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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
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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
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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
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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.
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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
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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.
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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
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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).
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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.
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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
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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.
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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.
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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
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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).
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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
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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.
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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.
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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.
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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.
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Part Three: Analyze Phase
Photo Courtesy of KLM Engineering and Maintenance
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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.
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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.
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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
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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.
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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.
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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)
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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.
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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.
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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.
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Part Four: Improve Phase
Photo Courtesy of KLM Engineering and Maintenance
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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
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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
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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
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Part Five: Validate & Control Phase
Photo NRC 18 May 2016
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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
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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.
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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)
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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
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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