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1. Adoption of Six Sigma DMAIC
to reduce cost of poor quality
Anupama Prashar
IILM School of Higher Education, Gurgaon, India
Abstract
Purpose – The purpose of this paper is to demonstrate the systematic application of Six Sigma tools for
identification and reduction of cost of poor quality (COPQ). The studyexamines one of the chronic problems
of failure of cooling fan assembly at repair division of a company dealing in helicopter components.
Design/methodology/approach – The case adopted Six Sigma Define-Measure-Analyze-Improve-
Control (DMAIC) methodology to achieve the goal of reduction in COPQ.
Findings – After completing the Define, Measure and Analyze phase, it was found that use of extreme
tolerances and cross-fitment of bearings are the root cause of cooling fan assembly failure. The major
recommendations made during the Improve phase were to design a bearing matching software for
improving the cross-fitment of bearings and to procure a hydraulic jig with electronic jig instead of
manual jig. The value of implementing these recommended solutions equate to a saving of INR 34 lacs
per annum. Since it was a chronic problem, the company expects this to be a recurring saving.
Originality/value – This specific case exhibits the successful application of Six Sigma DMAIC
methodology in repair and maintenance for driving down the cost of failure and improved processes.
Keywords Six sigma, DMAIC, Rework, Pareto analysis, Cost of poor quality (COPQ),
Capability analysis, Process failure mode effect analysis (PFMEA), Cause and effect diagram,
Control chart, Chronic
Paper type Case study
1. Introduction
Today, organizations strive for an improved level of process capability and a reduced
level of cost of poor quality (COPQ). The bottom-line objective is to generate a
profitable margin and sustainable competitiveness in the market. COPQ is the cost
associated with poor quality of products and services. For a manufacturing company,
COPQ is the total cost of repair, rework, scrap, service calls, warranty claims and
write-offs from obsolete finished goods. The concept of COPQ connects the improvement
priorities of a company with its strategic objectives of achieving improved financial
performance and greater customer satisfaction. As per statistics, COPQ is o10 percent
of sales for companies who are at “Six Sigma” level, about 15 to 20 percent of sales
for companies who are at “four sigma” level and about 20 to 30 percent of sales for
companies who are at “three sigma” levels (Clark, 1999).
One of the well-known approaches to reduce COPQ is a project-based approach
based on Six Sigma DMAIC methodology (Kumar and Sosnoski, 2009). Six Sigma
DMAIC methodology, is used to improve already existing processes and had been
proven to be successful in reducing costs, improving cycle times, eliminating defects,
raising customer satisfaction and significantly increasing profitability in every
industry and many organizations worldwide (Tong et al., 2004). There are number of
studies which show the successful application of Six Sigma DMAIC methodology in
automobile industry (Chen et al., 2005), small-scale enterprises (Sinthavalai, 2006;
Desai, 2006), manufacturing processes (Kumar et al., 2007; Li et al., 2008; Tong et al.,
2004; Kumar and Sosnoski, 2009) and services such as healthcare (Dreachslin and Lee,
2007; Taner et al., 2007) and retail (Kumar et al., 2008).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1741-0401.htm
Received 24 January 2013
Revised 24 January 2013
Accepted 25 January 2013
International Journal of Productivity
and Performance Management
Vol. 63 No. 1, 2014
pp. 103-126
r Emerald Group Publishing Limited
1741-0401
DOI 10.1108/IJPPM-01-2013-0018
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Sigma DMAIC

2. The basic statistical tools used in five phases of DMAIC methodology helps identify,
quantify and eliminate the root cause of waste or rejections and sustain the improved
performance of the production line with well-executed control plans in future (Desai
and Shrivastava, 2008). The goal is to stop the defects before they appear and reduce
the COPQ by adopting a predictive rather a reactive approach toward rejection
and rework.
