

Saven, 2004, p. 133).”

To improve processes in a business is essential for business development, management of

change and quality improvement. In its basis, business process improvement consists of

process mapping and analysis, resulting in greater understanding of the process and possible

re-design (Bendell, 2005).

Many different methodologies are known with the purpose of Process Improvement. A (non-

exhaustive) number of these methodologies are discussed in this section: Lean, Six Sigma,

Lean Six Sigma, Total Quality Management, Theory of Constraints and Creative Problem

Solving.

2.1.1. Lean

The first Lean techniques emerged at the Ford production plants in the 1920s; at the plants,

Henry Ford demonstrated focus on activities that were of service to the customer and

elimination of waste of time and material were possible. Lean is more well-known in

association with Toyota in Japan – a company that benefits greatly from the Lean philosophy

(Ayeni, Baines, Lightfoot, & Ball, 2011). At Toyota, the “Toyota Production System” was

invented. The actual term ‘Lean’ was popularized by (Womack, Jones, & Roos, 1990).

(Bendell, 2005, p. 972) provides a clear summary of Lean:

10

“Lean (…) is the systematic pursuit of perfect value through the elimination of waste in all

aspects of the organization’s business processes. It requires a very clear focus on the value

element of all products and services and a thorough understanding of the Value Stream”

(Womack, Jones, & Roos, 1990) identify the five core principles of the Lean Organization as

being the following:

1. The elimination of waste

2. The identification of the value stream

3. The achievement of flow through the process

4. Introducing pull

5. Achieving continuous pursuit of perfection

Another well-known principle of the Lean philosophy is the Toyota Production System (TPS)

House. This House, shown in Figure 2-1, represents the basic principles of Lean. The House

is built on a strong foundation: Stability and Standardization. Without these two conditions,

the system would collapse. The two pillars are formed by Just-in-Time and Jidoka (built-in-

quality). Just-in-time means getting the right amount of material at the right place at the

right time, whilst Jidoka is about detecting defects and repairing them early in the process.

The base and pillars of the House carry the roof: the aim for highest quality, shortest lead

time and lowest cost by continuous improvement: Kaizen (Stewart J. , 2011).

Figure 2-1: the TPS House based on (Stewart J. , 2011, p. 27)

The “TPS House” can be built using Lean tools available for each element. A selection of

these tools per “House element” is shown in Table 2-1.

Table 2-1: Lean Methodology tools

House element Goal Tools

Stability The foundation of the house –

improvement is impossible

without stability in the 4M’s

4M; TIMWOOD(S) ; SMED; Value

Stream Map

Standardization Create a standard process 5S ; Visual Management

Just-In-Time

To produce the right product at

the right time in the right

quantity

Takt Time; Kanban; Heijunka

Jidoka (Built-in

Quality)

To prevent or detect defects early

in the process

Poka-Yoke; Kaikaku (5 Why’s);

Andon;

Kaizen To achieve customer satisfaction

11

The main drivers of a (MRO) process taken from Lean, are waste – defined as TIMWOOD(S)

– 4M and flow. These drivers can be investigated after the identification of the Value Stream.

TIMWOOD(S) is an acronym for different forms of waste, namely Transport, Inventory,

Motion, Waiting time, Overproduction, Over-processing, Defects and Skill. 4M, in turn,

stands for Man, Machine, Method and Material.

As Ayeni et al. (2011, p. 2115) state in their paper, Lean is widely seen as a viable tool within

the aviation MRO industry, albeit not sufficient by itself to realize all the organization’s

goals. The paper suggests the use of Lean in combination with other business strategies,

such as Six Sigma. The next section will discuss this methodology.

2.1.2. Six Sigma

Six Sigma is a methodology developed by the Motorola Corporation in the 1980s to describe

the high level of quality the company was aiming to achieve (Reid & Sanders, 2010, p. 195).

The aim of the Six Sigma methodology is to decrease the variation and number of defects in

a process; statistically Six Sigma means that 3.4 defects per million occur in a process.

The principle of Six Sigma process improvement can be summarized in a straightforward

formula:

𝑌 = 𝑓(𝑋) + ε

In this formula, Y represents the output of a certain process. This output is a function f of

value drivers X and a factor of uncertainty or error ε (International Six Sigma Institute,

n.d.). Of course, a vast number of value drivers X have an influence on the process output

Y. The aim of Six Sigma is to screen the value drivers until a selection of main value drivers

(or root causes) remain that, upon improvement, positively influence the process output. This

screening of value drivers is conducted following the DMAIC cycle.

As stated before in section 0, the DMAIC cycle stands for Define, Measure, Analyze, Improve

and Control. Each of these steps works towards improving and controlling future process

performance and comes with a large number of available tools. An extensive overview of

different Six Sigma tools appropriate for every DMAIC phase is given by (iSixSigma, n.d.).

A selection of tools is shown in Table 2-2.

Table 2-2: Six Sigma tools (iSixSigma, n.d.)

DMAIC phase Goal Tools

Define Define project goals and customer

deliverables

SIPOC Diagram; Stakeholder Analysis;

Measure Measure the process to determine

current performance & quantify

the problem

Process Flowchart; Process Sigma

Calculation; Normality Plots;

Analyze Analyze and determine the root

causes of the defects

Histogram; Pareto Chart; Fishbone

Diagram; Statistical Analysis; 5 Why’s

Improve Improve by eliminating defects Brainstorming; Simulation; FMEA

Control Control future process

performance

Control Charts; Control Plan

Six Sigma not only relies on technical tools and data-analysis; the other important aspect of

Six Sigma considers people involvement. Training of employees to use the technical tools

and identify and solve the root causes to improve process quality is essential in Six Sigma;

Black Belts and Green Belts are examples of employees trained to apply the Six Sigma

methodology (Reid & Sanders, 2010, p. 195).

12

Six Sigma can be used to improve existing processes, in this research Engine MRO processes.

It provides an analytical framework that encompasses all stages of an improvement project.

2.1.3. Lean Six Sigma

The marriage between Lean and Six Sigma was introduced by (George, 2002). Lean Six

Sigma aims to maximize performance by improving customer satisfaction, quality, cost,

flexibility and process speed (Jong & Beelaerts van Blokland, 2016).

As stated before, Lean alone cannot effectively bring a process under control (Ayeni, Baines,

Lightfoot, & Ball, 2011, p. 2115), nor can it define a sustaining infrastructure for

implementation. The combination of Lean and Six Sigma can solve this issue; (Smith &

Hawkins, 2004) state that Lean Six Sigma provides the tools to create ongoing business

improvement, where Lean “brings action and intuition to pick low-hanging fruit”, while Six

Sigma “uses statistical tools to uncover root causes and to provide metrics as mile markers”.

2.1.4. Total Quality Management

Total Quality Management (TQM) originates from a new concept of quality which emerged

in the 1980s: proactive quality management, where quality is built into the product and

process design. This is a change from the old, reactive paradigm, where quality problems are

corrected after they occur (Reid & Sanders, 2010, p. 145).

As with many (process) management methodologies, TQM is a product of many different

philosophies and teachings which have developed throughout the years. This has resulted in

different concepts that make up the TQM methodology. The concepts, in summary, are the

following (Reid & Sanders, 2010, p. 149):

1. Customer focus – identify and meet customer needs

2. Continuous improvement – the cycle of improvement never ends

3. Employee empowerment

4. Use of quality tools – many different tools to measure quality are available

5. Design the products to meet customer focus

6. Quality needs to be built into the process – sources of problems are identified and

corrected

7. Quality concepts need to extend to the suppliers as well

Within TQM, projects to improve quality follow a (continuously repeating) cycle of four steps:

Plan, Do, Study and Act (PDSA). In the first step, the current process is evaluated,

improvement plans are made and performance goals are established. The next step is to

implement the improvement plans and to, importantly, collect data for evaluation. In the

third step the collected data is studied and assessed whether the performance goals are met.

In the final step, Act, measures are taken responding on the result of the previous step; this

starts the cycle again (Reid & Sanders, 2010, p. 150).

As stated in the listed core concepts, TQM uses different tools to measure quality in the Plan

step. The goal of using these tools is to identify, analyze and improve quality problems. The

main tools are called “the seven tools of quality control” and are shown in Table 2-3.

13

Table 2-3: Seven tools of Quality Control (Reid & Sanders, 2010, p. 153)

Tool Aim

Cause-and-Effect (Ishikawa) Diagram Identify potential causes of quality problems, related to

suppliers, workers, machines, environment, process,

material and, measurements and other causes

Flowchart Make the process visual so a clear picture is developed

Checklist Collect information regarding the observed defects,

identify main issues

Control Chart Measure whether a process is operating within

expectations relative to some measured value

Scatter Diagram Shows relation between two variables (correlation)

Pareto Chart Used to identify quality problems based to their degree

of importance

Histogram Shows the frequency distribution of observed values of a

variable

For this research, the focus of TQM lies too heavily on quality improvement and product

design instead of increasing other process performance, such as throughput (TAT). However,

TQM does cover useful tools that can be used to identify bottlenecks and root causes in this

research, such as Pareto analysis and Cause-and-Effect diagrams. From this last tool, a

Cause-and-Effect diagram, possible drivers can be derived: suppliers, workers, machines,

environment, process, material and measurements.

2.1.5. Theory of Constraints

The Theory of Constraints (ToC) is a management philosophy developed by Eliyahu Goldratt

in 1984, presented in his book called The Goal (Goldratt & Cox, 1984). In this work of fiction,

but with a very educational message, Goldratt introduces the concept with the aim to help

organizations achieve their goals.

The principle of ToC is to help find practical solutions to business problems: constraints that

limit the output of the entire system. The Theory of Constraints focuses on five steps to

increase the flow in a system. These steps are, after problem definition, the following:

1. Identify the system’s constraints

2. Exploit the constraint – maximize the utilization and productivity of the constraint

3. Subordinate everything to the constraint – avoid producing more than the constraint

can handle

4. Elevate the constraint – After the previous steps have been conducted, the constraint

can be expanded

5. Prevent inertia from becoming the constraint – After the previous steps, a new

constraint will appear, so the cycle must begin again.

These steps will be useful when bottlenecks (constraints) are identified in the MRO process;

by following the 5 steps different solution alternatives can be developed. A review of ToC

(Mabin & Balderstone, 2003) has shown that application of ToC in process improvement can

lead to significant improvements in companies, for example an average cycle-time reduction

of 66%, an on-time delivery increase of 60% and an inventory reduction of 50%.

2.1.6. Creative Problem Solving

Lean and Six-Sigma are two of the most well-known business process improvement

approaches. Although these previously discussed approaches have many strengths, (Bendell,

2005) argues that these approaches are mainly focused on ‘left-brain’ analytical, data-based

tools, while neglecting more ‘right-brain’ thinking such as creativity and innovation.

Therefore this research aims to incorporate creative problem solving into the approach to

14

develop solution alternatives for process improvement. Examples of tools for creative

problem solving (CPS) are brainstorming and mind mapping.

In this research, CPS can be used to complement more analytical, data-driven approaches

to generate solution alternatives. One can create, for instance, out-of-the-box solutions by

creating an “Ideal World”, unlimited by normal constraints such as time and money.

15

2.1.7. Summary of process improvement methodologies

This section will summarize the discussed process improvement methodologies and evaluate

the usefulness of each methodology for this research. The overview can be found in Table

2-4. In this table, the last column indicates in what part of the DMAIC cycle (see section 0)

the methodology can be applied. Two methodologies are mentioned in the table, but not

applied in this research: Business Process Reengineering and Business Process

Management. Background on these methodologies can be found in Appendix A.

Table 2-4: Overview of process improvement methodologies

Methodology Key elements Aim Usefulness DMAIC

Lean Eliminate waste,

identify value

stream, flow, pull,

continuous

improvement,

PDCA

Pursuit of perfect

value in processes

through

elimination of

waste

Lean is a viable tool,

however Lean alone

cannot adequately

improve processes

(Ayeni, Baines,

Lightfoot, & Ball, 2011)

-

Six Sigma Value drivers,

DMAIC cycle,

statistical

analysis, root

causes

Decrease

variation and

number of defects

in processes

Can be used to improve

existing processes. It

provides an analytical

framework that

encompasses all stages of

an improvement project

-

Lean Six Sigma Eliminate waste

on analytical basis

Combines Lean

and Six Sigma

Combination of Lean and

Six Sigma provides the

tools to create ongoing

business improvement

(Smith & Hawkins,

2004)

DMAIC

Total Quality

Management

Customer focus,

use of quality

tools, quality in

processes

Proactive quality

approach: build

quality into

process and

product design

Focus lies on product

quality improvement

instead of

throughput/TAT,

however TQM covers

tools that can be used to

identify bottlenecks and

root causes in this

research

A, I

Theory of

Constraints

Bottlenecks,

exploit and elevate

Increase flow in

system

Good method to find

solutions for bottlenecks

when these are found

A, I

Business

Process

Reengineering

Design new

process, identify

change levers,

innovative

solutions

Redesign whole

process (green

field)

Focuses on creating new

processes instead of

improving existing

processes. Can be useful

to find innovative, out-of-

box solutions, but not

used for other phases.

-

Business

Process

Management

Holistic view,

continuous

improvement,

process re-

engineering

Business

improvement

enabled by IT,

corporate-wide

impact and cross-

functional process

management

Not in line with current

improvement philosophy

at KLM E&M

-

Creative

Problem

Solving

Brainstorming,

generate ideas

Generating

creative solutions

Can complement

analytical, data-based

approach for creating

solutions with a more

creative approach

(Bendell, 2005)

I

16

2.2. Process Modelling

Whereas the previous section (2.1) discussed tools and methodologies used to improve

processes, this section will focus on methodologies to model processes within the integral

engine MRO chain to subsequently assess the effect of the improvements on the overall

turnaround time.

The modelling of processes is becoming increasingly popular, as value-adding processes have

become more and more the core of organizing a business, instead of a functional hierarchy

perspective. Process Modelling is used on a large scale to develop software that supports

business processes, and also to analyze and re-engineer processes where needed. However,

Process Modelling is a wide and extensive field, resulting in a vast forest of methodologies,

techniques and tools (Aguilar-Saven, 2004). A number of Process Modelling techniques will

be discussed in this section.

Flow Chart

(Aguilar-Saven, 2004) defines Flow Charts as graphical representations of a process in which

symbols are used to represent elements such as operations, equipment and flow direction.

Flow Charts are very flexible in use: there are standards, however the processes can be

described in many different ways. A strength of a Flow Chart is that it is easy to use, however

a weakness can be that Flow Charts tend to get very big and not good for giving a simple

overview of a process.

IDEF

IDEF, Integrated Definition for Function Modelling, represents a group of techniques that

enables process modelling of different applications following a fixed paradigm. Examples of

applications are IDEF0, which is used for making structural graphical representations of

business processes, IDEF1, which is used for information modelling, and IDEF2: used to

represent dynamic behavior of resources in a system (Aguilar-Saven, 2004, p. 137).

Gantt chart

A Gantt chart includes the time dimension in the process model; this makes it able to relate

a group of activities to a time scale. The downside of Gantt Charts is that they do not show

clear dependencies between process steps (Aguilar-Saven, 2004, p. 136).

Object Oriented methods

Object Oriented Process Modelling is used to describe processes that deal with different

types of objects and where corresponding actions depend on the type of object that is

manipulated. Or in other words: Object Oriented methods are “methods to model and

programme a process described as objects, which are transformed by the activities along the

process” (Aguilar-Saven, 2004, p. 138).

Modelling means representing the construction and working of a certain system of interest.

A main purpose of modelling is to enable the analyst to predict the effect of certain changes

to the system; a good model is a tradeoff between simplicity and realism.

Models can be categorized in a number of categories: deterministic versus stochastic, static

versus dynamic and discrete versus continuous models. (Birta & Arbez, 2013) describe the

differences between these classifications.

17

Static versus Dynamic

In a static model, the time dimension is not taken into account. In a dynamic model, on the

other hand, time-varying interactions in the system are taken into account (Maria, 1997).

Deterministic versus Stochastic

Models that include random elements are called stochastic models, while models that include

no random aspects are called deterministic models (Birta & Arbez, 2013, p. 48).

Discrete versus Continuous

Models that have changing values continuously over time are called continuous models. This

is in contrast to discrete models, where state changes happen in discrete intervals over time

(Birta & Arbez, 2013, p. 49). These intervals are not known beforehand: simulated time will

‘jump’ in unequal intervals, depending on state changes. In practice, (Enserink, et al., 2010,

p. 158) state that “Discrete Simulation is particularly applicable to description and analysis

of the operational aspects of systems, such as queuing problems, logistical analysis, workflow

management etc.”

2.3. Solution Evaluation

When improvements (solutions) are developed and modelled, a method needs to be followed

to evaluate and assess the different solutions. By following a method, a systematic

assessment comparison of alternatives can be made (Haan, et al., 2009). When one compares

solutions based on different criteria, a Multi-Criteria Analysis (MCA) is conducted. However,

MCA is a general label for many different methods. This section will discuss different MCA

approaches found in literature.

Impact Table

The most elemental form of Multi-Criteria Analysis consists of the impact table: a neutral

representation of values per criterion per alternative (Haan, et al., 2009). No (subjective)

conclusions are drawn from the table, it is a mere representation of objective data. Ideally,

the impact table is based on quantitative, well-founded analyses. It is important to prevent

overlap in the criteria, to keep a balanced and fair MCA.

The Score Card

The score card uses a simple representation of the alternatives, without weighted criteria.

Color schemes indicates whether a solution scores positive, negative or neutral on a certain

criterion, compared to other alternatives (Haan, et al., 2009) – simply put, the score card is

a (subjective) interpretation of the impact table. A disadvantage of the score card is that no

weights are given to different criteria, however in case of diverse interests of different

stakeholders, the method can be useful (Ministerie van Financien, 1992).

Simple Multi Attribute Rating Technique

Whereas the previous MCA approaches gave equal importance to each criterion, the Simple

Multi Attribute Rating Technique (SMART) will give weights to the criteria. SMART

consists of a number of steps: first, the different criteria are weighted. Next, the solution

values (per criterion) are normalized to a value between 0 and 1. This normalization creates

equal scores for criteria with different units (for instance days versus euros). Finally, the

normalized values are weighted and added up to a final score per solution (Haan, et al.,

2009). Ideally, the weights of the criteria are determined by different stakeholders. However,

it is necessary to test the sensitivity of the solutions to the weight factors, as the

determination of weight factors remains a subjective approach.

18

Evamix Method

The Evamix Method, for Evaluation of Mixed Data, can be applied to a situation where both

scores of a ratio and an interval scale are used – or in in other words: a situation where both

qualitative and quantitative measurements are used (Brakken, 2001). The Evamix method

follows seven steps (Darji & Rao, 2013).

First, an impact table is generated, containing the solution alternatives and criteria

(attributes). All criteria (quantitative and qualitative) are given weights. From this impact

table, the ordinal (qualitative) and ratio criteria are distinguished. The next step is to

standardize the scores to values between 0 and 1, where 1 represents the best score and 0

the worst score. The qualitative and quantitative criteria are separated, resulting in two

standardized scorecards. Next, using pairwise comparison, dominance of each alternative

over another alternative is determined for each separate criterion. Finally, the dominance

of each alternative is added, including the weights of the different criteria. This will result

in a total score and ranking of the alternatives (Brakken, 2001).

The advantage of the Evamix method are that the evaluation makes use of both the

qualitative and quantitative criteria in an adequate way. However, disadvantages are that

the criteria scores are standardized twice, resulting in possible information loss. Next to this,

the dominance of an alternative over another alternative is dependent on the whole set of

alternatives (Ministerie van Financien, 1992), and is more difficult to interpret due to the

needed computational steps (Commissie voor de milieueffectrapportage, 2002).

