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Purpose – Problem solving and continuous process improvement are key elements to achieve business excellence. Many problem solving and process improvement methodologies have been proposed and adopted by organisations, with DMAIC being the most widely used. The purpose of this paper is to present an empirical application of a modified version of DMAIC which enabled a world-class organisation to achieve an optimum reduction in the lead time of its aerospace engine assembly process. Design/methodology/approach – The paper reviews the most commonly used problem solving and process improvement methodologies and specifically, DMAIC, its variations and limitations. Based on this, it presents define, measure, analyse, improve, review, control (DMAIRC). Finally, DMAIRC is empirically applied through a case study, in a world-class manufacturing organisation. Findings – The results obtained from the case study indicate that DMAIRC is an effective alternative to achieve the maximum improvement potential of a process. In particular, DMAIRC helped the organisation studied to achieve a 30 percent reduction in the lead time of its engine assembly process. Originality/value – The novel problem solving and process improvement methodology presented in this paper can be used by organisations to undertake a more effective improvement project by assuring that the maximum potential of their improvement initiatives and processes is achieved.
A DMAIRC approach to lead time reduction in an
aerospace engine assembly process
Jose Arturo Garza-Reyes
School of Technology, The University of Derby
Derby, UK
Ashley Flint
School of Technology, The University of Derby
Derby, UK
Vikas Kumar
Department of Management, Dublin City University Business School
Dublin City, Republic of Ireland
Jiju Antony
Department of Design, Manufacture and Engineering Management,
University of Strathclyde, Glasgow, UK
Horacio Soriano-Meier
Northampton Business School, The University of Northampton
Northampton, UK
Abstract
Purpose Problem solving and continuous process improvement are key
elements to achieve business excellence. Many problem solving and process
improvement methodologies have been proposed and adopted by organisations,
with DMAIC being the most widely used. The purpose of this paper is to present
an empirical application of a modified version of DMAIC which enabled a world-
class organisation to achieve an optimum reduction in the lead time of its
aerospace engine assembly process.
Design/methodology/approach The paper reviews the most commonly used
problem solving and process improvement methodologies and specifically,
DMAIC, its variations and limitations. Based on this, it presents DMAIRC
(Define, Measure, Analyse, Improve, Review, Control). Finally, DMAIRC is
empirically applied through a case study, in a world-class manufacturing
organisation.
Findings – The results obtained from the case study indicate that DMAIRC is an
effective alternative to achieve the maximum improvement potential of a process.
In particular, DMAIRC helped the organisation studied to achieve a 30 percent
reduction in the lead time of its engine assembly process.
Original value – The novel problem solving and process improvement
methodology presented in this paper can be used by organisations to undertake a
more effective improvement project by assuring that the maximum potential of
their improvement initiatives and processes is achieved.
Keywords: civil aerospace industry, DMAIC, DFSS, lead time and variability
reduction, assembly process, Six Sigma.
Paper type: Case study
1. Introduction
Air travel is an essential part of modern and business life that spans from passenger
transportation to cargo freight. In particular, the civil aerospace industry plays a vital role in
the UK’s economy as it supports over 200,000 direct and 500,000 indirect jobs (UK
Aerospace Industry Survey, 2011). UK Trade and Investment (2011) indicates that the civil
aerospace industry is one of the UK’s highest value adding manufacturing sectors and the
largest aerospace industry within Europe, generating a profit of around £22 billion in 2009.
The UK Aerospace Industry Survey (2011) considers the civil aerospace market, particularly
the aircraft engine market, as fiercely competitive. For example, ‘The Times’ (2000) reported
how the intensity of competition in the aircraft engine market has increased. As a
consequence, manufacturers have tried to improve their production volumes and market share
(UK Aerospace Industry Survey, 2011). This fierce competition has forced aerospace
manufacturers to use and implement different methodologies, such as Six Sigma, to
systematically solve problems and thus improve key operational and quality aspects of their
manufacturing and business processes.
Six Sigma can be considered as one of the most important developments to quality
management and process improvement of the last two decades (Garza-Reyes et al. 2010a).
Six Sigma focuses on the product or process’ critical quality characteristics that are relevant
to the customers. Based on these characteristics, Six Sigma identifies and eliminates defects,
mistakes or failures that may affect business processes or systems. According to Brue and
Howes (2006), Six Sigma has three meanings. Firstly, Six Sigma is a statistical measure of
variation that when achieved, a process would produce 3.4 defects per million opportunities.
Secondly, Six Sigma is a management philosophy and strategy that allows organisations to
achieve lower cost, ensuring competitive operations. And thirdly, Six Sigma is a problem
solving and improvement methodology that can be applied to every type of process to
eliminate the root cause of defects.
One of the Six Sigma’s distinctive and essential approaches to problem solving and process
improvement is the DMAIC model. DMAIC refers to five interconnected stages (i.e. define,
measure, analyse, improve and control) that systematically help organisations to solve
problems and improve their processes. Basu (2004) briefly defines the DMAIC phases as
follows:
Define by identifying, prioritising and selecting the right project,
Measure key process characteristics, the scope of parameters and their performances,
Analyse by identifying key causes and process determinants,
Improve by changing the process and optimising performance,
Control by sustaining the gain.