The repair division of a company dealing in helicopter components based in India
was facing the challenge of high COPQ. The division was responsible for overhauling
of critical and technology intensive components of transport helicopter. The present
paper presents the case of a Six Sigma project as practiced in the repair division of the
referred company.
Purposes of this paper are:
. to describe the application of the Six Sigma DMAIC methodology for reducing
the rejection or rework (COPQ); and
. to report preliminary findings, and to examine conditions which contributed to
the successful implementation.
The remainder of the paper is laid down as follows: the first section briefly reviews
the literature on status and adoption of Six Sigma in a variety of context such as
manufacturing, healthcare, retailing, financial services, etc. The following sections
provide an overview of the five phases of the DMAIC methodology to provide a context
of the case study. The discussion of the implementation is concluded in the last section.
2. Literature review
Before taking up the case explanations, let us briefly review the studies that have
shown successful cases of Six Sigma DMAIC methodology applications in a variety
of contexts.
Cho et al. (2011) identified the key ingredients of Six Sigma based on a survey of
diverse sizes, industries and implementation phases of Korean companies using factor
analysis. Compared with other studies conducted by empirical methods, the survey
data for this study were statistically analyzed and the evaluation results were represented
as quantitative indicators.
Mohamed conducted an empirical survey to validate the effectiveness of the
most influential barriers to Six Sigma implementation in a developing country.
The study identified the soft impediments – knowledge and support, and hard
impediments – professionals and finance, as the most influential barriers to Six
Sigma implementation. The study found that decision makers and quality managers
should not waste their resources on overcoming all Six Sigma barriers since only
specific barriers significantly influence the Six Sigma implementation in relation
to dimensions of organizational factors.
Firka (2010) investigated the results of a study conducted by the Argentinean
Institute for Quality to determine the variables which influenced the evolution and
growth of Six Sigma methodology within a set of organizations. The study involved
a qualitative investigation of multiple cases through interviews with key players in
some organizations. The study found that there cannot be a “one size fits all” definition
of Six Sigma, particularly years after the initial deployment. The study highlighted the
need for specific ways by which Six Sigma core concepts can be consciously
incorporated in the organization through a planned path.
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3. Tjahjono et al. (2010) identified the latest trends, approaches, tools and techniques,
benefits and combinations of Six Sigma with other concepts by carrying out a
systematic and thematic literature review. The study proposed four interpretations
of Six Sigma: first, a set of statistical tools, second, an operational philosophy of
management, third, a business culture and fourth, an analysis methodology. The study
emphasized that the main goals of Six Sigma implementation, i.e. improving efficiency,
profitability and capability in the process, remained unchanged.
Antony and Desai (2009) presented the results from an empirical investigation
of Six Sigma status in the Indian industry. The results of empirical study reflected the
reasons for application of Six Sigma by Indian organizations, the most and least
commonly used tools and techniques, critical success factors (CSFs) for the implementation
of Six Sigma, and common impediments in the implementation.
Kaushik and Khanduja (2008) applied Six Sigma DMAIC methodology to specific
case of thermal power plant for conservation of energy. They implemented Six Sigma
project recommendations to reduce the consumption of demineralized (DM) make-up
water from 0.90 to 0.54 percent of maximum continuous rating (MCR) resulting in a
comprehensive energy saving of INR 304.77 lacs per annum.
Krishna et al. (2008) presented a case study illustrating how a multinational Indian
corporation was able to successfully implement Six Sigma principles to improve its
operations. The study provides an assessment of the importance of Six Sigma strategy
in Indian manufacturing companies.
Kumar et al. (2008) presented a case of implementation of the Six Sigma DMAIC
approach for improvement in service system by a major consumer electronics and
appliance retailing company in the USA. The study used a combination of design
methods which included creating multiple service blueprints and implementing the Six
Sigma DMAIC approach. A Service Quality (SERVQUAL) Survey was conducted and
analyzed the data, to give an understanding of customer satisfaction with the service
provided at the company, compared to its major competitors. The service blueprint was
analyzed and strategies were recommended to improve the present system, with the
goal of providing better customer service and an improved shopping experience. With
the recommendations, an “improved service model” for the company was created
along with fail safe mechanisms to ensure that service guarantees will be met.
