Exploring Barriers, Enablers, Justification and Planning Methods for Total Productive
Maintenance Implementation in Automated Production of Commercial Airplanes
by
David Michael Feliciano
B.S.E Chemical Engineering, Princeton University, 2008
Submitted to the MIT Sloan School of Management and the Engineering Systems Division in
Partial Fulfillment of the Requirements for the Degrees of
In conjunction with the Leaders for Global Operations Program at the
Massachusetts Institute of Technology
June 2015.
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William A. Coolidge Professor of Electrical Engineering and Computer Science
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MIT Sloan School of Management
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2
Exploring Barriers, Enablers, Justification and Planning Methods for Total Productive
Maintenance Implementation in Automated Production of Commercial Airplanes
by
David M. Feliciano
Submitted to MIT Sloan School of Management and the Department of Engineering Systems on
May 8, 2015 in Partial Fulfillment of the requirements for the Degree of Master of Business
Administration and Master of Science in Engineering Systems
Abstract
The 737 program is currently producing 42 airplanes per month and will increase that production
rate to 47 per month by 2017 and 52 per month in 2018 [1] [2]. In order to help meet these rates
Boeing plans to increase the amount of automation in the shop that produces front and rear spars
for all 737 variants. To mitigate risks associated with increased automation, the automation
design team would like to implement an approach to equipment maintenance and operation
known as Total Productive Maintenance (TPM). However the justification for TPM was not
clear and an implementation strategy did not exist.
The focus of this thesis therefore, is to clarify the justification for TPM, understand how TPM
could impact the production system, identify the barriers and enablers of TPM implementation at
Boeing, and present a TPM implementation plan that will be appropriate and effective for the
particular context in which it will be executed.
An analysis of current maintenance data and practices, case studies within the current factory,
and a qualitative analysis of the future production system suggest that TPM could provide many
quantitative and qualitative benefits and that the new production system is a good candidate for
TPM. Results from a discrete event model show that TPM poses minimal risk of disrupting the
future production system. Results of an employee survey show that the most important factors to
successful TPM implementation are related to planning and building support prior to
implementation. These findings influenced the design of the TPM implementation plan presented
in this thesis, which focuses initially on building support, planning, and training.
As the airline industry adopts automated equipment in response to increased competition, TPM
may become an important strategy for staying competitive. The multi-pronged analyses
demonstrated in this thesis for justifying TPM, the survey method used to understand the
company-specific barriers and enablers of TPM, and the method of designing a customized TPM
implementation plan based on the insights from these analyses can serve as a general model for
implementing TPM within Boeing and within the broader airline industry.
Thesis Supervisor: Bruce G. Cameron
Title: Director of the System Architecture Lab and Lecturer in the Engineering Systems Division
Thesis Supervisor: Steven J. Spear
Title: Senior Lecturer, MIT Sloan School of Management
3
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4
Acknowledgements
I would like to acknowledge the Leaders for Global Operations Program for its support of this
work. I would also like to acknowledge The Boeing Company for their support of this work. The
leadership and support of Aaron Jones and Bruce Moravec made my internship and this project
possible. The design team I was a part of provided me with tons of support and education,
especially Punit Shivji, Tim Thornton, Gavin Smith, and Evan Johnson. I would not have learned
a tenth of what I learned about the production system if it had not been for their extensive
knowledge and patient teaching. Megan Taylor taught me a lot about the Boeing Company and
was a great friend throughout my time at Boeing. Jeff Adams and Gail Jezek from the Renton
TPM planning team were instrumental in helping me learn about TPM and also became good
friends. Thank you everyone!
I would like to recognize both of my thesis advisors, Bruce Cameron and Steve Spear who
supported me throughout my internship. Bruce provided me with much guidance and practical
advice and Steve Spear helped me understand how to distill my experience into a thesis.
The LGO community was an incredible source of friendship and support throughout the
internship. I had an amazing time in Seattle with Ammar Asfour, Esther Mangan, and Erik
Charpentier. Ellen Ebner was always willing to listen to my experiences, contrast them with her
own, and offer thoughtful advice. Finally, I would like to thank all of the LGOs that helped me
and provided community through visits, phone calls, and blogs. Even though we were separated
by many miles we were never apart.
I am grateful for the support from my parents, brother and sister. I would not have made it so far
without your love. You always believed in me and supported me through my many years of
education, for which I am eternally grateful. You all were the ultimate source encouragement
when I needed it most.
Lastly, I would like to thank my fiance Elizabeth Cespedes. She was my adventure partner for
the Seattle summer and my thesis writing buddy when I returned to Cambridge. I don't have the
words to express how incredibly blessed I am to have you in my life.
5
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6
Contents
ABSTRACT ...................................................................................................................................................
3
ACKNOWLEDGEMENTS..........................................................................................................................5
CHAPTER 1: INTRODUCTION ..............................................................................................................
11
1.1 PROJECT BACKGROUND AND MOTIVATION....................................................................................
11
1.2 P RO B LEM STA TEM EN T ......................................................................................................................
11
1.3 T H ESIS O V ERV IEW ............................................................................................................................
13
CHAPTER 2: BACKGROUND INFORMATION ..............................................................................
15
2 .1 IN D U STR Y O V ERV IEW .......................................................................................................................
15
2.2 CURRENT 737 SPAR ASSEMBLY AT RENTON ..................................................................................
16
2.3 AUTOMATION OF 737 SPAR ASSEMBLY AT RENTON .......................................................................
18
2.4 TOTAL PRODUCTIVE MAINTENANCE OVERVIEW...........................................................................20
CHAPTER 3: THE CASE FOR TPM...................................................................................................25
3.1 BENEFITS OF TPM DOCUMENTED IN LITERATURE AND CASE STUDIES.............................................26
3.2 ANALYSIS OF CURRENT STATE SPAR SHOP PERFORMANCE ..............................................................
27
3.3 O RGANIZATIONAL A NALYSIS........................................................................................................
33
3.4 QUALITATIVE ANALYSIS OF FUTURE SPAR SHOP...........................................................................35
3.5 SUM M ARY AN D D ISCUSSION .............................................................................................................
37
CHAPTER 4: DISCRETE EVENT MODEL OF THE AUTOMATED SPAR PRODUCTION SYSTEM
W ITH T PM ..................................................................................................................................................
39
4.1 MODEL FORMULATION, INPUTS, AND OUTPUTS.............................................................................40
4.2 INVESTIGATIONS OF SYSTEM BEHAVIOR TO VARYING PM DURATIONS............................................46
4.3 INVESTIGATIONS OF SYSTEM BEHAVIOR TO VARYING MACHINE RELIABILITY.............................52
4.4 INVESTIGATIONS OF SYSTEM BEHAVIOR TO PM DURATION AND MACHINE RELIABILITY ................ 54
7
4.5 SUM M A RY AND D ISCUSSION ...........................................................................................................
58
CHAPTER 5: BARRIERS AND ENABLERS FOR TPM IMPLEMENTATION AT BOEING........61
5.1 LITERATURE REVIEW: TPM IMPLEMENTATION, SUCCESS FACTORS AND BARRIERS.........................62
5.2 SURVEY OVERVIEW AND M ETHODOLOGY......................................................................................63
5.3 SURVEY RESULTS AND D ISCUSSION ...............................................................................................
66
5 .4 CO N C L U SIO N .....................................................................................................................................
68
CHAPTER 6: PLAN FOR IMPLEMENTING TPM IN THE FUTURE PRODUCTION SYSTEM.70
6.1 METHODOLOGY FOR CREATING THE TPM IMPLEMENTATION PLAN .................................................
71
6.2 FRAMEWORK FOR IMPLEMENTING TPM ........................................................................................
71
6.3 KEY RECOMMENDATIONS FOR A SUCCESSFUL IMPLEMENTATION..................................................81
CHAPTER 7: SUMMARY AND NEXT STEPS...................................................................................83
7.1 SUMMARY AND G ENERAL APPLICABILITY ......................................................................................
83
7.2 SUGGESTIONS FOR FUTURE WORK .................................................................................................
85
R E FER EN C ES............................................................................................................................................87
APPENDIX A: TPM ENABLING FACTORS AND BARRIERS SURVEY..................91
SURVEY CATEGORY AND FACTOR AND DEFINITIONS...........................................................................91
8
List of Figures
FIGURE
1-
GENERIC ILLUSTRATION OF WING COMPONENTS.....................................................................................
FIGURE 2 - CHORDS AND WEBS BEING LOADED INTO AN AUTOMATED MACHINE FOR ASSEMBLY .............................
16
17
FIGURE 3 - CORRELATION BETWEEN WEEKLY DOWNTIME AND OVERTIME FOR ALL SPAR PRODUCTION MACHINES OVER
FO U R YE A R S ........................................................................................................................................................
29
FIGURE 4 - GRAPHIC SUMMARY OF THE DISCRETE EVENT M ODEL ..............................................................................
40
FIGURE 5 - BATHTUB CURVE DEPICTING RELIABILITY IN TERMS OF FAILURE RATE OF EQUIPMENT [19] ..................
43
FIGURE 6 - GENERIC REPRESENTATION OF THE MAINTENANCE AREA AND A PRODUCTION STAGE IN THE AUTOMATED
SU BA SSEM BLY
SPAR
P H A SE ...............................................................................................................................
44
FIGURE 7 - SYSTEM DOWNTIME VS. PM HOURS, M TBF = 32 HOURS...........................................................................47
FIGURE 8 - SYSTEM OUTPUT VS. PM HOURS, M TBF = 32 HOURS...............................................................................48
FIGURE 9 - SYSTEM OUTPUT VS. PM HOURS OVER A RANGE OF MACHINE RELIABILITY ASSUMPTIONS........................50
FIGURE 10 - NORMALIZED SYSTEM OUTPUT VS. PM HOURS OVER A RANGE OF MACHINE RELIABILITY ASSUMPTIONS 50
FIGURE
11
- SYSTEM OUTPUT VS. SYSTEM DOWNTIME, MF INDICATES THE PORTION OF EVENTS THAT ARE MAJOR
F A IL U RE S ............................................................................................................................................................
54
FIGURE 12 - MTBF AS A FUNCTION OF PM HOURS COMPLETED PER YEAR ..................................................................
56
FIGURE 13 - SYSTEM OUTPUT AS A FUNCTION OF PM AND M TBF............................................................................
57
FIGURE 14 - BAR CHART SUMMARIZING SURVEY RESPONDENTS' TPM EXPERIENCE. THE AVERAGE YEARS OF TPM
EXPERIENCE FOR THE GROUP WAS 6.4 YEARS......................................................................................................65
FIGURE
15
FIGURE
16 -
- SUMMARY OF THE TPM IMPLEMENTATION PLAN.....................................................................................72
EXAMPLE OF A TPM SUPPORT STRUCTURE ORGANIZATIONAL CHART .....................................................
FIGURE 17 - SUMMARY OF THE TPM IMPROVEMENT CYCLE ADAPTED FROM [10].......................................................79
List of Tables
TABLE
9
1 - TPM OUTCOMES
AND BENEFITS (ADAPTED FROM TPM: THE WESTERN WAY [10])................26
74
TABLE 2 - SUMMARY OF TPM BENEFITS DOCUMENTED IN CASE STUDIES ....................................................................
27
TABLE 3 - PARAMETERS USED TO DEFINE RELATIONSHIP BETWEEN PM AND MTBF...................................................
56
TABLE 4 - SURVEY CATEGORIES AND FACTORS .........................................................................................................
64
TABLE 5 - ORGANIZATION AND JOB FUNCTION OF THE RESPONDENTS .....................................................................
66
TABLE 6 - SUMMARY OF TOP FIVE MOST ENABLING FACTORS FOR TPM IMPLEMENTATION AT BOEING ......................
66
TABLE 7 - SUMMARY OF TOP FIVE LARGEST BARRIERS TO TPM IMPLEMENTATION AT BOEING ..................................
67
TABLE 8 - SUMMARY OF TOP FIVE MOST IMPORTANT FACTORS FOR TPM IMPLEMENTATION AT BOEING...................67
10
Chapter 1: Introduction
1.1 Project Background and Motivation
A Boeing team at the 737 factory in Renton, WA is designing a new automated production
system for the 737 front and rear spar. The new production system will leverage automated
technology in order to reduce the footprint of the spar shop, increase capacity, increase quality,
reduce injuries, reduce build flow days, and reduce unit costs. The research presented in this
thesis is focused on determining how to best implement a manufacturing approach called Total
Productive Maintenance (TPM), with the intention being to leverage this research to implement
TPM when the time comes to install and ramp up the new production system.
TPM is a comprehensive, life cycle approach, to maintaining and operating equipment that seeks
to eliminate equipment failures, quality defects, and productivity losses. The approach requires
the entire organization to engage in a strategic effort to continuously improve and prevent
degradation of equipment. To achieve these results TPM leverages several tools and routines
such as regular preventative maintenance, daily maintenance and inspection performed by
operators, and team problems solving sessions. Motivating the entire TPM effort is the idea that
if maximum equipment effectiveness is achieved, then manufacturing costs are reduced and the
business is more competitive.
The design of the future automated spar production system makes extensive use of automated
equipment and lean design principles. Therefore, in order to achieve the aggressive production
rates of the 737 program, the automated equipment must operate with very high reliability,
output quality and production efficiency. To ensure the production system can meet these
expectations, the design team identified TPM as a strategy that could enable the system's
automated equipment to operate at peak effectiveness. In summary, the design team sees TPM as
an important strategy for achieving a successful production system.
1.2 Problem Statement
Although the design team saw TPM as a potential strategy for achieving manufacturing success
and mitigating risk, the justification for TPM implementation was not clear, the potential impact
of TPM on the system productivity was not understood, the barriers and enablers for TPM
implementation were not well understood, and an effective implementation method was not
11
defined. TPM is a complex, long-term strategy that requires significant resources and
commitment to successfully implement. Without a clear understanding of the above factors, odds
of successfully establishing and sustaining an effective TPM program are low. This is because:
" Without clear justification it is difficult to get buy-in and support for TPM
implementation from key stakeholders and organizations.
" Without understanding the impact of TPM on the system productivity it is difficult for the
manufacturing manager, leadership, and manufacturing organization to be motivated to
support TPM and commit resources to it.
" Without understanding the enabling factors and barriers for TPM implementation, a TPM
implementation team will have no idea why their implementation is failing and they will
have no guidance for creating a structured and effective implementation plan.
" Without an implementation plan a TPM program is unlikely to successful since TPM is a
complex and long-term strategy requiring commitment from many stakeholders and a
staged, gradual implementation.
Furthermore, it is not a given that TPM is actually the correct strategy for achieving
manufacturing success for the new production system. TPM requires significant resources to
implement, and whether it is worthwhile to invest these resources depends on the design of the
production system, its baseline status prior to implementing TPM, the expected benefits of TPM,
and how TPM could impact system output. Therefore investigating the justification for TPM and
its impacts on system productivity are key first steps that should be taken prior to investigating
how to best implement TPM.
The objective of this thesis therefore, is to clarify the justification for TPM, understand how
TPM could impact the production system, identify these barriers and enablers, and present a
TPM implementation plan that will be appropriate and effective for the particular context in
which it will be executed. The research questions that will be addressed are the following:
*
What is the justification for TPM implantation in the new spar production system?
" How will TPM impact the productivity of the new production system?
" Why has TPM implementation succeeded or failed at Boeing in the past?
12
*
What kind of framework should be used to successfully implement TPM in the future
spar production system?
The case supporting the implementation of TPM will be made through a literature review, case
studies within the current factory, analysis of interview data, and analysis of current maintenance
data and practices. The impact of TPM on production system reliability and output will be
explored using the results of a discrete event model. The key barriers and enablers for TPM
implementation at Boeing will be understood from a survey on this topic that was conducted on
TPM experts within Boeing. Finally, a framework for a step-by-step implementation plan will be
presented which draws on the key insights gained from the analyses described above.
1.3 Thesis Overview
This thesis is segregated into the following chapters, the contents of each is described briefly.
Chapter 2: Background Information - This chapter provides the background necessary to
understand the context in which this research took place. In this chapter a brief overview of the
airplane manufacturing industry will be presented, the current spar assembly process will be
described, and the key aspects of the new automated spar production system will be reviewed.
Chapter 3: The Case for TPM - This chapter presents evidence and analysis that justifies the
implementation of TPM within the new spar production system in Renton. The evidence and
analysis presented in this chapter will include TPM benefits cited in literature and external cases
studies, analysis of maintenance data and practices from the current spar shop, an organizational
analysis of the spar shop maintenance and manufacturing groups, and a qualitative analysis of
the future spar shop.
Chapter 4: Discrete Event Model of the Automated Spar Production System with TPM - The
research question addressed in this chapter is: How will TPM impact the reliability and
productivity of the new production system? The impact of preventative maintenance and failure
rates of the automated equipment on the output of the system are explored using a discrete event
model. The results of this model are used to answer the research question and provide the "pilot"
results needed to gain support and commitment to TPM prior to implementation.