The DMAIC model indicates, step by step, how problems should be addressed, grouping
quality tools, while establishing a standardised routine to solve problems (Bezerra et al.,
2010). In this context, DMAIC assures the correct and effective process execution by
providing a structured method for solving business problems (Hammer and Goding, 2001).
This rigorous and disciplined structure, according to Harry et al. (2010), is what many
authors recognise as the main characteristic which makes this approach very effective. In
practice, the criteria for the completion of every DMAIC phase are defined. Subsequently, the
project is reviewed, and if the criteria are met, then the following DMAIC phase starts (Basu,
2004; Breyfogle III, 2003).
However, although there is extensive evidence which indicates that Six Sigma, and thus
DMAIC, help organisations to achieve significant improvements in performance, Wheeler
(2005) suggests that one of the main limitations of DMAIC is that it may fail to develop the
full potential of processes. For example, Montgomery (2009) reviews a case study, in the
corporate legal department of DuPont, where DMAIC was employed to develop an efficient
process to allow timely access to needed documents with minimal errors. As a result, the unit
cost of a page document was reduced by about 50 percent while 70 percent of the non-value
added activities were eliminated. Similarly, Kumar et al. (2006) also present a case study
where DMAIC was used to reduce the defects that occurred in a final product manufactured
by an SME Indian organisation. In this case, the percentage of defective products was
reduced from up to 5 percent to up to 0.0016 percent in different areas of the process.
Although in both cases DMAIC helped the corresponding organisations to achieve
important improvements, it is unclear whether the improvement targets could have been
reviewed and redefined in order to seek further improvements. Arguably, due to the linearity
and rigid nature of DMAIC, which requires an improvement team to move through explicitly
defined stages and carry out specific activities in every one of the phases, this may occur with
a significant number of improvement projects guided by this approach. In general, activities
traditionally performed during the improve phase of DMAIC include the identification,
selection, experimentation, implementation, risk analysis and assessments of potential
solutions as well as the documentation of all these activities (Nanda and Robinson, 2011;
Montgomery, 2009; Brue and Howes, 2006; Pyzdek, 2003; Breyfogle III, 2003). However,
neither DMAIC nor the activities involved within the improve stage require or suggest the
review of the improvement(s) achieved and an assessment to determine whether a “more
ambitious” target can be sought, if the full improvement potential of a process has not been
reached.
This paper presents a case study where the traditional DMAIC methodology has been
modified and adapted to the particular requirements, capabilities and culture of a world-class
aerospace manufacturer in order to reduce the lead time and variation of its engine assembly
process. In particular, due to the highly competitive nature of the civil aerospace industry, the
organisation studied needed to reduce the lead time and variability of its aircraft engine
assembly process to its minimum. The paper thus demonstrates that a flexible and adaptable
DMAIC approach (i.e. DMAIRC), as opposed to the traditionally lineal and rigid method,
will be far more beneficial and thus help an organisation to achieve the full potential of its
improvement efforts.
2. Review of problem solving, process improvement models and DMAIC
Different problem solving and process improvement models have been proposed in order
to help organisations improve their manufacturing and business processes. Some examples
include: Ford 8D’s team-oriented problem solving (Ford Motor Company, 1988), Japanese
QC STORY (Tadashi and Yoshiaki, 1995; Hosotani, 1992), Deming’s PDCA cycle (Imai,
1986; Sokovic and Pavletic, 2007; Sokovic et al., 2010), 7 steps method (Wescott, 2006;
Scholtes et al., 2003), RADAR scoring matrix (Hides et al., 2004; Sokovic et al., 2010),
Xerox quality improvement process and problem solving process (Palermo and Watson,
1993), ISO/IEC TR 15504 process improvement approach (Anacleto et al., 2004; van Loon,
2007), Raytheon Six Sigma (Smith et al., 2002), IDEALSM (McFeeley, 1996; Gremba and
Myers, 1997), ADDIE (Islam, 2006), FADE (Krasper, 1992; Schiller et al., 1994), Quality
Function Deployment (QFD) (Vonderembse and Raghunathan, 1997; Ross, 1988). However,
as DMAIC is required by Six Sigma, which has been extensively implemented in industry
(Black and Revere, 2006; Antony, 2004) and considerable attention has been paid to lean Six
Sigma (George et al., 2005; Arnheiter and Maleyeff, 2005; Näslund, 2008), which also
requires the use of this model, DMAIC may arguably be considered the most popular and
used problem solving and process improvement approach.
Harry et al. (2010) and Sokovic et al. (2010) consider DMAIC a linear and explicitly
defined approach to problem solving. However, Basu (2011) argues that the best results from
this problem solving approach are achieved when flexible and unproductive steps are
eliminated. Therefore, Langley et al. (2009) and Assarlind et al. (2012) comment that
different versions and cycles of the DMAIC roadmap have been developed in order to
accommodate different problem solving needs and purposes. One of these versions is
DMAICR (DMAIC Report). DMAICR includes an additional final step which “reports the
benefits of the re-engineered process” into DMAIC (Senapati, 2004). On the other hand,
Jarrar and Neely (2005) comment that the rigid nature of Six Sigma and its over-reliance on
methods and tools may inhibit organisational innovation. Therefore, Jarrar and Neely (2005)
also mention that some practitioners have included innovation as an extra element in the
DMAIC methodology, which have made the traditional DMAIC to be transformed into
DMAI2C. Other variations of DMAIC include P-DMAIC (Project-DMAIC), E-DMAIC
(Enterprise-DMAIC) and DMAIC Plus. In the case of P-DMAIC and E-DMAIC, these
variations of the traditional DMAIC have been adapted to more effectively deal with the
implementation of Six Sigma at two different levels, namely: project (smaller scope) and
enterprise (wider scope) (Chakrabarty and Tan, 2007; Ward et al., 2008; Breyfogle III, 2008).