Li et al. (2008) presented a specific case on implementation of Six Sigma DMAIC
approach to improve the capability of the solder paste printing process by reducing
variations in thickness from a nominal value. The study adopted the DMAIC approach,
which included the Taguchi method to reduce the estimated standard deviation of
solder thickness from 13.69 to 6.04. In addition, the process capability index Cpk was
enhanced from 0.487 to 1.432.
Kumar and Sosnoski (2009) highlighted the potential of DMAIC Six Sigma in
realizing the cost savings and improved quality by using the case study of a leading
manufacturer of tooling. The study examined one of the chronic quality issues on shop
floor by utilizing Six Sigma tools. The study showed that DMAIC Six Sigma process
is an effective and novel approach for the machining and fabrication industries to
improve the quality of their processes and products and ensuring profitability by
driving down manufacturing costs.
Kumar et al. (2007) presented a case on application of Six Sigma DMAIC
problem-solving methodology to identify the parameters causing casting defects and
to control these parameters. The results of the study were based on the application of
tools and techniques in the DMAIC methodology, i.e. Pareto Analysis, measurement
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4. system analysis, regression analysis and design of experiment (DOE). The results
showed that the application of the Six Sigma methodology reduced casting
defects and increased the process capability of from 0.49 to 1.28. The application
of DMAIC resulted in a significant financial impact (over US$110,000 per annum)
on the bottom-line of the company.
Dreachslin and Lee (2007) presented a case on application of Six Sigma DMAIC
techniques in determining the effectiveness of diversity initiatives in healthcare
management in the USA. The authors inferred that Six Sigma and DMAIC can be
applied to diversity initiatives such as ameliorating racial and ethnic disparities in
healthcare, and recruiting and retaining a diverse workforce.
Taner et al. (2007) designed five case studies in healthcare to show the performance
improvement accomplished by Six Sigma DMAIC presenting a road-map for problem
solving and service/process improvement. The findings showed that the healthcare
organization gained a greater ability to address challenges across the system; maximized
resource utilization; reduced redundancies, waste and rework; diminished bottle-necks
related to scheduling; improved working conditions for healthcare personnel. The results
showed that healthcare organizations are able to increase their market share in the long
run after Six Sigma implementation.
Martins et al. (2006) carried out an action research in a Brazilian cosmetic factory
to identify the enablers and inhibitors of Six Sigma project. The study identified
that the main enablers were the continuous support of Six Sigma champion who
was the company’s CEO, different backgrounds of team members, involvement of
team members, team members’ knowledge of process, preliminary results which
provided a positive feedback, and the intensive presentation of results of project.
The inhibitors were found to be difficulty to collect data, uncertainty regarding the
project payoff, difficulty to carry out experiments interfering in the production
process, lack of previous production process map, and the difficulty to apprehend the
Control phase of DMAIC. Moreover, the existence of an interaction among some
those factors was evidenced.
Sinthavalai (2006) developed and evaluated a model for implementation of Six
Sigma in small manufacturing enterprises (SMEs). The study highlighted that SMEs
have limitations when employing the Six Sigma program due to the limited time, cost
and effort, and other barriers typical of an SME (e.g. organization culture, structure
and strategies). Therefore, SMEs need a different methodology to guide and facilitate
the implementation of Six Sigma. The study proposed self-learning web-based system
(e-learning systems) for employees in order to reduce the costs of hiring the consultancy
and training, which are the high investment costs in Six Sigma implementation.
Desai (2006) presented real-life case where Six Sigma has been successfully applied
at one of the Indian small-scale industries (SSI) to improve one of the core processes.
The studies reflected that SSI sectors are constantly on the alert to gain a competitive
edge, using the many tools and techniques that have long been flaunted as a way to
beat the competitions.