13
Chapter 5: Barriers and Enablers for TPM Implementation at Boeing - This chapter describes the
methodology used to determine the key barriers and enablers for TPM implementation that exist
at Boeing. It describes the key barriers and enabling factors which are used later in the thesis to
guide the design and content of the TPM Implementation Plan.
Chapter 6: Plan for Implementing TPM in the Future Production System - In this chapter a
framework for implementing TPM at the Boeing Renton plant is presented. The key question
addressed in this chapter is: What kind of framework should be used to successfully implement
TPM in the future spar production system? Insights from the prior chapters are used to guide the
structure and content of the implementation plan.
Chapter 7: Summary and Next Steps - This chapter summarizes the key findings of this thesis,
discusses how these findings could be generally applicable within the airline industry, and
provides suggestions for follow-on work.
14
Chapter 2: Background Information
This chapter provides the background necessary to understand the context in which this research
took place. In this chapter a brief overview of the airplane manufacturing industry will be
presented, the current spar assembly process will be described, and the key aspects of the new
automated spar production system will be reviewed.
2.1 Industry Overview
Boeing and is a leading aerospace and defense company, with over 160,000 employees in over
65 countries. In 2014, revenues were $90.7B, representing steady growth from $86.6B in 2013
and $81.6B in 2012 [3]. Boeing is split into five primary business units: Boeing Commercial
Airplanes (BCA), Boeing Defense, Space & Security (BDS), Boeing Capital, Engineering,
Operations & Technology (EOT), and Boeing Shared Services Group. BCA is the commercial
aviation division of Boeing and is responsible developing and manufacturing five commercial jet
programs: 737, 747, 767, 777, and 787.
A few large companies, namely Boeing, Airbus, Bombardier, and Embraer, dominate the
commercial airplane manufacturing industry and compete aggressively for market share [3].
Within the large narrowbodyl segment of this industry, Boeing and Airbus are the two dominant
companies and currently split this market roughly 50-50. In 2014 Boeing delivered 485 of its
single-aisle 737, roughly matching Airbus's 490 deliveries of its competing A320 [4].
Furthermore, Boeing has 4,284 unfilled orders for the 737 [5] compared to Airbus's 5,085
unfilled orders for its single aisle airplanes [6]. In addition to competitive pressures from Airbus,
BCA may also faces future competition from new entrants such as Comac and United Aircraft
Corporation. In order to enhance their competitiveness in the large narrowbody market, Boeing
increased its 737 production rate to 42 airplanes per month in 2014 [1] and plans to reach 47 per
month in 2017 and 52 per month in 2018 [2]. These rate increases will help Boeing fill their
order backlog and compete for market share in the narrowbody market.
IRoughly defined
8,000km.
15
as single aisle jet airplanes with seating capacities of
110 to 240 people and ranges of 6,000km to
One strategy that Boeing is pursuing to meet these rate increases while reducing manufacturing
costs and achieving new standards for employee safety and first-time quality is to implement
more automation in their aircraft production systems [3]. To this end, BCA conducts in-house
automation research and partners with external equipment integrators to develop, construct and
install production-ready automated manufacturing systems. One such system is being developed
for the 737 spar production shop and is described in a section below. However, before describing
the automated spar shop, an overview of the current spar assembly process is appropriate.
2.2 Current 737 Spar Assembly at Renton
Spars are supportive beams running the length of the wing structure. The 737 wing contains two
spars, a front and a rear. Both 737 spars are produced in the same factory area, or shop, in the
Renton factory. In addition to the front and rear spars, the spar shop also manufactures the
leading edge slats which are aerodynamic surfaces on the leading edge of the wings. These slats
in combination with trailing edge flaps, modify the lift and drag characteristics of the wing. See
Figure 1 for a general illustration of how these components are integrated into a wing.
Leading
sun
Front spr
anelRow
spr
strnersWo
Figure 1 - Generic illustration of wing components2
The spar shop is managed by a general manager and is organized into production areas that
coincide with the major subassemblies and work statements for producing finished rear spars and
front spars with attached leading edges and strakelets 3 . Each shift in these areas is managed by a
2
Source: http://www.nomenclaturo.com/tag/airplane-wing
3 Strakelets are structures that hold landing and taxi lights to the wings of airplanes [28]
16
separate area manager. For proprietary reasons, the precise layout, order of operations, and
details of each shop cannot be presented nor can the overall production sequence within the spar
shop. However, to provide the necessary context to understand the automation project taking
place in the spar shop, a broad description of the major production areas is presented below.
" Spar Assembly Area - Machines join webs, chords and terminal fittings to produce the
main subassemblies that becomes the front and rear spars (see Figure 2).
* Drilling and Filling Area - Holes are drilled in the main spar subassemblies and various
brackets, fixtures, and fittings are attached.
*
Strakelet Area - Strakelets subassemblies are built
" Leading Edge Area - The leading edge is built onto the front spar, this entails attaching
brackets and an aluminum skin to the front spar. The strakelet is also attached to the spar
in this area.
Sealing Area - Front and rear spars are sealed to protect against water intrusion and fuel
*
leaks.
Web
4
Figure 2 - Chords and webs being loaded into an automated machine for assembly
Maintenance for the tools and equipment in these shops is handled by the maintenance
organization which is a shared resource. There is one maintenance group that focuses on
servicing the machines in the Spar Assembly Area and another maintenance group that services
4 Source:
17
http://aerospace.firetrench.com/2011/10/boeing-737-program-begins-production-at-higher-rate/
the other equipment and tools used in the spar shop. However, neither of these groups is fully
committed to the spar shop, maintenance is a shared service so the maintenance mechanics and
technicians also provide service to other shops in the Renton factory.
In order to meet the new production rates Boeing would like to reduce the amount of time
required to manufacture the four spars (known as a spar set) required for a wing set. This
requires that the takt time of these areas decrease or the order and timing of the material flow
through the areas change so that flow days can be cut out of the process by completing steps in
parallel. While, the spar shop does currently contain some automated equipment, the overall
production process does have a significant amount of manual drilling, assembly, and sealing
operations. Eliminating certain manual operations through automation is a strategy for reducing
injury, cost, and increasing throughput. Increased automation may change the amount and type
of maintenance service that the spar shop requires. Finally, reducing the physical footprint of the
spar shop is desired since this will open up factory floor space for new projects and/or more
optimal material flows.
2.3 Automation of 737 Spar Assembly at Renton
As described in Section 2.1 Industry Overview, automating the production system is a strategy
Boeing is pursuing to meet higher production rates, reduce manufacturing costs, reduce injuries,
and improve quality. An opportunity to increase the level of automation and achieve these goals
was identified in the 737 spar shop and a Boeing design team has been working with an external
equipment integrator to scope, design and build the next generation spar production system.
While the final design of entire the production system is not yet determined, certain phases of the
design are. For proprietary reasons, the new production system cannot be presented in detail.
However, broad descriptions of the major phases, proposed and expected, are presented below.
*
Automated Spar Subassembly Phase - This phase of the automation project will replace
the current spar assembly machines from the Spar Assembly Area, with machines that
incorporate more advanced technology. The new machines will have a faster takt time,
occupy a smaller footprint, and may include technology to ensure higher first-pass
quality.
18
*
Leading Edge Phase - Designs are being explored to automate the build of the leading
edge slats. The 737 has four slats installed on the leading edge of each wing [7], and a
proposal is being explored which would use automated equipment to build each of these
slats on a parallel line and then join them to the front spar at a later stage in the
production process. This phase could reduce injuries related to current leading edge build
process, eliminate flow days from the overall build process, and reduce quality defects.
*
Automated Spar Sealing Phase - Designs are being explored to automate the sealing
processes for the front and rear spars. Instead of completing sealing manually, robots
would be used to complete most or all of the spar seals.
The entire new spar production system is expected to occupy a faction of the space that the
current spar show occupies. Furthermore, the material flow through the new spar shop is
expected to follow a more efficient path, there will be fewer intermediate buffers, and the takt
time at each position in the new system will be shorter than the current shop. In summary, the
new production system is expected to incorporate more lean production principles into its design
than the current system. The increased "leanness" of this production systems is one of the
motivating factors behind exploring TPM for equipment operation and maintenance practices.
Because the system is leaner, reduced productivity from automated equipment, equipment
failure, or poor quality could significantly impact the throughput of the system. Therefore TPM
is an attractive manufacturing and maintenance approach since its focus is to achieve 100%
equipment productivity, availability, and quality.
While TPM could be implemented in all of three of the phases presented above, the focus of this
thesis is on the Automated Spar Subassembly Phase. The analysis of the current spar shop
presented in Chapter 3, focuses on the Spar Assembly Area since the work done in area is most
similar to that of the Automated Spar Subassembly Phase. The discrete event model presented in
Chapter 4 is also specifically based on the Automated Spar Subassembly Phase. Finally, the
TPM implementation plan presented in Chapter 6 was designed to be implemented on this part of
the future production system.
19
2.4 Total Productive Maintenance Overview
TPM is a comprehensive, life cycle approach, to maintaining and operating equipment that seeks
to eliminate equipment failures, quality defects, and productivity losses (i.e. TPM seeks to
achieve maximum equipment effectiveness). The approach involves everyone in the
organization-from top level managers to maintenance workers, support groups, and operatorsin a strategic effort to continuously improve and prevent degradation of equipment. To achieve
these results TPM leverages several tools and routines such as regular preventative maintenance,
daily maintenance and inspection performed by operators, and team problems solving sessions.
But the underlying philosophy of TPM is this: if equipment degradation is prevented through
continuous improvement efforts and effective inspection and maintenance carried out by small,
motivated, cross-functional teams, then the equipment will not break down, create defects, or run
below its rated speed. Pursuing this philosophy requires strong management support, training,
and continuous use of small group activities to achieve incremental improvements. Motivating
the entire TPM effort is the idea that if maximum equipment effectiveness is achieved then
manufacturing costs will be reduced and the business will be more competitive. TPM also has
the espoused added benefits of creating a learning and problem solving organization and
.
improving morale and teamwork 5
Total productive maintenance (TPM) began in Japan at the Nippon Denso Company, part of the
Toyota group, in 1971. TPM is considered an evolution of preventive maintenance, originally
conceived in the United States in the 1950s [8]. Seiichi Nakajima is considered by many to be
the father of TPM who pioneered the approach and spread its influence in Japan from the late
1970s. Nakajima taught that TPM rested on five essential pillars [9]:
1. Adopt small group improvement activities to increase the overall equipment effectiveness
(OEE) by attacking six big losses (breakdowns, setup and adjustment losses, scraps and
rework, startup losses, idling and minor stoppages, and reduced speed).
2. Improve existing planned and predictive maintenance systems.
5 For more on the benefits of TPM see Section 3.1 Benefits of TPM Documented in Literature and Case Studies
20
3. Establish a level of self-maintenance and cleaning carried out by highly trained operators.
This is known as "Autonomous Maintenance".
4. Increase the skills and motivation of operators and engineers by individual and group
development.
5. Initiate maintenance prevention techniques including improved design and procurement.
The underlying principle behind these pillars is to put in place routines and organizational
structures which continually prevent equipment degradation and continually improve equipment
design and maintenance practices in order to achieve and sustain flawless operation. TPM
emphasizes that all the assets on which production depends are kept in optimum condition and
available for maximum output [10], and it measures progress towards this goal by tracking a
metric known as overall equipment effectiveness (OEE).
OEE is a metric that measures the extent to which a piece of equipment is producing output at its
maximum theoretical limit. It calculated by multiplying equipment availability, quality rate, and
performance efficiency. Availability is the ratio of equipment operation time to scheduled
operation time, it is decreased by equipment downtime. Quality rate is the ratio of good parts
produced to total parts produced, it is decreased by quality defects. Performance efficiency is the
ratio of actual production rate to target production rate, it is decreased by operating equipment at
a rate lower than the target rate. OEE is a useful metric for measuring the overall performance of
equipment and understanding the efficacy of the TPM program. Furthermore, because OEE is
composed of three distinct components, it can provide some guidance as to what should be
improved. However, it does not provide enough information to determine why equipment may
be performing at a particular level.
There are many sources that will provide an in-depth description of the components that make up
TPM. Therefore, if the reader would like a detailed review of TPM concepts there are numerous
sources to refer to, but for the purposes of this thesis only a high level overview of TPM
concepts is required. Therefore, I have followed the methodology from a prior thesis on this
topic [11] and grouped the main TPM concepts into three groupings: Autonomous Maintenance,
Planned Maintenance, and Maintenance Reduction.
21
Autonomous Maintenance
The central idea in this concept is to use equipment operators to perform routine daily inspection,
cleaning, lubrication and minor maintenance activities. These routines are typically developed
and standardized cooperatively by operators and maintenance staff. The maintenance group
trains the operators in the correct procedures to inspect, clean, lubricate, and make minor
adjustments. Since operators are familiar with the equipment and also interact with it daily, they
are in the best position to carry out these types of minor maintenance tasks and notice anomalies
which require more technical attention from maintenance professionals. In this way operators are
transformed into carefully tuned "eyes and ears" which assist the maintenance group perform
better. Operators may also be tasked with collecting daily information on the health and
performance of their equipment. Through autonomous maintenance operators also learn more
about the equipment, develop more advanced skills, and develop a sense of ownership over their
equipment. By shifting these daily tasks to operators not only is the condition of the equipment
improved, but ideally, maintenance will have more time to focus on planned maintenance,
equipment analysis, and equipment design activities.
Planned Maintenance (PM)
The central idea in this concept is to repair equipment and replace components before the
equipment breaks down. This requires that maintenance coordinate their PM activities with
production schedules to accommodate planned equipment downtime. The theory behind why this
type of maintenance is preferred is that as planned maintenance increases, unplanned breakdown
maintenance decreases, and overall maintenance costs and production disruptions are reduced. In
TPM, planned maintenance routines are well documented and analyzed using maintenance
records and equipment data so they can be improved.
Maintenance Reduction
The main idea of this concept is to reduce the overall amount of maintenance that is required.
This is accomplished through three main strategies: equipment design, predictive maintenance,
and small group improvement activities. In TPM, small cross-functional groups are frequently
used to identify and execute process or equipment improvement ideas. Maintenance staff and
operators form teams with necessary support group staff. These teams utilize their expertise
22
along with equipment performance and maintenance data to identify projects that will improve
equipment performance and reduce required maintenance. Predictive maintenance utilizes
sensors and advance analysis tools to collect data that can be used to predict equipment failure.
Tools such as thermography, ultrasound, vibration analysis, allow technicians to detect early
warning signs and fatigue points so they can make targeted repairs before a catastrophic failure
occurs. Preventative maintenance goes beyond planned maintenance because it is based on the
actual equipment condition rather than a static schedule and as a result it reduces maintenance
burden. Finally, equipment design reduces maintenance requirements in the future because the
knowledge gained from maintaining equipment is incorporated into the next generation of
equipment designs.
Despite its name, TPM is meant to be a manufacturing lead initiative. TPM represents a new
attitude towards maintenance; in TPM, maintenance isn't a cost center but rather it is recognized
as a valuable resource and a necessary part of the production system. This is because TPM plays
a role in making the business more profitable and the manufacturing system more competitive by
continuously improving the capability of the equipment, as well as making the practice of
maintenance more efficient [11].
To gain the full benefits of TPM it must be applied in the proper situations and therefore prior to
implementation one must consider whether TPM makes sense for a particular manufacturing
system. For continuous flow manufacturing systems with little, to no, buffers TPM will likely
provide significant benefits. A manufacturing system that extensively leverages automated
equipment will also likely benefit from TPM. If a production system is plagued with high
amounts of unplanned downtimes or quality defects and limited data collection, then TPM
benefits could be very significant.
TPM is not likely to have large benefits for production systems that are composed off small
workshops where most of the work is manual. Additionally, if a piece of automated equipment
does not represent the bottleneck in a production system and there is ample time to repair the
equipment before affecting the rest of the production system, then TPM may not be justified
since increasing reliability will not translate into economic gains. TPM is a long-term
commitment and requires significant time and effort from many people to implement. Therefore,
23
for the manufacturing systems described above it might be more fruitful to identify and eliminate
process bottlenecks, improve worker training, eliminate sources of variability related to critical
components, or implement other lean principles such as streamlining product flow.
To determine if a production system is appropriate for TPM the nature of the production system
must be considered (i.e. extent of automation, existence of buffers, flow of product through the
system, etc.) and data on the current state of the system must be collected and analyzed. If a
production system has a design that is appropriate for TPM but the reliability, output quality, and
production efficiency of that system is already high then TPM may not be required. Therefore,
the next chapter analyzes the current state of the Spar Assembly Area, the manufacturing and
maintenance organizations, and the future Automated Spar Subassembly Phase in order to
determine if TPM is an appropriate strategy for the new automated spar production system.