DMAIC Plus, on the other hand, was developed by Honeywell. The main characteristic of
DMAIC Plus is that it integrates specific lean manufacturing tools such as value stream maps
and thought process maps into a general Six Sigma framework (Kumar et al., 2008).
Furthermore, Gonçalves et al. (2008) proposed the MiniDMAIC methodology to simplify
the traditional DMAIC method in order to address causes and resolutions of problems,
particularly, in software development projects. According to Bezerra et al. (2010), the main
difference between DMAIC and MiniDMAIC is that while DMAIC improves the standard
processes of an organisation and implements such improvements in a controlled manner, the
MiniDMAIC addresses the root cause of the problem only. In addition, DMAIC requires
statistical proof of the problems’ causes and achieved improvements while MiniDMAIC uses
simple tools (i.e. Ishikawa diagram and Pareto Charts) and analyses the obtained
improvements by simply observing the project’s indicators (Bezerra et al., 2009). Finally,
Deshmukh and Lakhe (2009) argue that training is a key element to achieve the expected
results from a Six Sigma project, particularly in small and medium size enterprises (SMEs).
Therefore, they suggest the DMAIC methodology to be customised and enriched with
training (T). This creates a customised process of T-DMAIC for SMEs.
Tjahjono et al. (2010) suggest DMAIC and DFSS (Design for Six Sigma) as the two most
commonly used methods by organisations to implement Six Sigma. However, while DMAIC
is a problem-solving approach that aims at improving already existing processes (Tjahjono et
al., 2010), DFSS is employed to aid in the development of new processes (Edgeman and
Dugan, 2008). Similarly as DMAIC, the DFSS methodology has also been developed into a
number of variations. One of these variations is DMEDI (define, measure, explore, develop,
implement). DMEDI deals with the creation of new processes, services or products (Brue,
2003). Furthermore, Samsung, the South Korean business organisation, employed DMADOV
(define, measure, analyse, design, optimise, verify) to redesign its processes and systems
(Kumar et al., 2008). However, as most of the projects within the supply chain management
(SCM) of Samsung involved the redesigning of processes, DMADOV was not able to
provide the necessary support to execute the entire range of SCM projects (Kumar et al.,
2008). To overcome this limitation, Samsung developed DMAEV (define, measure, analyse,
enable, verify) and incorporated process value chain map techniques and modelling, the
concept of five design parameters (i.e. process, operation rule and policy, organisation role
and responsibility, performance measure, and system) and SCM related investment value
analysis methods (Kumar et al., 2008). Some other variations of DFSS include DCOV
(design, characterise, optimise, verify), DMADV (define, measure, analyse, design, verify)
and DMADV (define, measure, analyse, design, verify/validate). However, Tjahjono et al.
(2010) comment that there are no significant differences amongst these three approaches.
According to Chakrabarty and Tan (2007), the selection of the most effective problem-
solving or design methodology depends on the specific requirements of every organisation
and project, as well as the objectives to be achieved. Similarly, Zhao (2005) comments that
“the best approach a company can take is to understand the critical elements contained within
each version (of DMAIC/DFSS), and then customise what they have learned to fit their
corporate culture”. This is what the world-class aerospace manufacturing organisation studied
in this paper has done as the traditional Six Sigma’s DMAIC model has been modified and
adapted to its specific needs and improvement targets.
3. Aerospace engine assembly process lead time reduction through DMAIRC
This section presents a case study where the traditional DMAIC methodology has been
modified, by including an additional phase called “review” (R), in order to reduce the lead
time and variability of an aircraft engine assembly process of a UK’s world-class aerospace
organisation fiercely competing in the aircraft engine and civil aerospace market. Cameron
and Price (2009) consider a single detailed case study a valid research methodology,
particularly when the focus of the study can not be detached from the organisational context
where it occurs. Even though a single case study might be considered as a limited approach to
prove the effectiveness of DMAIRC, if it is replicated again in this and/or different industrial
context a generalisation and validation of findings can be achieved. Therefore, it would fall to
a future research agenda to test DMAIRC through the use of multiple case studies in different
settings.