Chen et al. (2005) presented a case study in context of automobile industry in
Taiwan. The study used the DMAIC model of Six Sigma to measure the performance of
customer requirements by creating a questionnaire and analyzing the performance
of the product quality mechanism. The study used the key elements found in the
quality process as countermeasures for planning and improvement.
Hendry (2005) presented empirical evidence from 11 case study companies in
context of Asian industry to determine the issues associated with implementation
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5. process of Six Sigma methodology and how it differs between manufacturing and
service processes. The study found that while the Six Sigma phenomenon is generally
perceived positively, Six Sigma is more appropriate for high risk, complicated, large
scale and cross-functional projects and its costs cannot be justified for smaller
projects. The evidence also suggested that Six Sigma is less successful if it is too
much geared toward shortsighted financial targets that do not have management
involvement.
Hensley and Dobie (2005) proposed a model for assessing the readiness for Six
Sigma in service organizations. The study identified two components of organizational
readiness: organizational experience with improvement programs and organizational
understanding of processes. The proposed model was applied in an urban public
transit company by using a survey. The results of the survey identified the perceptual
differences between customers and the organization which provided the basis for
the development of a process improvement program that employees should be able
to undertake.
McAdam and Evans (2004) examined the principles and practice used in the
implementation and operation of Six Sigma. The study critically evaluated the application
of Six Sigma in two sites and at four levels within a case organization. Both qualitative and
quantitative research data were used for an investigation of Six Sigma implementation
and deployment. Qualitative data took the form of in-depth interviews of the key Six
Sigma senior management personnel in both plants. The quantitative data were obtained
from questionnaires distributed to four levels and two sites in the organization.
Tong et al. (2004) designed a case on improvement on the sigma level of the
screening process, which is regarded as the most critical process in printed circuit
boards (PCB) manufacturing through the DMAIC approach. At the early stages,
process capability analysis (PCA) and statistical process control (SPC) were used to
measure and analyze the current printing performance of the screening machines.
During later stages, DOE was conducted to determine the optimal settings of the
critical to quality (CTQs) factors in the screening process. The study showed that by
using these optimal settings, Six Sigma performance can be achieved.
Goh and Xie (2004) presented an overview of the Six Sigma approach as it is taken
today, followed by a discussion on how this approach can be taken further to enhance
the competitiveness of organization. The study emphasized that in an increasingly
competitive and globalized environment, the traditional Six Sigma concept will be
outright irrelevant since in many situations “sigma levels” cannot be measured. The
study emphasized that a thriving, growing and winning organization cannot be
operating on the basis of error avoidance aspects such as imagination, vision,
leadership, passion and creativity. As per the study, these aspects are irrelevant to
the DMAIC straight jacket.
3. Case study
The case is about repair division of a company engaged in overhauling of
critical components of transport helicopters. The division was struggling with
a high COPQ. It identified three areas for improvement and based on Project
Prioritization Index (PPI) selected the project on reduction in failure of cooling fan
assembly (Table I).
A cooling fan is a vital sub-assembly of a helicopter and its function is to cool
down the lubricating oil of aero-engines and gearbox by forced circulation of the
ambient air. The division was repairing 80-90 cooling fan assembles in a year and
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6. the failure rate of cooling fan assembly at the testing phase was as high as 9 percent.
Thus, resulting in a COPQ (rework cost) of about INR 42 lacs per annum as shown in
Table II. This was the chronic problem for the company.
The division initiated a project in January 2011 with the aim of reducing this high
COPQ. A project team comprising of members of functions impacted by the project
implementation was formed. The team adopted project by project approach using Six
Sigma DMAIC methodology for solving the problem.
3.1 Methodology: Six Sigma framework
The project team followed the DMAIC process within a Six Sigma framework. The five
phases (Define-Measure-Analyze-Improve-Control) and key tools used in each phase
are listed in Figure 1.
These phases are discussed in detail in the following sections.