24
Chapter 3: The Case for TPM
The new spar production system design team identified TPM as a promising strategy to achieve
manufacturing success, but detailed and rigorous analysis to determine if TPM is truly an
appropriate strategy was not carried out. Such an analysis is important because, as described in
the section above, the benefits TPM will yield are dependent on particular details of the
production system in which it is implemented. Furthermore, TPM requires significant time,
resources and effort to implement, so it is important to ensure that the potential benefits from
implementation justify the costs of implementation. Therefore, this chapter will present analysis
that explores the justification of TPM implementation on the future Automated Spar
Subassembly Phase.
A second reason it is important to explore the justification of TPM has to do with the way that
people and organizations accept and adopt new ideas and processes. The Stages of Commitment
framework developed by Daryl Conner and Robert Patterson [12], describes how people within
organizations accept and adopt a new idea or organizational change such as TPM. It is composed
of three phases, the "Preparation Phase", the "Acceptance Phase", and the "Commitment Phase".
As people move from one phase to another they pass through "thresholds". For example, during
the Acceptance Phase people must truly begin to understand the change and develop a positive
perception of the change by weighing the costs and benefits in order to break through the
"Action Threshold" and into the Commitment Phase. Therefore, in order to move through the
stages of commitment and embrace TPM implementation, people have to not only understand
what TPM is, but they must understand the benefits and justification for TPM. This model of
organizational change suggests that without this clear justification it will be difficult to get buyin and material support for TPM implementation from key stakeholders and organizations.
Since it is not clear if TPM is an appropriate strategy for achieving manufacturing success in the
new production system and since buy-in is a pre-requisite for TPM implementation, the research
question I seek to address in this chapter is:
*
What is the justification for TPM implantation in the new spar production system?
This chapter presents evidence and analysis that justifies the implementation of TPM within the
new spar production system in Renton. The evidence and analysis will include TPM benefits
25
cited in literature and external cases studies, analysis of maintenance data and practices from the
current spar shop, an organizational analysis of the spar shop maintenance and manufacturing
groups, and a qualitative analysis of the future spar shop. A quantitative analysis of the future
spar shop will follow in the next chapter.
3.1 Benefits of TPM Documented in Literature and Case Studies
I.P.S. Ahuja has written extensively about TPM, and specifically has written extensively about
the benefits of implementing TPM. In a book chapter in the Handbook of Maintenance
Management and Engineering [13] he summarizes the benefits of TPM in modem
manufacturing. He explains that "TPM harnesses participation of all the employees to improve
production equipment availability, performance, quality, reliability, and safety". He couches
these TPM benefits within the context of the changing needs of modem manufacturing and
increased global competition to emphasize that TPM is needed now more than ever. In addition
to the benefits cited above, he also enumerates other benefits such as improving work culture and
mindset, improving productivity, realizing flexibility requirements, more effective use of human
resources, and improving human resource development. Analysis by McKone, Schroeder, & Cua
[14] indicate that TPM has a strong positive impact on multiple dimensions of manufacturing
performance such as low cost inventory positions, high internal quality, and responsive delivery.
Table 1 summarises the outcomes that TPM targets and the tangible and intangible benefits that
result from those outcomes.
TPM Outcomes
Machines run close to name-plate capacity
Ideas to improve are proposed by operators
Breakdowns rare, flawless operation is
achieved
Machines are adapted to the needs by our
people
Operators and maintainers solve problems
independently
Cleanliness and pride in continuous
improvement
Increased output potential from existing
equipment.
Benefits of the Outcome
Reduced capital expenditure
Sense of ownership and success is promoted.
Morale improves.
Output and quality are maximized. Rare
breakdowns become learning opportunities.
The machines improve beyond "like new"
condition over time
Fewer delays and stoppages. Enhanced selfesteem and morale.
Good and safe physical and psychological
working environment.
Increased profits, increased manufacturing
flexibility, reduced WIP requirements.
Table 1 - TPM Outcomes and Benefits (adapted from TPM: The Western Way [101)
26
Case studies such as the one conducted by Chan et al. [15] document both tangible benefits of
TPM such as a reduction in machine stoppages and increased training hours for skill
enhancement, as well as intangible benefits like improved morale and teamwork. While the
benefits cited in case studies such as these vary by site, implementation and the initial baseline of
the plant, the results are often positive and impressive. Table 2 summarizes the range of benefits
documented in multiple case studies. The data for the table below come from review articles by
Ahuja & Khamba [16] and McKone, Schroeder, & Cua [14].
Benefits Realized from TPM
Overall Equipment Effectiveness
Breakdowns
Equipment Availability
Equipment Productivity
Process Defects
Equipment Capacity
Maintenance Costs
Production Output
Labor Productivity
Inventory Reduction
Accidents
Employee Suggestions
% Improvement
14-45% Increase
50-90% Reduction
50% Increase
40-50% Increase
65-90% Reduction
25-40% Increase
15-60% Reduction
22-50% Increase
40-50% Increase
45-58% Reduction
90-98% Reduction
32-65% Increase
Table 2 - Summary of TPM benefits documented in case studies
If the benefits cited in TPM literature and case studies could be expected to materialize in the
future 737 spar production shop upon successful implementation of TPM, then there is a strong
justification to support TPM. In order to gain a better understanding of whether opportunities for
these types of benefits will actually exist, an analysis of the current baseline condition of the spar
shop is necessary. Since the new spar production system has not been constructed or installed,
baseline analysis was carried out on the current spar shop. Although these analyses won't
directly prove that TPM benefit opportunities will exist in the future production system, they can
at least provide some indication as to whether the benefits opportunities will exist.
3.2 Analysis of Current State Spar Shop Performance
Understanding the current state of maintenance and machine performance in the 737 spar shop
can illuminate real opportunities where TPM could expect to result in benefits for the future spar
shop. Therefore, maintenance, machine, and manufacturing data in the current spar shop was
analyzed to determine if opportunities for improvement exist. Any improvement opportunities
27
that exist in the current shop could suggest TPM benefit opportunities for the future spar
production system and thus bolster the case for TPM implementation.
Correlating Machine Downtime with Overtime
In order to determine if TPM had the potential to reduce manufacturing costs in the spar shop, an
analysis to determine the correlation between spar production machine downtime and overtime
was completed. If a strong correlation exists, a case can be made that TPM could reduce
overtime costs since TPM is focused on reducing equipment downtime. This would provide
strong justification for TPM in the 737 spar shop since according to a manufacturing manager
from that shop, "overtime is a driver and motivator of behavior. [Overtime] metrics are
consistently tracked and discussed at daily and weekly meetings. [Overtime] considerations
influence management decisions."
Hourly downtime data for all of the current spar production machines from September 2011 to
August 2014 was collected and aggregated by week. Overtime data for the spar production
machine shop during that same period was also collected and aggregated by week. A graph of
that data along with the line of best fit is shown in Figure 3. The linear regression analysis shows
that indeed equipment downtime is positively correlated to shop overtime. While the correlation
is statistically significant (significance F value of 5E-6), and the magnitude of the correlation
coefficient is relatively large (each hour of downtime correlates to an increase of 0.3% overtime),
the adjusted R2 value indicates that downtime only accounts for about 12.5% of the variation
observed in weekly overtime (adjusted R2 value equals 0.125). This analysis suggests that there
are other important factors, besides equipment downtime, that are important for predicting and
controlling shop overtime. The fact that there are weeks with no overtime despite experiencing
over 30 hours of collective equipment downtime shows that there are other factors which stack
on top of equipment downtimes to result in overtime. Interviews with manufacturing managers
and industrial engineers on site confirm that there are indeed other factors not related to machine
downtime which can result in overtime such as absenteeism, and late or defective parts. Finally,
the decision to schedule overtime is ultimately a management decision and is therefore subject to
inherent variability of human judgment.
28
45%
35%
-
30%
-
40%
25%
,*
,
9
020%
15%
+
*
%
*Preccted
596
+*Overtime
+
0%
0
10
20
30
40
50
60
70
80
90
WeeWly Downtime of all Spar Machines (hr)
Figure 3 - Correlation between weekly downtime and overtime for all spar production machines over four
years.
This analysis supports the general idea that minimizing equipment downtime, perhaps with TPM,
is an important strategy for controlling overtime costs. It also provides a rough order correlation
coefficient that could be used to develop an initial cost-benefit analysis of TPM on the basis of
overtime reduction. However, since this analysis also shows that equipment downtime alone is a
weak predictor of shop overtime, strictly reducing future equipment downtime through TPM
may not necessarily result in low overtime unless the other sources of overtime are determined
and also controlled. Furthermore, this analysis was completed from data on the current spar
production shop and it may be a poor assumption to think that this relationship will hold for the
future production system as well. Finally, if the future spar production shop machines are
inherently reliable, then there will be little room for TPM to yield overtime savings regardless of
how strong the correlation between reliability and overtime actually is.
Despite these limitations, this analysis could be used to connect the ideas of reliability with
overtime costs in manufacturing manager's heads. Changing mental models like this can be a
powerful way to change behavior and get manufacturing to care more about reliability and TPM.
Although this analysis was carried out on data from the current spar shop, it still represents the
most reasonable assumption at how strong the correlation between downtime and overtime could
be for the new production system. In fact, the sensitivity of overtime to machine reliability in the
future spar shop could be higher as a result of planned production rate increases. If this is the
case the value of TPM for maintaining high reliability could be significant.
29
Analysis of Planned to Unplanned Maintenance
It is often cited in maintenance literature that planned maintenance is significantly cheaper than
unplanned maintenance. Therefore, TPM programs typically aim to achieve a planned to
unplanned maintenance ratio of 80% to 20%. In order to determine if there is an opportunity for
TPM to improve this ratio in the spar production shop three years of data from the maintenance
database was analyzed.
This analysis showed that the average ratio of planned to unplanned maintenance was 56% to
44% for all the spar production machines between the years 2011 and 2013. If it is accepted that
a ratio closer to 80/20 will result in lower maintenance costs and if it is assumed that a ratio
similar to the current ratio will exist for the new spar production system, then TPM could yield
tangible benefits for the future production system. Determining the ideal ratio of reactive and
planned maintenance would require a detailed analysis, but it is not unreasonable to accept that
generally a higher percentage of planned maintenance is preferred over unplanned. Finally, to the
extent that the current ratio of planned to unplanned maintenance is a result of the norms and
processes of the maintenance organization, it is not unreasonable to assume that a similar ratio
could evolve for the new spar production system. Therefore, this analysis indicates that TPM
could benefit the future production system and supports TPM implementation.
Accuracy of Maintenance Data
Keeping accurate and accessible maintenance data can greatly improve the daily performance of
a manufacturing system. This data can be used to optimize maintenance activities and
maintenance costs and help ensure that the equipment upon which production relies is reliable
and continuously improved. Therefore, to determine if an improvement opportunity exists an
analysis of the maintenance data in the current spar production shop was carried out. This
analysis looked at the accuracy of maintenance data recorded about equipment breakdowns.
Since TPM heavily relies on the collection and usage of accurate machine data, successfully
implementing TPM by necessity will improve data systems. Therefore, if the current spar
production shop shows evidence of inaccurate maintenance data, then justification for TPM
implementation exists on the basis of improving these systems.
30
In order to determine the accuracy of maintenance data systems the duration of downtime events
recorded in the maintenance database were compared to cycle times recorded by the machine
data collection systems. If the maintenance data systems are accurate, then the downtimes
recorded in that system should agree with the cycle time data recorded by the machines. That is,
for a given date and time of an event recorded in the maintenance database, the duration of that
event should coincide with a long cycle time or gap between operations in the machine data
collection system.
Twenty-seven downtime events recorded in the maintenance database for a single spar
production machine during March 2013 were compared to the machine data from the same
period. The data from these two databases did not agree. Ignoring two outlier downtime values
from the machine database, the average discrepancy between the two databases was about 30
minutes per event and ranged from a couple minutes to 3.2 hours. An average discrepancy of 30
minutes is significant given that a shift lasts only 8 hours. Furthermore, since this 30 minute
discrepancy per event is compounded over hundreds of events, a large amount of systemic error
likely exists in the maintenance database. The error likely exists in the maintenance database
since the machine database is recorded automatically and the values entered in the maintenance
database are manually entered by maintenance personnel.
The machine database required a significant time-intensive of manual manipulation to crosscheck it against the maintenance database. Therefore, time constraints prevented the analysis of a
larger sample of data. However, there is strong reason to believe the conclusions drawn from
even this small sample of data. Firstly, the belief that the downtime values recorded in the
maintenance database do not accurately reflect the actual downtime of the assets was held by
multiple manufacturing managers, industrial engineers, and equipment engineers interviewed.
Secondly, the process used to record this data is prone to error since it is manual and relies on the
maintenance personnel filling out a job report after-the-fact and remembering precisely how
much time the machine was down for. Thirdly, it was not uncommon to find downtime records
in the maintenance database for which no duration was recorded. Finally, it is likely that the
downtime perceived by maintenance staff is not the same as the actual downtime of the system.
For example, when a machine goes down the operator may try to fix the problem for a couple of
minutes before calling maintenance dispatch. Then maintenance staff will take time to travel to
31
the asset before diagnosing and fixing the problem. At that point the maintenance person may
travel back to their shop to record the job. Meanwhile the operator of the machine may have to
run re-referencing and coupon routines to ensure the machine is properly calibrated before
resuming work on the spar. The maintenance staff may only consider the time from when she
began working on the machine to when she finished as the downtime, but in reality there is a
significant amount of additional downtime that the maintenance person might not or could not
know about.
The importance of accurate data collection to a TPM program means that if TPM is properly and
successfully implemented the accuracy of downtime data, in addition to productivity and quality
data, will certainly be improved. TPM will not only necessitate accurate data collection, but it
will require effective processes for storing, analyzing, and making data regularly available. Since
the analysis above shows that downtime data collected in the spar shop is not accurate, there is
an opportunity for TPM to improve maintenance data systems.
Summary of Current State Analyses
The analysis completed on the current state of the spar production system suggests that there
may be some opportunities for TPM to help control overtime costs, improve the planned to
reactive maintenance ratio, and improve the accuracy, accessibility and use of machine
availability, quality, and productivity data. In weighing how this analysis bolsters the case for
TPM, it must be taken into consideration that the tangible opportunities identified in this section
are based on an analysis of the current production system and not the future spar production
system. Never the less, the analyses in this section provides the best indication possible at this
time that there is a reasonable likelihood that TPM can lead to tangible benefits for the future
production system.
In this section the potential benefits of TPM were not monetized, the analysis in this section
sought only to determine if there are reasonable indications that opportunities for benefits exist.
Therefore, future work could include quantifying or estimating benefits more rigorously. For
example, the correlation between downtimes and overtimes could be used to calculate rough
order of magnitude cost savings for improved reliability. Another opportunity for future work
could be to monetize costs savings associated with increasing the ratio of planned maintenance to
32
unplanned maintenance. These analyses could be carried out by Boeing employees since
publishing these analyses would include disclosing sensitive information around labor and
overtime rates. In contrast to the tangible benefits of TPM analyzed in this section, in the next
section the intangible benefits and organizational issues that justify TPM implementation will be
explored.
3.3 Organizational Analysis
Extensive observations of the manufacturing and maintenance groups within the current spar
shop revealed opportunities for intangible benefits that TPM could provide. As explained below,
two promising opportunities for providing intangible benefits are related to improving morale
and improving teamwork.
Improving Morale
Observations of the manufacturing and maintenance personnel in the spar shop indicated that
morale can be improved. For example, during Employee Involvement 6 (EI) meetings there were
seldom indications of vibrancy or interest from the group of 15 to 20 operators and mechanics
that were in attendance. These meetings were supposed to mobilize front-line workers to take on
continuous improvement projects, but instead of fruitful ideation and excitement, meetings
involved a group of people sitting in a conference room waiting for the meeting to end so they
could continue with their day. In another instance, the Renton TPM focal was organizing an
autonomous maintenance workshop (AMW) and was having a difficult time trying to recruit
maintenance personnel, mechanics, and operators to participate. The AMW is an opportunity for
people to clean and inspect the machine, identify problems, and implement process improvement
ideas. Within a shop with a high degree of pride and morale, finding people to participate in such
an event would have been easy. However, this was not the case, and multiple people responded
that they would participate only if their managers required them to. A final example low morale
came from an interview with a manufacturing manager from the spar shop. During the interview
6
The Employee Involvement program (and associated meetings) is a continuous improvement initiative in which
front-line workers form small teams that are supposed to identify improvement opportunities and implement
solutions. Improvement opportunities are generally related to processes, equipment, or safety.
33
he expressed frustration with his workers because they were not buying into a significant
production scheduling change he helped implement and stated that this change was actually
causing a major rift to develop between him and his shop workers.
The costly results of low morale can include lower productivity, counterproductive behaviors,
high absenteeism, and a disengaged workforce. Within a new production system low morale can
be especially costly. This is because an engaged and motivated workforce is necessary to learn
the new processes and technologies of the new production system. A highly motivated workforce
with high morale will also greatly facilitate trouble-shooting during ramp-up and are more likely
to continuously improve the equipment and processes during steady-state production and when
production rate increases are necessary.