Figure 1 illustrates the DMAIRC model and the main activities involved in the review
phase. Within the context of this case study, the review phase allowed the project’s team not
only to compare the improvement results with the target but also to evaluate whether the
initial project’s target could still be improved further in order to achieve additional lead time
and variability reductions. As illustrated in Figure 1, after the improvements are performed,
the review phase requires the team to compare the improvement results against the original
target. If the original target is achieved or exceeded (i.e. r t), then an investigation to
evaluate the feasibility of performing further improvements is required. At this stage, the
improvement team must consider factors such as willingness, capability (i.e. in terms of
resources commitment, for example: time, personnel, further investment, expertise, etc.) and
cost-benefit as part of the feasibility evaluation, before the new targets are defined and the
improvement team performs an additional improvement cycle, which starts on the improve
phase, or in an earlier phase if required. On the other hand, if the original target is not
achieved (i.e. r < t), it is recommended, if the organisation’s elements of willingness,
capability and cost-benefit allow it, to consider repeating the improvement cycle initiating
from the improve phase, or in an earlier phase if needed. This will be an ongoing process
until no further improvements can be made or the organisation determines that no further
improvements can be pursued due to the lack of willingness, capability or cost-benefit. In this
case, the DMAIRC cycle can be finalised by moving into the control phase.
Insert Figure 1 in here
Figure 1 – DMAIRC model
3.1 Define phase
This stage in the traditional DMAIC framework consists of identifying the problem,
clarifying the project’s scope and defining goals (Taner et al., 2007). In general, the aircraft
engine production process consists of four manufacturing stages, namely: (1) module build,
(2) fancase dressing, (3) vertical stack build and (4) horizontal build. Particularly, due to the
strategic importance for the manufacturing organisation studied, this project focused on the
vertical stack build stage (i.e. stage 3) of the engine assembly process as a priority to reduce
lead time and its variability. The vertical stack build stage of the engine production process is
a critical part of the overall fabrication of a large aircraft engine. It consists of the assembly
of five main core modules (i.e. large parts of the aircraft engine) which are those that generate
the engine’s thrust. The study aimed to reduce lead time and variability in order to provide a
more cost effective and efficient assembly process. The historical lead times from past
production runs for the vertical stack build process are shown in Figure 2. In general, the
historical data shows a high degree of instability and variation in the assembly lead time.
Insert Figure 2 in here
Figure 2 – Vertical stack assembly historical lead time
According to Nonthaleerak and Hendry (2006), Murugappan and Keeni (2000) and
Banuelas and Antony (2002), Six Sigma projects should not only focus on monetary savings
but also on having a significant, positive and direct impact to customers. Specifically, the aim
of this study was to deliver a minimum of 20 per cent reduction in lead time for the vertical
stack and to provide a more stable and sustainable assembly process by reducing its
variability to its possible minimum. In the case of the variability target, no specific reduction
target was set as top management and the improvement team recognised that a considerable
proportion of it came from other sections (i.e. module build, fancase dressing, and horizontal
build) of the engine assembly process. From the studied organisation’s point of view, the
reduction of engine assembly lead time and variability were required to meet a product
volume increase planned for the following years and to offer its customers a shorter delivery
time, which is vital in the civil aerospace industry.
3.2 Measure phase
The measure phase includes selecting the measurement factors to be improved (Omachonu
and Ross, 2004), providing a structure to evaluate current performance, as well as assessing,
comparing and monitoring subsequent improvements and their capability (Stamatis, 2004). In
addition, Bertels (2003) suggests that in the measure phase the data collection method to be
used must be defined and implemented. In the case of this project, the main performance
elements to be improved (i.e. reduced) were the assembly process lead time and its
variability. Therefore, lead time and variability (i.e. lead time process range) were considered
the metrics to be used when measuring the success of the improvement activities performed.
According to Chernatony et al. (2000), authors such as Band (1991), Gale (1994) and
Naumann (1995) have advocated the element of “value added” as a strategy for
achieving competitive advantage and have also advised organisations on designing
processes that create value for their customers. Therefore, although it was beyond the
scope of this project to reduce the value added and/or non-value added activities of the
vertical stack assembly process, they were closely monitored not only to study the
DMAIRC model proposed in this paper but also in order to be addressed in future
improvement projects, with the intention of subsequent replications of the DMAIRC
model. In terms of the data collection method, the data was collected through a streamer
and a 3C chart, which were completed by the operators involved in the production
process. This data was then analysed in order to determine the optimum sequence and
timing of the assembly process. The streamer listed all the operations involved in the
workstation, which gave an indication of how much work had been completed in each
shift. This aided in the study of the engine assembly time. The 3C chart, on the other
hand, helped to identify any delays in the assembly process and/or further improvements
to the method and sequence of operations. This gave an indication of the typical delays
observed during each workstation, which prevented a sustainable and constant “takt”
time (Womack and Jones, 2003; Iwayama, 1997).
Once the streamers were completed, the information was recorded by the production
leaders into a Trend (Krichbaum, 2007), Pareto (Hill, 2000), Paynter matrix (Krichbaum,
2007) and Action log (Krichbaum, 2007) chart – TPPA chart. The TPPA chart displayed, in a
single page report, the information collected and the metrics’ performance for the different
workstations that comprise the vertical stack assembly process, allowing an easy tracking and
charting of the data (see Figure 3 as an example of a TPPA chart).
Insert Figure 3 in here
Figure 3 – TPPA chart example
3.3 Analyse phase
This phase involves the analysis of the system, in this case the one related to the vertical
stack assembly process, in order to identify ways to reduce the gap between the current
performance and the desired goal(s) (Garza-Reyes et al., 2010a). Garza-Reyes et al. (2010a)
mention that in order to carry out the analysis of the system, different approaches and
techniques can be used. In particular, Pyzdek (2003) comments that process mapping,
brainstorming, design of experiments (DOE), cause-and-effect diagrams, simulation,
hypothesis testing and statistical process control (SPC) charts are the most commonly used.