Giving weights to criteria

For the SMART approach, it was briefly discussed that weights are given to the different

criteria. However, different methods are available to generate the weights in a systematic

manner.

Ranking

The simplest way to determine criteria weights is through ranking, in ascending or

descending order. An example is to rank from 1 to 5, where the most important criterion is

given rank 5, and the least important rank 1. This is called the Rank Sum method. Usually,

the criteria weights are standardized, so the total weights add up to one. Another way to

rank criteria, is through the Rank Exponent method, where a parameter describes the

weights (Roszkowska, 2013). It is recommended to use this method as a first approximation

only (GITTA, 2013).

Paired Comparison

Weights of criteria can be defined by Paired Comparison (Brown, 2007). It is an easy to use

and widely accepted method. First a basic ordering is made in a small set of criteria. Next,

relative importance is decided by the team. Subsequently it is necessary to express the

importance of a criterion with the criteria of lower importance in terms of equal to, smaller

than or larger than relationships. To compute the weights, the resulting linear expressions

are solved by giving the least important criterion a value of 1, and working through the

expressions. The values are finally standardized, resulting in weights adding up to one.

Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process (Saaty, 2008), is a method where criteria are measured

through pairwise comparison. Inclusion of experts or stakeholders is necessary to drive the

priority scales. Saaty uses a “fundamental scale of absolute numbers” to compare two criteria

or activities. This scale ranges from 1 to 9, where 1 stands for “equal importance” and 9

stands for “extreme importance” over the other alternative. The inverse of these numbers is

19

used when a criterion is less important than another criterion. In this way, a score matrix is

formed. Next, the scores per column are normalized. To find the criteria weights, the average

normalized score per row is computed. As a final step, a statistical consistency test is

conducted.

2.4. Literature framework

From the combination of literature to improve business processes, to model business

processes and to evaluate solution alternatives, a comprehensive framework is created. This

framework is shown in Figure 2-2.

From Lean Six Sigma, the DMAIC stages are followed and complemented with other

methodologies. Within these stages, seven research steps are followed. First, the system

needs to be defined: the scope of the research is demarcated and criteria to later on evaluate

the different solutions, must be determined. In step two, the current state of the predefined

system is measured. This measurement serves as input to find the constraints in the system,

but also as a basis for the current state model in step five. The third step is to identify the

constraints, limiting the output of the system. When these constraints are found, solutions

are created based on the Theory of Constraints and Creative Problem Solving: solutions are

found to exploit and elevate the constraints, but also an “Ideal World” is created - solutions

that are found without limitations of money, location etc. To create the solutions within these

three domains, different tools from literature, such as Lean, can be used.

When the solution alternatives are created they are modelled in step five. A model of the

current state is used as a reference. Again, different modelling tools and approaches are

available, depending on the specific problem. The sixth step consists of evaluating the

different solution alternatives. For this evaluation, previously defined criteria are used. For

evaluation, multiple methods are available as well – again, the chosen method depends on

the specific problem. The last and seventh step in the framework is to implement the right

solution alternatives and to control the process. To achieve continuous improvement, the

cycle will start again from the beginning – as indicated by the dotted arrow.

20

Figure 2-2: Literature Framework

21

Now that a comprehensive framework is created, it is necessary to apply the framework to

a real-world case study. This case study takes place at KLM Engineering & Maintenance

Engine Services. In the next chapter (3), the case study is introduced and the first step of

the framework (define the system and determine the criteria) is taken.

In each step in this research applied to the case study, a detailed explanation will be given

on the methods used to fulfill the step. As shown in this chapter, multiple methods are

available for each step. For the case study, specific methods and tools are chosen from the

selection given in this chapter.

This chapter answered the following research question: “What framework can be built

from literature with the aim of finding and evaluating solutions to improve the output of

an aircraft engine MRO process?”

The framework is based on methodologies to improve processes, model processes and

subsequently evaluate improvement solutions. The comprehensive framework consists

of seven steps:

I. Definition of the system and determination of criteria

II. Measurement of the current state of the system

III. Identification of the constraints limiting the output of the system

IV. Creation of solution alternatives for the constraints

a. Exploiting the constraint

b. Elevating the constraint

c. Creating the Ideal World solution

V. Model the solution alternatives

a. Current state (based on step II)

b. Future state exploit

c. Future state elevate

d. Ideal state

VI. Evaluate the solution alternatives based on the criteria (step I)

VII. Implement the solutions and control the process

22

23

Definition of the Case Study at KLM E&M Engine

Services

This chapter indicates the starting point of the case study at KLM E&M Engine Services, in

which the previously defined literature framework is applied to find solution alternatives to

decrease the overall aircraft engine MRO turnaround time at KLM E&M Engine Services.

This chapter aims to give a broader overview of the context of the case study at KLM E&M

Engine Services, by first discussing the technological design of the system (section 3.1), the

surrounding market (section 3.2) and the organizational (institutional) design (section 3.3).

From this broader context, criteria to evaluate the solution alternatives later on will be

derived, thus answering the second sub-question: “What criteria can be used to assess the

different solution alternatives for KLM E&M Engine Services?” The set-up of this chapter is

shown in Figure 3-1.

Figure 3-1: Chapter 3

3.1. Technological Design

(Ayeni, Baines, Lightfoot, & Ball, 2011, p. 2109) give the following definition of the aviation

MRO industry: “The aviation MRO industry is responsible for the retaining or restoring of

aircraft parts in or to a state in which they can perform their required design function(s). This

includes the combination of all technical and corresponding administrative, managerial,

supervisory and oversight activities.” This section will discuss the MRO relevant to this

research: engine MRO.

3.1.1. Turbofan engines

The Engine Shop at KLM E&M maintains turbofan engines: a specific type of aircraft engine

useable for medium-high speeds. A turbofan engine is a tradeoff between the concepts of a

pure turbojet and a propeller engine (Anderson, 2008, p. 722), as it combines the high thrust

of a turbojet engine with the higher efficiency of a piston engine-propeller combination. A

turbofan engine consists of a number of main modules: the fan, the compressor, the

combustor (burner), the turbine and the exit nozzle. A schematic view of a Turbofan engine

is shown in Figure 3-2.

24

The turbine drives both the fan and the compressor, while the fan accelerates a large mass

of air that flows through and outside of the engine core. The ratio between flow through and

around the core is called the ‘Bypass Ratio’. The air that flows around the engine core mixes

with the air that is burned in the combustor and leaves via the nozzle. The thrust of a

Turbofan engine is a combination of the airflow from the exhaust nozzle and the thrust

produced by the fan (Anderson, 2008, p. 722).

Figure 3-2: Schematic view of a Turbofan engine (Ackert, 2011)

3.1.2. Turbofan engine Maintenance, Repair and Overhaul

The maintenance of aircraft represents around 10-15% of an airline’s operating budget, of

which 35-40% of these costs are engine maintenance related (Ackert, 2011, p. 9). The reasons

for engine maintenance are threefold: Operational, which is needed to keep the engine in a

serviceable and reliable condition, Value Retention, which means to maintain the current

and future value of an engine, and finally Regulatory Requirements, meaning meeting the

minimum required demands and standards of inspection and maintenance. The health of an

engine is generally measured following a number of indicators (Ackert, 2011):

EGT (Exhaust Gas Temperature)

This indicator is a common condition or health parameter. A high EGT can indicate degraded

engine performance. The manufacturer gives a maximum allowed temperature; the

temperature is measured at the engine exhaust in degrees Celsius.

EGT Margin

The EGT margin is the difference between maximum allowed EGT and peak EGT during

takeoff. The required margin after repair is part of the contract with the client.

EPR (Engine Pressure Ratio)

This indicator is sometimes used to measure the thrust of the engine.

N1-Speed

The N1-speed measures the rotation speed of fan.

3.1.3. Serviced engine types at KLM E&M Engine Services

The Engine Shop of KLM E&M serves a limited number of engines. The engine types are

described in Table 3-1. It is necessary to differentiate between the different engine types in

this research, as not every engine type follows exactly the same process throughout the MRO

chain. The CFM56-7B is an engine made by the CFM joint venture (General Electric and

SNECMA) and is commonly applied at Boeing 737 aircraft. The 8F6-80E1 engine is made by

25

General Electric (GE) and is used for the Airbus A330. The third engine type serviced at the

KLM E&M Engine Shop is the CF6-80C2, also by GE, used for wide body aircraft such as

the Boeing 747. The last mentioned engine type, the GEnx-1B64, or GEnx for short, will be

serviced at KLM E&M in the future and is used for the Boeing 787 Dreamliner.

This research will take into account the first engine type: CFM56-7B. In this way, the

research can contribute to existing and ongoing projects to reduce the TAT – such as the

TAT45 project; next to this, this engine type is an important capability in the portfolio of

KLM E&M, as the volume of this engine type is expected to increase over the following years.

Table 3-1: Engine types at KLM E&M Engine Shop

Engine type OEM Applications #engines per

aircraft

CFM56-7B SNECMA-GE (CFM) B737/A320 2

CF6-80E1 GE A330 2

CF6-80C2 GE B747 4

GEnx-1B64 GE B787 2

3.2. The engine MRO Market

The current turbofan engine manufacturing market is led by three main parties: General

Electric (GE), Rolls-Royce and Pratt & Whitney (Ackert, 2011). These parties operate

independently, but also in joint ventures. GE and SNECMA have formed the joint venture

CFM International, while Rolls-Royce and Pratt & Whitney joined forces in International

Aero Engines (IAE). The global market for turbofan engine production is shown in Figure

3-3. It shows that the three mentioned OEM’s dominate the global market, either

independently or through their joint ventures.

Figure 3-3: Global market shares for commercial engine production (Flightglobal, 2015)

As stated before in the introduction (section 1.1), the engine maintenance market (also called

aftermarket) is highly competitive and dominated by OEM players, such as General Electric

and Rolls-Royce; an estimate of 55% of the engine MRO market is taken by OEMs, which is

the highest share in the whole aircraft MRO market. When comparing this share to Airframe

MRO, for instance, it is estimated that OEMs take up only 2% of the global market (Stewart

D. , 2015).

26

3.2.1. Competition landscape engine MRO

To compare the main players in the Engine MRO market, landscapes are created using

figures from their respective annual reports; these landscapes are shown in Figure 3-4 and

Figure 3-5.

The first figure shows the company’s profitability versus its growth. It shows how General

Electric achieves both high growth and high profitability in its engine MRO segment. The

second figure shows the company’s revenue versus its product strategy. An important note

is that the numbers for OEM’s consist of only engine MRO (!), whilst the Airline 3rd party

numbers consist of all MRO activities, thus including airframe maintenance, for instance.

Taking into account this fact, it becomes apparent how large especially General Electric (GE)

is as a player, with a revenue of over $9 billion in 2015.

Figure 3-4: Competition landscape Profitability versus Growth (2015 figures)

Figure 3-5: Competition landscape Turnover versus Product strategy (2015 figures)

3.2.2. Different strategies in the engine MRO landscape

As shown in Figure 3-5, different strategies exist in the Engine MRO market. The OEM’s,

for instance, focus on mono-product MRO: only engine maintenance. With this focus, they

can compete on technology and a cost competitive service. Next to this, the OEM’s are

responsible for giving out maintenance licenses to independent parties, such as KLM E&M.

OEM’s focus increasingly on engine MRO, increasing their internal MRO revenue share and

27

building MRO backlog. They are achieving this through the sales of engines accompanied by

long-term service agreements.

For independent party service providers, competing with the OEM’s is difficult, but possible.

Unique selling points for independent parties are the fact that they can offer multi-product

solutions: not only engine maintenance, but also airframe and component maintenance, for

instance. Next to this, their independence from OEM’s is a reason for airlines to choose

independent parties – it prevents the OEM’s from gaining too much market power.

Independent airline 3rd party companies have an extra selling point: from own experience,

they have knowledge on owning and operating an airline, and may have a better

understanding of a client’s wishes from this expertise.

As stated before, maintenance providers have to compete on cost, throughput time (TAT) or

quality of delivered services (Ayeni, Baines, Lightfoot, & Ball, 2011, p. 2122). These aspects

can be visualized in a triangle (Figure 3-6), as cost, quality and TAT are all interrelated

outputs of the MRO process. Ideally, one would achieve a process where cost and TAT are

low, while achieving high product quality. However, trade-offs are necessary: the next

section will discuss the objectives of KLM Engineering & Maintenance that follow from its

strategic goals.

Figure 3-6: Process output objectives

3.3. The Organization

As described before, KLM Engineering & Maintenance is a branch of the maintenance

division of Air France-KLM. To safeguard the future of KLM and its divisions, the High

Performance Organization is being implemented: a project aiming for a more agile, lean and

efficient company (KLM, 2015b). In practice, this has many implications for the organization

within divisions: management layers will be decreased and functions centralized, for

instance. Next to this organizational optimization plan, which has a large impact within

E&M, KLM E&M aims for “growth through work for third parties, particularly on General

Electric next generation engines and components for third parties flying Boeing 787s.”

The current organization of KLM E&M Engine Services is based on its three main goals:

Engine Availability, Engine MRO and Parts & Accessories MRO. Each of the three divisions

of Engine Services has its own management team. Within each division, different

departments are in place. For engine MRO, these are the four main stages as shown in

Figure 1-1: Work Scope, Disassembly, Repair and Assembly.

Currently, the performance of Engine MRO at KLM E&M is measured using a Connected

Business Balance Score Card (CBBSC). Table 3-2 shows the currently used performance

indicators for Engine MRO in the CBBSC. For each indicator, the definition as used by KLM

E&M is given.

28

Table 3-2: Engine MRO performance indicators KLM E&M

MRO Performance

Indicator

Explanation

On time (TAT) % Percentage of engines that is delivered within the agreed TAT.

Different for different clients and engine types

Product quality (EGT) % Percentage of engines with EGT that matches the contract EGT,

if this metric was included in the contract (not all engines)

Test Cell % Measures the percentage of engines that pass the test cell final

test the first time (first time right). Collection of different quality

indicators dictated by engine MRO manual.

Productivity % Planned man-hours versus spent man-hours

At the time of this research, the exact effects of the High Performance Organization on the

organization of Engine Services are still unknown. However, an important change is clear:

at Engine MRO, Teaming will be applied.

The principle of Teaming, is that a single team will be responsible for the entire engine MRO

chain. In the current situation, different teams work on the disassembly, repair and

assembly of one single engine. With Teaming, the same team will disassemble and assemble

the engine. However, a handover will keep existing between the disassembly team and the

repair of the engine parts – a different team will stay responsible for the repair process. The

current state of Engine MRO will be based on the organization before HPO, as the available

and used data is a result of the “old” situation.

3.4. Criteria to evaluate solution alternatives

This research aims to provide different solution alternatives, improving the Engine MRO

process at KLM Engineering & Maintenance. To evaluate the different solution alternatives,

criteria need to be established. From the previous sections, a number of criteria can be

derived. To determine criteria, a goal tree is used (Haan, et al., 2009). From the main goal

of KLM E&M Engine Services, “to be a competitive player in the Engine MRO Market”, sub-

goals can be derived. These sub-goals are based on the triangle of Cost, Quality and Time,

as stated in section 3.2. Next, this goals are transformed in to lower-level goals from the

perspectives of KLM E&M and from client’s perspectives. These lower level goals can be

translated into criteria. The overview is found in Figure 3-7.

Figure 3-7: Goal tree KLM E&M Engine Services

29

These criteria are used to evaluate the found solution alternatives in chapter 8. The next

chapter will describe the next step from the literature framework (section 2.4): measuring

the current state of the engine MRO chain.

To answer the sub-question “What criteria can be used to assess the different solution

alternatives for KLM E&M Engine Services?”, this chapter explored the context of the

engine MRO process. First, the technology of aircraft engines is defined, next the engine

MRO market is discussed and finally the organizational design at KLM Engineering &

Maintenance engine services is explored. From this context, a number of solution criteria

can be defined:

MRO process Cost

Implementation Cost

Product Quality

Process Quality –variation in the processes

Turnaround Time

In the case study at KLM E&M Engine Services, Turnaround Time and Process quality

are measured on a quantative basis, whilst the other criteria are assessed on a

qualitative basis.

30

31

Part Two: Measure Phase

Photo Courtesy of KLM Engineering and Maintenance

32

33

Current state – Case study at KLM E&M Engine

Services

This chapter aims to answer the sub question “What is the current state of the Engine MRO process

at KLM E&M Engine Services?” This will be achieved by first explaining the used tools to

measure the current state in section 4.1, next the general current state of the overall engine

MRO chain is investigated in section 4.2. As in-house repairs are an element of the MRO

chain, previous work and current performance are summarized in section 4.3. This research

will focus in-depth on the outsourced repair stage – the current state of these stages will be

discussed in section 4.3.2 and 4.3.2. The chapter will summarize the findings and answer

the above stated sub question in section 4.4. The overview of the chapter is shown in Figure

4-1 below.

Figure 4-1: Chapter 4

4.1. Methods used for current state measurement

In the literature study in chapter 2, a number of tools are identified that can be used to

measure the current state of the Engine MRO chain at KLM E&M. The different tools are

explained briefly in this section.

SIPOC Diagram

A SIPOC diagram is a tool taken from the Six Sigma methodology (iSixSigma, n.d.). It stands

for Supplier – Input – Process – Output – Control; it gives an overview of the inputs and

subsequent process deliverables to a certain customer.

BPMN Flowchart

A flowchart is a tool taken from Total Quality Management (Reid & Sanders, 2010, p. 153),

but is also a part of Business Process Modelling (section 2.2). As (Aguilar-Saven, 2004)

defines, a Flow Chart is a graphical representation of a process, using symbols to represent

elements such as flow direction, operations and equipment.

34

Histograms & Probability Plots

Histograms are used to show the frequency distribution of observed values of a variable (Reid

& Sanders, 2010, p. 153). A Probability Plot is another statistical tool useful tool to show the

distribution of observed values. Multiple groups of variables can be compared using

estimated cumulative distribution functions; using the plots, the normality of the sample

values can be tested and the percentiles can be estimated (Minitab, 2016b).

Used datasets

To measure the current stage and to generate input into the current state model in a later

step in this research, different datasets from different stakeholders are used, all originating

from the SAP system. A description of the various used datasets can be found in Appendix

B.

In the next sections, these tools will be applied to measure the Current State of the Engine

MRO Process at the Engine Shop of KLM Engineering and Maintenance.

4.2. General current state engine MRO

As previously discussed in section 1.2, the Engine MRO chain considered in this research

consists of four different stages: Work Scope determination (0), Disassembly (1), Repair (2)

and Assembly (3). When illustrating the problem and its context, it already became clear

that the current output performance of these four stages is insufficient; this section will

elaborate on the current state of the whole engine MRO chain, using a selection of the tools

described in the previous section. This section will end with qualitative observations made

during the research – observations that are needed, next to the analytical results, to improve

the process.

4.2.1. SIPOC of the engine MRO chain

Figure 4-2 shows the SIPOC diagram of the overall Engine MRO chain. The four main stages

are indicated in the process box, while the suppliers box shows the three different client

types: Internal pool, which are Air France and KLM engines, External Airlines, and GE

(General Electric) Offload: GE Maintenance engines subcontracted to KLM E&M. On this

level, the customers are equal to the suppliers: this is typical for MRO. Unserviceable and

Serviceable engines serve as physical input and output of the process, while Engine Order

Data and the Engine Overhaul Report make up the information in- and output.

Figure 4-2: SIPOC diagram of the Engine MRO Chain

35

4.2.2. Flow Chart engine MRO Process

In Figure 4-3, the main processes and choice moments in the Engine MRO chain are shown.