An effective TPM program can improve morale. This is because TPM explicitly places a high
value on the front-line worker and puts in place structures, training, and processes that allow
workers to increase and apply their value in their daily job functions. Additionally, TPM
improves morale because it creates opportunity for meaningful work by offering front-line
workers three key qualities: autonomy, complexity and a connection between effort and reward.
Within TPM the machine operator has a great deal of autonomy to care for their machine and
improve process. The small group problem solving teams and continuous training that TPM
emphasizes introduces a healthy degree of complexity. The opportunity to work hard to
implement a process improvement and then see the impact in the OEE metric and be rewarded
for success by management creates a strong connection between effort and reward. Finally, TPM
increases morale by improving the overall work environment by making it safer and encouraging
a higher degree of teamwork within and between organizations.
Improving Teamwork
Observations of the 737 spar shop support the idea that teamwork can be improved both within
and between the manufacturing and maintenance organizations. The working relationship
between manufacturing and maintenance can be succinctly described as "I fix the equipment;
you operate it". There were strict division of roles that were reinforced by union affiliations. EI
team involvement was an opportunity for inter-organization teamwork, but the spar shop EI team
did not include members from the maintenance organization. Within the manufacturing
34
organization, El teams were also a great opportunity for teamwork, but as described in the
section above, people were not engaged during El team meetings and there weren't any small
teams working on continuous improvement projects during the period of observation.
Improving teamwork has the potential to not only improve morale, but it can lead to more
innovation and increased productivity within the shop. A strong characteristic of TPM is that it
encourages teamwork, both within the maintenance and manufacturing organizations and
between the organizations. The main way it does this is by eliminating the attitude of "I fix, you
operate" and replacing it with the attitude that "we are all responsible for equipment condition
and operation". The way this attitude is applied in practice is through a reliance on small, crossfunctional teams for problem solving. The autonomous maintenance program is also an example
of how teamwork is built into the fabric of TPM. With autonomous maintenance operators take
on a small amount of maintenance responsibility and are expected to communicate equipment
issues with maintenance personnel and collaborate with them to complete root-cause analysis,
identify solutions and implement them. Shifting the manufacturing and maintenance strategy to a
system that implements routines which rely on teamwork could be effective in better establishing
teamwork as a central tenant of the manufacturing and maintenance organizations.
Summary
The organizational analysis of the current 737 spar production shop identified two opportunities
for TPM to yield important intangible benefits for the future spar production system.
Implementing TPM has the potential to improve the morale and teamwork. Both of these
benefits support the case for TPM implementation in the future production system since a high
level of moral and teamwork will be required to effectively operate and maintain the complex
equipment, diagnose and solve problems during ramp-up, and continuously improve the system
as production rate targets increase. In the next section I will present a qualitative analysis of the
future spar production system to further explore the caser for TPM implementation.
3.4 Qualitative Analysis of Future Spar Shop
TPM results in the biggest gains when it is applied in a production system that leverages a large
amount of automation or machines, and is designed to have continuous flow of material, just-intime supply, low amounts of buffer or WIP, and other lean features. This is because in such a
35
system the reliability, productivity, and output quality of the machines becomes crucial to
meeting production targets. In a lean system the lost production from a poorly functioning
machine is difficult, costly or impossible to recover and therefore, systems like TPM, which
ensure that equipment operates flawlessly, generally represent good value propositions. The new
spar production system leverages lean principles and will rely on automated equipment;
therefore, from a qualitative perspective, TPM seems to be well-aligned with the design of the
new production system.
The new spar production system will replace current spar production machines with higher
performing, more advanced, and more complex machinery. It may also utilize automated robots
to build the wing's leading edge and seal the spar, both of which are done manually today.
Therefore, the new spar production system not only represents a potentially large increase in the
amount of automated equipment, at the very least it will introduce faster and more complex
machinery. Given this reality, the amount and complexity of maintenance work in the future spar
shop could increase.
TPM represents can address future challenges related to the amount and complexity of
maintenance work. Firstly, since TPM focuses on pursuing preventative or predictive
maintenance over reactive maintenance, it offers a strategy to help the maintenance organization
better manage the additional maintenance work that could result from an influx of new
automated equipment. Secondly, TPM's focus on continual skill evaluation and improvement
can help both maintenance and manufacturing personnel deal with the increased complexity of
the automation.
The future spar production system is also being designed to be leaner than the current system. It
will feature a continuous flow line with less in-line buffer and a faster takt time than the current
system. While these features will result in a more efficient and productive system, they also
mean the system could be more sensitive to disruptions. Since the aim of TPM is to minimize
machine disruptions by targeting 100% reliability, 100% productivity, and 100% output quality,
TPM represents a risk mitigation strategy for the new production system. Minimizing equipment
disruptions in a future production system, which could be more sensitive to disruptions, is
36
especially important since it will likely leverage more automated equipment than the current
system.
A qualitative analysis of the future spar production system reveals that TPM is well-aligned with
the design based on its extensive use of automated equipment and lean design principles. TPM
can help to better manage the amount, cost and complexity of maintenance by emphasizing a
proactive maintenance strategy and investment in employee skill development. It can also help
mitigate production disruption risks by ensuring flawless equipment operation.
3.5 Summary and Discussion
The analysis presented in this chapter aimed at exploring the justification for TPM
implementation within the new spar production system in Renton. Understanding the potential
benefits of TPM is important since TPM requires significant time and effort to implement.
Understanding the justification for TPM is also important because, according to the Stages of
Commitment framework, clear justification is necessary to get buy-in and material support for
TPM implementation.
The justification for TPM was explored using a combination analyses which included: a
literature review of benefits and external cases studies; an analysis of maintenance data and
practices in the current spar shop; an organizational analysis of the maintenance and
manufacturing groups; and a qualitative analysis of the future spar shop.
The literature review and case studies indicated that TPM could yield many significant tangible
and intangible benefits. The analysis of maintenance data and practices of the current spar shop
showed that there may be opportunities for TPM to control overtime costs, reduce maintenance
costs (by improving the planned to reactive maintenance ratio), and improve the accuracy of
machine downtime data. However, the main limitation of these current state analyses is that they
were performed on the current production system and therefore may not hold true for the future
production system. However, they provide the best indications of opportunities for TPM benefits
possible at this time.
The organizational analysis highlighted opportunities for TPM to improve morale and teamwork
within the spar shop. Finally, the qualitative analysis of the future production system showed that
37
TPM is a well-aligned manufacturing strategy to implement because it can help manage the
amount, cost and complexity of maintenance work as well as mitigate equipment disruption risks
associated with the increased automation and lean design.
38
Chapter 4: Discrete Event Model of the Automated Spar
Production System with TPM
The previous chapter sought to understand the justification for TPM implementation in the future
production system through a series of analyses that ranged from broad (literature review of
benefits) to specific (qualitative analysis of future production system). The conclusions from that
chapter are important for providing the rationale for moving from the Acceptance Phase to the
Commitment Phase within the Stages of Commitment framework but they may not be sufficient
to move through the Commitment Phase towards full implementation. This is because the
Commitment Phase involves executing small scale pilots that allow stakeholders to experiment
with the change and understand how it will affect their organization or system. Understanding
how a change is going to impact the system in this way is an important step prior to investing
large amounts of time and resources into full implementation. Therefore, the research question
addressed in this chapter is:
*
How will TPM impact the productivity of the new production system?
The answer to this question is important for gaining full commitment prior to executing the TPM
implementation plan.
Ideally, TPM implementation would improve machine reliability, increase productivity, and lead
to lower production costs. However, if preventative maintenance activities are too frequent or too
time consuming the output of the system could be negatively impacted as the redundancy of the
system (as measured by the amount of spare automation equipment available) is temporarily
decreased. On the other hand, if preventative maintenance activities are not effective or not
frequent enough, this could lead to accelerated degradation and increased breakdowns which will
reduce system output.
To explore these effects a discrete event model of the system, which simulates system output
over an entire year, was used. In this model the duration, frequency and impact of preventative
maintenance policies can be varied and the impact to system reliability and output can be
understood. The results of this model will be used to address the research question in this chapter
39
and provide the "pilot" results needed to further justify full commitment prior to TPM
implementation.
4.1 Model Formulation, Inputs, and Outputs
The new production system is not yet constructed, so in order to explore how TPM could impact
the reliability and output of the system it was simulated using a discrete event model. A discrete
event model simulates the operation of a system as a discrete sequence of events in time. Each
event occurs at a particular instant in time and marks a change of state in the system [17]. This
simulation approach is well equipped to help users diagnose issues and performance drivers in
complex environments such as manufacturing systems. Therefore, a discrete event model of the
Automated Spar Subassembly Phase was created to determine how drivers relevant to TPM such
as preventative maintenance activities and machine failure rates, affect spar output of the system.
A graphic summary of the model is presented in Figure 4 and a description of the model is
provided below.
Variables
Preventative
Equipment
Maintenance
Failure Rate
Assumptions
* Build process
~
- System design
- Production schedule
*
Breakdown policies
- System output capability (spar sets)
- Machine Availability/System Downtime
Figure 4 - Graphic Summary of the Discrete Event Model
The discrete event modeling software ProModel was used to create a simulation of the
Automated Spar Subassembly Phase. This model is based on the actual design specifications of
this production system. It incorporates information about the build process, the build sequence,
the yearly production schedule, the takt time of various operations, the orientation of the shop
floor, the material flow through the system, the number of machines that will be installed on the
40
production floor, how those machines coordinate to complete a statement of work, and
procedures and policies for dealing with equipment failures. This model also incorporated
information on how additional "maintenance" machines-which are serviced and stored in a
maintenance bay but can be swapped into the production floor if necessary-will be used. Due to
the proprietary nature of the production system details beyond these cannot be disclosed.
However, the main point is that the model captures the key details about how the production
system uses equipment and time to transform incoming parts into finished product. The model
also captures the processes and policies the production system uses to deal with equipment
failures.
The two main independent variables that can be changed in the model are the failure frequency
of the machines and the number of hours of preventative maintenance (PM) performed each
year. In practice there is typically a relationship between these two variables (i.e. the amount of
preventative maintenance performed impacts the frequency of failures). But for simplicity most
of the analyses completed with the model were run as if PM and failure rates were independent
variables so that the impact of changing just one of these variables could be understood. In the
last analysis described in Section 4.4 a function that describes the relationship between these two
variables was developed so that the impact simultaneously varying PM hours and equipment
failures could be explored. A full understanding of how equipment failures and PM hours were
modeled is necessary to understand the results so a more detailed treatment of both variables is
described in two subsections below.
Once the parameters for the independent variables are set, the model simulates an entire year's
worth of scheduled production hours. Breaks, crew meetings, lunch time, and inter-shift time
were not simulated. Instead, the model simulates only the number of hours available for
production over all shifts and manufacturing days in a year.
The key outputs of the model are system output measured in number of spar sets completed, and
system downtime. The number of spar sets completed metric is straightforward to interpret but
the system downtime is less so and deserves additional explanation. The model generates output
that captures the amount of downtime each automated position in the production system
experiences. But since multiple machines work in concert to complete a spar, the downtime of an
41
automated position is not a useful metric for understanding the system. The important metric is
the downtime that the system experiences as a result of machine failures. For example, if a
machine fails for 30 minutes and after it comes back online a different machine working on the
same spar fails for 30 minutes the system has lost 60 minutes of production time. However, if
one machine fails for 30 minutes and ten minutes into the failure another machine working on
the same spar fails for 30 minutes the system loses only 40 minutes. Tracking the automated
positions, both of these scenarios result in 60 minutes of downtime, but the impact to the system
for these scenarios is 60 minutes and 40 minutes of downtime, respectively. When analyzing the
performance of the production system the system downtime is the important metric not the
equipment downtime. Therefore, the discrete event model uses equipment downtimes to generate
and report system downtimes.
Modeling Equipment Failures
The model only simulates the failure of the automated machines, other potential tooling failures
are not taken into account. In the absence of performance data or production experience with the
machines, it was assumed that machine failure rates were constant and therefore an exponential
distribution was used to model their reliability. The exponential distribution is a very commonly
used distribution function in reliability engineering because it effectively represents the time-tofailure distribution of components, equipment, and systems of complex nature with components
of different and/or mixed life distributions, exhibiting a constant failure rate [18]. The reliability
function for the exponential distribution is given by
R(t) = e-t, A > 0
(4.1)
Where A is the failure rate (the inverse of the mean time between failures). Equation 4.1 also
illustrates another appealing aspect of using the exponential distribution, namely that it can be
used by specifying only a single parameter, A.
42
Yar-ou falses
d ance faikros
4
+
EWly ailures
erdnce qaIures
ChOMMufsflg
Equip inent Operaing Life
(Agu)
Figure 5 - Bathtub curve depicting reliability in terms of failure rate of equipment [19]
Figure 5 depicts the "bathtub" curve which illustrates the incidence of failure over the life of
equipment. This curve is criticized in the literature for failing to effectively model the
characteristic failure rate for most machines in an industrial plant over equipment lifetime.
Equipment rarely progress (or deteriorate) deterministically through a well-defined sequence of
states as the curve suggests. However, the curve is useful for explaining three basic failure rate
characteristics: declining, constant, or increasing. At the left, so-called "infant mortality" failures
are plotted. Failure rates in early life can decline as "bugs" in the system are worked out. Failure
rates are low and constant throughout the useful life of a piece of equipment, and rise towards the
end of life [19]. Therefore, yet another reason the exponential distribution was used to model
reliability is because most machines spend a large part of their life in the flat part of the bathtub
curve.
By running the discrete event model using different mean time between failures (the inverse of
failure rate) values in the exponential function, the production system output and system
downtime can be explored as a function of machine reliability. It is valuable to understand this
because if the system is sensitive to machine reliability then TPM could yield important benefits.
43
Failure mitigation policies and the system specifications related to the time it should take to
remove a machine from the production line and replace it with a spare machine, were built into
the model. However, the details of these specifications cannot be disclosed. Nevertheless, it is
still useful to discuss these topics in general terms. The machine swap times used in the model
are based on the supplier's specification. The policies and procedures coded into the model for
dealing with machine failures are based on the design of the system and input from the Boeing
equipment engineer and manufacturing manager working on the design team. When a machine
fails it is assumed that some percentage of the time the failure is "minor" and the rest of the time
it is "major". The repair times that designate a minor failure from a major failure and the portion
of the time that one type of failure occurs over the other is based on an analysis of 3 years' worth
of maintenance data from the current spar assembly machines.
Modeling Preventative Maintenance
Before describing how preventative maintenance (PM) was modeled, it is helpful to review a
generic representation of where PM will be carried out in the Automated Spar Subassembly
Phase and how it could impact system productivity. Such a representation is presented in Figure
6 below.
Legend
An unspecified
Domachines.
number of
A spar in progress
Swap=
A machine
unavailable for
.
=
production due to
breakdown or
maintenance.
Figure 6 - Generic representation of the maintenance area and a production stage in the Automated Spar
Subassembly Phase
44
This diagram shows that the production system will have some amount of spar machine capacity
and that those machines will be located in a maintenance area that is separate from the
production system. Since all PM will be performed offline, production will not be directly
interrupted by it.
Figure 6 also depicts how PM could indirectly impact production. If a machine on the production
line fails, depending on the severity of the failure, it may be swapped out for a machine in the
maintenance area. However, if there are no production ready machines available in the
maintenance area due to preventative maintenance activities then the system downtime would
increase since a lengthy repair would have to be made on the line. Therefore, PM indirectly
affects system downtime and output by decreasing the availability of the spare machine capacity.
The severity of this impact can be explored by increasing or decreasing the duration of PM
activities. It is valuable to understand this because if the system is sensitive to PM duration then
"too much" TPM could be detrimental to productivity.
Key Model Limitations
This model is designed to investigate the sensitivity of the production system to machine failure
rates and total duration of PM activities. Understanding these sensitivities will shed light on how
much benefit TPM could offer as well as whether TPM could have negative unintended
consequences on productivity. However, the model does have limitations which are important to
keep in mind when reviewing and interpreting the results.
The first important limitation of the model is that it does not capture all the activities of TPM.
TPM activities that could enhance reliability such as predictive maintenance, autonomous
maintenance and kaizen-type continuous improvement activities are not included. The only TPM
activity explicitly captured in the model is PM.
The second important limitation in the model is that the relationship between the amount PM
performed and reliability is not explicitly captured. That is, the reliability parameters assigned to
the machines do not automatically change according to some empirical or theoretical function
when the amount of PM is varied. The reason this limitation was not addressed is because this
relationship is difficult to estimate accurately without empirical data. In the final analysis
presented in this chapter this relationship is modeled, but it is modeled in a very simplistic way.
45
Thirdly, failures other than machine failures are not included in the model nor are the disruptive
impacts of other factors such as absenteeism, defective and late parts, or quality defects.