The nature of the project and the way in which it is conducted will normally dictate the
selection of the most appropriate approaches (Pyzdek, 2003). As part of the analysis, the
historical lead times of past production runs previously illustrated in Figure 1 where plotted
in Individual (I) values and Moving Range (MR) control charts (Carey, 2003; Breyfogle III,
2004) in order to understand their mean (i.e. average) and degree of variability (see Figure 4).
The overall and historical variability’s range from the past vertical stack runs was found to
be 72.4 hours while the average lead time was found to be 110 hours, as illustrated in Figure
4. As previously commented, the aim of this improvement project was to reduce the lead time
by 20 per cent and stabilise (i.e. reduce variability) the assembly process. The baseline for the
lead time reduction was defined based on the average of the last 6 engines assembled; this
can be seen in Figure 4, where the average lead time was 65.84 hours and variability (i.e.
range) was 14.46 hours. Therefore, the average assembly time had to be reduced to 56 hours
and balanced over the four workstations that comprise the vertical stack process.
Insert Figure 4 in here
Figure 4 Individual (I) values and moving range (MR) control charts for the vertical
stack assembly process
After understanding the historical average lead time and variability in the vertical stack
assembly process, a cause-and-effect analysis was carried out to identify the process inputs
that were likely to be causing long lead times and instability (i.e. variation) in the process.
Dhillon (2003) indicates that cause-and-effect analyses and diagrams are effective methods to
identify root causes of problems as they provide guidance for further inquiries, production of
relevant ideas and orderly arrangement of theories. The key input variables that were
determined, by the key stakeholders of the engine assembly process, as those that caused
unstable lead times are presented in Figure 5.
Insert Figure 5 in here
Figure 5 Cause-and-effect analysis for the engine assembly process unstable lead times
Omachonu and Ross (2004) consider the prioritisation of improvement opportunities a key
element within the analyse phase. Therefore, the information obtained from the cause-and-
effect analysis was then fed into a Failure Modes and Effects Analysis (FMEA) in order to
prioritise improvement initiatives. FMEA is a systematic process for identifying potential
process failures, with the intent to eliminate or minimise their risk of occurrence
(Narayanagounder and Gurusam, 2009; Tiwari et al., 2006). Tiwari et al. (2006) comment
that FMEA is a method used to prioritise potential defects and that such prioritisation is based
on scoring factors such as defects severity, occurrence and detection. The score in relation to
these factors are then combined to calculate a Risk Priority Number (RPN) (Blank, 2004). In
this study, FMEA allowed the identification of the most critical problems, which were used
as a baseline to carry out further analyses to determine the root cause of long assembly lead
times. Based on the highest RPN score, the FMEA results suggested that the main cause of
long assembly lead times and variability in the vertical stack assembly process came from the
“method branch” of the cause-and-effect diagram, relating particularly to the workstations’
load balance and process sequence documented in the Assembly Control Record (ACR).
Therefore, the workstations’ load balance and process sequence were selected as the main
priorities for improvement.
During the analyse phase of the study, a specific engine trial (i.e. before improvements)
was run and analysed in order to obtain a more accurate understanding of the effect of the
current workstations’ load balance and sequence on the lead time, variability and value
added/non-value added activities of the engine assembly process. The vertical stack engine’s
assembly process workload is divided into 4 workstations. An illustration of the results from
this initial engine trial can be seen in Figure 6, which was populated from the streamer and
the 3C charts. The results indicated that the current workstations’ load balance and process
sequence provided a lead time of 64.61 hours and a variability of 7.38 hours. In terms of
value added and non-value added activities, the assembly time (i.e. value added) was found to
be 29.5 hours above the required target, and the non-value added was 35 hours. These results
were taken as the baseline for analysing the results of any improvement made.
Insert Figure 6 in here
Figure 6 Engine trial performance analysis
3.4 Improve and Review phases
The traditional improve phase of DMAIC consists of proposing, testing and implementing
creative solutions to eliminate the root causes of problems (Garza-Reyes et al., 2010a). For
the case of this improvement project, after the improvements were performed, the results
obtained were reviewed, as suggested by the DMAIRC methodology (see Figure 1), in order
to determine whether the goals had been achieved or whether further improvements could
still be carried out. As further improvements were determined to be needed and/or possible,
the measure, analyse, improve and review phases were repeated, at workstation level, over a
period of five cycles (i.e. five engine trials).
3.4.1 Trial engine 1 – improvement
The first improvement trial took into account the lessons learned from the engine trail
“before improvements” ran in the analyse phase. The results of this improvement trial, which
were taken from the TPPA chart, are illustrated in Figure 7. In this figure, the x axis
represents the target “takt” time and the bars represent time over/under the target. Womack
and Jones (2003) define “takt” time as the time that precisely matches the production rate to
the customer demand while Iwayama (1997) defines it as the production time allocated for
the production of a part or product in a line or in a cell. Garza-Reyes et al. (2012) argue that
in order to create a continuous process flow, and thus reduce the lead time of a process,
workload must be evenly distributed among the different workers, operations, or stages that
comprise the production lines. In order to do this, Feld (2001) suggests comparing the “takt”
time against the operation’s time in order to determine if the operation’s cycle time is greater
than the “takt” time. Feld (2001) comments that if this is the case, then action must be taken
to change processes, off load, change the available time, reduce the cycle time, split demand,
add equipment or staff, etc. In the case of this project, the workstations’ load was tried to be
balanced and their cycle time and non-value added activities reduced by testing different
sequences and workloads using computerised builds.