After work scope determination and disassembly of engine modules and subsequently

components, the components are assessed at the Parts & Disposition (P&D) department.

From here, it is decided whether a component is serviceable, or whether it needs repair or

replacement. When repair is possible, a decision is made to either repair the component in-

house, or to outsource it to an external vendor. Another option could sometimes be to replace

a component from un-used stock: this is generally only done for AF-KLM pool engines. After

repair, the components are collected at the APrep department. The components are

assembled into modules, and these modules are assembled into a full engine. After assembly,

the engine is inspected and tested in the test cell. In the indicated flow chart, the flow as it

should be is indicated – it may, for example, happen that an engine fails the final test and is

sent back into the process.

Figure 4-3: Flow chart of the overall Engine MRO process

4.2.3. Current control of the engine MRO chain

Figure 4-4 shows the current organizational control of the MRO chain. This layout will

change in the near future through the implementation of HPO (High Performance

Organization), however this layout is used for the current state as the historical data are

resulting from this layout.

The green line shows the steps that fall under the control of the MRO department, led by

René Kruithof (manager ES MRO). The orange lines indicate the areas falling under the

responsibility of different managers that were included in the discussion to form a clear

picture of the MRO chain: Harry Akermans (manager MRO stage 1), Saskia Verschuren

(manager MRO stage 2) and Erik Dirksen (manager MRO stage 3).

36

Figure 4-4: current organizational (institutional) control of the MRO chain

Currently, no clear agreements exist on the TAT per main stage. These agreements should

depend on the contract TAT and the work scope of an engine; an example for a TAT of 60

days (excluding stage 0) is shown below in Table 4-1. It is important to note that the control

of the process is thus based on the TAT per stage, and that the agreements are not clear or

consistent: different stakeholders give different values for agreed TAT per stage.

Table 4-1: TAT agreements per stage - TAT60

Stage 0 Stage 1 Stage 2 Stage 3 Total

5 days 11-12 days 30-35 days (28 in-

house)

13 days 60 days

4.2.4. Currently measured output performance

Figure 4-5 below shows the performance of the Engine MRO chain in 2015. The time aspect

is represented by On Time (TAT) percentage, quality by EGT and TestCell and cost by the

productivity metric. However, productivity is not sufficient to measure the actual cost of the

process: the main cost driver for MRO is represented by material cost – this could amount

to 60-70% of the total cost (Ackert, 2011).

Figure 4-5: CBBSC KLM E&M ES MRO

37

Turnaround Time (TAT)

An important output measure of the Engine MRO chain – and the main focus of this case

study - is the turnaround time. In the CBBSC, it is only measured whether an engine is

completed within the contract TAT (on time performance), however in this section the actual

TAT will be shown. Figure 4-6 shows the actual total TAT per engine type. It can be observed

that the average TAT for the CFM56-7B engine is 62 days, with a large standard deviation

of 23 days. The used dataset is described in Appendix B.1.

Figure 4-6: Actual TAT per engine type

As shown in Figure 1-1, the engine MRO process consists of four separate stages: Work Scope

(0), Disassembly (1), Repair (2) and Assembly & Testing (3). For each of these stages,

probability plots of the TAT can be created. These are shown in Figure 4-7, Figure 4-8, Figure

4-9 and Figure 4-10, respectively.

Figure 4-7: Actual TAT stage 0 per engine type

38

Figure 4-8: Actual TAT stage 1 per engine type

Figure 4-9: Actual TAT stage 2 per engine type

39

Figure 4-10: Actual TAT stage 3 per engine type

The actual TAT performance of CFM56-7B engine MRO for the different stages can be

compared to the handshakes, shown in Table 4-1. It can be observed from the actual average

TAT that the sum of all stages does not add up to the total average TAT. This can be

explained by the fact that, in reality, overlap is used within the stages – even though the

actual control is stage-based.

Table 4-2: Handshake versus actual TAT – CFM56-7B engines

Stage 0 Stage 1 Stage 2 Stage 3 Total

Handshake 5 days 11-12 days 30-35 days (28

in-house)

13 days 60 days

Actual average 4 days 14 days 49 days 24 days 62 days

4.2.5. General Observations engine MRO chain

From interviews with different employees, a number of more qualitative observations can be

made. An important observation is that there are many (forthcoming) changes in the Engine

MRO chain, due to the HPO reorganization. This results in a challenging period, where the

final process and organizational structure is still unclear. It is clear, however, that the

reorganization will have a major impact on the organizational structure. Management layers

and responsibilities are changing, and self-steering teams will be implemented. Even though

the current organizational structure will change, the current state analysis is based on the

old structure, as the data is also a result from the old organizational structure (2015).

Next to this, it was observed that no clear consensus exists on the TAT per stage, and how

the norms are defined. However it can be observed that, regardless of the agreed norm, the

current TAT performance is sub-optimal, and that the Repair Stage contributes to the

largest TAT share in the whole chain.

4.3. Research focus: Repair Stage

From the measurement of the current state of the engine MRO chain, it can be observed that

the largest portion of the whole Turnaround Time is taken by the repair stage (Stage 2). To

40

reach a TAT of 45 days, it is expected that large improvements can be found in this stage.

Therefore, this research will aim to find structural improvements in this stage.

Repair of engine components takes place both in-house and outsourced, at external vendors.

Previous research of (Meijs, 2016) and (Mogendorff, 2016) focused on in-house repairs, of the

fan blades and combustors, respectively. This research will therefore contribute to

improvement of the repair stage by focusing on outsourced repairs. A recap of the research

on in-house repairs is given in section 4.3.1, while the current state of outsourced repairs is

discussed in section 4.3.2.

4.3.1. Current state of In-House Repairs – Summary of previous research

In-House repair is an important element of the engine MRO chain. This section will

summarize previous work conducted by (Meijs, 2016) and (Mogendorff, 2016), and will give

the performance of In-House repairs in 2015. Next to this, the current performance

measurement structure is discussed.

In-House repair: Combustors & Fan Blades

The In-House repair of combustors was investigated by (Mogendorff, 2016). The research

showed the importance of defining the KPI correctly: from part level to full set level. The on

time performance on part level amounted to 72%, however on set level it was a mere 14%.

The researcher found a waiting time percentage of around 90% in the whole process – 5 days

processing (touch) time against 34 days of waiting time. By implementing a number of

solutions, for example re-evaluation of maintenance routes and a reduced number of

inspections, the on time performance could possibly improve to 91%.

A following research by (Meijs, 2016) investigated the In-House repair of fan blades. Again,

the same issue regarding KPI definitions arose: the KPI was measured on part level,

resulting in an on time performance of 52%, however when looking at a complete set of fan

blades, the performance dropped to a mere 29%. 80% of the throughput time was defined as

waiting time, with a processing time of 6.3 days against a waiting time of 25.2 days. After

implementation of solutions such as using smaller batch sizes, better utilization of the shot

peen machines and using the drum-buffer-rope principle, an on time performance of 98%

could be achieved.

In-house repair 2015 performance – CFM56-7B Engines

Currently, the performance of In-House repairs is based on the TAT per work center. As

stated before in section 4.2.3, there are agreements in place on the TAT per stage. For In-

House repair, this agreement, or handshake, is 28 days. For the CFM56-7B the performance

of the work centers relative to this handshake of 28 days is shown in Figure 4-11 below.

Work center 2400 is indicated as an underperforming work center: this is the combustor

repair work center. An important note is that the fan blades do not show up in this graph:

the fan blades of the CFM56-7B engine are outsourced.

41

Figure 4-11: On Time Performance CFM56-7B In-House Repairs (HS=28 days) – 2015

A similar graph can be created when looking at the modules of CFM56-7B engines instead

of just the work centers, this is shown in Figure 4-12. Again, the combustor is indicated.

Notice how the performance of some modules is lower than the performance of previously

stated work centers: for example 52X – the high pressure turbine rotor. This module has an

OTP of 86%, however only uses work center 2600 – which reaches an OTP of 97% in total.

This suggests that by looking only at the work centers, some under-performing modules can

be missed.

Figure 4-12: On time performance of CFM56-7B modules (HS=28 days) – 2015

Observations In-House repairs

From previous research it has become clear that major improvements can be achieved for in-

house repair of fan blades and combustors. However, due to performance measurement on

work center level, under-performance of modules such as the high pressure turbine rotor

(52X) could be missed.

Next to this observation it became clear from discussions that the decision to repair a module

or component in-house is made purely based on in-house capability: can we do it in-house?

42

No clear cost factor is taken in to the decision-making process, and there is no clear insight

whether in-house repairs are priced according to market value or at a competitive price

point. In other words: the make-or-buy decision is not based on all the possible decision

factors. The next sections will discuss the process Outsourced Repair.

4.3.2. Current state of Outsourced Repairs

This section will discuss the current state of Outsourced Repairs, focusing on CFM56-7B

engines. First, the current processes are defined using a SIPOC diagram. Next, the current

state in terms of TAT performance is discussed. The section will conclude with general

observations on Outsourced Repair.

SIPOC diagram

The diagram shown in Figure 4-13 shows the relevant suppliers, inputs, process, outputs

and customers of the Outsourced Repairs process. After assessment, a purchase order is

created for an unserviceable module. In the data, this is shown as the “created on” date.

Next, the module goes through the logistics Export step, to be delivered to a specific vendor

– for instance GE. KLM E&M has a contract with the vendor which considers the throughput

time of the repair step at the vendor. When the module is repaired, it is delivered back to

KL E&M after another logistical process (Import). The customer of the Outsourced Repair

process is APrep – the department where all serviceable modules are gathered.

Figure 4-13: SIPOC Diagram Outsourced Repair

Performance Current State Outsourced Repair

Figure 4-14 shows the current TAT performance of outsourced components. A difference is

made between contract TAT, actual TAT and Grand TAT – this last variable includes

logistics times to and from KLM E&M. As shown, the contract TAT follows a normal curve,

with an average of about 23 days. However, the actual TAT shows more variation – an

average of 23 days with a standard deviation of around 10 days. The grand TAT includes

logistics times; naturally, this curve is shifted more to the right. The average grand TAT is

31 days – 9 days more than the average actual TAT. As shown in Table 4-1, the agreed

handshake for this stage is 35 days. However, only 75% of all Outsourced Work is conducted

within these 35 days.

43

Figure 4-14: Current TAT Outsourced Repair

Observations Outsourced Repairs

A number of qualitative observations can be made on Outsourced Repairs. First of all, vendor

management is an issue – up till now, KLM E&M has had a very (in their own words)

reactive attitude towards vendors and agreements; in the near future, focus lies on

improving vendor management, hopefully resulting in better delivery on contracts. From

various discussions, it became clear that the supply chain is an issue; the current provider

does not provide insight in transport status, and lacks performance. The constraints causing

the sub-optimal performance for Outsourced Repairs on both vendor and logistics aspect will

be analyzed in chapter 5.

44

4.4. Conclusions current state

This section gives the main conclusions of the Measurement phase: the identification of

the Current State of the Engine MRO chain at KLM E&M. It will answer the following

sub-question: “What is the current state of the Engine MRO process at KLM E&M Engine

Services?”

This sub-question can be answered from multiple perspectives. First, the general

current state of the Engine MRO chain is discussed. Next, In-House repair performance

is investigated and finally Outsourced Repair is discussed.

General MRO Chain

The current performance of the general MRO chain is sub-optimal, with the average

TAT for CFM56-7B engines being 62 days, with a large standard deviation of 23 days.

The current control is based on stages, giving no insight in the integral value stream.

However, agreements on the TAT per stage are inconsistent and unclear. Next to this,

the quality performance is sub-optimal, and cost are not adequately measured in the

scorecard. The Repair stage contributes to the largest share of the total TAT.

In-House Repairs

Previous research has contributed to improving the in-house repair processes for fan

blades and combustors. The researchers found that in the repair TAT, 80% to 90% is

waiting time. The current performance for in-house repair is measured only on work

station basis, resulting in loss of information of the value stream.

Outsourced Repairs

The current grand TAT for outsourced repairs, including logistics, is 31 days, however

25% of orders have a TAT larger than 35 days. The average TAT at vendors lies at 23

days, while the whole logistics process has a TAT of 9 days on average.

45

Part Three: Analyze Phase

Photo Courtesy of KLM Engineering and Maintenance

46

47

Identification of constraints in the engine MRO chain

at KLM E&M Engine Services

This chapter aims to answer the following sub-question: “What constraints are limiting the

turnaround time of the Engine MRO process at KLM E&M Engine Services?” To answer this question,

an in-depth analysis will be made of the MRO chain and subsequently Outsourced Repair.

First, a method to measure performance is discussed in section 5.1. Secondly, the general

MRO chain is analyzed in section 5.2. Next, constraints of Outsourced Repair are analyzed

in section 5.3. Section 5.4 will give the conclusions of this chapter. The overview of this

chapter is shown in Figure 5-1 below.

Figure 5-1: Chapter 5

5.1. Methods used for constraint identification

Various tools and methods are available to identify, measure and observe the constraints in

the process of engine MRO. A constraint is anything that limits the output of a process. In

the literature review (section 2.1), different drivers are identified. Through measurement

and observation, first a Value Stream Map is built.

Value Stream Mapping

Value Stream Mapping (VSM) is taken from the Lean methodology (section 2.1.1). As stated

before, a Value Stream Map is a tool applied to contribute to the stability of the process (the

foundation of the TPS House). A Value Stream Map is used to analyze the flow of goods

through different processes, indicating the total throughput time and the processing time.

In this way, constraints can be shown in the process.

Different constraint types

Constraints can be formed by many different drivers. An overview of these drivers is shown

in Figure 5-2. The drivers are based on Total Quality Management and Lean Six Sigma, as

discussed in chapter 2.1.

48

Figure 5-2: Different internal drivers that can constrain a process

In the next sections, the constraints in the whole MRO chain are identified, and

subsequently constraints in the repair process are found through measurement and

observation.

5.2. Analysis of the engine MRO Chain – Value Stream Based

The Value Stream needs to be defined before bottlenecks, and subsequently, root causes can

be identified.

The Value Stream of Engine MRO is the flow of goods through the Engine MRO process.

Naturally, this flow consists of the aircraft engines. However, the engine is disassembled

into modules, which consist in turn of assemblies (WBS elements) and finally parts. As an

engine consists of more than 10,000 separate parts – too many for individual analysis – the

level of detail for the Value Stream is decided to be focused on assemblies (WBS elements).

The main assemblies of the engine are shown in Figure 5-3 below. The Fan module, for

instance, consists of the assemblies 01X (the fan major module), 22X (bearings and support)

and 23X (fan frame and blades).

Figure 5-3: CFM56-7B Engine with assemblies

As these assembly codes are also present in SAP, the throughput time and other data can be

matched to the different assemblies, for different stages in the MRO process. Currently,

measurement of the process is conducted stage-based. As previously shown, TAT norms per

stage are inconsistent. Furthermore, no measurements are conducted on the integral value

stream of the different assemblies: from disassembly, to repair, to assembly. Taking into

account the disassembly order, shown in Appendix C, the value stream based on the different

WBS assemblies can be recreated. This value stream is shown in Figure 5-4. Datasets, on

which this figure is based, are discussed in Appendix B.

49

Figure 5-4: Current State Value Stream of WBS assemblies

This shows that individual measurement of stages is not enough: what matters is the entire

chain, as the output of the engine MRO process is limited by the moment the critical

assembly is ready for assembly: one cannot build a complete aircraft engine when not all

material is ready. Furthermore, assembly of the engine starts with the Fan module – 01X,

22X and 23X – the module that is disassembled last. Analysis shows that in general the

largest share of Turnaround Time is formed by outsourced repairs (indicated in light blue in

Figure 5-4), therefore forming the main constraint to the overall engine MRO chain.

The next sections will again zoom in to the Outsourced Repair stage and will identify the

constraints limiting the throughput time of that stage.

5.3. Constraints in the Outsourced Repair Stage

As shown in section 4.3.2, the average throughput time of Outsourced Repair for the CFM56-

7B engines is 31 days – measured from the moment orders are created until the goods are

received again after repair. Only 75% of the orders are completed within the set handshake

of 35 days for Stage 2 (see Table 4-1).

Bottlenecks and root causes within Outsourced Work will be analyzed in a number of steps.

First, the overall TAT is analyzed per assembly. Next, the performance of the vendor is

shown. Thirdly, the contract agreements are analyzed, and finally logistics to and from the

vendor is discussed.

For the performance in this analysis, for logistics a goal of three days to and from the vendor

is agreed upon. For the repair itself, performance is based on a handshake of 28 days – the

same amount of time as In-House repairs. In total, this amounts to 34 days – within the

handshake of 35 days for Stage 2:

Used Dataset

The analysis of Outsourced Work is based on a dataset retrieved from SAP with the help of

Ronald Oostveen (senior project buyer). A more extensive description is given in Appendix

B.

3 days logistics 28 days repair 3 days logistics

50

Grand TAT performance

The performance of Outsourced Work for different assemblies – as described in section 5.2 –

is shown in Figure 5-5. The Grand TAT is the turnaround time between the moment an order

is created (“created on” date) and the moment an order is received again (“goods received”

date). This thus includes both logistics and repair time at the Vendor. To establish the On

Time Performance, the Grand TAT is compared to the handshake of 35 days.

Figure 5-5: OTP Outsourced Work (based on 35 day handshake Stage 2)

It can be observed that the Grand TAT on time performance is sub-optimal for all different

assemblies; the next sections will analyze the underlying logistics and vendor performance.

Vendor TAT performance

This section discusses the performance when looking at the Vendor TAT – the time an

assembly has been at a vendor for repair. This TAT should fit within the handshake of 28

days. Figure 5-6 shows the performance for the assemblies based on this handshake. Again,

no assembly reaches a 100% performance.

Figure 5-6: OTP Vendor TAT (based on 28 day handshake)

The next section will explore the performance of the vendors based on the made contract

agreements.

51

Contract TAT performance

When looking at the vendor performance, it is important to analyze whether the agreements

are met. To create Figure 5-7, actual TAT is compared to the agreed contract TAT. It is

shown that no vendor meets all the agreements. For example GEAN, one of the main

vendors, reaches the agreement in a mere 53% of all orders.

Figure 5-7: OTP vendors (based on contract agreements)

Even if contracts agreements are met by the vendor, still issues rise – as is shown in Figure

5-8. Here, the contract agreements per assembly are compared to the handshake of 28 days.

For assembly 22X – part of the fan module - for instance, 25% of all agreements is longer

than 28 days for repair.

Figure 5-8: OTP contracts (based on handshake of 28 days)

It can thus be concluded, that orders exist with an agreement above 28 days; these orders

are described in detail in Table 5-1 below. For instance, a set of HPT rotor blades, repaired

by GEAN, can have a contract agreement of 32 to 35 days, excluding logistics. When such an

agreement is met, and logistics are six days in total, the Grand TAT for such a set would

amount to 38 to 41 days – impossible to fit in the whole goal MRO chain of 45 days.

52

Table 5-1: Contracts outside of handshake of 28 days (percentage below 100%)

WBS Part Vendor Contract TAT Count

22X Tcc Valve Triumph,

Honeywell

30 days 14 orders

52X HPT Rotor

Blades (set)

GEAN 32-35 days 4 orders

51X HPT Nozzle Vane

Segments (set)

GEAN 32-35 days 5 orders

32X HPC stator seal

assembly (stage

1-3)

GE-Hungary 33 days 14 orders

21X Various LRU GEAN, Honeywell 30 days 25 orders

11X Various LRU AAT, Triumph,

Eaton

30 days 20 orders

Logistics general performance

The overall average TAT of logistics to and from a vendor to Engine Services is 8.8 calendar

days, of which the logistics towards the vendor (export logistics) have an average TAT of 3.8

days, and the logistics from the vendor (import logistics) an average TAT of 5 days.