Essentially, the model assumes that all other aspects of the production system operate perfectly
except for the machines. This simplifying assumption is necessary to zero in on the impacts of
machine reliability, but it is not realistic.
Finally, this model cannot be used to determine the optimum design of the TPM program. It
cannot be used to determine the optimum amount of PM to pursue nor can it determine if the
costs of implementing TPM are outweighed by the benefits from TPM. This is because 1) the
relationship between PM and machine reliability is not modeled in a robust way, 2) all the
elements of TPM are not captured, 3) the costs of implementing TPM are not included, and 4)
the benefits (or costs) of increasing (or reducing) output are not monetized.
4.2 Investigationsof System Behavior to Varying PM Durations
Question
As discussed above, it is valuable to understand how sensitive the system output is to total
annual PM duration because of the risk that "too much" TPM could indirectly reduce
productivity. Therefore, to explore this concern the analysis in this section will answer the
following question:
0
Will increased hours of preventative maintenance activity negatively impact system
output and availability?
Method
Because PM will be performed offline, it won't directly reduce system productivity; but it is
possible for excessive PM to indirectly reduce output7 . To explore the severity of this impact, the
system was simulated for an entire year with different inputs for PM hours ranging from 0 hours
per year to 3,425 hours per year. In the extreme scenario, there is always a machine unavailable
for production due to maintenance. For all of these scenarios the mean time between failures
7 See
46
the section on Modeling Preventative Maintenance above for a more detailed explanation.
(MTBF) for each machine was set to 32 hours of machine use time. All of the scenarios were run
25 times and the average values are reported.
Results
Figure 7 and Figure 8 show graphic results of the system output for all the scenarios run. System
downtime is represented as a percentage of total production hours scheduled. System output is
represented as a percentage of the maximum system output.
Total PM Hours vs. System DT (%)
3
........
.................
1. ...
.......
..............................
0
0
500
1000
1500
2000
2500
3000
Total PM Hours Per Year
Figure 7 - System Downtime vs. PM Hours, MTBF = 32 hours
47
3500
4000
Total PM Hours vs. Spar Sets Produced
100 0%
........
.
.....
......
...............
... ,0............................0
97.5%-
90.0%
95.0%
0
500
1000
1500
2000
2500
3000
3500
4000
Total PM Hours per Year
Figure 8 - System Output vs. PM Hours, MTBF = 32 hours
Discussion
The results above show that both the system downtime and system output are not affected by the
amount of preventative maintenance performed when the MTBF is 32 hours. Both Figure 7 and
Figure 8 depict relatively flat curves over the entire range of PM hours. Indeed, the largest
difference in system downtime for all scenarios run was 0.04% and the largest difference in
output was 0.1%. These slight variations are not due to actual differences in system performance
over the PM range explored but rather the random error associated with stochastically modeling
failure rates. It is important to keep in mind however, that for all the scenarios run, the MTBF
was held constant; the reality is that in a mature production system if no PM is performed the
failure rates of the machines will likely increase. However, this analysis simply aims to
determine if increasing PM will indirectly affect system downtime and output by virtue of
reducing the number of machines that are available to be swapped in the event of a machine
failure. The results show that even though increasing the amount of preventative maintenance
will effectively decreases system redundancy, the system has enough redundancy that at
reliability levels consistent with a MTBF 32 hours, system performance is not indirectly
impacted by PM activities.
48
The implication of this conclusion is that as long as machine reliability can be maintained,
manufacturing and maintenance can experiment with different lengths or frequencies of PM
routines without worrying about reducing manufacturing output. Although, it is not likely that
PM can be varied over the ranges explored in this analysis without affecting machine reliability,
it is reasonable to expect that PM durations can be experimented over smaller ranges with
minimum impacts to reliability.
Question
The scenarios explored above assumed a relatively high MTBF. It is because of this high level of
machine reliability that productivity is not impacted by the negative affect that increased PM
hours has on the redundancy of the system. Essentially, machines fail so infrequently that even if
a machine in the maintenance bay is unavailable for the entire year due to PM, the system is
unaffected. However, this situation is not likely to persist at lower levels of machine reliability.
And since machine reliability will most certainly be impacted by changing PM duration and
frequency, it would be useful to understand the indirect effect that PM hours have on system
performance over a wide range of machine reliability characteristics. Therefore, the analysis in
this section will answer the following question:
*
How sensitive is the system output to PM hours at different levels of machine reliability?
Method
To explore the question above, the system was simulated for an entire year with different inputs
for PM hours ranging from 0 hours per year to 3,425 hours per year. For each set of PM inputs
the MTBF was set to a different value ranging from 32 hours to 3 hours. All of the scenarios
were run 25 times and the average values are reported.
Results
Figure 9 and Figure 10 show graphic results of the system output for all the reliability scenarios
run. In Figure 9, system output is represented as a percentage of the maximum system output. In
Figure 10, normalized system output is presented; all the values were normalized by the
maximum output value within each reliability scenario.
49
Total PM Hours vs. Spar Sets Produced
100.0%
P
&98.0%*
960
. oa
0 ............
.........
....................
4*
.......
4
40--41.. .........
94.0%
*fMTBF
092.0%
90.0%
*
0
"
..-..
.
3
0
12h
=
TBF = 8h
.....
.......
MTBF=4h
BF = 3h
8400-0..............
84.0%
"
.
8
16h
*MTBF
.
'*"o*.
88.0%
= 32h
e MTBF =
-NIB---4
86.0%
80.0%
0
500
2000
1500
1000
2500
3500
3000
4000
Total PM Hours per Year
Figure 9 - System Output vs. PM Hours over a range of machine reliability assumptions
Total PM Hours vs. Spar Sets Produced (Normalized)
1.00
I
m
*...
......
.
.
1.01
......
.
................
.
0.99.........
...............
4
#lfMTBF = 32h
0..
-.4.
-097
*M TBF = 16h
.
--..--
.fTBF=12h
*IMTBF
- 0-6
--.
= 8h
*MTBF = 4h
-0*
MTBF
=3h
0959
0-94
0
500
1000
1500
2000
2500
3000
3500
4000
Total PM Hours per Year
Figure 10 - Normalized System Output vs. PM Hours over a range of machine reliability assumptions
Discussion
As expected, the results in Figure 9 and Figure 10 show that as machine reliability decreases, the
system becomes more sensitive to the decreased redundancy that results from longer PM
durations. Figure 9 shows that the first noticeable impact to system output from increased PM
50
occurs when the MTBF is 12 hours. In this scenario system output changes by slightly less than 1
percentage point over the entire range of PM values simulated. System output sensitivity to PM
hours is more pronounced for the 4 hour and 3 hour MTBF scenarios. System output changes by
3.8 and 5.5 percentage points for these two scenarios respectively, over the range of PM values
simulated. However the change in system output for these two scenarios as a result of lower
machine reliability is still larger than the impact caused by PM. Poor machine reliability alone
reduces output from the maximum theoretical output by 10 and 13.2 percentage points in the
cases where no PM is being performed. This suggests that even though increased PM can reduce
output when machine reliability is low, reduced machine redundancy is only a secondary driver
compared to the primary driver represented by machine reliability.
Figure 10 more clearly isolates the impact that increased PM has on system output given
different reliability assumptions. The steepness of the slopes in Figure 10 indicate the system's
sensitivity to decreased redundancy (as a result of longer PMs). However, even in the worst
reliability scenario, system output only decreased by about 6% from where it would have been if
all extra machine capacity were available all the time.
Taken together, these results show that system output is extremely robust to reduced redundancy
from increased PM over a large range of machine reliability characteristics. These results also
show that the system is robust the primary driver of reduced system output which is machine
reliability. Even in the worst case, where machine redundancy is reduced by the equivalent of
one machine and the MTBF is 3 hours, system output is only reduced by 18.7%. To appreciate
this better, a MTBF of 3 hours means that in an 8 hour shift if the machines are operating for 6
hours they will all fail on average twice 8 . However, these results are based assumptions around
how many failures will be major vs. minor, how long it will take to repair minor failures on the
floor, and how often machines will have to be swapped out as a result of failures. If these
8
This is merely a hypothetical example, these values do not reflect how long the machines will operate during each
shift nor does it reflect the expected reliability of the machines.
51
assumptions are realized then the system will be robust, but whether the reality will resemble
these assumptions is difficult to say.
Based on the current expectations for the production system, the implication of these results are a
double-edged sword in terms of justifying TPM. On the one hand these results further reinforces
the notion from the previous analysis that different lengths and frequencies of PM routines can
be tested without significantly risking reduced output as a result of lower redundancy (even if
failure rates increase to high levels). On the other hand, these results suggest that the system is so
inherently robust that even if machine failure rates are high, it can produce at a relatively high
rate. Within such a robust system, the benefit of high machine reliability from implementing
TPM is diminished.
To summarize, the key question raised by this analysis is the following: If an investment has
already been made to build a robust production system, will there be motivation or justification
to invest in TPM implementation to achieve high levels of reliability? A response to this question
might be to point out all of the other benefits of TPM related to quality, production efficiency,
morale, etc. that were not included in this analysis. Another important wrinkle to remember
when responding to this question is that this analysis assumes that all other processes within the
system are behaving perfectly, which is unlikely to be the case. It is conceivable that the system
would not be as resilient to high frequencies of machine failures if it also had to cope other
sources of variability, stress, and delay such as labor resource constraints, production pressures,
quality issues, etc. The argument supporting TPM implementation is that in addition to
addressing machine reliability, it can also mitigate (either directly or indirectly) some of these
other drivers of reduced productivity.
4.3 Investigationsof System Behavior to Varying Machine Reliability
Question
Understanding the relationship between machine reliability and system performance is key to
assessing the some of the potential benefits from implementing TPM. In the last section this
relationship was analyzed to some degree. The results suggested that system output is robust to
changes in machine MTBF over a wide regime of PM hours. In this section the relationship
between machine reliability and system output will be explored in greater depth to determine if
52
this finding holds over a wider range of machine reliability characteristics. The analysis in this
section will address the following question
*
How sensitive is system output and availability to machine reliability characteristics?
Method
To explore the question above, the system was simulated for an entire year with different inputs
for MTBF and with different assumptions for the portion of failures that are major failures. The
MTBF assumptions tested in this analysis ranged from 32 hours to 3 hours, but a greater number
of MTBF assumptions were analyzed in this section compared to Section 4.2. Furthermore, in
contrast to the prior section where the portion of major failures were held constant, in this
analysis the portion of major failures ranged from 5% to 30%. In the model, major failures
require that equipment is swapped into the maintenance bay in order to be repaired. Therefore, at
a given MTBF as the portion of major failures is increased, the negative impact of machine
failures on performance is exacerbated. For all scenarios, the PM hours were held at a constant
value of 1,120 hours per year. All of the scenarios were run 25 times and the average values are
reported.
Results
Figure 11 depicts a graph of the results from all of the scenarios run. The bottom axis, system
downtime, was calculated for each scenario and serves as a proxy for the reliability
characteristics that were changed in each scenario. The MTBF parameter used for each scenario
is indicated on the graph. The results from the scenarios on the far left of the graph are so close
that some of the markers fall on top of one another. Therefore, only the range of MTBF
parameters are indicated in this portion of the graph. The assumption for portion of major
failures used is also indicated in the graph (abbreviated by "MF"). The lower data point is
associated with the higher MF value. Where a range is indicated the MF was changed by 5% for
each scenario. The MF is not indicated for the far left data points but they ranged from 5% to
20%.
53
100%
MTBF =12h; MF = 10%, 20%
MTBF=
950
'..
32h--20h
9000
MTBF = 4h
MF 20%.25%.30%
*..
MTBF =16h
MTBF = 8h
MF5%-20%
MF= 10%. 20%
8000
MTBF =3h
=20%, 30%
7;0
700o
0
2
4
6
S
10
12
14
16
is
0
System Downtime (%)
Figure 11 - System Output vs. System Downtime, MF indicates the portion of events that are major failures
Discussion
The results in Figure 11 support the conclusion made in the previous section. The system is
robust to machine failures. At moderate levels of PM (1,120 hours per year) and when the MTBF
of all machines was 3 hours and 30% of those events resulted in a major failure, the system
output only decreased 18.6% relative to the theoretical output. While it is highly unlikely that the
reliability of the machines would ever reach these extreme failure characteristics, this analysis
shows that even over a broad range of reliability, the robustness of the system holds. Again, this
resilience would reduce the risk of failure for a team trying to implement TPM, but
simultaneously it would detract from the sense of urgency and necessity that is helpful for
creating support and justification for TPM.
4.4 Investigationsof System Behavior to PM Duration and Machine
Reliability
In the analyses presented in the prior sections, either machine reliability characteristics were held
constant while the effect of changing PM hours was explored or vice versa. It is clear that this
will not be the case in practice, in practice there will be a relationship between reliability
characteristics and the amount of PM performed (otherwise PM would not be justified).
Therefore, in order to simulate the system more realistically, a model is used to describe the
relationship between these two factors and this model is incorporated into the simulation.
54
Updating the simulation in this way is important for verifying the results and conclusions that
have been presented in the prior analyses. The main question addressed in this section is:
0
How will the system perform under different PM durations if PM impact on reliability is
taken into account?
Method
Ideally, empirical data would be used to determine the relationship between PM activities and
machine reliability. Since empirical data on the new system is not available, a simplistic and
intuitive model was adapted from Meller and Kim [20]. Based on interviews, the authors found
that failure rate generally increases as PM frequency increases. The authors then assume that
long-run MTBF is a function of PM frequency (T) and the following three parameters:
1. present MTBF with no PM program (min)
2. maximum MTBF possible with a very frequent PM program (max)
3. shape factor for the asymptotic gain in PM (p).
Adjusting their methodology so that the independent variable is total PM hours completed in a
year (D) rather than PM frequency, and given the above assumptions the relationship between
the PM program and MTBF can be modeled according to this form:
MTBF = min + (max - min) - [1 - e-k]
(4.2)
In order to specify the shape factor (f) the user only has to identify a point on the curve defined
by equation 4.2. Thus, if the user can specify an amount of PM (R) that must be performed to
achieve some percentage of the maximum MTBF (a), the following expression can be used to set
-In1= (1 - a
Rrmax-
x-
min(43
(4.3)
Using equations 4.3 and 4.2, two relationships were defined. The parameters used to define these
relationships are presented in Table 3 below.
55
Parameters
Maximum MTBF (max)
Minimum MTBF (min)
Reference PM amount (R)
% of max MTBF achieved
with reference PM (a)
Relationship 2 (alpha =
Relationship 1 (alpha =
85%)
45%)
36 hours
4 hours
1200 hours per year
45% (16 hours)
89% (32 hours)
Table 3 - Parameters used to define relationship between PM and MTBF
In Relationship 1 from Table 3, a MBTF of 32 hours is achieved through a moderate PM
program consisting of 1,200 hours of labor per year. In Relationship 2 a MTBF that is half of the
first is accomplished through the same effort. These two relationships are plotted in Figure 12
below.
MTBF as a function of PM hours per year
40.0
35.0
30.0
2 5.0
L.
20-0
1 5.0
5.0
---
Alpha= 89%o
-
Alpha= 45%
0.0
0
500
1000
1500
2000
2500
3000
3500
4000
PM hours per year
Figure 12 - MTBF as a function of PM hours completed per year
These two curves represent different behaviors in terms of the effectiveness of PM. For the
relationship where alpha is set to 89%, PM is moderately effective; initially PM improves
reliability rapidly and diminishing returns don't begin to set in until about 1,100 hours. This
relationship can be considered a base case for this production system in that this is not an
unreasonable expectation. For the relationship where alpha is set to 45%, PM is not very
effective. Slope of the curve is fairly shallow over the entire range, and it takes significantly
more PM, compared to the other relationship, to reach the same levels of reliability. This
relationship can be considered a low case in that it represents a PM program that is on the low
56
end in terms of effectiveness. The PM parameters and corresponding MTBF parameters defined
by the two curves above were entered into the discrete event model and the output of the system
was simulated for a year. All of the scenarios were run 25 times and the average values are
reported.
Before reviewing the results, it is important to emphasize that the purpose of this analysis is not
to calculate the optimal amount of PM that should be performed. The parameters in Table 3 are
not based on empirical data, therefore they may not reflect the reality of the production system
once it is constructed and operational. As a result they cannot and should not be used to
determine the optimal PM program. The values in Table 3 were selected in order to explore the
full space of possible relationships between PM and machine reliability. They two curves in
Figure 12 define a space over which the performance of the production system can be analyzed.
The goal of this analysis is to determine if the conclusions reached from the prior analysis hold
within this space which more closely reflects reality.
Results
Figure 13 graphs the results of running the system simulation using PM hours and MTBF
parameters defined by the two relationships discussed above.