3.1.1 Define phase. The Define phase involves creating a project charter, identifying
customers to the project and their needs and requirements (CTQs) and creating a high
level process map (Benbow and Kubiak, 2010).
Project charter. The problem statement, mission statement, project goals, process
boundaries, project team composition and the project milestones are specified as part
of the project charter.
Potential
savings (INR)
Cost to
complete (INR)
Probability of
success
Time to
complete (year)
Projects S C P T
PPI ¼
(S/C) (P/T)
Reduction in cost
of flight test 2,000,000 350,000 0.7 0.5 8.00
Reduction on spurious
fire warning
in helicopter 1,600,000 200,000 0.8 0.75 8.53
Reduction in cooling fan
assembly failure 4,200,000 300,000 0.8 0.5 25.20a
Note: a
Highest PPI (reduction in cooling fan assembly failure project)
Table I.
Project selection (PPI)
Average production of cooling fans per year 82
Average rework per year (9%) 8
Cost of overhaul INR 7.1 lac
Cost of man hours at INR380/man hr (460 std man hours) 174,800
Cost of spares 387,000
Cost of consumables 124,000
Cost of testing (on test rig) 24,460
Cost incurred in rework INR 12.57 lac
Cost of man hours at INR 380/man hr (460 std man hours) 315,400
Cost of spares 696,600
Cost of consumables 196,800
Cost of testing (on test rig) 48,400
Loss incurred in rework or COPQ per year INR 42.18 laca
Note: a
COPQ per year
Table II.
Calculation of COPQ
(cooling fan assembly)
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7. Problem statement. The problem statement framed considering the aspects of
time-period, specificity and measurability (Snee, 2001) is as follow:
On an average, 9% of cooling fan assemblies failed during the final testing. This cost a
financial loss of INR 42 Lacs per annum to the company.
Mission statement. The rule of thumb is to solve 50 percent of the problem within four
to five months of time (Eckes, 2001). The mission statement is framed as:
The mission of the present project is to reduce the failure of cooling fan assembly during final
testing from 9% to less than 2% to achieve an annual gain of INR 34 Lacs, within five months
( January to May 11).
The details of project goals, process boundaries, project team composition and project
milestones are specified as part of the project charter in Appendix 1.
Identification of CTQ factors. A CTQ is an attribute of an assembly, sub-assembly,
product or process that has direct or significant impact on its direct or perceived quality.
The CTQ concept in Six Sigma enables to improve the quality from the customer’s
perspective (Lucas James, 2002). The CTQ tree for the present project is shown in Figure 2.
High-level process map (SIPOC). The high-level process map or SIPOC is a high-level
picture of the process that depicts how the given process is servicing the customer
(Desai, 2006). The SIPOC chart is illustrated in Figure 3.
3.1.2 Measure phase. This phase involves mapping the existing process (“as is”
process map), analysis of measurement system (MSA), data collection plan, calculation of
baseline sigma (present sigma level) and capability analysis of CTQs (Benbow and Kubiak,
2010). The “as is” flow chart of overhauling the cooling fan assembly is shown in Figure 4.
Process description. The overhauling process commences on receipt of the cooling
fan unit. The fan assembly is the dismantled, cleaned and inspected for any damage
and if found satisfactory, it is sent for micrometry or non-destructive testing. If the
parts are found damaged, they are replaced with new and serviceable ones. After
micrometry/NDT, if these parts are found within specification limits, they are treated
with appropriate electroplating/anti-corrosive treatments. After these processes,
final cleaning and subsequently post-micrometry is done. Parts which fail during
post-micrometry are to be again sent for protective treatment. If the parts are found
to be within specification limits, they are sent for assembly. After the assembly is
Phases Tools
Project Charter, CTQ Tree, SIPOC
“As is” Process Map, Measurement System
Analysis (MSA), Data Collection Plan, Baseline
Sigma, Capability Analysis
Brainstorming, Cause and Effect diagram,
Pareto Analysis
Counter Measure Matrix, Impact/Effort Matrix,
Solution Prioritization Matrix, Process Failure Mode
Effect Analysis (PFMEA), Implementation Plan,
Cost Benefit
Control Plan, Control chart
DEFINE
MEASURE
ANALYSE
IMPROVE
CONTROL
Figure 1.