Figure 5-9: Logistics average TAT per vendor

The various vendors are located world-wide: from Singapore to the USA, but also very close

to Engine Services: EPCOR BV is a subsidiary of KLM E&M. Interesting to note is, that

even though EPCOR is located very close to Engine Services, logistics to and from EPCOR

still have a TAT of 3.1 and 2.5 days on average, respectively. The logistics portion of

Outsourced Repairs can be divided into internal and external logistics. The next section will

analyze the constraints limiting the logistics TAT.

Constraints in the logistical chain

This section will describe the general logistical chain, and subsequently focus on the internal

logistical processes at KLM E&M Engine Services, both on the import and export side. The

logistics stream consists of a number of steps, including many different stakeholders. In

general, the different steps can be categorized as follows, starting from export of a package

at Engine Services:

1. Internal logistics - Engine Services Export

2. External logistics - Logistics Centre E&M

3. External transport – Bolloré (3rd party logistics service provider)

53

4. Repair at the vendor

5. External transport - Bolloré

6. External logistics - Logistics Centre E&M

7. Internal logistics - Engine Services Import

This research will focus the search for constraints in the first and last step: the internal

logistics processes at KLM E&M Engine Services.

Logistics Engine Services Import

Using observations and measurements from previous research by (Klaassen, 2012), the

constraints in the import logistics process are identified. First, a Value Stream Map is

created. This Value Stream Map is shown in Figure 5-10 below. The time on the lower bars

indicate the estimated process time, while the time on the higher bars indicate the non-value

added time: waste. The estimated process time was acquired by real-life observation of the

process.

The import logistics process at Engine Services consists of three steps: after delivery of a

package at ES by Sodexo, the package needs to be accepted at the Maintenance Unit – or

MU. In this step, packages are imported into the system, checked for transport damages and

unpacked. Next, the repaired parts require an Inspection Incoming Goods (IIG) by certified

inspectors. In this step, certification is checked, along with detailed part counts and other

administrative tasks. The next step is to transport the parts to APrep in another part of the

building – this is done on regular times: three times per day shift, and two times per night

shift.

Figure 5-10: Value Stream Map import logistics Engine Services

By observation, different possible constraints – as shown in Figure 5-2 – were identified. The

observations are listed in Appendix C.2. From Figure 5-10, it can be observed that the largest

waiting time exists before the inspection incoming goods. This is also observed in real life:

the IIG buffer is very large. Based on the data and observations, it can be concluded that the

main constraint for the import logistics process is the inspection incoming goods (IIG) step.

The import logistics at Engine Services has an average TAT of 3.8 days (Klaassen, 2012).

This means that, on an average of 5 days for import logistics, slightly more than a day is

spent on external logistics. This can be feasible when looking at the estimated transport

times towards different vendors, as shown in Appendix C.2.

54

Logistics Engine Services Export

The total export logistics process has an average TAT of 3.8 days. Based on observations,

interviews and estimates on the transport time to different vendors, it is estimated that

around three days is non-value added time in the process. The build-up of value-added time

(process time) in the export logistics is as follows: 30 minutes of packing and readying for

export at Engine Services. Next, around one hour to pick up the packages and deliver them

to the KLM E&M Logistics center. This is based on the Sodexo delivery rounds, shown in

Appendix C.2. The process at the Logistics Center takes around 2 hours, based on an

interview with a stakeholder. The transport times to the vendor are based on estimates

shown in Appendix C.2. Figure 5-11 shows the Value Stream Map of this process.

Figure 5-11: Value Stream Map export logistics Engine Services

From interviews and observations, it becomes clear that the export logistics process is

constrained by cut-off times determined by outgoing flights to various regions in the world.

These cut-off times are shown in Appendix C.2. If, for example, a package needs to be

transported to Asia, the cut-off time for KLM Cargo flights is 10:00 AM. As KLM Cargo is

the preferred way of transport and most intercontinental flights are once a day, missing the

cut-off time often results in a delay of at least 24 hours.

However, observations at Engine Services do not show a priority system based on these cut-

off times. Packages are handled following a single piece flow and not including possible

prioritization based on package destination.

The next section will give the overview of the different main constraints found in the Engine

MRO process.

55

5.4. Conclusions of constraint identification

This chapter aimed to answer the sub-question: “What constraints are limiting the

turnaround time of the engine MRO process at KLM E&M Engine Services?”

To identify the constraints, different methodologies are applied, amongst others Value

Stream Mapping and identification of waste.

For the overall MRO chain, it is shown that no consistent agreements are in place to

measure different stages in the MRO process. Furthermore, control is based on work

stages, however it is necessary to control the full value stream. When the value stream

is measured, outsourced repair is currently the largest constraint.

Outsourced repair consists of outgoing (export) logistics, repair at a vendor, and incoming

(import) logistics. Using measurement and observation, the main constraints for these

three steps are identified. Export logistics is limited by outgoing flights at fixed times

and the pick-up times of Sodexo towards the logistical center. Repair at the vendor is

constrained by vendor performance, caused by internal sub-optimal performance and

caused by contract agreements being too long. The TAT of import logistics is mainly

constrained by manpower: a lack of capacity for Incoming Goods Inspection (IIG) causes

a large waiting time in this process.

56

57

Part Four: Improve Phase

Photo Courtesy of KLM Engineering and Maintenance

58

59

Creation of solution alternatives for KLM E&M

Engine Services

In this chapter the next step in the framework is conducted: to create solutions for the

different constraints (step IV). This chapter aims to answer the sub-question: “What are

solution alternatives to optimally reduce the turnaround time at KLM E&M Engine Services from the

current towards 45 days?” First, the methods and tools used to create the solutions are

described in section 6.1. Secondly, solutions for the whole MRO chain are created in section

6.2, while subsequently solutions for the repair stage are created in section 6.3. Section 6.4

will give the conclusions to this chapter. The overview of the chapter can be seen in Figure

6-1 below.

Figure 6-1: Chapter 6

6.1. Methods used to create solutions

As a result from the framework, the aim is to create solution alternatives to the constraints

defined in chapter 5. The different solutions aim to exploit or elevate the constraint, or to

create an “Ideal World” solution without limiting factors such as money, time or location.

Solutions to the constraints can be created using Lean tools, such as Just-In-Time or

standardization, tools to create stability or to introduce pull (section 2.1.2). Next to this,

solutions are created using Theory of Constraints (2.1.5), and Creative Problem Solving

(2.1.6).

6.2. Solutions for the MRO chain

As stated in chapter 5, the main constraint of the MRO chain is currently formed by

outsourced repairs. Solution alternatives are created through exploit, elevate or ideal state

identification; the alternatives will be described in this section.

60

Exploit the constraint

To make optimal use of the current constraint, it is necessary that the output of the MRO

chain is limited by the longest critical TAT agreement for outsourced repair. Next to this, it

is necessary that all agreements are consistent in the chain to effectively plan and control

the MRO chain.

Elevate the constraint

To elevate the constraint, the outsourced repair stage throughput needs to be increased by

decreasing the throughput time for this stage. This can be achieved by realizing different

solutions for this stage, as will be defined in section 6.3.

Ideal world

In the perfect world, where no limitations are formed by money, issues in communications

etc., the highest level of Lean Six Sigma can be achieved in the MRO chain, devoid of all

forms of waste. Next to this, continuous flow exists in the system, while process steps can be

conducted perfectly as planned resulting from full availability of Man, Machine, Materials

and the right Method at the right place and time.

6.3. Solutions for the repair stage

In this section, the solutions for the repair stage are created. First, a recap of previously

developed solutions for in-house repairs is given, based on research by (Meijs, 2016) and

(Mogendorff, 2016). Next, the solutions for outsourced repairs are developed.

6.3.1. Summary of previously developed solutions for In-house Repair

Fan Blades

The in-house repair of fan blades is investigated by (Meijs, 2016). In this research, the aim

was to decrease the TAT of in-house fan blade repair. The main constraints were found to be

the shot peening machine, the blending step, seals replacement and inspections. To solve

these constraints, the research advises KLM to use smaller batch sizes, better utilize the

capacity of the shot peen machines, and to use a drum-buffer-rope principle for plating. For

fan blades, the current state TAT was 31.5 days, with an actual process time of 6.3 days –

resulting in a waiting time of 80% in the process.

For the CFM56-7B engines, the fan blades are not repaired in-house – caused by a different

need of capabilities. However, lessons from this case can be used to create assumptions for

ideal state alternatives.

Combustor repair

Repair of the combustor at Engine Services is investigated by (Mogendorff, 2016). Again, the

aim of this research was to decrease the TAT of in-house repair of combustors. The main

constraints found are inspections, bench work and carrousel turning. Several solutions are

proposed, amongst others re-evaluation of maintenance routes, reduction of inspections,

multi-skilled teams and planning and control. Of the whole combustor repair process, the

current TAT amounts to 39 days, while the process time is a mere 5 days – resulting in a

waiting time of 90%. This can be used to create assumptions for the Ideal World solutions.

6.3.2. Solutions for Outsourced Repair

This section will present the proposed solution alternatives for outsourced repair, based on

the found constraints and described in section 5.3. As described before, outsourced repair

consists of logistics (export and import) and repair at a vendor.

61

Solutions Vendor

As analyzed in chapter 5.3, the main constraint for vendor repair is formed by the vendor

performance, caused by a lack of vendor management relative to the current agreements and

contracts agreements that have a long TAT.

Exploiting the Constraint

To exploit the current constraints, constraining the output TAT of the vendor, it is

necessary to exploit the current agreements. As shown in the previous chapter, no vendor

reaches 100% of the agreed TAT on their orders. By implementing more proactive and

strict vendor management, the constraint can be exploited to its maximum: the current

contract agreements.

Elevating the Constraint

Another solution alternative is to negotiate with the vendors to elevate the constraints.

(Kraljic, 1983) defines four different types of purchased resources: non-critical items,

leverage items, bottleneck items, and strategic items. The researcher states that each of

these resource types requires a different purchasing approach. Depending on the position

of KLM E&M versus the various vendors, different strategies can be applied to elevate the

constraint. For example, when “the company plays a dominant market role and suppliers’

strength is rated medium or low, a reasonable aggressive strategy is indicated” (Kraljic,

1983, p. 113) – meaning that the company has a larger change to renegotiate for better

contract and pricing agreement, resulting in a positive profit contribution.

On the other hand, when suppliers have a stronger position, “a company must go on the

defensive and start looking for material substitutes or new suppliers” (Kraljic, 1983, p. 113).

Part of this strategy, on a longer term, is to search for alternative sources or consider

backwards integration – starting to repair strategic components in-house.

For the “elevate” solution alternatives, this research will assume the first strategy, where

KLM has a sufficient market position to renegotiate contracts. The other, more defensive

option, will be explored in the Ideal World solution. To elevate the contracts, this research

proposes three different solution alternatives: all contracts have a maximum TAT of 28

days at the vendor, all contracts have a maximum TAT of 21 days at the vendor, and lastly

all contracts have a maximum TAT of 14 days at the vendor. Next to these contract

agreements, it is assumed that vendor management, as explained in the “exploit” solution,

is in effect.

Ideal world

In the Ideal World where the solutions are not limited by initial monetary or other

objections, different ideas are generated to come to the Ideal World alternative for repair

at the vendors. In the ideal world, the vendors are partners of KLM E&M Engine Services,

partners that are fully Lean and that are able to integrate the planning with KLM E&M

Engine Services and deliver Just-in-Time through an integrated, Lean supply chain. An

example of a vendor that is currently applying Lean is EPCOR (Jong & Beelaerts van

Blokland, 2016). For strategic parts that remain critical and where partners cannot be

fully Lean, backwards integration is applied as described by (Kraljic, 1983) – meaning that

the parts will be repaired in-house, under full control of Engine Services MRO. Further

research is recommended to investigate which critical parts need to be integrated in the

in-house repair process.

62

Solutions Logistics – Export

The Export logistics step is constrained by outgoing transport times. Three different solution

alternatives are created in this section: a solution to exploit the constraint, a solution to

elevate the constraint, and finally an ideal world solution.

Exploiting the constraint

To exploit the current constraint, pull is introduced. Pull is a system from the Lean

methodology, as described in section 2.1.1 and section 6.1. In short, Pull is established in

the export logistics step by creating a clear priority system for outgoing packages based on

the cut-off times per destination. These cut-off times are a result of flight departure times

for, for example, KLM Cargo flights, combined with Sodexo’s pick-up schedule and

handling times at the Logistics Center.

Elevating the constraint

To elevate the constraint, Pull is introduced and, next to this, direct dedicated transport

to the Logistics Center is enables – thus bypassing the delivery schedule of Sodexo.

Ideal World

In the Ideal World, an automated integrated priority system is developed based on the

critical path, giving the priority of export package handling at Engine Services. This

system also communicates with the disassembly, cleaning and inspection steps, to create

a flow parts that need to be outsourced. Next to this, direct dedicated transport from

Engine Services to KLM Cargo or other logistics providers is enabled, to eliminate time-

consuming logistical steps from the process.

Solutions Logistics - Import

As shown in section 5.3, the main constraint for import logistics at Engine Services consists

of the capacity for Incoming Goods Inspection. Next to this, other forms of waste besides

waiting time are observed as well: transport, motion, lack of FIFO, no flow, etc. To create

solution alternatives for Import Logistics, the constraint is exploited, elevated, or an ideal

world is created.

Exploiting the constraint

To exploit the current constraint, the Inspector Incoming Goods (IIG) capacity can be

increased by creating multi-skilled teams. The current worker capacity can be utilized

more efficiently by combining the DGO and the IIG steps to one single activity.

Elevating the constraint

Another alternative is to elevate the current constraint. This is achieved by increasing the

IIG capacity from 5x2 shifts per week, to 7x2 shifts per week – this also creates a more

synchronization with the engine shop itself, which operates on a 7x2 basis. Next to this

capacity increase, the flow of incoming logistics must be safeguarded by creating dedicated

lanes for priority packages, AOG packages and other problematic packages.

Ideal World

In the Ideal World, the parts are delivered Just-in-Time by Lean partners. The IIG

capacity is optimal, and delivery to Engine Services is enabled directly from KLM Cargo

or other logistics providers. Next to this, FIFO flow is created using conveyor or roller belts,

where automatic – RFID based – selection of package destination takes place: New,

Repaired or Used parts, IIG or no IIG needed, etc. A buffer will be created only at the input

of the selection system, and a Kanban system is created for the DGO+IIG process step.

Ideally, no flow disturbances caused by priority or AOG packages occur.

63

6.4. Conclusions and overview of solutions

This chapter aimed to answer the sub-question: “What are solution alternatives to

optimally reduce the turnaround time at KLM E&M Engine Services from the current

towards 45 days?”

For the integral MRO chain the following solutions are formulated:

The main constraint of the current MRO chain is the Outsourced Repair stage. To

exploit this constraint, the planning and output of the MRO chain must be limited by

the critical repair TAT (insourced or outsourced). To elevate the constraint, the

turnaround time of the repair stage must be decreased. In an ideal world, the process

steps in the chain can be conducted as planned as a result of full availability of Man,

Machine, Method and Materials, and as a result of zero waste.

For the Outsourced Repair chain again solutions are formulated following the four

mentioned steps:

Vendor solutions: Constraint: the turnaround time of repairs at the vendor

Solution exploit – Vendor management to make sure TAT agreements are met

Solution Elevate – Renegotiate critical contracts to fit in the MRO chain; from a

maximum of 28 days to 21 days and 14 days.

Ideal State – Vendors are fully Lean, production happens Just-In-Time for

perfect logistics integrality, in-source critical strategic parts when vendors

cannot comply

Logistics Export solutions: Constraint: Outgoing flights

Solution exploit - Introduce Pull, create clear priority system of packages at ES

logistics based on cut-off times at Logistics Center

Solution elevate – Pull from cut-off times at Logistics center, dedicated direct

transport from ES to the Logistics Center, eliminating Sodexo transport time

Ideal State – Automatic priority system based on package and destination,

integrated in cleaning & inspecting process; Direct dedicated transport from ES

to KLM Cargo or other logistics providers to bypass Logistics Center.

Logistics Import solutions: Constraint: Inspection Incoming Goods capacity

Solution exploit – Make optimal use of manpower by creating multi-skilled teams

(IIG and DGO) and integrate the DGO and IIG steps.

Solution elevate – Increase the IIG capacity to 7x2 shifts per week, create

dedicated lanes for priority packages, so the regular flow is not disturbed.

Ideal State – JIT delivery of parts by Lean vendors, direct delivery from airside

to ES; enough Man capacity, create FIFO flow by using conveyor/roller belts.

Enable automatic work station selection per package using RFID. Buffer only at

input of ES logistics, Kanban system for DGO/IIG inspectors.

64

65

Modelling and results of solution alternatives for

KLM E&M Engine Services

This chapter aims to answer the sub-question: “What is the effect of these improvements on the

turnaround time of the integral MRO chain at KLM E&M Engine Services?” To achieve this, step V of

the framework is conducted: modelling the solutions. First, methods used to model the

different solution alternatives are described in section 7.1. Next, the current state of the

MRO chain is modelled in section 7.2. In section 7.3, the different solution alternatives of

outsourced repair are modelled, to subsequently serve as input to the future state models of

the MRO chain, described in section 7.4. This chapter will conclude with an overview of the

model results in section 7.5. The overview of this chapter is shown in Figure 7-1.

Figure 7-1: Chapter 7

7.1. Methods used for solution modelling

Different methods to model solutions are described in section 2.2. The methods used

specifically for this case study are described in this section. Different considerations are

made to model the processes at KLM E&M Engine Services. First of all, the main objective

of the research at Engine Services is to decrease the Turnaround Time of Engine MRO,

implying that the model needs to have TAT as an output. Next to this, the model needs to be

useable for KLM, easy to interpret and insightful to all stakeholders. Therefore, it is decided

not to use expensive modelling and simulation software.

It is decided to use a static, deterministic model, producing average turnaround times for

different stages in the MRO process. Although the main model output is average turnaround

time, variation in the repair process – a metric for process quality - can be measured in the

model on the level of Outsourced Repairs. The used tool to create the static, deterministic

model of the MRO chain is a Gantt chart, as described in section 2.2.

66

In the next sections, first the current state of the MRO chain is shown. Afterwards, the

solution alternatives for outsourced repair are modelled, which subsequently serve as input

to the different future states of the whole MRO chain.

7.2. Modelling of the current state - engine MRO chain

To model the current state of the MRO chain, the different steps in the value stream, from

work scope determination to assembly of the engine, are tied together on the level of engine

WBS assemblies. This is also shown in section 5.2. In the next sections, first the specification

of the current state model is given, while subsequently the results are described and the

model is verified and face validated with the real world current state.

7.2.1. Current state model specification and assumptions

In the model, four different MRO stages are classified: Work scope determination (0),

disassembly (1), repair (2) and assembly & testing (3). Using datasets, as described in

Appendix B, for each stage the current average turnaround time for each WBS assembly can

be determined. The average turnaround time for the WBS assemblies for disassembly and

repair can be found in Appendix D.1.

Next to this, a number of assumptions is made for the model. First, work scope determination

is conducted on engine level – resulting in an equal turnaround time for all WBS assemblies.

Next to this, assembly can only start when all material is ready, and assembly starts with

the module that is disassembled last: the fan module, or WBS assemblies 01X, 22X, and 23X.