Pyzdek (2003) suggests force field diagrams, 7M tools, project planning and management
tools and prototype and pilot studies as the most commonly used tools in the improve phase.
However, in the case of this improvement project and in order to improve the engine
assembly method as suggested by the cause-and-effect analysis, theoretical computerised
builds were employed. The computerised build improvement activity was carried out with the
aid of a specialised “in-house developed” computer simulation software. This allowed
experimenting with different assembly sequences and workload distributions in order to
determine the optimum ones to be later tested in the assembly line. Garza-Reyes et al.
(2010b) comment that although experimentation in a real manufacturing scenario may be
more desirable, this approach of investigation and analysis would be expensive, time-
consuming, disruptive, unpractical or dangerous to reproduce directly. Therefore, the
theoretical computerised builds were an effective and efficient complementary method to
optimise the vertical stack assembly process to reduce its lead time and variability. In general,
the optimised assembly sequence consisted of defining a process where the shop-floor
operators could simultaneously work on the assembly of the engine with their own specific
instructions.
Insert Figure 7 in here
Figure 7 – Trial engine 1 - improvements results
The analysis from the data engine illustrated in Figure 7 shows that the value added time
in all workstations was reduced when compared to the initial trial “before improvements” ran
in the analyse phase. In particular, significant improvements in workstation 4, where the
value added assembly time was reduced below the “takt” time target, were achieved.
However, workstations 1, 2 and 3 still required further improvements to reduce assembly
times. In terms of the overall value added and non-value added activities, the reduction on
value added activities was 10.5 hours while the non-value added activities were reduced by
3.8 hours. On the other hand, the lead time and variability were found to be 39.84 and 24.76
hours respectively. Even though the results’ review indicated that the target of 56 hours lead
time had not only been achieved but exceeded (i.e. rtrial 1 > t), the improvement team evaluated
the feasibility of performing further trials, as indicated by the review phase of DMAIRC, in
order to pursue a greater reduction in lead time. Therefore, after consultation with top
management and process experts, the team decided to perform further assembly trials in order
to define an even more effective workstation’s load balance and process sequence to reduce
the overall assembly process lead time below 39.84 hours and achieve greater stability.
3.4.2 Trial engine 2 – improvement
Based on the first improvement trial and the experience obtained and feedback provided
through the 3C charts, further computerised builds were performed on workstations 2 and 3.
The results are shown in Figure 8. The results showed a value added time reduction for
workstations 2 and 3 when compared to the improvement cycle 1 (i.e. trial engine 1).
However, workstations 1 and 4 experienced a slight increase in value added time. The non-
value added times also increased dramatically, due to a major shortage of parts causing a
significant late delivery to the assembly line. The overall value added activities were reduced
by 5.4 hours when compared to the previous engine (i.e. trial engine 1). However, the non-
value added assembly time increased by 43.5 hours, reflecting in a poor overall build time of
93.23 hours and variability of 53.38 hours. The review phase of DMAIRC allowed the
improvement team to compare the previous lead time obtained with the assembly sequence
defined in trial 1 and determine that a further reduction in the process lead time was not
achieved in trial 2 (i.e. rtrial 2 < ttrial 1). Since the second trial was negatively impacted by a
special cause variation (Montgomery 2009, Christensen et al., 2007) related to the delivery of
assembly parts, the improvement team decided to continue seeking further improvements by
performing a third trial.
Insert Figure 8 in here
Figure 8 – Engine 2 - improvements results
3.4.3 Trial engine 3 – improvement
The improvements on this trial were also based on computerised builds which particularly
focused on operations in workstations 1 and 4 with an adaption of the improvements made in
workstation 3, as these were highlighed in the 3C charts as improvement opportunities from
the previous engine trial (i.e. trial engine 2). The results achieved after the third trial are
illustrated in Figure 9. The results showed an overall reduction of 11.65 hours in value added
time and a reduced in non-value added time of 58.1 hours. In terms of the overall process
lead time and variability, they were found to be 39.30 hours (i.e. rtrial 3 > t) and 53.92 hours
respectively. Similarly as in trial 1 and as indicated by the review phase of DMAIRC, the
improvement team evaluated and considered the possibility of performing further reductions
in the assembly process lead time. Once more, top management and process experts were
consulted and an agreement to carry out another trial was concurred.
Insert Figure 9 in here
Figure 9 – Engine 3 - improvements results
3.4.4 Trial engine 4 – improvement
Once that all of the computerised build optimisations were completed across all four
workstations, the structure around the following improvements in trial 4 were based on
“practical” experience and feedback from the process experts and operators. Particular
attention was focused on workstation 3, as this recorded the most critical issues on the 3C
charts. The results of the improvement activities carried out in trial 4 are illustrated in Figure
10. As shown in Figure 10, the results from this trial were unsuccessful. In this case, both the
overall value added and non-value added times increased by 9.58 and 24 hours respectively.