Six Sigma framework
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8. done, impeller clearance between the cooling fan blades and the housing is checked
and if found ok, the cooling fan unit will be passed for final testing. In case, the
clearance is not within the specification limit, the assembly has to be dismantled
again and has to go through all the process as mentioned above. After the final testing,
if the assembly fails, it has to go through the similar procedures. After successful
testing, the cooling fan assembly is sent for preservation/packing and after proper
documentation, it is passed out to either operating unit/helicopter final assembly.
Data collection plan for CTQs. A data collection plan was formulated depicting
the data type, sample size, data collection method and responsibilities. Format used for
data collection is illustrated in Table III. Data collected using this plan are given
in Appendix 2.
Need
Reduce COPQ
(Failure of cooling
fan assembly)
Prevented
impeller rubbing
Improved airflow
Prevented spline
damage
Extended dents,
notches
Flow rate
(4.85 + 0.25 m3
/sec)
Blade cleaner
with housing
(0.2 + 0.03 mm)
Driver Requirement
Figure 2.
A CTQ tree
Kit Marshal
Supplier Input Process Output Customer
Can ‘D’ Fan
Units
Dismantling
Overhauled
Fan Units
H/C Assembly
Line
OEM
Initial
Cleaning
Defectation
Coating
Final
Cleaning
Assembly
Testing
Spares
Consumables
Local
Supplier
H/C
Operators
Figure 3.
High-level process
map or SIPOC
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9. Capability analysis of CTQs. The capability analysis of the data on CTQs was done
using the Mini-Tab. For both the CTQ parameters namely impellor blade clearance (the
capability index, Cp ¼ 0.64, Cpk ¼ 0.53 which is o1.33) and flow rate (the capability
index, Cp ¼ 0.78 Cpk ¼ 0.53 which is o1.33) the process was found to be incapable.
The capability plots are shown in Figure 5.
Base line sigma level. The present sigma levels were calculated for attribute CTQ
based on the following defect per million opportunities (DPMO) method (Benbow and
Kubiak, 2010).
DPMO ¼ Number of defects 106
/number of opportunities number of units
Considering:
. number of units (cooling fan units assembled) ¼ 40;
. defect (fans with clearance beyond specification limits) ¼ 9; and
. opportunity(CTQ as arrived in Define phase) ¼ 3.
The baseline sigma for attribute CTQ was found to be 3.1.
START
Receipt
Dismantling Cleaning
Viewing
Parts
OK ?
NDT
Micrometry
Checks
Parts
OK ?
Final
Cleaning
Parts
OK ?
Assembly
Test
OK ?
Pass Out
End
Yes
Yes
Yes
Yes
No
No
No
Impeller
check OK ?
No
Yes
Electro-plating /
Anti-corrosive
Post-
Micrometry
Replace
Parts
Functional
testing
Packing/
Preservation
Note: COPQ due to rework is detected in two stages mentioned above and are
marked in red line in the diagram
Figure 4.
“As Is” process map
CTQ Operational definition Data type Size (each) How collected By whom
Table III.
Data collection plan
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10. 3.1.3 Analyze phase. During this phase, the project team examined the measurements
collected in the Measure phase and sought explanations for the various readings by
using the following tools and techniques: brainstorming, cause and effect diagram,
Pareto analysis (Benbow and Kubiak, 2010).
Cause and effect diagram. The project team conducted brainstorming to identify
the probable cause of failure. In order to narrow down the focus, these causes were
categorized in different categories in the cause and effect diagram (Figure 6).