Another assumption is that no waiting time occurs between the stages, but that all waiting

time is included within the average turnaround time for the different stages. For the actual

disassembly order timing, no actual data could be retrieved, so the disassembly order is

based on the norm times and previous research by KLM. And lastly, when a WBS assembly

has parts that are repaired both in- and outsourced, the longest average TAT is taken of the

two – which always constitutes to outsourced repair in the current state.

This specification of the Value Stream can be summarized in the following formula:

𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑀𝑅𝑂 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇0 + max(𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑊𝐵𝑆 1−2) + 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇3 (7.1)

In this equation, average TAT0 is defined by the average Turnaround Time of the work scope

stage (0), on engine level, whilst average TAT3 is defined as the average Turnaround Time

of the Assembly & Testing stage (3), also on engine level.

The average TATWBS 1-2 is the average Turnaround Time of stage 1 and 2 (disassembly and

repair) per WBS assembly, and can be defined by the following equation:

𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑊𝐵𝑆1−2 = 𝑇𝐴𝑇𝑊𝐵𝑆 𝑑𝑖𝑠.𝑜𝑟𝑑𝑒𝑟 + 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑊𝐵𝑆1 +

max( 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑊𝐵𝑆 𝑖𝑛−ℎ𝑜𝑢𝑠𝑒 , 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑊𝐵𝑆 𝐺𝑅𝐴𝑁𝐷) (7.2)

TATWBS dis.order is the fixed disassembly order of the different WBS elements from the engine.

This disassembly order can be observed in Appendix C.1. Average TATWBS1 is the average

turnaround time per WBS assembly for the disassembly, clean and inspection stage. Average

TATWBS in-house is the average Turnaround Time of in-house repairs per WBS assembly, whilst

average TATWBS GRAND is the average grand TAT of outsourced repairs, consisting of both

logistics and repair at the vendor. This is explained further in section 7.3.

The next section will demonstrate the results of the current state model of the MRO chain.

67

7.2.2. Current state model results

Taking into account the specification (equation 7.1 and 7.2) of the model and the different

assumptions discussed in the previous section, the current state model is developed. The

result is shown in Figure 7-2. The total average Turnaround Time of the MRO chain amounts

to 65 days in this model.

Figure 7-2: Current state model MRO chain

In the figure, WBS elements with mainly outsourced repairs are indicated in light blue.

Analysis of the dataset shows that the average turnaround time for outsourced repairs is 31

days, with a standard deviation of 10 days for all orders combined.

7.2.3. Model verification & face validation

The average actual turnaround time for CFM56-7B engines is measured to be 62 days in

2015 – see section 4.2.4. Differences between the modeled TAT and the real-world TAT can

be explained by a number of reasons: first of all, different types of datasets were used to

generate the output. In the real world, TAT is measured on engine order level whilst in the

model, the overall TAT is built from smaller process steps. Next to this, in the real world not

all engines go through the full work scope as is shown in the model, where all WBS

assemblies are disassembled and repaired. This will result in a lower turnaround time in

reality.

This being said, from discussions with stakeholders it has become clear that the current

state model representation is accurate and insightful enough to be used to model the

different solution alternatives: the model gives a good insight into the basic functioning of

the integral engine MRO chain. The next step in the modelling process is to model the

detailed solution alternatives of the outsourced repair stage – described in the next section.

7.3. Modelling of Outsourced Repair Future State TAT and process quality

To be able to model the future state of the MRO chain, first the future state of the detailed

solution alternatives for outsourced repairs needs to be modelled. For each alternative –

Exploit, Elevate and Ideal – assumptions and results are given.

7.3.1. Future state Exploit

To model the future state exploit alternatives, first assumptions are given in this section for

logistics export, vendor repair and logistics import and subsequently the results are

discussed.

68

Logistics Export

The exploit solution for export logistics is based on introducing pull in the process. Assuming

the average waiting time for outgoing flights is 12 hours – based on a flight once a day – the

TAT is decreased by 12 hours in this future state. As a result, the average export TAT

decreases to 3.5 days.

Vendor repair

The exploit solution of vendor repair is vendor management, which means assuring that the

contract agreements are kept. In the dataset used for outsourced repairs, as described in

Appendix B, contract agreements for each repair order are indicated. The average

Turnaround Time of these agreements is 22.9 days, for all orders.

Logistics Import

The exploit solution for import logistics is to apply a multi-skilled team, combining the DGO

inspection and Inspection Incoming Goods steps. This results in a doubling of capacity for

IIG, decreasing the overall TAT of the Logistics Import process with an estimated 1.8 days.

When reducing the current import logistics TAT with 1.8 days, this results in an average

future TAT of 5 days.

Results

When applying the exploit solution alternatives, the average outsourced repair grand TAT

decreases to 29 days, with a standard deviation of 7.2 days: a decrease compared to the

current state. The average grand TAT is calculated using the following formula:

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝐺𝑅𝐴𝑁𝐷 = ∑ 𝑇𝐴𝑇𝐺𝑅𝐴𝑁𝐷𝑖

𝑛𝑖=1

𝑛 (7.3)

In which n is the number of outsourced repair orders and TATGRANDi is determined using the

following formula:

𝑇𝐴𝑇𝐺𝑅𝐴𝑁𝐷𝑖 = 𝑇𝐴𝑇𝐸𝑋𝑃𝑂𝑅𝑇𝑖 + 𝑇𝐴𝑇𝑉𝐸𝑁𝐷𝑂𝑅𝑖 + 𝑇𝐴𝑇𝐼𝑀𝑃𝑂𝑅𝑇𝑖 (7.4)

The results per WBS assembly can be found in Appendix D.2, along with more detailed

assumptions per solution alternative.

7.3.2. Future state Elevate – 28 days

The same process is followed for the Elevate solution alternatives, starting with the

maximum contract of 28 days alternative.

Logistics export

For export logistics, the proposed solution consists of implementing pull in combination with

dedicated transport from Engine Services towards the logistics center, thus decreasing the

export TAT with one hour. This solution decreases the average export TAT only very slightly

below 3.5 days.

Vendor repair

As stated, the contract TAT is capped at 28 days. Next to this, vendor management is applied

– resulting in future TAT of 28 days or less. This solution results in an average vendor TAT

of 22.8 days – a slight decrease from the exploit solution.

Logistics import

For import logistics, it is proposed to implement a multi-skilled team in combination with a

capacity increase of 40% by increasing the amount of shifts to a 7x2 schedule (work in

69

weekends). Next to this, dedicated lanes are created for priority packages to not disturb the

flow of regular repaired components. Another estimated 0.8 days can be detracted from the

current TAT. This results in an average import TAT of 4.3 days.

Results

The grand TAT for outsourced repair can again be computed by following formula (7.3) and

(7.4). This results in a total average grand TAT of 28 days with a standard deviation of 7

days. Again, the results per WBS assembly can be found in Appendix D.2.

7.3.3. Future state Elevate – 21 days

For the future state Elevate - 21 days solution, the contract TAT is capped at 21 days. The

same solutions are used for import and export logistics, so only the vendor repair TAT is

affected. Capping the repair TAT to 21 days leads to an average repair TAT of 19.8 days.

When including both import and export logistics, the average grand TAT decreases to 25

days, with a standard deviation of 6 days. Once again, the results per WBS assembly can be

found in Appendix D.2.

7.3.4. Future state Elevate – 14 days

The last Elevate solution consists of capping the contract agreements to 14 days. Again, the

same elevate solutions for import and export logistics are applied. The cap of 14 days results

in an average repair TAT of 13.8 days. When computing the grand TAT by including logistics,

the result is an average TAT of 19 days with a standard deviation of 5.8 days. Once more,

the detailed results can be found in Appendix D.2.

7.3.5. Future state Ideal World

For the Ideal World solution alternatives, creative solutions were developed to minimize the

turnaround time in the chain. This time, the ideal state is also applied to in-house repairs –

based on process times found by (Meijs, 2016) and (Mogendorff, 2016).

Logistics export

The Ideal World for export logistics is estimated in the same way as import logistics. A

differentiation is made based on the different vendors. The average Ideal TAT for export

logistics is around 0.5 days – or 12 hours.

Vendor repair

In the ideal world, KLM E&M Engine Services works with fully Lean partners that integrate

the planning and deliver Just in Time. When a vendor cannot comply, the repair is

integrated in the in-house repairs at Engine Services. To model this situation, an estimate

is made in discussions with stakeholders. As (Meijs, 2016) and (Mogendorff, 2016) have

shown in their research, 80%-90% of the TAT consists of waiting time. For the Ideal State,

it is decided to use a more conservative estimate of 60% waiting time, meaning that the

process time is 40% of the actual TAT. Implementing solutions such that the TAT decreases

to 40% of the current TAT result in an average vendor TAT of 9.1 days.

Logistics import

The Ideal World solution for import logistics aims to decrease the TAT to a minimum, using

JIT delivery, delivery directly from airside and a smooth flow in the process. The import

logistics TAT is based on transport times from the vendor, assuming a smooth process.

Vendor transport times can be found in Appendix C.2. In de Ideal World, the resulting TAT

for import logistics is on average 0.6 days, or 14 hours.

70

In-House Repairs

For in-house repairs, the Ideal World means that Lean is fully implemented in the

workshops, not only for combustor repair (Mogendorff, 2016), but also other repairs. A

similar assumption as for outsourced repair is made, where 40% of the current TAT is

processing time – which can be achieved in a Lean operation. Implementing this into the

current state of in-house repairs, results in an Ideal World TAT of on average 4 days for in-

house repairs.

Results

Again, the Grand TAT for outsourced repair in the Ideal World needs to be calculated. This

Grand TAT amounts to 9.6 days, with a standard deviation of 3.3 days. The average ideal

state TAT for in-house repairs is 4 days, as previously stated. An overview of the results can

be found in Appendix D.2.

7.3.6. Probability plots Turnaround Time Outsourced Repairs

The results of the different solution alternatives can be visualized in probability plots, shown

in this section. Figure 7-3 shows the effect of the different solution alternatives on the

turnaround time of Outsourced Repair at the vendor. Figure 7-4, in turn, shows the effect of

the different solutions on the logistics steps. These plots are a gathering of all orders, without

differentiation between assemblies (WBS elements).

Figure 7-3: Probability plot Outsourced Repair - vendor TAT

71

Figure 7-4: Probability plot Outsourced Repair – Logistics

7.4. Modelling of the integral MRO chain Future State

The previously described results of the outsourced repair solution alternatives can be used

to create the different future states of the whole MRO chain. The future state models are

built using equation (7.1) and (7.2).

7.4.1. MRO Chain – Future State Exploit

The exploit solution alternatives are used as input to the whole MRO chain model, as

described in the current state model in section 7.2. Next to this, it is assumed that the

assembly stage (stage 3) can be conducted following the norm when all orders are received

in time and capacity is sufficient. Appendix D.3. shows the Future State Exploit model of the

integral engine MRO chain. From this model, the resulting average TAT is 57 days,

compared to the original 65 days in the Current State Model.

7.4.2. Future State Elevate

As with the Future State Exploit model, the Elevate models are created using the input from

the outsourced repair solution alternatives. The Elevate model with a cap of 28 days on

contract agreements, reaches an average MRO chain TAT of 56 days. The 21 days-maximum

model, generates an average MRO chain TAT of 54 days, whilst the 14-days maximum model

enables an average MRO TAT of 46 days. The models can be found in Appendix D.3.

7.4.3. Determining the Ideal World turnaround time of the MRO chain

The Ideal World model of the MRO chain is based on the Ideal World solutions for outsourced

and in-house repair, combined with a number of assumptions on the stages work scope,

disassembly and assembly. For the Work Scope determination stage (stage 0), the average

actual TAT is used – the actual TAT is shorter than the norm TAT for this stage. The same

is applicable to the disassembly step; from previous research it has become apparent that

most WBS assemblies are disassembled faster than the norm. There are some exceptions,

but the main causes for delays in these stages are a lack of capacity and a last-minute Bill

of Work change. In the Ideal State, it is assumed that these issues do not occur. Therefore,

when a disassembly takes longer than the norm in the current state, the norm TAT is used

in the Ideal State.

For the assembly stage (stage 3), norm times are used as well. From discussions with

stakeholders, it was concluded that, when capacity is sufficient and all material is ready on

72

time, the assembly stage can be conducted in the norm TAT. Next to this, the research

assumes that in the Ideal World, no rework is needed after engine testing.

Considering these assumptions and the Ideal World input of the repair stage solutions, the

Ideal World model shows that an Engine MRO TAT below 38 days is achieved – and possibly

even less when more detailed analysis of the disassembly and assembly stages is conducted.

The Ideal World model is shown in Appendix D.3.

The next section will provide the overview of the results of the modelling step. The next

chapter will discuss the sixth step of the framework: the evaluation of the different solution

alternatives.

7.5. Modelling results

This chapter aimed to answer the sub-question “What is the effect of these

improvements on the turnaround time of the integral MRO chain at KLM E&M Engine

Services?” This sub-question is answered by modelling the solution alternatives. First,

the current state MRO chain model is defined, while subsequently the future states of

the different solution alternatives for outsourced repair are modelled. These future

states serve as input to the future state of the MRO chain model: a bottom-up

approach.

The results of the model are summarized in the table below:

Solution Average Grand

Turnaround Time

Outsourced Repair

Standard

deviation

outsourced

repair

Average

Turnaround

Time MRO

Current state 31 days 10 days 65 days

Exploit 29 days 7.2 days 57 days

Elevate 28 days

max contract

28 days 7 days 56 days

Elevate 21 days

max contract

25 days 6 days 54 days

Elevate 14 days

max contract

19 days 5.8 days 46 days

Ideal State 9.5 days 3.3 days 38 days

73

Part Five: Validate & Control Phase

Photo NRC 18 May 2016

74

75

Evaluation of the solutions for KLM E&M Engine

Services

In this chapter, the sixth step of the framework is conducted: to evaluate the solutions

against the previously defined criteria. The aim is to answer the following sub-questions:

What is/are the optimal solution alternatives to be implemented for KLM E&M Engine

Services?

What is the theoretical performance of the whole Engine MRO chain at KLM E&M Engine

Services?

What are new focus areas to further improve the MRO chain performance at KLM E&M Engine

Services?

Fist, the method used to evaluate the solutions is discussed in section 8.1. Next, the solutions

are evaluated in section 8.2. Finally, section 8.3 will give the future state of the Engine MRO

chain at KLM Engineering & Maintenance Engine Services. The layout of this chapter is

shown in Figure 8-1.

Figure 8-1: Chapter 8

8.1. Method used for solution evaluation

Various different methods and tools to evaluate solutions are described in section 2.3. For

this specific case study, the Evamix method is applied: Evaluation of Mixed Data is suitable

when both qualitative and quantitative criteria are applied. In this evaluation, this is the

case. The weighting of the different criteria is done by the Analytic Hierarchy Process (AHP):

the process is easy to use with a limited number of criteria, in this case six, and is insightful

to stakeholders. The next section will evaluate the different solution alternatives.

8.2. Evaluation of solutions

In this section, first the criteria – as defined in chapter 3 – are repeated. Next, the different

criteria are given weights using AHP, and subsequently the Multi-Criteria Analysis using

Evamix is conducted. Next to this, the sensitivity of the MCA is discussed and the final

chosen solutions are discussed.

76

8.2.1. Criteria

The criteria as defined by the system definition in chapter 3 are the following:

C1: MRO cost

C2: Implementation cost

Q1: Product quality

Q2: Process quality – variation in the process

T1: Turnaround Time

In the next section, the criteria are weighted, using the Analytic Hierarchy Process.

8.2.2. Giving weights to the criteria using AHP

The weights to the five different criteria are determined using the Analytic Hierarchy

Process (Saaty, 2008). Together with stakeholders, pairwise comparisons are made between

criteria. The comparison is then scored on a 1-9 scale, 1 for equal importance and 9 for

extremely more important. The opposite, so 9 for extremely less important, is scored with

the inverse 1/9. After scoring, the columns are normalized and subsequently the average

normalized score per row is computed. The last step is to check for consistency of the matrix,

using the consistency ratio. For the AHP method, the matrix is only accepted when the

consistency ration is below 0.1 (Alonso & Lamata, 2006).

The matrices used to score and give weights to the criteria can be found in Appendix E.1.

The overview of criteria weights is shown in Table 8-1 below. It is important to note that

these criteria are produced from the perspective of KLM E&M – a client’s perspective

(airline) is used in the sensitivity analysis later on.

Table 8-1: Criteria Weights Process Owner

Criterion Weight factor

C1 MRO cost 0.12

C2 Implementation cost 0.07

Q1 Product quality 0.21

Q2 Process quality 0.24

T1 Turnaround Time 0.36

8.2.3. Multi-Criteria Analysis scores and results using Evamix

Evamix consists of a number of steps. First, all criteria need to be weighted – as shown in

the previous section. Secondly, a division is made between the quantative and qualitative

criteria: both categories are treated independently – by pairwise comparison the dominance

of each alternative over another alternative is determined. The total dominance score is then

determined by weighting the standardized qualitative and quantitative dominance scores.

The final ranking of the alternatives is based on the total dominance matrix (Commissie

voor de milieueffectrapportage, 2002, p. viii). An overview of the Evamix steps can be found

in Appendix E.2.

Scoring the alternatives

Before the MCA is conducted, the scores of the solution alternatives on the different criteria

need to be determined.

Two criteria are quantitative: T1 – turnaround time, and Q2 –variation in the process

(process quality). The scores are previously determined in chapter 7. These quantative scores

need to be standardized to values between 0 and 1, to enable a fair comparison.

77

The qualitative criteria require a different approach: as the exact values of the criteria and

the exact differences between the criteria cannot be determined in this research, ranking is

applied on an ordinal scale. Theoretically, ranking is the best method to include the

qualitative criteria in the MCA (Commissie voor de milieueffectrapportage, 2002, p. 28). Per

criterion, the best and worst alternative is selected. The best alternative will receive a score

of 6, while the worst alternative receives a score of 1. All other alternatives are then

compared and scored within this scale.

The overview of the qualitative scoring rationale per alternative can be found in Appendix

E.3. The overview of all scores is shown in Table 8-2 below.

Table 8-2: Unweighted scores per alternative

Overview Current

state

Exploit Elevate

28

Elevate

21

Elevate

14

Ideal

World

Weight

Quantitative

Q2 0.00 0.42 0.45 0.60 0.63 1.00 0.24

T1 0.00 0.30 0.33 0.41 0.70 1.00 0.36

Qualitative

C1 5 6 4 3 2 1 0.12

C2 6 5 4 3 2 1 0.07

Q1 1 6 6 6 6 6 0.21

Evamix - Determining the dominance matrix

Once the scores and criteria weights are determined, the dominance scores of the different

alternatives needs to be established. This is done separately for the quantitative and

qualitative criteria. For the quantitative dominance, the dominance score is the product of

the criterion weight and the difference between the standardized scores (Commissie voor de

milieueffectrapportage, 2002).

The qualitative dominance scores are created by pairwise comparison, where not the

difference in values is used, but merely whether an alternative is better than another on a

certain criterion. If an alternative scores better on a criterion, the weight of this criterion is

added to the dominance, and if an alternative scores worse, the weight of the criterion is

subtracted (Reinhard, Vreke, Wijnen, Gaaff, & Hoogstra, 2003, p. 54). Both created

dominance matrices (qualitative and quantitative) are standardized to make the scores

comparable. The dominance scores can be found in Appendix E.3.

The final step is to calculate the total dominance using the weights of the criteria,

differentiating between qualitative and quantitative weights. The total weight of the

quantitative criteria is equal to 0.24 + 0.36 = 0.60. The total weight of the qualitative criteria

is equal to 0.12 + 0.07 + 0.21 = 0.40. These steps result in a final dominance matrix as shown

in Table 8-3.