990-0
978%
9600
9306
--
Alpha = 890%
920 o
---
Alpha = 45%
91P
90%
0
500
1000
1500
2000
2500
3000
3500
4000
Total PM Hours per Year
Figure 13 - System output as a function of PM and MTBF
Discussion
The results in Figure 13 verify the results of the analyses presented in prior sections. The prior
analyses showed that the system performance is resilient to high machine failure rates as well as
57
the decreased system redundancy that results from increased PM. Therefore, it should not be too
surprising that when both of these parameters are changed according to the relationships defined
in the methods section, the system still proves to be resilient. Even in the low PM effectiveness
case (alpha = 45%) the system reaches 95% of its theoretical output with only about 300 hours of
PM per year.
It is important to understand that this analysis cannot be used to determine the optimum amount
of PM to perform in the future system. This is because the relationships defined between PM
hours and reliability are based on a simple model and best guess parameter estimates. In order to
determine the optimum PM regime, at the very least empirical data from the future system is
required to define the relationship between PM and machine reliability.
While this analysis cannot determine the optimal amount of PM to perform, the results do
illustrate what could be considered a "safe zone" for setting the initial PM program. The area
bounded by the two curves and by ~300 hours of PM on the left and ~1,100 hours of PM on the
right bounds a region with acceptable system performance (>95%) and where additional PM
hours would likely lead to a region of diminishing returns. When setting the initial PM program,
determining the yearly PM hours from within this range provides reasonable likelihood that
performance won't be endangered. This range also provides a high likelihood that excessive PM
will not be specified.
4.5 Summary and Discussion
The key results from the analysis presented in this chapter are the following:
" The production system performance is resilient to reduced machine redundancy that
increased PM causes.
*
The production system performance is resilient to machine failure rates.
" Reduced machine redundancy (from increased PM) is a secondary driver of reduced
system output compared to machine failure rates.
" The resiliency of the system holds even when the relationship between PM and machine
failure rates are incorporated into the model.
58
The first implication of these insights with regard to TPM implementation is that TPM
implementation won't likely represent a risk to system output if machine reliability suffers a
slightly while TPM is going through its growing pains during early implementation. This would
be a good thing to point out to someone who might be averse to TPM for fear that it could
compromise machine reliability and system productivity if implementation didn't go well.
Another key implication of these results is that TPM does not pose a risk to system output from
the perspective of reducing machine redundancy as a result of increase PM activities. Finally,
because the system is so inherently robust, management, manufacturing, maintenance, and other
support groups can take time to explore and experiment with the most appropriate TPM
implementation without fear of jeopardizing system output.
However, these results also suggest that there may be limited upside to machine reliability
benefits of TPM since the system productivity has already been designed to be extremely robust
to machine failures. If the production system proves to be as robust as this analysis suggests it is,
a lack of urgency could blunt the push for implementing TPM. However, this analysis focused
solely on machine redundancy and reliability and there are many other factors that impact system
output which TPM affects such as quality, and production efficiency. Furthermore, a more indepth analysis of how TPM can reduce maintenance costs could provide tangible "bottom line"
benefits that could justify implementation. Finally, TPM can yield other tangible and intangible
benefits as discussed in chapter 3, and these may also bolster the case for pursuing TPM.
While the analysis presented in this section does not provide strong support for TPM, it does not
suggest that TPM should be avoided either. In fact, the analysis eliminates one of the potential
arguments against pursuing TPM. It shows concerns about TPM indirectly reduce productivity
by taking machines out of service for PM are not justified. Therefore, even though the analysis
cannot be used to support the reliability benefit of TPM, it can be used to defend TPM
implementation from certain criticism pertaining to unintended consequences.
Key limitations of this analysis and possible areas for future research include:
" The impacts of other aspects of TPM such as autonomous maintenance and small group
improvement activities were not analyzed.
*
59
The impact of TPM on quality and production efficiency was not analyzed.
*
The costs for implementing TPM were not considered and the benefits, in terms of
increased system output, were not monetized.
" Only machine failure rates and machine redundancy were analyzed, other drivers of
system output were not considered.
60
Chapter 5: Barriers and Enablers for TPM Implementation at
Boeing
The prior two chapters have explored justification for TPM implementation based on analyses of
the current state of the spar shop and the future spar production system. These analyses have
identified the benefit opportunities that TPM could provide. These chapters have also highlighted
potential limitations of TPM as well as future work that could be executed to further explore the
case for TPM. According to the Stages of Commitment framework, the results of these analyses
should be presented to key stakeholders and decision makers within the 737 program to build
support for TPM and solidify full commitment to TPM implementation. This chapter switches
gears and assumes that commitment to implementation has been secured and explores the factors
that contribute to (or detract from) implementation success.
Boeing has been implementing TPM at commercial airline factory locations since the early
1990s. For example, MIT Leaders for Manufacturing student Eugene Hamacher completed an
internship at Boeing in 1996 that analyzed four different TPM projects that were active in the
Boeing Commercial Airplane Group. Today, TPM routines that involve operators in daily
machine maintenance are being implemented in the 737 spar shop and in a shim mill shop in the
Renton plant. Furthermore, Boeing TPM assessment documents from 2014 show that TPM is
being implemented in more than a dozen different Boeing facilities. Similar to what Hamacher
discovered in 1996, today TPM implementation success (as measured by TPM implementation
progress) varies widely from site to site. Recent TPM assessments prepared by the Boeing
Enterprise TPM Focals show that there is a large gap between the best performing and worst
performing sites in terms of TPM implementation progress.
Given the historic and current variation in TPM implementation outcomes, the research question
I seek to address in this chapter is:
*
Why has TPM implementation succeeded or failed at Boeing?
This chapter seeks to understand the key barriers and enabling factors for implementing TPM at
Boeing. Understanding the key barriers that prevented full implementation as well as the
61
enabling factors that lead to success will provide guidance and rationale for the strategy
documented in the TPM implementation plan presented in the next chapter.
5.1 Literature Review: TPM Implementation, Success Factors and
Barriers
There is extensive literature that explores and summarizes methods for TPM implementation,
success factors, and barriers. Ahuja & Khamba [16] provide an extensive literature review which
includes a complete overview of TPM implementation practices. While they reiterate that a
highly structured approach and careful planning are keys to successful implementation, they also
acknowledge the problems regarding a "cookbook-sytle" TPM implementation strategy due the
the variability in cultures, skillsets, conditions, production systems, managements styles, etc. that
exist among organizations. They summarise various implementation methodologies that have
evolved since Nakajima [9] published the first text that summarised the main princicples and
pillars of TPM in 1988. The implementation methods covered range from strict implementation
of the eight pillars of TPM, to Nakajima's original twelve step model, to simplified or expanded
versions of Nakajima's model, to plans that focus on revitalizing existing but floundering TPM
programs. There is also an extensive array of books published that focus on TPM
implementation and adopt TPM to different contexts. For example Peter Willmott's TPM: The
Western Way [10] adopts TPM implementation for western oriented companies. While there
have been many methodologies for TPM implementation presented by researchers and
practicioners, it important that the specific organization implementing TPM adopt a given
methodology to their particular context and work out the correct sequence of steps to
successfully deploy TPM.
Identifying the factors that influence TPM adoption has also been a rich area for research.
McKone, Schroeder, and Cua [14] analyzed the managerial, environmental, and organizational
factors that influence TPM adoption. They found that managerial factors such as Just-in-Time,
Total Quality Management, and employee involvement are most important to the execution of
TPM programs. Ahuja & Khamba [16] provide an extensive literature review on the "stumblingblocks in TPM implementation" as well as the "success factors for TPM implementation" and
have classified barriers into different categories such as organisational, cultural, behavioural,
technological, operational, financial and departmental barriers. Attri, Grover, and Dev [21]
62
provide further documentation of literature which identifies various barriers to TPM
implementation, they explore the interaction of one barrier to another barrier, and group a
selected set of barriers into five broader categories. Finally, in his Master's thesis Eugene
Hamacher [11] identified key TPM implementation barriers and success factors within the
Boeing Commercial Airline Group. While commonly cited factors from this extensive literature
can be identified, the shear number of unique factors that are identified suggest that enablers and
barriers are as diverse as the companies that attempt to implement TPM. Therefore, in order to
successfully implement TPM the mission shouldn't be to attempt to identify all of the barriers
and enablers within a company, but to identify a subset of key barriers and enablers. This is the
goal of the analysis presented below.
5.2 Survey Overview and Methodology
The mechanism for collecting data to complete this analysis was a survey that asked TPM
professionals at Boeing to rate various common TPM implementation factors found in the
literature.
This survey was sent to 124 Boeing employees who were selected based on their demonstrated
expertise, experience and/or interest in TPM. Since a central skills database that lists whether
employees have expertise in TPM does not exist at Boeing, participants were identified through
several different strategies. First, any employee who held a role as a TPM focal at manufacturing
facility was included. Next, any employee that had completed training and been certified as a
"TPM coach" was included in the survey. Various managers from the maintenance and
manufacturing organizations at Renton who showed interest in TPM were included. Finally,
people who appeared on an invite list for a monthly TPM meeting were included. This initial list
was then sent to the Enterprise TPM focal and the Renton TPM focals for review and additional
names were added.
The survey design was based on the academic literature review of TPM implementation barriers
and success factors presented above. Based on a compiled list of commonly cited factors found
in the literature and interviews with Boeing TPM focals, a list of 26 factors was created. An
effort was made to ensure that the factors developed for the survey were mutually exclusive and
collectively exhaustive. The five broad categories defined in Attri et al. [21] were adopted to
63
organize the factors. Detailed definitions of each factor were create and included in the survey so
that all survey respondents had the same clear understanding of the factors they were rating.
These categories and factors are summarized in Table 4. The definitions of the survey categories
and factors are included in Appendix A: TPM Enabling Factors and Barriers Survey.
Behavioral &
Strategic
Organizational Factors
Factors
Top
Effective longmanagement
term planning
commitment
Sense of job
security
Cultural
Factors
Operational
Factors
Technical
Factors
Standard
operating
procedures
Well educated
workforce with
strong technical
knowledge
Clarity of
organizational
objectives and
policy with
Tendency to
cooperate,
communicate
and collaborate
in small teams
Ability of TPM
to harmonize
with or change
prevailing
Effective
preventative
maintenance
schedules
Effective and
substantial
training and
education
Adept use of
continuous
improvement
tools,
techniques and
methodologies
Strong followup on progress
of TPM
initiatives
Understanding
of TPM
concepts and
principles
regard to TPM
culture
Coordination
between
maintenance
and
manufacturing
organizations
Stable
leadership
Allowing
sufficient time
for TPM
development
Employee
acceptance of
new methods
and procedures
Appropriate
and realistic
expectations set
Employee sense
of
empowerment
Union support
TPM support
structure
Reward and
recognition
mechanisms
Broad
ownership of
TPM planning,
execution, and
sustainment
Table 4 - Survey categories and factors
64
Other
lean/improvem
ent processes
and initiatives
are well
coordinated
with TPM
Effective
computerized
maintenance
management
system
(CMMS)
Accessible
performance
measures and
actionable
reliability,
quality, and
production data
The survey participants were asked to reflect on their experience with TPM implementation at
Boeing and rank each factor on a scale from -3 to 3. A rank of -3 indicates that lack of the factor
was a key barrier to TPM implementation. A rank of 3 indicates that the presence of the factor
was a key enabler for TPM implementation. A rank of 0 indicates that the factor was neither a
barrier nor enabler for TPM implementation.
Survey results were collected for 4 weeks from November 7 th to December
8 th
2014. The survey
response rate was 24% and represented a diverse group in terms of job function, location, and
years of experience. Figure 14 summarizes the TPM experience of all the respondents and Table
5 summarizes the organization and job function of the respondents. The respondents worked at
18 different Boeing sites across North America. Even though the total number of respondents
was low (31), they did represent a significant portion of the total TPM experts at Boeing in
addition to representing a diverse cross-section of the company.
Respondents' TPM Experience
10
W
:5
SU.
00
I1
2
M
4
.i
6
8
, ,II
10
More
Years of Experience
Figure 14 - Bar chart summarizing survey respondents' TPM Experience. The average years of TPM
experience for the group was 6.4 years.
65
Leadership - Manufacturing
2
Leadership - Equipment Engineering
1
Leadership - Other Engineering
1
Management - Manufacturing
1
Management - Maintenance
1
Management - SSG
1
Manufacturing
3
Maintenance
2
Equipment Engineering
1
Other Engineering
4
PPMO
3
SSG
5
Other
6
Table 5 - Organization and job function of the respondents
5.3 Survey Results and Discussion
The ranking scheme used in this survey allowed the most enabling factors, the largest barriers,
and the most important factors overall to be determined simply by calculating the average score
or the absolute average score for each factor. The average scores and top five factors for each of
these three categories are presented in Table 6 through Table 8.
0.77
0.42
0.39
0.35
0.19
Well educated workforce with strong technical knowledge
Effective computerized maintenance management system (CMMS)
Employee sense of empowerment
Union support
Effective preventative maintenance schedules
Table 6 - Summary of top five most enabling factors for TPM implementation at Boeing
66
Broad ownership of TPM planning, execution, and sustainment
Reward and recognition mechanisms
TPM Support Structure
Understanding of TPM concepts and principles
Stable leadership
-1.03
-0.87
-0.81
-0.81
-0.74
Table 7 - Summary of top five largest barriers to TPM implementation at Boeing
core
1.87
1.87
1.84
1.77
1.61
1.61
op 5 'Most 11mportant Fa,:ctors lit Boeing
Effective preventative maintenance schedules
Top management commitment
Coordination between maintenance and manufacturing organizations
TPM Support Structure
Well educated workforce with strong technical knowledge
Clarity of organizational objectives and policy with regard to TPM
Table 8 - Summary of top five most important factors for TPM implementation at Boeing
A statistical analysis of the results was not practical for two main reasons. First, there aren't
enough data points to achieve statistical power. Secondly, there isn't enough range in the ranking
scheme to yield average values that differ by a significant amount. For example, the differences
in average values of adjacent factors is typically on the order of 0.1 points, while the standard
deviation for each factor is on average 0.9. Therefore, it cannot be stated that the first factor on
these lists are statistically more important than the second factor. However, these results are still
insightful and were used to inform the content and structure of the TPM implementation plan.
In addition to these qualitative questions about implementation factors, the survey also included
nine quantitative questions about TPM outcomes. These questions asked recipients to provide the
values for metrics such as equipment availability and performance efficiency before and after
TPM implementation. The original intention was to use this data to link successful and
unsuccessful TPM outcomes to the implementation factors that lead to those outcomes.
However, very few respondents provided this data probably because too much effort was
required to collect it. Therefore, this analysis could not be completed.
67
5.4 Conclusion
The top barriers to TPM implementation are related to a lack of broad ownership of TPM
planning, execution and sustainment. This finding agrees well with the opinion of a TPM focal
interviewed in Renton, "TPM doesn't work here because it's always a small group of people that
believe in TPM and are actually pushing it. If TPM is going to be successful all the people within
manufacturing and maintenance have to embrace it." These sentiments were reinforced by the
written response of one of the survey respondents "TPM is tough and requires dedication and
participation from all stakeholders. Some sites implement better than others and some
[organizations] work together, some don't."
The other top barriers to TPM implementation at Boeing are related to, and are perhaps
contributing factors to the primary reason (lack of broad ownership). For example, without a
reward and recognition system it can be difficult to sustain broad ownership of an initiative.
After all, incentives are not only strong shapers of behaviors, they implicitly signal to an
organization what is important and what isn't. Similarly, without a formal organizational
structure that supports TPM it is difficult, from a practical perspective, to broadly establish
ownership of a system as complex as TPM. Finally, without a clear understanding of TPM
concepts and principles there is no way that people can move through the "Stages of
Commitment to Organizational Change" 9 which moves through awareness and understanding
and ends with what amounts to ownership (i.e. adoption, institutionalization and internalization).
Thus, the results from the survey shown in Table 7 suggest that the reasons that TPM
implementation has failed at Boeing in the past are strongly interrelated.
Far and away the most important factor in TPM implementation success at Boeing listed in Table
6 has to do with having a well-educated workforce with strong technical knowledge. In contrast
to the barriers where the score difference between the top factor andfifth factor was 0.29, the
difference between the top enabling factor and second factor was 0.35. This top factor for TPM
success at Boeing agrees well with the central theme of TPM. TPM at its core is a people
9 As
described by Daryl Conner and Robert Patterson (1982) in their article "Building commitment to organizational
change." Training & Development Journal,36(4)
68
centered methodology which empowers and relies heavily on the teamwork of well trained
employees capable of identifying technical issues and implementing solutions.
The factors for TPM implementation identified in Table 6 through Table 8 provide key input for
the strategic implementation plan and should also guide the efforts of the implementation team
once this plan is ready to be executed. One broad insight that can be derived from the key
barriers and key overall factors in Table 7 and Table 8 is that initial planning prior to
implementing TPM routines and effective communication will be critical to implementation
success. Securing top management support, setting up a TPM support organization, and creating
a clear TPM policy and goals, and ensuring that everyone understands TPM concepts and
principles are key planning steps that should take place before and implementation activities can
takes place.