In reference to the assembly process lead time, it was found to be 57.62 hours (i.e. rtrial 4 < t)
while the variability was 18.3 hours. Therefore, up to this trial, the process optimisation
resulted from trial 3 was still considered the best option to reduce the assembly process lead
time. This resulted in the method and sequence reverting back to the same performance as in
engine trial 3 for workstation 3. After reviewing the results obtained from this trial, as
indicated by the review phase of DMAIRC, and recognising that further improvements could
still be achieved in the reduction of lead time, the improvement team was expected to run one
last trial.
Insert Figure 10 in here
Figure 10 – Engine 4 - improvements results
3.4.5 Trial engine 5 – improvement
Once more, the improvement approach for this trial was based on the issues raised on the
3C charts, with a particular focus on workstation 1. The results of the improvement activities
carried out in trial 5 are illustrated in Figure 11. This figure shows that the value added time
in workstation 1 was reduced when compared to trial 4, obtaining an overall improvement of
5.5 hours. In addition, the non-value added time was also decreased by 28.8 hours. However,
the results presented in Figure 11 were misleading as the trial engine was moved to
workstations 3 and 4 without the full assembly work from station 2 being completed due to a
part shortage. This resulted in quick assembly times for workstations 2 and 3 and a heavy
workload in workstation 4 as the operators had to recover from the work missed in the
previous workstations. Although the lead time was reduced to 37.54 hours, which represented
the lowest lead time achieved through all the five trials, no reliable conclusions were able to
be obtained in relation to the assembly process optimisation due the special cause of variation
(Montgomery 2009, Christensen et al., 2007) that affected the process. As a consequence, the
workstations’ work load and process sequence defined in trial 3 were considered as the
optimum ones and best options to reduce the current lead time of the engine assembly process
from 65.85 to 39.30 hours.
Insert Figure 11 in here
Figure 11 – Engine 5 - improvements results
3.4.6 Trials and improvements review
Table 1 summarises the historical measures and target set for this improvement project as
well as the lead time and variability achieved through the different trials performed.
Table 1 Improvement project objectives and results obtained from the different trials
performed
In general terms, the aim of this improvement project was to reduce the engine
assembly lead time by 20 per cent. The final results achieved through this project indicate
that the aim was not only achieved but exceeded through a reduction of about 30 per cent.
This has aided the organisation studied to not only attain a significant operational cost saving
but it has also contributed to enhance its reputation with its main customers as the aircraft
engine orders are now delivered within a shorter timeframe. Figure 12 illustrates the “before”
and “after” scenarios in relation to the lead time improvement carried through this study.
Insert Figure 12 in here
Figure 12 - Verification of the vertical stack assembly lead time reduction
In particular, the workload distribution and process sequence tested in trial 3 were
considered as the optimum and most effectives to reduce the current lead time of the engine
Historical Last 6
engines
Improvement
project target
Trial before
improvements
Improvement trials
1 2 3 4 5
Lead time
(hrs) 110 65.84 56 64.61 39.84 93.23 39.30 57.62 37.54
Variability
(hrs) 72.4 14.46 N/A 7.38 24.76 53.38 53.92 18.3 20.08
assembly process from 65.85 to 39.30 hours. In this context, the DMAIRC approach helped
to direct the improvement team through the evaluation of results and critical assessment as to
whether further improvements could be performed. In terms of the process variability,
although it was not reduced and can even be considered large (i.e. 53.92 hours), the
improvement team accepted, based on experience and consultation with process experts, that
as the shop-floor operators start getting used to the new workload and process sequence, the
variability would naturally be reduced. In addition, other improvement projects targeting
other sections (i.e. module build, fancase dressing, and horizontal build) of the engine
assembly process were determined by the improvement team to have a positive future effect
on reducing some of the variability experienced in the vertical stack process. In terms of
value added and non-value added assembly operations, the workload distribution and process
sequence defined in trail 3 offered a reduction of 11.65 hours of value added time and 58.1
hours of non-value added time. Highlighting the non-value added activities contributed to
plan and drive future improvements within the problematic business areas.
3.5 Control phase
According to Taner et al. (2007), this phase involves setting the mechanisms for ongoing
monitoring as well as institutionalising improvements. For this particular study, the
sustainment of the improvements was achieved by modifying the Assembly Control Records
(ACRs) to document the optimised workstations load and process sequence defined in trial 3.
To enable the improvement to continue and for the appropriate information to be recorded, a
control plan was also created and signed by the stakeholders. Particularly, the production
leaders took responsibility for collecting streamer and C3 information while the business
improvement teams compiled the TPPA charts. The results were presented to the senior
management to review the progress of the assembly process and identify which business
functions were contributing to long lead times so a plan could be drawn and resources could
be dedicated to improve them.