78

Table 8-3: Total Dominance score matrix

Solution Current

state

Exploit Elevate

28

Elevate

21

Elevate

14

Ideal

World

total

Current state - -0.05 -0.02 -0.02 -0.03 -0.04 -0.14

Exploit 0.05 - 0.02 0.02 0.01 0.00 0.11

Elevate 28 0.02 0.03 - 0.02 0.02 0.00 0.08

Elevate 21 0.02 -0.02 -0.02 - 0.02 0.01 0.00

Elevate 14 0.03 -0.01 -0.02 -0.02 - 0.01 -0.01

Ideal World 0.04 0.00 0.00 -0.01 -0.01 - 0.01

From this table, a ranking can be created in solution favorability based on dominance. The

ranking for the set of weights from the KLM perspective is as follows:

Table 8-4: Resulting ranking from KLM E&M perspective

Rank Solution Dominance score

1 Exploit 0.11

2 Elevate 28 0.08

3 Ideal World 0.01

4 Elevate 21 0.00

5 Elevate 14 -0.01

6 Current State -0.14

The Exploit solution can reduce the overall TAT with 8 days, by improving only the

outsourced repair stage on the vendor and logistical aspects. This is not yet near the 45 day

TAT, however with relatively low cost and high ease of implementation, the largest

improvement that can be made from the current state. Next to this, more days can be found

in other stages, such as the disassembly and assembly stage. The Elevate 28 solution comes

as a close second with a nearly equal dominance score, however the further reduction in TAT

compared to the Exploit solution is slight. A combination could be ideal.

As this ranking is a result from a certain subjective weight set, a sensitivity analysis needs

to be conducted. In the next section both the sensitivity to the criteria weights and the

sensitivity to the quantitative values is tested.

8.2.4. Multi-Criteria Analysis sensitivity test

To validate the MCA, a sensitivity test is conducted: both the sensitivity of the outcomes to

different scores and to different criteria weights is tested, by varying the scores and weights.

The different matrices used can be found in Appendix E.4.

79

Sensitivity to criteria weights

Two different sets of criteria weights are used: one set from the perspective of an external

client, and one set with only equal weights. An external client highly values the product

quality, turnaround time and MRO cost, whilst not being very interested in the

implementation cost of a new solution. From a client’s perspective, the ranking is as follows:

Table 8-5: Resulting ranking from a Client's perspective

Rank Solution Dominance score

1 Ideal World 0.15

2 Elevate 14 0.06

3 Exploit 0.02

4 Elevate 21 0.01

4 Elevate 28 0.01

6 Current state -0.23

As expected, an external client favors the Ideal World where the MRO provider is fully Lean,

the Quality is as required and the Turnaround Time is really low. However, the price of the

MRO service will be an issue – the investments KLM E&M will have to make to achieve the

Ideal State will affect the cost of MRO for an external client, at least on the short term. When

equal weights are used, the ranking looks as follows:

Table 8-6: Resulting ranking with equal weights

Rank Solution Dominance score

1 Ideal World 0.08

2 Exploit 0.03

2 Elevate 28 0.03

3 Elevate 14 0.01

4 Elevate 21 0.00

5 Current state -0.12

The most robust solution alternatives scores the highest on the three sets of criteria weights.

The highest scoring alternative overall is the Ideal World solution, with the Exploit and

Elevate 28 solutions following closely.

Sensitivity to solution scores

Next to the subjective nature of the weights, the scores have uncertainties. The sensitivity

of the MCA outcome to the quantitative scores is tested, by increasing or decreasing a

number of these values. Not all values of one criterion are adjusted, as this would have no

effect on the standardized outcomes. It is therefore decided to always keep the current state

as-is, while changing the values of the turnaround time and process variation for all other

alternatives. The resulting dominance scores for each increase or decrease can be found in

Appendix E.4.

The sensitivity analysis shows no changes in ranking order – the top two solutions from the

perspective of KLM E&M remain the Exploit and Elevate 28 solutions. From the different

weight sets, it is already observed that these two solutions are relatively robust. The next

section will elaborate on the advised solution for KLM E&M Engine Services.

8.2.5. Chosen solution

From the Evamix approach, combined with the sensitivity tests, it is concluded that the

optimal, robust solution is either the Exploit solution, the Elevate 28 solution or a possible

combination of both. In this section, the detailed descriptions of the solutions are repeated

from section 6.3.2.

80

Exploit solution

To exploit the current constraints, constraining the output TAT of the vendor, it is necessary

to exploit the current vendor agreements. By implementing more proactive and strict vendor

management, the constraint can be exploited to its maximum: the current contract

agreements.

To exploit the current constraint of Export logistics, pull needs to be introduced. In short,

Pull is established in the export logistics step by creating a clear priority system for outgoing

packages based on the cut-off times per destination. These cut-off times are a result of flight

departure times for, for example, KLM Cargo flights, combined with Sodexo’s pick-up

schedule and handling times at the Logistics Center.

To exploit the current constraint of Import logistics, the Inspector Incoming Goods (IIG)

capacity can be doubled by creating multi-skilled teams. The current worker capacity will be

utilized more efficiently by combining the Decentralized Goods Receipt (DGO) and the IIG

steps to one single activity.

Implementing the Exploit solution to both the vendors and the logistics at KLM E&M Engine

Services will lead to a total average Turnaround Time of 57 days, a decrease of 8 days of the

total TAT by only improving the Outsourced Repair stage. Next to this, the variation in the

outsourced process is decreased.

Elevate 28 solution

The vendor constraint is elevated by capping all contract agreements to a maximum of 28

days. Next to these contract agreements, it is assumed that vendor management, as

explained in the “Exploit” solution, is in effect.

To elevate the constraint of Export logistics, Pull is introduced and, next to this, direct

dedicated transport to the Logistics Center is enabled – thus bypassing the delivery schedule

of Sodexo.

The constraint of Import logistics is elevated by increasing the IIG capacity from 5x2 shifts

per week, to 7x2 shifts per week – this also creates a more synchronization with the engine

shop itself, which operates on a 7x2 basis. Next to this capacity increase, the flow of incoming

logistics must be safeguarded by creating dedicated lanes for priority packages, AOG

packages and other problematic packages.

Implementing the Elevate 28 solution will result in a total average Turnaround Time of 56

days, a decrease of 9 days from the current state. Next to this, the variation in the

Outsourced Repair process is slightly less than in the Exploit solution. However, the needed

implementation costs are significantly higher than for the previous solution.

Estimated monetary benefits to KLM E&M and airlines

The monetary benefits for both KLM and other clients (airlines) resulting from the

implementation of these solutions are not included in the criteria. These monetary benefits

can be estimated based on the decrease in Turnaround Time. From the perspective of an

airline, the monetary benefits can be estimated in several ways. First, imagine an airline

leasing its engines at a rate of $3,000 per day (Mattijssen, Boerrigter, & Klokkers, 2016) – a

decrease in TAT of 8 to 9 days would result in a saving of $24,000 to $27,000 per engine.

Next to this, a lower MRO TAT means that airlines need less engines in their engine pool to

keep their fleet in the air: the availability of the engines is higher.

81

From the perspective of KLM Engineering & Maintenance Engine Services, a lower engine

MRO TAT will result in a higher shop visit capacity: more engines can be serviced in one

year. A decrease of 8 days, for instance, will increase the capacity with 13%, resulting in

66*1.13=74 shop visits. When looking at the estimated budget norm revenues for 2016

(Mattijssen, Boerrigter, & Klokkers, 2016), these eight extra serviced engines can result in

an extra revenue of around €30 million per year.

8.3. Control – Towards a new integral MRO chain control structure

The provided solution alternatives – Exploit and Elevate 28 – are a good first step towards

decreasing the Turnaround Time of the MRO chain to 45 days. Besides the solutions that

consider Outsourced Work, changes in the control of the MRO chain are necessary. As stated

before, it is essential that all agreements are consistent in the chain to effectively plan and

control the MRO chain.

Next to this, the method of measurement needs to be changed from a stage approach to a

Value Stream approach, as demonstrated by the integral MRO chain models. This Value

Stream Approach can be summarized by the following equation:

𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑀𝑅𝑂 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇0 + max(𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑊𝐵𝑆 1−2) + 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇3

Even though the Ideal State solutions are not viable to the process owner right now, they do

need to be kept in mind as a “dot on the horizon”. After all, the Ideal State has shown that

the theoretical Turnaround Time of the Engine MRO chain could be less than 38 days by

focusing mainly on improvement of the repair stage.

In the light of continuous improvement, new focus areas can be identified. No in-depth

research has been conducted to the Disassembly and Assembly stages. However,

stakeholders have suggested that a lot of optimization possibilities lie in these stages as well.

Next to this, this case study has focused on the CFM56-7B engines, but improvements can

and must be made for the other engine types as well.

This concludes the case study at KLM E&M Engine Services, as the implementation phase

is beyond the scope of this research. In the next chapter, the literature framework as was

defined in section 2.4 is evaluated against the case study.

82

What is/are the optimal solution alternatives to be implemented for KLM E&M Engine

Services?

From the multi-criteria analysis, conducted using a combination of the Analytic Hierarchy

Process for criteria weight determination, Evamix for dominance establishment and

sensitivity analysis, it is concluded that the optimal solution from the perspective of KLM

E&M Engine Services is either the Exploit solution or the Elevate 28 solution.

For the Exploit solution, vendor management is needed to maximally exploit the current

contract agreements. Next to this, Import logistics can be improved by implementing

multi-skilled teams, and Export logistics can be streamlined by introducing Pull.

The Elevate 28 solution consists of vendor management in combination with a limit on

the repair contracts of 28 days. Next to this, Export logistics can be improved by

introducing Pull and enabling direct dedicated transport to the Logistics Center. And

finally, Import logistics can be improved by increasing the IIG capacity from 5X2 to 7X2

shifts per week and safeguarding the regular flow of incoming logistics by creating

dedicated lanes for priority packages, AOG packages and other problem cases.

What is the theoretical performance of the whole Engine MRO chain at KLM E&M Engine

Services?

Implementing the Exploit solution will result in a total Turnaround Time of 57 days,

compared to a current Turnaround Time of 65 days. The standard deviation of the

Outsourced Repair process will decrease from an average of 10 days to an average of 7.2

days. Implementing the Elevate 28 solution will result in a total Turnaround Time of 56

days, with an average standard deviation of 7 days for the Outsourced Repair process.

What are new focus areas to further improve the MRO chain performance at KLM E&M

Engine Services?

This research has focused on improving the main constraint limiting the Turnaround

Time of the MRO chain of CFM56-7B engines at KLM E&M Engine Services: Outsourced

Repairs. A lot of optimization possibilities lie in the stages Disassembly and Assembly.

Next to this, improvements can and must be developed for other serviced engine types.

83

Evaluation of the literature framework

The seven-step literature framework as defined in chapter 2 has been applied to a case study

at KLM Engineering & Maintenance Engine Services. In this chapter, the steps taken in the

case study, following the literature framework, are summarized and discussed.

First of all, the framework was applied in two iterations to achieve a good result: on the level

of the integral MRO chain, and next on are more detailed level for the Outsourced Repair

stage. The results of the Outsourced Repair solutions served as an input for the MRO chain

modelling step.

Used tools

The framework served as a comprehensive backbone to the case study. A large selection of

available tools and methods for each step was described in the literature review, the tools

used for this specific case study are listed below:

Step I – The system was defined by discussing the technological design of turbofan engines,

the engine MRO market and the organization of KLM E&M Engine Services. The evaluation

criteria were determined using a goal tree.

Step II – The current state was measured using SIPOC diagrams, flowcharts, and probability

plots based on data analysis of CFM56-7B engine data of serviced engines in 2015.

Step III – The constraints were identified using Value Stream Mapping and observation of

various types of constraints: 4M, TIMWOOD(S) and others

Step IV – Solutions to the constraints were created by Exploiting and Elevating the constraint

and by imagining the Ideal World solutions. The solutions within these categories were

developed using tools from Lean, Theory of Constraints and Creative Problem Solving.

Step V – The solution alternatives were modelled using a static, deterministic model, using

Gantt charts as a tool.

Step VI – The different solution alternatives were evaluated by a Multi-Criteria Analysis,

using Evamix in combination with the Analytic Hierarchy Process.

Step VII – The implementation and control of the solution alternatives is beyond the scope of

this research, however recommendations are given in chapter 10.

All steps and used tools for the case study at KLM Engineering & Maintenance Engine

Services are indicated in Figure 9-1.

Framework added value and considerations

Overall, the developed seven-step framework provided a comprehensive, step-by-step

backbone to develop, test and evaluate a wide set of solution alternatives to the problem.

However, for each different case study, the framework needs specific tailoring: the used tools

and methods are specific to individual case studies.

To determine the added value of the seven-step framework, it is useful to compare the

framework to previously existing frameworks. The seven-step framework is in its core based

84

on the DMAIC cycle from Lean Six Sigma. However, no explicit evaluation of the solutions

is part of the DMAIC cycle – whereas the seven-step framework forces the researcher to take

an explicit evaluation step by evaluating the solutions against different criteria.

Another valuable addition is the fact that the framework forces the researcher to develop

the widest range of solutions possible – from the current state to the ‘Ideal World’. This is a

result from combining Theory of Constraints with Creative Problem Solving.

Due to the general and comprehensive nature of the seven-step framework, this framework

can be applied to other existing processes, where constraints limit the output of the system.

One can think of other, non-aviation, MRO processes, or production processes where a wide

set of solutions is required.

Many different researches on aircraft MRO have preceded this research. A lot of frameworks

developed in these researches, could be integrated into the comprehensive seven-step

framework.

The research by (Mogendorff, 2016), for example, proposed a method to decrease the TAT of

combustor maintenance through process improvement and simulation. This coincides with

step IV and V in the framework. Another research by (van Rijssel, 2016), proposed a

framework to improve component MRO processes by selection improvement methodologies

based on the flow type and subsequently simulating the process – this can be integrated in

steps IV and V. And finally, (Meijs, 2016), identified the main conditions and factors of

influence on the TAT of an MRO process – which is a valuable framework to identify and

solve the different constraints in step III and IV.

85

Figure 9-1: Applied framework and tools at KLM E&M Engine Services

86

87

Conclusions and Recommendations

This chapter presents the conclusions and recommendations of this research. First, the

answers to the research questions are given in section 10.1. Next, the recommendations and

suggestions for further research are given in section 10.2, while subsequently the limitations

to the research are discussed in section 10.3.

10.1. Answering the research questions

To answer the main research question “How can the output of aircraft engine Maintenance, Repair

and Overhaul processes be optimized from an integral perspective?’ first the sub-questions are

answered in this section.

What framework can be built from literature with the aim of finding and evaluating solutions

to improve the output of an aircraft engine MRO process?

A seven-step comprehensive framework is developed based on process improvement

methodologies, process modelling methodologies and solution evaluation methodologies.

First, the system and evaluation criteria need to be defined [I]. Next, the current state of the

system is measured [II], and subsequently constraints in the system are analyzed [III]. The

fourth step is to create solution scenarios for the constraints [IV], by exploiting, elevating or

creating the Ideal World. Next, the solution alternatives are modelled [V], and evaluated in

the sixth step [VI]. The seventh and last step consists of implementing the optimal solution

and controlling the process [VII].

What criteria can be used to assess the different solution alternatives for KLM E&M Engine

Services?

The seven-step framework is applied to a case study at KLM Engineering & Maintenance

Engine Services. This case study considers the Engine MRO process of the CFM56-7B

engines, which consists of four main steps: Work scope determination, Disassembly of the

engine, Repair and Assembly of the engine. Based on the system definition, five different

evaluation criteria are determined: MRO cost, Implementation cost, Product quality, Process

quality and Turnaround Time.

What is the current state of the Engine MRO process at KLM E&M Engine Services?

The current state of the MRO process is measured on two levels: first on the level of the

integral chain, and subsequently on the Repair stage level. The current turnaround time of

the integral MRO chain is 62 days, with a large standard deviation of 23 days. Currently,

control is based on measurement of the different stages, however, the norm agreements are

inconsistent. The largest share in the total TAT is realized by the repair stage, and more

specifically outsourced repairs. The average TAT of outsourced repairs is 31 days, including

logistics.

What constraints are limiting the turnaround time of the Engine MRO process at KLM E&M

Engine Services?

Again, constraints are identified on two different levels: that of the MRO chain and on the

repair stage. In the overall MRO chain, no consistent agreements for control are in place and

control is based on stages instead of the value stream of an engine and its parts. When the

value stream is measured, outsourced work forms the largest constraint to the TAT output

88

of the chain. Within outsourced repairs, constraints are found in the logistical process and

at the vendors. For export logistics, this constraint is formed by fixed outgoing transport

times. At the vendor, the constraint is formed by lack of internal performance and the

contract agreements. For import logistics, the main constraint is formed by incoming goods

inspections.

What are solution alternatives to optimally reduce the turnaround time at KLM E&M Engine

Services from the current towards 45 days?

Different solution alternatives are created based on exploiting the constraint, elevating the

constraint and creating the Ideal World solution – thus generating a wide spectrum of

alternatives. Five different alternatives are generated: the exploit alternative, elevate 28

days, elevate 21 days, elevate 14 days, and Ideal World.

What is the effect of these improvements on the turnaround time of the integral MRO chain at

KLM E&M Engine Services?

The effect of the different solution alternatives on the TAT is modelled using a static,

deterministic model. First, the effect of the detailed solution on the outsourced repair TAT

is modelled. This subsequently serves as an input to the model to generate the overall MRO

TAT. The results are shown in Table 10-1 below.

Table 10-1: Results of the solution alternatives

Solution

Average Grand

Turnaround Time

Outsourced Repair

Standard deviation

outsourced repair

Average

Turnaround Time

MRO

Current state 31 days 10 days 65 days

Exploit 29 days 7.2 days 57 days

Elevate 28 days max

contract

28 days 7 days 56 days

Elevate 21 days max

contract

25 days 6 days 54 days

Elevate 14 days max

contract

19 days 5.8 days 46 days

Ideal State 9.5 days 3.3 days 38 days

What is/are the optimal solution alternatives to be implemented for KLM E&M Engine

Services?

To answer this research question, the different solution alternatives are evaluated using

multi-criteria analysis, conducted using the Evamix method combined with the Analytic

Hierarchy Process. The previously defined evaluation criteria are used for the multi-criteria

analysis. From the perspective of KLM E&M as a process owner, the optimal solution is the

Exploit solution, with Elevate 28 as a close second.

For the Exploit solution, vendor management is needed to maximally exploit the current

contract agreements. Next to this, Import logistics can be improved by implementing multi-

skilled teams, and Export logistics can be streamlined by introducing Pull.

89

The Elevate 28 solution consists of vendor management in combination with a limit on the

repair contracts of 28 days. Next to this, Export logistics can be improved by introducing

Pull and enabling direct dedicated transport to the Logistics Center. And finally, Import

logistics can be improved by increasing the IIG capacity from 5x2 to 7x2 shifts per week and

safeguarding the regular flow of incoming logistics by creating dedicated lanes for priority

packages, AOG packages and other problem cases.

What is the theoretical performance of the whole Engine MRO chain at KLM E&M Engine

Services?

Implementing the Exploit solution will result in a total Turnaround Time of 57 days,

compared to a current Turnaround Time of 65 days. The standard deviation of the

Outsourced Repair process will decrease from an average of 10 days to an average of 7.2

days. Implementing the Elevate 28 solution will result in a total Turnaround Time of 56

days, with an average standard deviation of 7 days for the Outsourced Repair process. If the

Ideal State would be achieved, the turnaround time of the engine MRO chain is 38 days.