A second insight that can be derived from the results is that good communication among
stakeholders will be important for establishing TPM. Ensuring that there is broad ownership of
TPM, ensuring that the manufacturing and maintenance organizations are coordinated, clarifying
TPM organizational objectives and policy, and ensuring that people understand TPM concepts
and principles requires that there is effective communication early on in the implementation.
Therefore, the first six of nine steps in the implementation plan outlined in the next chapter are
devoted to planning and communication activities. Ensuring that these key implementation
factors are addressed in the plan will increase the likelihood of a successful TPM
implementation.
69
Chapter 6: Plan for Implementing TPM in the Future
Production System
The analysis completed in chapter 3 and chapter 4 explored the justification for TPM
implementation. The key insights from chapter 3 that support TPM implementation include the
following:
*
The literature review showed that TPM has yielded significant benefits in case studies
*
Correlations of downtime to overtime in the current spar shop show that TPM reliability
benefits can translate into manufacturing cost savings
" The ratio of preventative maintenance to reactive maintenance can be improved which
may reduce maintenance costs
*
There is opportunity to improve morale and teamwork
" A qualitative analysis of the new production system shows that it is a good candidate to
benefit from TPM as a result of its lean design and significant use of automated
equipment.
Although the quantitative analysis presented in Chapter 4 suggested that while the resiliency of
the system to machine failures may blunt TPM benefits from increased reliability, the results
from this chapter also show that implementing TPM poses little risk to the throughput of the
production system. Furthermore, the analysis in Chapter 4 did not consider other aspects of TPM
beyond PM or other benefits beyond reliability that could justify TPM implementation.
Referring back to the Stages of Commitment framework, it is reasonable to believe that the
analyses, insights, and justifications from these two chapters are sufficient to move key decision
makers through the Commitment Phase towards adoption and institutionalization. Therefore, in
chapter 5 the focus shifted from justification to implementation and the key barriers and enabling
factors for TPM implementation at Boeing were identified. The results from that chapter guided
the content and structure of the framework for implementing TPM which is presented in this
chapter. The key question addressed in this chapter is:
*
What kind of framework should be used to successfully implement TPM in the future
spar production system?
70
6.1 Methodologyfor Creating the TPM Implementation Plan
There are many TPM implementation frameworks available in the literature so rather that
reinventing the wheel, this chapter presents a framework created by adopting the first 6 of the
original 12 steps developed by Nakajima [9] and combining them with the execution
methodology described by Peter Willmott in his book, Total Productive Maintenance: The
Western Way [10].
The reason behind structuring the plan in this way has to do first of all with the barriers to TPM
identified from the survey in chapter 5. The key barriers Boeing faced were all related to a lack
of planning and preparation prior to implementing TPM. Since the first six steps of Nakajima's
implementation method focus on planning, setting up the organizational structure, educating
people, and gaining buy-in, it is makes sense to start the Boeing implementation plan by
emphasizing those steps.
The execution steps from Willmott were adopted because his execution methodology can be
readily used by the existing El teams that exist in each production shop at the Renton plant.
Willmott's execution methodology can be executed according to the weekly cadence that El
teams typically use and it provides actionable tasks and clear routines for implementing TPM
that El teams can immediately take advantage of. As will be seen, this execution methodology is
designed to be iterative and therefore, if dutifully followed, it will result in a sustained and
constantly improving TPM program.
6.2 Framework for Implementing TPM
The TPM implementation plan is summarized in Figure 15. It has nine steps divided into two
phases, the planning phase and the execution phase. The planning phase is critical to TPM
success and lays the foundation for the execution phase. This part of the implementation plan is
meant to be performed only once. The execution phase is a three step cycle that is meant to be
performed iteratively for as long as the TPM program is in place. The execution phase is
dominated by step seven which is a nine step cycle meant to be performed iteratively as well.
The final two steps of the execution phase provide an opportunity for review by leadership and
management and continuous improvement of the TPM program. Thus, this plan will result in a
continuously improving and learning TPM program.
71
Planning Phase
,dW*"Execution Phase
Figure 15 - Summary of the TPM Implementation Plan
This plan should be managed and executed by a relatively small core group of people (6 to 8
people) who are committed to TPM and extremely knowledgeable about the new production
system. This group could include the manufacturing manager for the system, the equipment
engineer, the industrial engineer, an equipment operator team leader, and a team leader from
maintenance 10 . In the following subsections, each step in the TPM implementation plan will be
explain in greater detail.
Step 1: Initial TPM Awareness and Buy-in
In this first step, a half-day to full-day initial TPM awareness session is held with senior
management, first-level managers, team leads, and key people who will be part of the TPM
Management and Implementation teams (see step 3). The primary goal of this meeting is to fully
10 The composition of the TPM teams is discussed in greater detail in step 3 below.
72
introduce what TPM implementation will entail and to get buy-in from management. Analysis
that explore TPM justification such as those presented in chapter 3 and 4 should be presented.
It is crucial that top management understand and believe in the TPM concept before
implementing it. Leaving this session management must also understand that their key
responsibility at this stage is establishing a favorable work environment that supports TPM and
autonomous activities. That is, they must understand the type of day-to-day changes, resources,
and teamwork required for TPM and communicate to management and front-line workers that
they will support these changes.
Buy-in from top management is frequently cited in the literature and in classic TPM texts as
being crucial. At Boeing, "Top Management Commitment" was the single most important factor
for TPM success identified by in TPM survey. Therefore, this is a crucial first step towards a
successful TPM program.
Step 2: General TPM Awareness Campaign
In this step all of the people who will work on the new spar production system should receive
formal TPM awareness training that introduces the concepts, goals, and expected benefits of
TPM. The presentation could include recorded personal statements from top managers to
employees since establishing a favorable work environment that supports TPM is the primary
responsibility for management at this stage. Establishing this favorable work environment is
accomplished by making public statements of support and giving employees the time to
complete this training.
This step is important because it will directly address the most important barrier to TPM
identified in the previous chapter: "Broad ownership of TPM planning, execution, and
sustainment". Only by bringing everyone involved in new spar production system up to a
baseline level of understanding about TPM, can the TPM planning team expect to engender
broad ownership of its planning, execution and sustainment. People cannot support that which
they have no awareness of. This step also addresses another key barrier to TPM: lack of
understanding of TPM concepts. If the training in this step is designed to emphasize how TPM
empowers employees and improves working conditions, then it can contribute to the third most
important enabling factor identified by the survey: "Employee sense of empowerment".
73
Step 3: Establish TPM Support Structure
Once the initial TPM awareness training has been completed by management, the building of a
TPM organizational support structure can begin. Based on conversations with the current Puget
Sound TPM focals, by the time this plan is implemented an Enterprise TPM Steering Team and a
Renton TPM Planning team will have been created. Therefore, the purpose of this step is to
create a Management Team and Implementation Teams (one per shift) for the new spar
production system and then tie-in these teams with the enterprise and site-wide organizations.
Figure 16 depicts an example of an organizational structure that could effectively support TPM
implementation and sustainment.
Figure 16 - Example of a TPM support structure organizational chart
An efficient way to coordinate and connect the various teams is to have the team leader from
each organization participate in the meetings of the team the next level up. Since the Renton and
Enterprise teams will already be established, this means that the leaders from the Implementation
Teams participate in the Management Team meetings and the leader from the Management
Team participates in the Renton TPM Planning Team meetings. This step addresses one of the
key barrier to TPM success: lack of TPM support structure. Because this step sets up a
Management Team and integrates teams into the Enterprise and Renton teams it also addresses
74
the key factor of ensuring top management support. Finally, since the teams are made up of
members from both the maintenance and manufacturing groups this step ensures that
maintenance and manufacturing are well coordinated.
Step 4: Establish Basic Policy and Goals
Establishment of basic TPM policies and goals is necessary to achieve successful TPM
implementation. Indeed, one of the top factors identified in the survey was clarity of
organizational objectives and policy with regard to TPM. A basic TPM policy is important
because it provides high level direction towards which all of the TPM activities should be
striving toward. This policy should be in line with the larger manufacturing goals and policies of
the 737 program. Since TPM implementation is a long-term commitment, one basic management
policy should be to commit to TPM and incorporate concrete TPM development procedures into
the medium to long-range management plan [9]. The TPM Management team should choose a
basic TPM policy statement and additional policies that capture the most essential elements of
the TPM implementation and which resonate with the teams. Seeking the input from the
Enterprise Steering Team and Renton TPM Planning team will facilitate this exercise and ensure
that TPM policies in the new production system are consistent with those of Boeing and other
Renton shops. Examples of basic TPM policies and additional policy statements are provided
below:
*
To aim at zero-defects, zero-breakdowns and zero-losses through small groups activities
[15].
0
To reduce losses by eliminating breakdowns, defects, and accidents while enhancing
company profitability and creating a favorable working environment for all employees
[9].
0
100% commitment to TPM by all employees in order to achieve zero-defect, zerobreakdown, zero-loss, and zero-accident operation on ISAL and create an environment of
teamwork, engagement, and continuous learning and improvement.
Although policies can consist of abstract written or verbal statements, the goals should be
quantifiable and precise, specifying the target (what), quantity (how much), and time frame
(when) [9]. Goals such as these are important for demonstrating the progress of the
75
implementation and for keeping the implementation team motivated and on track. Because it can
be difficult to set specific goals with a specific timeframe before a baseline condition is
understood, the Management Team may want to agree on a small set of goal metrics and then
leave it to the implementation team to determine the level and timeframes associated with each
goal metric. It may be tempting to create a long laundry list of goals, but at least initially, a
shorter and simpler list of goals will be more manageable and more useful as a diagnostic and
motivational tool. An example of some metrics to set goals around include the following:
*
Overall Equipment Effectiveness (OEE)
*
Mean Time Between Failures (MTBF)
0
Mean Time to Repair (MTTR)
0
% of preventative maintenance jobs executed on time
9
Ratio of planned to unplanned maintenance work
Finally, it is important that goals are not confused with deployment milestones. Goals measure
the impact TPM is having on equipment operation and performance, while milestones measure
the extent of TPM deployment. Milestones will be covered in the Finalize Master Plan step.
Step 5: TPM Training for Implementation Teams
In this step the members of the Implementation Teams receive comprehensive and in-depth TPM
training. This training should provide teammates with a thorough understanding of TPM and
how to put it into practice. The team should be trained in applicable concepts and skills that will
directly help them execute the TPM Implementation Process (step 7). For example, team
members should leave this training with the skills required to lead autonomous maintenance
workshops, execute root cause analysis, and implement continuous improvement ideas.
Examples of content that this training should cover include the following:
*
"
"
"
"
"
"
76
OEE evaluation
Condition appraisal
Refurbishment and asset care
Data recording
Assessment of the six losses
Problem resolution
Best practice routines
It should be emphasized during this training that the concepts and skills in the course will be
directly applied in the shop. It is important that the participants appreciate that the training they
are receiving isn't for show, but rather will be used and reinforced when they begin to implement
TPM. This step addresses the following factors identified by the survey: well educated work
force and ensuring an understanding of TPM concepts.
Step 6: Finalize the Master Plan
After the Implementation team is trained, the next responsibility of the Implementation Team is
to create and finalize the Master TPM Implementation Plan. It is recommended that the Master
TPM Implementation Plan include:
*
A detailed time phasing of the training program (dates, duration, purpose, and attendees)
" A description of how meetings will be run, the topics that will be covered, and how
frequently the team will meet (I suggest the team meet weekly at the minimum)
" A description and time phasing of the "TPM Improvement Cycle" (See step 7)
" A detailed description and timeline of implementation milestones
" A detailed description and timeline of TPM program goals; including targets (what), level
(quantity), and timing (when)
*
The dates of quarterly reviews
While the first draft of this plan should be developed by the Implementation Team with support
from the Renton TPM Planning Team, it should be reviewed by and finalized with the
Management Team. Getting the Master Plan finalized by the Management Team is an
opportunity to keep them involved, solicit helpful feedback, solidify their buy-in, and most
importantly communicate the resources that will be required to begin implementing TPM in the
near-term and secure those resources.
Thinking about the key factors identified by the survey, documenting the implementation plan in
this way ensures broad ownership, ensures that everyone understands the organization's TPM
policy and objectives, ensures top management commitment, and ensures that the manufacturing
and maintenance organizations are coordinated.
77
Step 7: Execute TPM Implementation
The first six steps of this plan are absolutely essential for creating the conditions for TPM
success. The Planning Phase directly addresses some of the most important implementation
factors identified by the survey. If the Planning Phase is essential for creating the conditions for
success, then step 7 is essential for actually achieving success. This is because this is the step
where the Implementation Team begins the engaging and transformative work of implementing
TPM through daily and weekly activities.
While most implementation frameworks simply step through each the five pillars of TPM by
explaining how each one could be establish individually, the framework presented in this phase
is a nine step, three phase cycle designed to naturally establish these five pillars. The benefit of
the framework presented here is that it sets up a natural cadence and iterative process that the
Implementation Team can work through each week. This framework lends itself better to the
time-constrained reality of the shop floor and provides clear path with concrete tasks that a small
team can execute. Finally, since this cycle is designed to be executed over and over again, the
team will get better at executing with each pass and the TPM program, as well as the benefits,
will continue to improve overtime.
The implementation cycle summarized in this section is called the "TPM Improvement Cycle"
and is adopted from a book by Peter Willmott [10]. This book should serve as a valuable
reference for a more in-depth explanation. The three phases of the cycle are:
" Condition Phase (4 Steps) - Determine the present condition of the equipment and
identify areas for improvement and future care. During this phase, the Implementation
team will determine the autonomous maintenance (AM), and planned, preventative
maintenance (PPM) routines and determine the activities necessary to measure equipment
deterioration.
" Measurement Phase (2 Steps) - This phase assesses the performance history and current
OEE of the equipment. This phase provides a baseline for the measurement of future
improvements.
*
Improvement Phase (3 Steps) - In this phase, the Implementation team will analyze the 6
major sources of losses and develop and implement improvements that will improve the
78
OEE of equipment. During this phase, best practices are standardized and documented as
well.
A summary or the TPM Improvement cycle is depicted in Figure 17. By working through the
steps in these phases, the Implementation Team will have a clear and straightforward method for
implementing and continuously improving the TPM program. The TPM Improvement Cycle will
provide the Implementation Team with a clear set of goals and activities to pursue during time
that is set aside each week for EI team meetings.
Measurement
Phase
5. Equipment histury
6, 6OEE
Improvement Phase
condition Phase
1, Criticality
assessment
a
7. Assessment
of 6
losses
"island". That is, they should communicate their plans with other people, they should solicit
feedback and suggestions, they should communicate progress, and they should recruit people to
help implement solutions. In short, communication is key to TPM implementation, when in
doubt the Implementation Team should err on the side of over-communicating.
The first iteration through these steps may take longer than expected since the Implementation
Team will be establishing the key pillars of TPM for the first time. However, the education
campaign and the public support of leadership should greatly facilitate this work. If the team
finds that they need more time to implement TPM they can communicate these needs with the
79
Management Team and adjust the Master TPM Implementation Plan (step 6) as appropriate.
Finally, this step strongly leverages the two top enabling factors at Boeing: a well-educated
workforce and employee sense of empowerment.
Step 8: Quarterly Review
After the Implementation Team has been iterating on the TPM Improvement Cycle for about a
month, the first quarterly review meeting should be scheduled with the Management Team. Even
though the Implementation Team may not have made a great deal of progress by this point, it is
important to have this early touch point with the Management Team to discuss how TPM
Implementation has been progressing, to review road blocks, success stories, and determine if
plans need to be altered in any way. The focus of the Implementation Team in this meeting
should be on providing candid feedback on progress and the focus of the Management Team
should be on identifying ways to support and enhance the TPM implementation. Topics of
interest at the initial Quarterly Review meeting might include:
*
Autonomous Maintenance implementation progress
" Preventative Maintenance program implementation progress
*
Review of OEE
" Cooperation between maintenance and manufacturing organizations
" Fostering broad ownership of TPM implementation and sustainment
" The TPM organizational structure
" Training needs
After the initial Quarterly Review meeting these quarterly review meetings will be held every
three months. The meetings should adopt a standard agenda that focuses on reviewing:
implementation progress, performance against goals and milestones, success stories, barriers,
training needs, TPM promotion needs, implementation plan modifications and next steps.
The Management Team should be looking to provide any necessary support and to review
progress with regard to the following objectives:
*
Ensuring TPM implementation is coherent and consistent with TPM policy
" Ensuring a consistent and measurable introduction of TPM
80
"
Reviewing progress and achievements against agreed milestones and goals
*
Removing any blockages to progress
" Ensuring that progress, plan intentions and publicity material are regularly communicated
to all employees.