4. Conclusions
Dinsmore and Cabanis-Brewin (2010) comment that problem solving and continuous
process improvement are key elements to achieve business excellence. For this reason, many
problem solving and process improvement methodologies have been proposed and adopted
by organisations in their quest for business excellence. The Six Sigma’s methodology
DMAIC may be considered the most important and widely used problem solving approach in
industry. However, although the effectiveness of DMAIC has been widely recognised by
academics and practitioners, different authors and organisations have proposed different
versions of DMAIC in order to adapt it to specific improvement projects and organisational
needs. This paper has presented a case study where DMAIC has been modified and adapted
to the specific needs and improvement goals of a world-class organisation fiercely competing
in the civil aerospace industry. In particular, a “review” (R) phase has been integrated to the
DMAIC methodology. This phase allows organisations not only to review the results
obtained from the improvement initiatives undertaken but also to evaluate the possibility of
performing further improvements in order to achieve the maximum possible potential of a
process. Within the context of the case study presented in this paper where DMAIRC was
applied, the review phase guided the improvement team to carry out five improvement cycles
(i.e. five engine trials) in order to test and validate different workstations loads and assembly
sequences in order to define the optimum ones to reduce the lead time of an engine assembly
process.
In the context of the application of DMAIRC in the organisation chosen as a case study for
this paper, it proved to be extremely beneficial to this large manufacturing enterprise (LME)
by proving an effective method to seek the maximum possible potential of its engine
assembly process. LMEs traditionally benefit from “vast” resources to continuously
undertake improvement projects within their organisations. Therefore, as DMAIRC may
require undertaking several improvement cycles, which by definition will also require the
allocation of resources (i.e. staff, time, investment, etc.), it could be used by LMEs more
effectively and efficiently, as shown in this case study. Finally, the application of DMAIRC
in different industrial settings and organisations is considered to be part of the future research
agenda.
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Define Measure Analyse Improve r ≥ t Control
No
r = improvement results
t = target
Further
improvement
possible?
Re-evaluation and
re-setting of targets
Yes
Yes
No
Review
Figure 1 – DMAIRC model
Figure 2 – Vertical stack assembly historical lead time
Figure 3 – TPPA chart example
Figure 4 Individual (I) values and moving range (MR) control charts for the
vertical stack assembly process
Lead Times
Unstable
Environment
Measurements
Methods
Material
Machines
Personnel
Support
Inspectors
Manpow er
Tooling
Av ailable Workstation
Tooling
C C lass parts
Consumables
Modules
Kits
Adherence
Build Process
Work Cont ent
Documentat ion
Draw ings and
AC Rs
Accurate Lead Tim e
Figure 5 Cause-and-effect analysis for the engine assembly process unstable lead times
2321191715131197531
0
O b se r v a t i on
I n di v idu a l Va l ue
2321191715131197531
0
O b se r v a t i on
M o vi ng R a ng e
2
2
1
1
2
1
1
Engines used as a
performance baseline
to set projects aim
Figure 6 – Engine trial performance analysis
Figure 7 – Trial engine 1 - improvements results
Total engine
assembly time
Non-value added
time
Value added
time
“Takt” time
time
Figure 8 – Engine 2 - improvements results
Figure 9 – Engine 3 - improvements results
Figure 10 – Engine 4 - improvements results
Figure 11 – Engine 5 - improvements results
28252219161310741
0
Ob se r v a t io n
I nd ivid ua l V alu e
1
1
Figure 12 - Verification of the vertical stack assembly lead time reduction
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Over the last decades, there has been increasing tremendous pressure on organizations to carry out their operations in a reliable way to enhance their ecological and societal performance. This has encouraged organizations and researchers identically to recognize ways to execute sustainable operations. Green Lean Six Sigma (GLSS) is a comprehensive approach that not only improves environmental sustainability but also leads to improvement in the financial stability of the organizations. However, before implementing this comprehensive GLSS strategy, each organization needs to identify the critical success factors for the execution of this approach. The current study deals with the identification of critical success factors through literature review, lessons pursued by the experiences of authors and authenticated by a survey of organizations. Six CSFs were identified in this present study to implement this strategy. The study reveals that ‘dedication of higher management and employees’ and ‘organizational willingness to execute Green Lean Six Sigma program’ were considered as most important CSFs for the implantation of GLSS approach. The results have assisted to development of framework for executing GLSS strategy. Academic researchers and practitioners can utilize the outcomes of this manuscript for future survey development. It also investigates CSFs for effectively execution of this strategy and dispenses a brief explanation of each success factors that will be very beneficial for organizations to understand and execute this approach.
... Lean Six Sigma provides a combination of measurement: quality, process efficiency, responsiveness, and cost [1]. Indeed, this method is widely used in manufacturing firms throughout the world and is applied in different industrial fields, that include manufacturing [2][3][4][5][6][7][8] services [9][10], commercial [11] health care [12][13] and logistics [14]. However, the literature on LSS and its application in small-and medium-sized enterprises and is limited [15][16][17]. ...
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Chapter
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Chapter
This study explores how Lean Six Sigma and Simulation can be integrated together based on Sim-Lean approach, using a process improvement effort in a Tunisian Clothing Small Medium Enterprise (SMEs). A structured framework integrating these research methodologies is developed, which might benefit a variety of future clothing process improvement efforts, and could inform quality improvement efforts in other industries. The aim is to allow a successful implementation of the approach in Tunisian Clothing Industry in order to improve the Lead Time, the daily output and the average staying times (min) of jobs waiting in queues.KeywordsLean Six SigmaDiscrete event simulationModelingSewing processProcess improvement
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