What are new focus areas to further improve the MRO chain performance at KLM E&M Engine

Services?

This research has focused on improving the main constraint limiting the Turnaround Time

of the MRO chain of CFM56-7B engines at KLM E&M Engine Services: Outsourced Repairs.

A lot of optimization possibilities lie in the stages Disassembly and Assembly. Next to this,

improvements can and must be developed for other serviced engine types.

The main research question - “How can the output of aircraft engine Maintenance, Repair and

Overhaul processes be optimized from an integral perspective?’ - can now be answered. A

comprehensive framework, consisting of seven steps, is created to develop, model and

evaluate solutions to optimize engine MRO processes. This seven-step model is successfully

applied to a case study at KLM E&M Engine services, wherein different solution alternatives

are created to decrease the turnaround time of the MRO process. The recommended solution

alternatives consist of either exploiting the constraints in the MRO chain, focusing on

Outsourced Repairs, or elevating the constraints with a cap of 28 days in the contract

agreement. The potential reduction in turnaround time in the integral MRO chain by

implementation of these solutions is 8 or 9 days. The developed comprehensive seven-step

framework has added value over existing frameworks on two aspects: it, on one hand, forces

researchers to create the widest possible array of solution alternatives – from the current

state to the ‘Ideal World’, and on the other hand it forces researchers to evaluate their

solutions against different criteria in the evaluation step.

10.2. Recommendations and Further Research

For KLM E&M Engine Services, it is recommended to implement the solution Exploit or

Elevate. Next to this, it is essential that all stage agreements are consistent in the chain –

meaning that all stakeholders have the same view of the agreements - to effectively plan and

control the MRO chain.

Next to this, the method of measurement needs to be changed from a stage approach to a

Value Stream approach, as demonstrated in the integral MRO chain models. The Value

Stream Approach can be summarized by the following equation:

𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑀𝑅𝑂 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇0 + max(𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇𝑊𝐵𝑆 1−2) + 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝐴𝑇3

90

Next to implementing these solutions, it is necessary to implement the previously developed

solutions for in-house repairs by (Meijs, 2016) and (Mogendorff, 2016).

Implementation of the Outsourced Repair solutions can result in a decrease in Engine MRO

TAT of 8 or 9 days. More potential days can be found in the Disassembly and Assembly

stages, so it is recommended to apply the same framework to these stages to find more

optimization strategies. In this way, by continuous improvement, the Ideal World can be

achieved – with a potential Engine MRO TAT of 38 or less days.

From this research, various recommendations for further research can be given: after all,

Kaizen, continuous improvement, needs to be held in mind.

First, it is recommended to conduct research on the qualitative criteria used to be able to

measure the criteria on a quantitative, ratio scale. Next, it is recommended to develop

different models for different engine work scopes. Furthermore, it is necessary to investigate

which critical parts need to be repaired in-house to achieve the Ideal State. And finally for

further research it is useful to apply the framework to other engine types.

From a scientific aspect, it is useful to fit previously developed frameworks for aircraft MRO

into the comprehensive seven-step framework developed in this research. Examples of these

frameworks are given by (Meijs, 2016), (Mogendorff, 2016) and (van Rijssel, 2016). And

finally, it is useful to apply the comprehensive framework to other processes in other

industries and subsequently compare and evaluate the used methods and tools within the

framework.

10.3. Research limitations

In each step of the framework applied to the case study at KLM E&M Engine Services,

limitations occur. First of all, the outcome of the case study is limited by the availability of

data. For the case study, engine data of 2015 is used, however sometimes for certain WBS

assemblies the available data was limited or unreliable.

Next, the research is limited by the focus on main constraints for the development of solution

alternatives. Many different smaller constraints were observed – which makes sense when

looking at the whole MRO chain – but only the main constraints were used to develop

solutions.

A third limitation is formed by the assumptions made when modelling the different

solutions, as described in the modelling chapter. And finally the evaluation of the different

solution alternatives is limited by the use of qualitative criteria and subjective weights. Even

though the use of Evamix enabled the use of qualitative criteria, ideally one would have an

objective, quantitative basis to all criteria. Furthermore, the use of Evamix is not very

straightforward or immediately insightful, and it is not possible to easily add or remove

different alternatives as the dominance is determined relative to the whole set of solutions.

91

Bibliography

Ackert, S. (2011). Engine Maintenance Concepts for Financiers - elements of Turbofan Shop

Maintenance Costs. Aircraft Monitor.

Aguilar-Saven, R. S. (2004, July 28). Business process modelling: Review and framework.

International Journal of Production Economics 90, 90(2), 129-149.

doi:10.1016/S0925-5273(03)00102-6

Air France Industries KLM Engineering & Maintenance. (n.d.). Aircraft Engine

Maintenance. Retrieved May 20, 2016, from AFI KLM E&M:

http://www.afiklmem.com/AFIKLMEM/en/g_page_tabs/GlobalSolutions/Engines/En

gines.html

Air France KLM. (n.d.). Maintenance. Retrieved May 30, 2016, from www.airfranceklm.com:

http://www.airfranceklm.com/en/activities/maintenance

Alonso, J., & Lamata, M. (2006). Consistency in the Analytic Hierarchy Process: A New

Approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based

Systems, 14(4), 445-459.

Anderson, J. D. (2008). Introduction to Flight (6 ed.). New York, NY: McGraw-Hill.

Ayeni, P., Baines, T., Lightfoot, H., & Ball, P. (2011). State-of-the-art of 'Lean' in the aviation

maintenance, repair and overhaul industry. Proc. IMechE, 225(B), 2108-2123.

doi:10.1177/0954405411407122

Bendell, T. (2005). Structuring business process improvement methodologies. Total Quality

Management & Business Excellence, 969-978. doi:10.1080/14783360500163110

Birta, L. G., & Arbez, G. (2013). Modelling and Simulation - Exploring Dynamic System

Behaviour (2 ed.). London: Springer-Verlag.

Brakken, B. (2001). Evaluatie van ICT en Business Scorecards. Bedrijfskunde, 73(4), 71-78.

Brown, S. (2007). Standardized Technology Evaluation Process (STEP) - User’s Guide and

Methodology for Evaluation Teams . Mitre. Retrieved October 5, 2016, from

http://www2.mitre.org/work/sepo/toolkits/STEP/files/StepUsersGuide_09.pdf

CAPA Centre for Aviation. (n.d.). MRO - Maintenance, Repair & Overhaul. Retrieved May

20, 2016, from Centre for Aviation: http://centreforaviation.com/profiles/hot-

issues/mro---maintenance-repair--overhaul

Chellappa, S. (2015, February 4). Aftermarket to drive aerospace engine manufacturers'

growth. Retrieved May 20, 2016, from Societe Generale:

https://www.privatebanking.societegenerale.com/en/strategy/equity-

solutions/equity-research-watcher/the-watcher-details/news/aftermarket-drive-

aerospace-engine-manufacturerse-growth/

92

Commissie voor de milieueffectrapportage. (2002). Geactualiseerde notitie over multicriteria-

analyse in milieueffectrapportage. Utrecht: MER. Retrieved from

http://api.commissiemer.nl/docs/mer/diversen/mca-brochure.pdf

Darji, V. P., & Rao, R. V. (2013). Application of AHP/EVAMIX Method for Decision Making

in the Industrial Environment. American Journal of Operations Research(3), 542-

569. Retrieved from http://file.scirp.org/pdf/AJOR_2013111917132863.pdf

Davenport, T. H. (1993). Process Innovation:Reengineering work through Information

Technology. Boston, Massachusetts: Harvard Business Press.

Enserink, B., Hermans, L., Kwakkel, J., Thissen, W., Koppenjan, J., & Bots, P. (2010). Policy

Analysis of Multi-Actor Systems. The Hague: Lemma.

Flightglobal. (2015). Commercial Engines - Turbofan focus 2015. Flightglobal. Retrieved

May 17, 2016, from https://globalaviationaerospace.com/2015/10/25/commecial-

aircraft-engines-insights-2014-2015/

George, M. L. (2002). Lean Six Sigma: Combining Six Sigma Quality with Lean Speed. The

McGraw-Hill Companies, Inc.

GITTA. (2013, November 26). Weighting by ranking. Retrieved October 10, 2016, from

Gitta.info:

http://www.gitta.info/Suitability/en/html/Normalisatio_learningObject1.html

Goldratt, E. M., & Cox, J. (1984). The Goal: A Process of Ongoing Improvement. Great

Barrington, MA: North River Press.

Haan, A. d., Willemse, W., de Heer, P., Vos, S., Bots, P., Bas, G., . . . Veltman, J. (2009).

Inleiding technische bestuurskunde - een raamwerk voor het systematisch oplossen

van complexe multi-actorproblemen. Den Haag: Uitgeverij LEMMA.

Hung, R. Y. (2006). Business Process Management as a competitive advantage: a review and

empirical study. Total Quality Management & Business Excellence, 17(1), 21-40.

doi:10.1080/1478330500249836

IATA. (2015, November 26). IATA Air Passenger Forecast Shows Dip in Long-Term Demand.

Retrieved May 20, 2016, from IATA: http://www.iata.org/pressroom/pr/Pages/2015-

11-26-01.aspx

International Six Sigma Institute. (n.d.). Retrieved June 1, 2016, from http://www.sixsigma-

institute.org/What_Is_The_Focus_Of_Six_Sigma.php

iSixSigma. (n.d.). Six Sigma DMAIC Roadmap. Retrieved June 1, 2016, from iSixSigma.com:

https://www.isixsigma.com/new-to-six-sigma/dmaic/six-sigma-dmaic-roadmap/

Jeston, J., & Nelis, J. (2014). Business Process Management - practical guidelines to succesful

implementations (3rd ed.). New York: Routledge.

Jong, S. J., & Beelaerts van Blokland, W. A. (2016). Measuring lean implementation for

maintenance service companies. International Journal of Lean Six Sigma, 7(1), 35-

61.

Klaassen, M. (2012). Improving Inbound Logistics at KLM Engine Services.

93

KLM. (2008, December). Onderhoud Vliegtuigmotoren bij KLM - Logistieke uitdaging met

10.000 onderdelen. VNSG Magazine.

KLM. (2013, June 14). Vliegtuigonderhoud. Retrieved May 30, 2016, from www.klm.com:

http://www.klm.com/corporate/nl/about-klm/students/aircraft-maintenance.html

KLM. (2015a, June 26). Air France KLM. Retrieved May 30, 2016, from www.klm.com:

http://www.klm.com/corporate/nl/about-klm/air-france-klm/index.html

KLM. (2015b). Annual Report 2015. Retrieved from

https://www.klm.com/corporate/nl/images/Printversion-

Annual%20Report%202015_tcm730-653699.pdf

Kraljic, P. (1983). Purchasing must become supply management. Harvard Business Review,

61(5), 109-117.

Mabin, V., & Balderstone, S. (2003). The performance of the theory of constraints

methodology: analysis and discussion of successful TOC applications. International

Journal of Operations & Production Management, 23(6), 568-595.

Maria, A. (1997). Introduction to Modeling and Simulation. 29th Winter Simulation

Conference, (pp. 7-13). Atlanta, Georgia.

Mattijssen, I., Boerrigter, E., & Klokkers, T. (2016, February). TAT45 Presentation. KLM

Engineering & Maintenance.

Meijs, P. C. (2016). Reducing the Turnaround Time of In House Repairs of Aircraft Engine

MRO Services - A Case Study at KLM Engineering and Maintenance Engine Services.

Delft: Delft University of Technology.

Ministerie van Financien. (1992). Evaluatiemethoden - een introductie (4rd ed.). Ministerie

van Financien.

Minitab. (2016a). What is a boxplot? Retrieved July 20, 2016, from support.minitab.com:

http://support.minitab.com/en-us/minitab/17/topic-library/basic-statistics-and-

graphs/graphs/graphs-that-compare-groups/boxplots/boxplot/

Minitab. (2016b). What is a probability plot? Retrieved July 20, 2016, from

support.minitab.com: http://support.minitab.com/en-us/minitab/17/topic-

library/basic-statistics-and-graphs/graphs/graphs-of-distributions/probability-

plots/probability-plot/

Mogendorff, W. A. (2016). Aircraft Engine Combustor Maintenance - A model to measure

MRO turnaround time. Delft: Delft University of Technology.

Reid, R. D., & Sanders, N. R. (2010). Operations Management - An Integrated Approach

(Fourth ed.). John Wiley & Sons (Asia).

Reinhard, S., Vreke, J., Wijnen, W., Gaaff, A., & Hoogstra, M. (2003). Integrale afweging van

ruimtegebruik - ontwikkeling van een instrumentarium voor het beoordelen van

veranderingen in aanwendig van ruimte. Den Haag: LEI. Retrieved October 15,

2016, from http://library.wur.nl/WebQuery/wurpubs/fulltext/19921

94

Roszkowska, E. (2013). Rank ordering criteria weighting methods - a comparative overview.

Optimum Studia Ekonomiczne(5), 14-33.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. Int. J. Services

Sciences, 1(1), 83-98. Retrieved October 5, 2016, from

http://www.colorado.edu/geography/leyk/geog_5113/readings/saaty_2008.pdf

Shay, L. A. (2015, December 24). Global 2016 Commercial Aviation MRO Market Forecast

By Region. Retrieved May 20, 2016, from Aviation Week:

http://aviationweek.com/mro/global-2016-commercial-aviation-mro-market-

forecast-region

Smith, R., & Hawkins, B. (2004). Lean maintenance. New York: Elsevier.

Stewart, D. (2015, November 3). MRO Market Forecast & Trends. Retrieved June 10, 2016,

from ICF International: http://www.slideshare.net/ICFI/icfs-mro-market-forecast-

and-trends-58767697

Stewart, J. (2011). The Toyota Kaizen Continuum. A Practical Guide to Implementing Lean.

Boca Raton, FL: CRC Press.

The Guardian. (2013, June 11). Number of planes to double in next two decades, Boeing

forecasts. Retrieved May 20, 2016, from The Guardian:

https://www.theguardian.com/business/2013/jun/11/boeing-commercial-planes-

double-asia-pacific

United States General Accounting Office. (1997). Business Process Reengineering Assessment

Guide. Retrieved from http://www.gao.gov/assets/80/76302.pdf

van Rijssel, R. (2016). Lowering the Turnaround time for Aircraft component MRO services:

A case study at KLM Engineering & Maintenance. Retrieved from

http://repository.tudelft.nl/islandora/object/uuid:7ec271ca-2a51-4b89-b4ef-

9e2badeb0493?collection=education

Womack, J., Jones, D. T., & Roos, D. (1990). The Machine that Changed the World. UK.:

Simon & Schuster Ltd.

95

Appendix

96

97

A. Process Improvement Methodologies

A.1. Business Process Re-engineering (BPR)

Business Process Re-engineering (BPR) is related to Business Process Management, but

focuses more on innovative, fundamental and radical re-design of processes instead of

refining existing processes. Or in other words: optimizing sub-processes can result in some

benefits, but BPR focuses on redesigning the process as a whole in order to achieve the

maximum possible benefits for the organization and its customers (United States General

Accounting Office, 1997, p. 6).

(Davenport, 1993) has developed a five step framework for innovative process re-

engineering. These five steps are shown in the figure below:

The first step considers the identification of processes for innovation. In this step, major

processes in the organization are investigated resulting in a selection of processes most in

need of a fundamental change.

Next, it is necessary to identify the main levers (enablers) for change in the organization.

According to (Davenport, 1993), the three main change levers are IT, information and

organizational (human resources).

The third step consists of developing visions for the new processes: the new goal a process

should reach, fitting in the company strategy.

To avoid repeating old mistakes, the current processes need to be investigated in the fourth

step; this will also serve as a baseline for improvements.

The fifth and last step consists of designing and prototyping the new process. Designing

innovative processes is best conducted through brainstorming. When designs are developed,

it is essential to assess the feasibility, risk and benefits of the designs and select the

preferred design.

For this research, BPR focuses too much on redesigning complete processes, from a green-

field approach; this research is aimed towards improving or optimizing current processes.

However, the creative approach of BPR towards designing solution alternative can be a

Designing and prototyping the new process

Understanding existing processes

Developing process visions

Identifying change levers

Identifying processes for innovation

98

useful addition to the research. Generating creative solutions is covered in section 2.1.6:

Creative Problem Solving.

A.2. Business Process Management (BPM)

Business Process Management (BPM) considers a wide field, focused on improving business

performance by managing and improving business processes. One of the definitions of BPM

is given as follows:

“A management discipline focused on using business processes as a significant contributor to

achieving an organization’s objectives through the improvement, ongoing performance

management and governance of essential business processes (Jeston & Nelis, 2014, p. 4).”

BPM has its roots in both Total Quality Management (section 2.1.4) and Business Process

Re-engineering, however BPM is more incremental and evolutionary in nature than the

radical approach of BPR. (Hung, 2006, p. 22) states that “BPM integrates TQM and a BPR

approach, and can be regarded as suitable for performance improvement in most

circumstances”.

The concept of Business Process Management can be summarized in a number of defining

principles as stated by (Hung, 2006, p. 23):

1. Holistic View – it looks further than isolated parts of business processes

2. Strategic Imperative – BPM should focus on a coherent process to strategy

3. Enabled by IT – BPM uses IT extensively to manage business processes

4. Corporate-Wide Impact – BPM does not end at one department. The impact should

be corporate wide, from structure to management.

5. Cross-functional Process Management

6. Process Alignment – arrange the parts of the company to work in harmony in pursuit

of common organization goals

7. Horizontal Structure, IT and Strategic alignment

8. People Involvement, Executive Commitment and Employee Empowerment

These principles can be summarized as “a holistic view – Strategic Imperative – enabled by

information technology, corporate-wide impact, and emphasizes cross-functional process

management (Hung, 2006, p. 23).”

Typical BPM activities or initiatives can be Continuous Improvement, Process Re-

engineering or Benchmarking. Any activity to improve the business processes can be a BPM

activity, as long as it follows the previously stated principles.

Business Process Management serves as a less radical, more incremental counterpart to

Business Process Re-engineering, including the continuous approach principle of Total

Quality Management. It is a holistic philosophy, having the best effect when implemented

corporate wide. The current holistic philosophy at KLM E&M lies in the direction of Lean

Six Sigma, which makes BPM less practical to implement and use for this research.

99

B. Used datasets for current state measurement

B.1. Used Datasets for current state measurement MRO chain

Various datasets, on various levels of detail are used to measure the current state of the

engine MRO chain and, more detailed, the repair stages.

Overview of 2015 engines – SAP Theo Dorlandt

The data used to generate the TAT per stage stems from the SAP system used at KLM

Engineering & Maintenance. Data was used from the whole year 2015. In 2015, 148 different

engines were serviced at KLM E&M Engine Services. Of these 148 engines, 66 engines were

of the CFM56-7B type, the scope of the case study as described in section 1.2. From this

dataset, the overall TAT and TAT per stage, on engine order level, can be determined.

B.2. Used Datasets for current state measurement Repair stage

In-House repair dataset – SAP Alex Gortenmulder QlikView

To generate the performance of in-house repairs, again data from 2015 for CFM56-7B

engines is used. However, in the used dataset, the performance is measured on the level of

detail of engine WBS elements (an assembly of different parts of an engine). The used

dataset for in-house repairs is extracted from SAP. After eliminating faulty data, the dataset

consists of 3738 separate service orders (work orders). These 3738 service orders are a part

of 66 engine orders, thus corresponding to the 66 engine orders that were investigated in the

dataset mentioned above.