" Determining if the Master Implementation Plan needs to be altered in any way
*
Recognizing and reward effort
This step is designed to address the key implementation factors related to top management
commitment and reward and recognition.
Step 9: Continuous Improvement
Continuous improvement of the TPM program should happening on a day-to-day basis, however,
the opportunity for continuous improvement are especially high during and after each Quarterly
Meeting. During the Quarterly Meeting the Implementation Team should not only present
progress and achievements, but they should also present road blocks and missed milestones and
goals. Presenting the later opens up the opportunity to discuss solutions and continuous
improvement ideas with the Management Team in order to remove roadblocks and create plans
to achieve the missed milestones and goals. After the Quarterly Meeting, the Implementation
Team should take time to document the specific steps that they and/or the Management team is
going to take to remove road blocks and/or change the TPM Implementation Plan. Clearly
documenting these plans and sharing the document with the Management Team creates
accountability and will help ensure that support is received and action items are completed.
6.3 Key Recommendations for a Successful Implementation
By following the implementation plan outlined in this chapter TPM can be successfully
implemented and sustained in the future automated spar subassembly system. This plan was
designed to address the key barriers and leverage the key enabling factors identified in the
survey. This plan was also designed to leverage the existing organizational structures in place in
the Renton factor such as existing TPM organizations and Employee Involvement teams. In
addition to the plan outlined above, the following three key recommendations will ensure that
TPM implementation has the greatest opportunity to succeed.
81
1. The First 6 Steps - The first six steps of this plan represent the planning phase and they are
the most important steps for future success. These steps directly address the main enabling
factors and barriers identified in the survey and set the groundwork for the entire TPM
implementation. It is exciting to execute, but executing TPM without first educating, building
support, and winning the hearts and minds of the people involved will result in a floundering
TPM program. TPM is more about people than equipment and the first six steps of this plan
are about engaging people.
2. Training - TPM requires that people are engaged and empowered. The first step toward
engaging and empowering people is to train them. Therefore proper training is key to
success. The training described in the first six steps of this plan is important, but ensuring
that operators and maintenance personnel continue to be trained during implementation is
equally important. Only by continually assessing and increasing the skills of operators and
maintenance personnel, can continuous improvement of the TPM program be achieved.
3. Metrics of Success - The primary aim of TPM is to improve the effectiveness of equipment
as measured by OEE. Additionally, there are other goals that a TPM program might aim to
achieve, all of which should be objectively and quantifiably measureable. To achieve a
successful TPM implementation it is important that the metrics for success are measured and
broadly shared. Without objective metrics of success, the momentum driving the TPM
program will quickly dry up. Furthermore, without metrics of success the people
implementing and executing TPM will become demotivated and disengaged. It is the
awareness and improvement of metrics like OEE that fuel the TPM program.
82
Chapter 7: Summary and Next Steps
This chapter summarizes the key findings of this thesis, discusses how these findings could be
generally applicable within the airline industry, and provides suggestions for follow-on work.
7.1 Summary and General Applicability
The design team for the automated spar production system identified TPM as a strategy that
could ensure the system's automated equipment operates at peak effectiveness so the production
system can meet its target production rate. However, the justification for TPM implementation
was not clear, the potential impact of TPM on the systems output was not understood, the
barriers and enablers for TPM implementation were not well understood, and an effective
implementation method was not defined. The purpose of this thesis therefore, was to explore the
justification for TPM, understand how TPM could impact the production system, identify the
barriers and enablers, and synthesize this information into a TPM implementation plan that is
appropriate for the context in which it will be used.
Implementing TPM is a significant and long-term endeavor with real costs and potential tradeoffs, so convincing key decision makers to support it and truly commit to implementation is both
critical and potentially challenging. Therefore, without justification and an understanding of the
potential impacts of TPM on the system, it is not possible to move key decision makers through
the Stages of Commitment such that they support and commit to TPM implementation.
In chapter 3 a combination of methods were used to provide justification for TPM
implementation. A literature review showed that TPM has yielded significant benefits in multiple
case studies. Case studies have shown benefits from TPM such as 14-45% increase in OEE, 5090% reduction in breakdowns, 65-90% reduction in defects, and 15-60% reductions in
maintenance costs. While these benefits are highly context specific, they are encouraging and
provide compelling reasons for exploring TPM implementation further. An analysis of
maintenance data from the current spar shop showed a positive correlation between equipment
downtime and overtime. Although this analysis was not done on data from the future spar
production system, it was done on a system that is very similar, and demonstrated that reliability
improvements from TPM could translate into real manufacturing cost savings. Similarly, an
analysis of the preventative maintenance to reactive maintenance ratio in the current spar shop
83
suggested that there is an opportunity for TPM to lower maintenance costs by improving this
ratio from the current 56%:40% planned to unplanned, to the target 80%:20%. Observations of
the maintenance and manufacturing groups revealed that there is an opportunity for TPM to
improve morale and teamwork. Finally, a qualitative analysis of the new production system
showed that it is a good candidate to benefit from TPM as a result of its lean design and
significant use of automated equipment.
The results from the discrete event model analysis presented in chapter 4 further supported the
case for TPM implementation since they showed that reduced system redundancy from increased
PM posed little risk to system output. However, the results also demonstrated the resiliency of
the system to machine failures which may diminish the value of reliability benefits from TPM. It
is important to acknowledge that the analysis in this chapter did not consider other aspects of
TPM beyond PM or other benefits beyond reliability that could justify TPM implementation;
these are important topics of future research.
Referring back to the Stages of Commitment framework, it is reasonable to believe that the
analyses, insights, and justifications from chapters 3 and 4 are sufficient to move key decision
makers through the Commitment Phase towards adoption and institutionalization. In order to
implement TPM a detailed strategic plan is required since TPM is a long-term, complex, multipronged, and multi-stakeholder strategy. While there are many resources available which outline
strategic plans, it is important that the plan is tailored to the specific context in which it will be
applied. Therefore, prior to designing a plan, chapter 5 presented the results of a survey that was
used to determine the barriers and enabling factors that have impacted Boeing's past TPM
implementations. The key lessons from this survey are that the largest barriers were related to a
lack of planning and preparation prior to implementing TPM and the capability and
empowerment of the people implementing and executing TPM (i.e. the operators, mechanics,
and maintainers) are key enablers for the success of TPM. As a result of these insights, the
implementation plan developed and presented in chapter 6 devotes the first six steps (collectively
referred to as the Planning Phase) to building support and awareness, planning, and training
people to implement TPM. The second part of the implementation plan (the Implementation
Phase) is built around a framework called the TPM Improvement Cycle [10] which can be
readily adopted by the El team structure that currently exists within the Renton factory.
84
Justifying TPM implementation through diverse analysis methods and understanding the impacts
of TPM on a system through discrete event modeling can serve as a model and strategy for
gaining commitment to TPM implementation across Boeing and the commercial airline industry
in general. As the competition within the industry continues to intensify and pushes production
systems towards automation in an effort to increase productivity and reduce costs, TPM could
become an ever more important strategy for maintaining and operating these systems while also
minimizing maintenance and manufacturing costs. However, TPM is a long-term commitment
that requires significant investment so stepping through a multi-pronged analysis, such as the one
demonstrated in this thesis, to determine if investment in TPM is justified is both prudent and
necessary to secure commitments.
TPM is not a new concept and many industries have experience implementing it. However,
based on the number of scholarly articles, books, and consulting services devoted to helping
companies implement TPM, it is not unreasonable to state that implementation is difficult even
for those companies with experience. This is because implementing TPM essentially involves
significant organizational change and no matter how many times a company experiences
organizational change it remains a difficult process to undergo. Therefore, the process
demonstrated in this thesis of using a targeted company survey to understand the barriers and
enablers for TPM implementation prior to creating an implementation plan can also serve as a
general strategy that can be used across Boeing and the commercial airline industry. The insights
gained from such a survey are instrumental in designing a customized implementation plan that
will be more likely to succeed than simply using a "cookie cutter" plan available from a book or
literature.
7.2 Suggestions for Future Work
The suggestions for future work fall into three categories: presenting the analysis to key decision
makers, further modeling of TPM benefits, and executing the strategic implementation plan.
*
Presenting the analysis to key decision makers - Chapter 3 and 4 of this thesis show
analysis which support the implementation of TPM for the future spar production system.
Furthermore, this thesis argues that taken together the analysis presented should be
enough to move key decision makers through the Stages of Commitment to adoption and
85
institutionalization. The next natural step in this work therefore, is to actually socialize
this analysis with key leaders within the 737 organization to test the hypothesis that they
will be convinced to adopt and institutionalize TPM. Some of this analysis has been
presented to a limited number of leaders within the manufacturing organization and the
feedback was positive. However, in order to truly achieve the type of buy-in necessary to
implement TPM many more leaders must be exposed to this analysis, asked to critique it,
and provide feedback. It may be the case that some leaders and managers have legitimate
concerns which will require further analysis.
" Additional modeling of TPM benefits and impacts - Chapter 4 provides a list of
additional analysis that should be completed to more fully explore the benefits of TPM
and its impacts on the new production system. The key recommendations from this
chapter include: doing more analysis to understand the costs of implementing TPM and
to monetize the benefits of TPM; analyzing the TPM benefits to output quality and
production efficiency; analyzing the benefits from other aspects of the TPM program
such as autonomous maintenance and continuous improvement efforts. Once empirical
data from the new production system is available, all of these analyses can be made more
accurate.
" Executing the strategic implementation plan - This thesis argues that the TPM
implementation plan presented in Chapter 6 will be effective because it was designed
based on Boeing specific TPM barriers and enabling factors in mind and because it was
designed to integrate into the existing continuous improvement structures already in
place. The only way to test this hypothesis is to actually execute the strategic plan. This
job will likely be the joint responsibility of the manufacturing manager and the industrial
engineer who are currently members of the design team. However, execution will have to
wait until closer to the date on which the new production system will begin ramping up.
86
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90
Appendix A: TPM Enabling Factors and Barriers Survey
Survey Category and Factor and Definitions
*
Behavioral/Organizational Factors - These factors are related the behaviors of employees
and groups of employees as well as the organizational structures or incentives that
influence those behaviors.
*
Top management commitment - Top management from maintenance and manufacturing
understand the business reasons for implementing TPM, feels there is a need to
implement, and understands the consequences of changing or not changing. They clearly
communicated their support to all stakeholders and provided resources (time, money,
input) to support TPM implementation.
" Sense of job security - Employees feel that their job position and function is stable and
secure and do not worry about losing employment when asked to collaborate with or
transfer skills to others.
" Coordination between maintenance and manufacturing organizations - Employees from
these organization communicate regularly about equipment performance and problems.
There is a meeting or tie-in-sheet to communicate equipment problems. Mfg. and
maintenance work together on continuous improvement teams to ensure equipment stays
in proper working condition and coordinate schedules so that preventative maintenance is
done on time and does not interfere with production.
"
Stable leadership - Leadership that supports TPM has a low turnover rate that does not
hamper or disrupt TPM support or implementation. When leadership turnover does
happen there is a smooth transition of TPM support and policy to the new leadership.
" Union support - Site TPM leadership communicated with union leadership or
representatives early in the TPM implementation process. The union understands TPM
concepts and principles and supports the implementation of TPM.
" Reward and recognition mechanisms - Employees are regularly recognized and rewarded
by leadership, management or team leaders for implementing or executing TPM
concepts. For example, there is recognition and rewards for the best process improvement
idea, for the team with the best AM compliance, or for the team with the highest or most
improved OEE metrics. TPM is part of the formal performance management process.
91
"
Strategic Factors - These factors are related to strategic planning by the TPM steering
team, leadership, and champions.
" Effective long-term planning - Prior to launching the TPM initiative a robust and realistic
TPM implementation plan was documented. This plan defined the TPM organizational
support structure, defined roles, laid out a communication and training plan, explained
how the six pillars of TPM would be established, and contained dates and milestones in
order to track implementation progress.
" Clarity of organizational objectives and policy with regard to TPM - Site leadership,
managers, team leads, and site associates understand the corporate TPM program vision,
understand their role in contributing to the success of the corporate TPM program vision,
and understand the measures of "success" as well as the long-term goals associated with
those measures.
" Allowing sufficient time for TPM development - An appropriate amount of time is
allocated to properly implement TPM. Time is allocated by management for training,
AMWs, PMs, small group activities, and root cause analysis. 3 to 5 years is allowed for
the full development of the TPM program.
" Appropriate and realistic expectations set - Site leadership, managers, team leads, and site
associates have an appropriate and realistic understanding of the magnitude and timing of
TPM results. It is accepted that full TPM development can take 3 to 5 years, there are
reasonable and measurable intermediate milestones, and there are frequent updates on the
progress of TPM for their site.
*
TPM support structure - A formal TPM organizational structure exists and supports the
implementation and sustainment of the TPM program. This structure could include an
executive steering team, a site TPM focal, and shop TPM champions which form the
shop TPM teams. This organization is responsible for communicating and planning the
TPM program, ensuring proper training is provided, managing TPM implementation, and
tracking/communicating TPM progress.
*
Broad ownership of TPM planning, execution, and sustainment - Everyone in the shop is
committed to TPM and had some stake in planning, executing and sustaining TPM. In
addition to site leadership and the formal TPM organization, operators, maintenance
92
mechanics and techs, managers and team leads were all trained in TPM concepts and
have a role to play.
*
Cultural Factors - These factors are related to the prevailing attitudes, values, symbols,
and perceptions of a workplace.
*
Tendency to cooperate, communicate and collaborate in small teams - Employees
communicate important equipment information at daily team meetings, there is a standard
tie-in sheet used to communicate equipment information between shifts, there are regular
meetings between operators and maintenance personnel to discuss equipment
performance and condition. Employees regularly teach and transfer skills to each other.
Employees participate in small process improvement teams.
" Ability of TPM to harmonize with or change prevailing culture - When TPM was initially
introduced the concepts, principles, and practices meshed well with the existing culture.
For example, employees were used to working in teams, communicating with people
from other organizations, employees were open to learning new techniques and new
processes, and employees were used to seeing and interpreting data and metrics.
" Employee acceptance of new methods and procedures - In general employees are open to
learning new techniques and new procedures. When new procedures are introduced
employees quickly and readily adopt them as the new standard.
* Employee sense of empowerment - Employees actively participate on employee
involvement teams or other continuous improvement teams. Employees often will
suggest ideas for new ways of performing their work or new tools for performing the
work.
" Operational Factors - These factors are related to the day-to-day processes, methods,
tools, and techniques practiced on the shop floor.
" Standard operating procedures - Operators and maintenance personnel routinely follow
standardized and well documented procedures to accomplish their daily tasks.
" Effective preventative maintenance schedules - Preventative maintenance schedules exist
for all critical equipment. The preventative maintenance schedules are executed in a
timely manner and are coordinated with production schedules. The timing of the
preventative maintenance is frequent enough to keep the equipment operating with high
availability and performance.
93
*
Adept use of continuous improvement tools, techniques and methodologies - Operators,
maintenance personnel, and supporting engineering functions arc well trained in the use
of lean and continuous improvement tools and methodologies.
*
Strong follow-up on progress of TPM initiatives - TPM champions and team members
take timely and frequent action to ensure that TPM initiatives are being executed. The
TPM team communicates progress on TPM initiatives on a weekly or bi-weekly basis.
*
Other lean/improvement processes and initiatives are well coordinated with TPM - Other
lean, continuous improvement, and safety initiatives are well integrated into the TPM
initiative so as to not duplicate efforts, strain resources, or create administrative burden
on managers, operators, or maintenance personnel.
* Technical Factors - These factors are related to the technical competency, knowledge,
and technology present on the shop floor.
" Well educated workforce with strong technical knowledge - The workforce is well
educated and has strong technical knowledge of the equipment and processes required to
complete their work. They have strong analytic and problem solving skills that they
utilize regularly.
" Effective and substantial training and education - All managers, operators, maintenance
personnel, and production support engineers are encouraged or required to complete
training and have ample allocated time for training. The training programs offered are of
high quality and teach skills that are directly applicable to daily work on the shop floor.
* Understanding of TPM concepts and principles - All managers, operators, maintenance
personnel, and production support engineers have a clear understanding of basic TPM
concepts and principles. All personnel were required or encouraged to take an
introduction to TPM training to build this baseline competency.
*
Effective computerized maintenance management system (CMMS) - A CMMS is in
place which documents all required PMs, manages work orders, tracks breakdown
maintenance and PMs, and maintains the spare parts inventories. The CMMS is used to
determine which machines require maintenance, to calculate maintenance cost, and to
analyze breakdown data in order to improve maintenance practices and the equipment
condition.
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*
Accessible performance measures and actionable reliability, quality, and production data
- Production and equipment performance measures (e.g. equipment availability,
efficiency, quality, equipment status, cost of rework, overtime, labor hours, jobs behind)
are tracked and made available to managers and shop floor personnel in a clear and easy
to visualize way. These performance measures provide useful insight are used to manage
daily shop activity.
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