Outsourced Repair dataset – SAP Ronald Oostveen

The dataset used for Outsourced Repairs was again retrieved from SAP. It consists of

Outsourced Repair orders of 2015, for the CFM56-7B engine types. A total of 3624 repair

orders is used for analysis, corresponding to 60 different engine orders. In this dataset,

amongst others vendors are indicated, accompanied by “created on” date, accepted at vendor

date, expedited at vendor date and goods received date.

B.3. Used Datasets for other stages

Disassembly stage – Idris Mattijssen SAP and previous research

To model the current state of the engine MRO chain, data is necessary to measure the

detailed performance of the disassembly stage. This is done by using previous research by

Idris Mattijssen (Black belt) on disassembly of WBS elements. Next to this, the TAT of

cleaning & inspection (part of the disassembly stage) is retrieved from SAP for various WBS

elements.

Assembly stage – detailed data SAP Theo Dorlandt

The current stage of the Assembly stage is retrieved from SAP. For all 2015 CFM56-7B

engines, it is registered what the TAT is for the assembly stage, the TAT for testing and the

TAT for rework.

100

B.4. Output performance current state - Quality

An important quality measure is the EGT margin: the difference between the engine exhaust

gas temperature after MRO and the specific EGT limit for that engine type. The higher the

margin, the higher the engine quality, simply put. At KLM, certain agreements are made

with clients on the EGT margin that needs to be reached. The difference between the agreed

margin and the actual margin is called the EGT delta. The delivered EGT delta at KLM

E&M is shown in the figure below. It can be observed that around 30% of the engines do not

reach the required EGT margin – they have a negative EGT delta.

101

C. Constraint observations

C.1. General MRO Chain constraints

Disassembly order

The disassembly order of different WBS elements is shown in the figure below. This order is

specific for the CFM56-7B engine. Disassembly is started with the hosing and piping around

the engine: the QEC. Next, the engine is disassembled from back to front: starting with the

Low Pressure Turbine, and ending with the Fan module. The assembly procedure starts –

in reverse order - with the last module: the fan.

C.2. Constraints in Outsourced Repair

Observations Import logistics KLM E&M Engine Services: Waste & Flow 4M/TIMWOOD(S) Observation

Man No inspector incoming goods (IIG) capacity in weekend, shifts 5x2 (weekdays)

– mismatch with 7x2 Engine Shop

Machine Not observed

Method Set-up of “arrived at maintenance” unit (AM) buffer enables LIFO, batching

of components, flow disrupted by priority packages and aircraft-on-ground

(AOG) deliveries

Material Not observed

Transport Transport of components on carts between stations

Inventory Buffers before AM, IIG, transport to APrep, Quarantine area

Motion Employees go retrieve packages for each step

Waiting Time Buffers before AM, large buffer IIG, transport to APrep waiting time

Overproduction Not observed (not applicable)

Over-processing Both digital and hard-copy certification used

engine QEC

72X QEC

03X 56X 55X 54X

Core

02X 53X 52X 51X 42X 32/33X 31X

AGB

63X

LPC

21X

TGB IGB

62X 61X

01X 22X 23X

Fan

LPT

HPT uit Core

102

Defects PIGs (packages with problems), incorrect number input. Unexpected

exchange parts due to delivery delay

Skill Potential to use more skills (multi-skilled team)

Flow Flow disrupted by AOG/Prio deliveries and PIGs. No FIFO method applied.

Batching of packages, three different buffers.

Estimated transport times per vendor Vendor Country/Region Current TAT

export (days)

Current TAT

import (days)

Transport time

estimate (touch

time)

Chromalloy Tilburg/EU

Thailand/Asia

5.1 6.3 2 hr. (NL), 14 hr.

(TH)

CRMA Elancourt/EU 3.6 3.6 4 hr.

EPCOR BV SPL/NL 3.1 2.5 0.5 hr.

GE-ATI Singapore/Asia 6.7 2.9 15 hr.

GE-Hungary Hungary/EU 4.8 4.5 4 hr.

GE ACSC Cincinnati/US 6.0 4.5 12 hr.

GEAN storefront 2.5 4.9 0 hr.

GEASO Singapore/Asia 6.9 4.7 15 hr.

Honeywell USA/Canada 6.8 8.1 14 hr.

LHT Hamburg/EU 5.0 8.5 3 hr.

Meggitt Milwauki/US 6.1 6.1 13 hr.

SKF EU/US 4.9 4.4 4 hr. (EU), 12 hr.

(US)

StandardAero Cincinnati/US 4.7 8.2 12 hr.

Triumph Wellington/Grand

Prairie US

5.5 4.7 13 hr.

Unison Alpha/Jacksonville

/US

6.5 6.1 12 hr.

103

Agreed cut-off times per region – Export logistics

Sodexo transport rounds schedule – Export and Import logistics

104

105

D. Modelling of the Outsourced Repair solutions

D.1. Current state average TAT per process step MRO

106

D.2. Future state outsourced repair – assumptions & results

Assumptions per solution Vendors Constraint: Vendor Repair TAT

Exploit Vendor management – meet the current TAT agreements as stated in SAP per work

order

Elevate Contract renegotiations towards maximum (cap) 28, 21 or 14 days, in combination

with vendor management.

Ideal Fully Lean partners that integrate the planning (integrated supply chain) and

deliver Just In Time, or repairs conducted in-house when vendors are unable.

Resulting TAT: Touch time * 2 40% of current TAT. Estimates based on previous

research by (Meijs, 2016) and (Mogendorff, 2016). In-House repairs: combustor TAT

decreased to 10 days, based on previous research by (Mogendorff, 2016).

Current Average TAT 22.7 days, average contract TAT 22.9 days

Logistics

Import

Constraint – IIG capacity

Exploit Combining DGO + IIG (Decentralized Goods Receipt and Inspection Incoming Goods)

which doubles capacity, thus halves the buffer before IIG – resulting in an estimated

reduction from 3.8 to 2 days – so minus 1.8 days of all import logistics

Elevate Increasing from 5x2 to 7x2 schedule for Incoming Goods Inspections, increases the

IIG capacity by 40%. Decreases the TAT with 0.8 days more, on top of the previous

decrease (total decrease 2.6 days)

Ideal Just-in-Time delivery through the integrated, Lean supply chain. Touch times:

15+30+5 minutes (50 min total), add buffer for waiting time * 2 = 100 minutes.

Delivery directly from airside (bypass Logistics Center KLM). Add touch times

external logistics: Sodexo (1 hour), Logistics center (2 hours) and transport to vendor

(12-24 hours depending on location, see Appendix C.2.)

Current Average 3.8 days ES Import logistics, total import logistics 5 days

Logistics

Export

Constraint – Outgoing transport times (cut-off times)

Exploit Reduce the current TAT with average waiting time 24/2=12 hours, assuming one

intercontinental flight per day per destination (Appendix C.2)

Elevate Reduce TAT with one hour more due to bypassing of Sodexo delivery round (total

13 hour reduction of current TAT)

Ideal Assume touch time at ES 30*2 minutes, add transport to vendor (12-24 hours) –

Differentiate transport time per vendor (see estimated transport times Appendix

C.2)

Current 3.8 days average (including Sodexo, LC and transport to vendor)

107

Results Exploit solution alternative – Outsourced Repairs

Results Elevate 28 solution alternative – Outsourced Repairs

108

Results Elevate 21 solution alternative – Outsourced Repairs

Results Elevate 14 solution alternative – Outsourced Repairs

109

Results Ideal World solution alternative – Outsourced Repairs

110

D.3. Future state MRO chain – Results

Future State – Exploit

Future State – Elevate 28 days

Future State – Elevate 21 days

111

Future State – Elevate 14 days

Future State – Ideal World

112

113

E. Solution evaluation

E.1. Criteria weight determination

Unstandardized weights using AHP

KLM E&M Engine Services (Process Owner)

Client’s perspective

Equal Weights

C1 C2 Q1 Q2 T2

C1 1.00 1.00 1.00 0.33 0.33

C2 1.00 1.00 0.33 0.20 0.20

Q1 1.00 3.00 1.00 1.00 1.00

Q2 3.00 5.00 1.00 1.00 0.33

T1 3.00 5.00 1.00 3.00 1.00

sum 9.0 15.0 4.3 5.5 2.9

C1 C2 Q1 Q2 T2

C1 1.00 7.00 0.25 2.00 0.33

C2 0.14 1.00 0.13 0.20 0.14

Q1 4.00 8.00 1.00 5.00 3.00

Q2 0.14 5.00 0.20 1.00 0.33

T1 3.00 7.00 0.33 3.00 1.00

sum 8.29 28.00 1.91 11.20 4.81

C1 C2 Q1 Q2 T2

C1 1 1 1 1 1

C2 1 1 1 1 1

Q1 1 1 1 1 1

Q2 1 1 1 1 1

T1 1 1 1 1 1

sum 5 5 5 5 5

114

Standardized weights and consistency index

KLM E&M Engine Services (Process Owner)

Client’s perspective

Equal Weights

E.2. Evamix approach

The Evamix approach as defined by (Commissie voor de milieueffectrapportage, 2002, p. ix).

C1 C2 Q1 Q2 T2 Weights lambda CI CR Limit=0.1

C1 0.11 0.07 0.23 0.06 0.12 0.12 1.1 0.10 0.09 Consistent

C2 0.11 0.07 0.08 0.04 0.07 0.07 1.1

Q1 0.11 0.20 0.23 0.18 0.35 0.21 0.9

Q2 0.33 0.33 0.23 0.18 0.12 0.24 1.3

T1 0.33 0.33 0.23 0.54 0.35 0.36 1.0

sum 1.00 1.00 1.00 1.00 1.00 1.00 5.4

C1 C2 Q1 Q2 T2 Weights lambda CI CR Limit=0.1

C1 0.12 0.25 0.13 0.18 0.07 0.15 1.2 0.08 0.07 Consistent

C2 0.02 0.04 0.07 0.02 0.03 0.03 0.9

Q1 0.48 0.29 0.52 0.45 0.62 0.47 0.9

Q2 0.02 0.18 0.10 0.09 0.07 0.09 1.0

T1 0.36 0.25 0.17 0.27 0.21 0.25 1.2

sum 1.00 1.00 1.00 1.00 1.00 1.00 5.3

C1 C2 Q1 Q2 T2 Weights lambda CI CR Limit=0.2

C1 0.20 0.20 0.20 0.20 0.20 0.20 1 0 0 consistent

C2 0.20 0.20 0.20 0.20 0.20 0.20 1

Q1 0.20 0.20 0.20 0.20 0.20 0.20 1

Q2 0.20 0.20 0.20 0.20 0.20 0.20 1

T1 0.20 0.20 0.20 0.20 0.20 0.20 1

sum 1.00 1.00 1.00 1.00 1.00 1.00 5

115

E.3. Qualitative criteria scores per solution alternative & dominance matrices

This section will discuss the scoring of the criteria and the resulting dominance matrices for

the qualitative and quantitative criteria.

Qualitative criteria scores

The qualitative criteria are:

C1: MRO cost

C2: Implementation cost

Q1: Product quality

Per criterion, the different alternatives are ranked on a scale from 1 to 6, where 6 represents

the best alternative, and 1 the worst alternative. The next sections will give the rationales

to the given scores.

C1: MRO cost

MRO cost is about MRO cost for the clients, on a short to middle-long term. The best

alternative is scored 6, in this case the alternative representing the lowest MRO cost, whilst

the worst alternative is scored 1 – representing the highest MRO cost.

Score Solution Reason

6 Exploit The current contracts can be used meaning that no extra fee needs to

be paid to vendors – which results in a higher fee for the clients -while

no delays occur, the use of the current constraints is exploited and no

large new investments need to be made

5 Current state In the current state, no higher fee needs to be paid to vendors, however

delays and inefficiencies do occur

4 Elevate 28 Some vendors need higher fees for shorter contracts, and the price for

logistics increases due to dedicated transportation and more logistics

shifts

3 Elevate 21 More vendors need higher fees for shorter contracts and the prices for

logistics go up

2 Elevate 14 Most vendors will ask for a very high fee for shorter contracts, prices for

logistics go up

1 Ideal state Very short TAT at vendors will cause high fees, and also in-house

integration with short TAT will be more expensive on the short term for

the client. Next to this, dedicated, decentralized logistics will be more

expensive.

C2: Implementation cost

Implementation cost considers the investments that need to be made by KLM E&M to

achieve the solutions. The alternative with the lowest implementation cost receives a score

of 6, whilst the alternative with the highest implementation cost receives a score of 1.

Score Solution Reason

6 Current State No solutions, so no implementation cost

5 Exploit Only investments needed are to allocate resources on vendor

management and training logistics employees for multi-skill and Pull

order

4 Elevate 28 Extra needed investments are renegotiation of some contracts, likely

against a higher fee, investing in more logistical shifts and dedicated

transportation

3 Elevate 21 Extra needed investments are renegotiation of more contracts, likely

against a high fee, and investing more in logistics (see elevate 28)

2 Elevate 14 Extra needed investments are renegotiation of many contracts, likely

against high fee, if even possible, and investing more in logistics (see

elevate 28)

1 Ideal State A lot of investments are needed in creating a Lean, integral supply

chain with Lean vendors, and integrating critical repairs to in-house

activities. Next to this, new logistical processes need to be defined to

enable direct airside-engine service transport.

116

Q1: Product quality

The product quality is about meeting the required standards for the client. Assumed is that

all solutions will enable a better product quality than the current state, due to a better

control of the process. However, the differences between the different solutions cannot be

estimated. Therefore, all alternatives receive the same score. Important to note is that the

difference between score 1 and 6 is irrelevant for the Evamix method, as Evamix only checks

whether an alternative is better or worse than an alternative, for qualitative criteria.

Score Solution Reason

6 Exploit See above

6 Elevate 28 See above

6 Elevate 21 See above

6 Elevate 14 See above

6 Ideal See above

1 Current State In the current state, 30% of the EGT margins are below par

Dominance matrices

Qualitative dominance matrix

Qualitative dominance matrix – standardized

Quantitative dominance matrix

Dominance QualitativeCurrent

stateExploit Elevate 28 Elevate 21 Elevate 14

Ideal

World

Current state -0.26 -0.03 -0.03 -0.03 -0.03

Exploit 0.26 0.19 0.19 0.19 0.19

Elevate 28 0.03 0.19 0.19 0.19 0.19

Elevate 21 0.03 -0.19 -0.19 0.19 0.19

Elevate 14 0.03 -0.19 -0.19 -0.19 0.19

Ideal World 0.03 -0.19 -0.19 -0.19 -0.19

Standard

dominance

Current

stateExploit Elevate 28 Elevate 21 Elevate 14 Ideal World

Current state -0.06 -0.01 -0.01 -0.01 -0.01

Exploit 0.06 0.04 0.04 0.04 0.04

Elevate 28 0.01 0.04 0.04 0.04 0.04

Elevate 21 0.01 -0.04 -0.04 0.04 0.04

Elevate 14 0.01 -0.04 -0.04 -0.04 0.04

Ideal World 0.01 -0.04 -0.04 -0.04 -0.04

Dominance QuantitativeCurrent

stateExploit Elevate 28 Elevate 21 Elevate 14

Ideal

World

Current state -0.21 -0.23 -0.29 -0.40 -0.60

Exploit 0.21 -0.02 -0.08 -0.20 -0.39

Elevate 28 0.23 0.02 -0.06 -0.18 -0.37

Elevate 21 0.29 0.08 0.06 -0.11 -0.31

Elevate 14 0.40 0.20 0.18 0.11 -0.20

Ideal World 0.60 0.39 0.37 0.31 0.20

117

Quantitative dominance matrix – standardized

E.4. Sensitivity analysis matrices

Different Weights

Client’s Perspective standardized weights & consistency

Client’s Perspective Total Dominance Matrix

Equal Weights standardized weights & consistency

Equal Weights Total Dominance Matrix

Standard

dominance

Current

stateExploit Elevate 28 Elevate 21 Elevate 14 Ideal World

Current state -0.03 -0.03 -0.04 -0.06 -0.08

Exploit 0.03 0.00 -0.01 -0.03 -0.05

Elevate 28 0.03 0.00 -0.01 -0.02 -0.05

Elevate 21 0.04 0.01 0.01 -0.02 -0.04

Elevate 14 0.06 0.03 0.02 0.02 -0.03

Ideal World 0.08 0.05 0.05 0.04 0.03

C1 C2 Q1 Q2 T2 Weights lambda CI CR Limit=0.1

C1 0.12 0.25 0.13 0.18 0.07 0.15 1.2 0.08 0.07 Consistent

C2 0.02 0.04 0.07 0.02 0.03 0.03 0.9

Q1 0.48 0.29 0.52 0.45 0.62 0.47 0.9

Q2 0.02 0.18 0.10 0.09 0.07 0.09 1.0

T1 0.36 0.25 0.17 0.27 0.21 0.25 1.2

sum 1.00 1.00 1.00 1.00 1.00 1.00 5.3

TOTAL DOMINANCECurrent

stateExploit Elevate 28 Elevate 21 Elevate 14

Ideal

Worldtotal

Current state -0.05 -0.03 -0.04 -0.05 -0.07 -0.23

Exploit 0.05 0.01 0.00 -0.01 -0.03 0.02

Elevate 28 0.03 0.01 0.00 -0.01 -0.03 0.01

Elevate 21 0.04 0.00 0.00 0.00 -0.02 0.01

Elevate 14 0.05 0.01 0.01 0.00 -0.01 0.06

Ideal World 0.07 0.03 0.03 0.02 0.01 0.15

C1 C2 Q1 Q2 T2 Weights lambda CI CR Limit=0.2

C1 0.20 0.20 0.20 0.20 0.20 0.20 1 0 0 consistent

C2 0.20 0.20 0.20 0.20 0.20 0.20 1

Q1 0.20 0.20 0.20 0.20 0.20 0.20 1

Q2 0.20 0.20 0.20 0.20 0.20 0.20 1

T1 0.20 0.20 0.20 0.20 0.20 0.20 1

sum 1.00 1.00 1.00 1.00 1.00 1.00 5

TOTAL DOMINANCECurrent

stateExploit Elevate 28 Elevate 21 Elevate 14

Ideal

Worldtotal

Current state -0.03 -0.01 -0.02 -0.03 -0.04 -0.12

Exploit 0.03 0.01 0.01 0.00 -0.02 0.03

Elevate 28 0.01 0.02 0.01 0.00 -0.01 0.03

Elevate 21 0.02 -0.01 -0.01 0.01 -0.01 0.00

Elevate 14 0.03 0.00 0.00 -0.01 0.00 0.01

Ideal World 0.04 0.02 0.01 0.01 0.00 0.08

118

Different quantitative values dominance matrices

Increasing and decreasing TAT and standard deviation (T1 and Q2)

Increasing and decreasing only TAT (T1)

Solution 120% 110% 100% 90% 80%

Current state -0.10 -0.12 -0.14 -0.16 -0.17

Exploit 0.08 0.10 0.11 0.12 0.13

Elevate 28 0.06 0.07 0.08 0.09 0.10

Elevate 21 -0.01 0.00 0.00 0.01 0.02

Elevate 14 -0.01 -0.01 -0.01 -0.01 -0.01

Ideal World 0.03 0.02 0.01 0.00 -0.01

Current state 120% 110% 100% 90% 80%

Current state -0.11 -0.13 -0.14 -0.16 -0.17

Exploit 0.09 0.10 0.11 0.12 0.12

Elevate 28 0.07 0.07 0.08 0.09 0.10

Elevate 21 -0.01 0.00 0.00 0.01 0.01

Elevate 14 -0.01 -0.01 -0.01 -0.01 -0.01

Ideal World 0.02 0.02 0.01 0.00 -0.01

119

120