Pareto analysis. In order to prioritize the cause, the team plotted a Pareto chart using the
data collected for 31 rework cases during last four years (2006-2011) as shown in Figure 7.
Based on the results of Pareto analysis, the team selected the problem of “extreme
tolerances” and “wrong fitment” for the improvement phase.
Identifying key process input variables (KPIVs). The project team used above
mentioned causes as basis for further detailed analysis to identify the contributing
Process Data
LSL USL
LSL USL
5.0 5.1
4.9
4.8
4.7
4.6
0.16 0.17 0.18 0.19 0.20 0.21 0.22 0.23
Between/Within Capability of Flow Rate
Between/Within Capability of Impeller Blade clearance
LSL 4.6
B/W
B/W Capability
Overall Capability
Pp 0.81
0.55
1.07
0.55
*
PPL
PPU
Ppk
Cpm
Cp
CPL
CPU
Cpk 0.53
1.03
0.53
0.78
Overall
B/W
B/W Capability
Overall Capability
Pp 0.65
0.55
0.75
0.55
*
PPL
PPU
Ppk
Cpm
Cp
CPL
CPU
Cpk 0.54
0.73
0.54
0.64
Overall
*
5.1
4.76875
40
0.106684
0.106684
0.102961
0
Target
USL
Sample Mean
Sample N
SD(Between)
SD(Within)
SD(B/W)
SD(Overall)
Process Data
LSL 0.17
0.23
0
0.0157125
0.0157125
0.0153518
0.195474
95
*
Target
USL
Sample Mean
Sample N
SD(Between)
SD(Within)
SD(B/W)
SD(Overall)
Observed Performance Exp. B/W Performance Exp. Overall Performance
Observed Performance Exp. B/W Performance Exp. Overall Performance
PPM LSL 0.00
0.00
0.00
PPM USL
PPM Total
PPM LSL 0.00
0.00
0.00
PPM USL
PPM Total
PPM LSL 52,483.20
13,996.60
66,479.79
48,524.68
12,255.96
60,780.64
PPM USL
PPM Total
PPM LSL
PPM USL
PPM Total
PPM LSL 56,850.15
951.49
57,801.64
PPM USL
PPM Total
PPM LSL 50,609.70
647.15
51,256.85
PPM USL
PPM Total
Notes: (a) Capability plot of impeller blade clearance; (b) capability plot of flow rate
Figure 5.
Capability plots of CTQs
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11. factors for each major cause. The team identified five KPIVs for the cooling fan assembly
based on the know-how of process owners with the objective of funneling the input
variables to identify most critical inputs and measuring their performance. The KPIVs,
its type and specifications are given in Table IV.
The team prepared a KPIV Impact Matrix to analyze the impact of the KPIVs on the
performance of cooling fan assembly as shown in Table V.
30
25
20
Vital Few
Useful Many
Extreme
tolerances
Frequency
Cum. Frequency 67.7
67.7
80.6
12.9
21 4
87.1
6.5
2
93.5
6.5
2
96.8
3.2
1 1
100
3.2
Percent
Wrong
Fitment
Bad profile
of blade
Ineffective
Iubrication
Test rig
faulty
Wrong
Handling
0
10
20
Cum.
Percent
30
40
50
60
70
80
90
100
Frequency
15
10
0
5
Figure 7.
Pareto analysis
Impeller rubbing
Loose
attachment
Wrong fitment
on Test bed
Use of extreme
tolerances
Play in
bearing
Wrong fitment
Operator error
Ineffective
Iubrication Damaged
blade
Bad profile
of blade
Ineffective
cleaning
Lesser air flow
Test rig
faulty
Reduced
speed
Wrong
fan fitment
Wrong
handling
Wrong fitment
Non-adherence
of SOPs
Use of extreme
tolerances
Spline damage
Play in
bearing
Failure of
Cooling Fan
Figure 6.
Cause and effect diagram
KPIVs Type of data Specifications (mm)
Inner diameter of sleeve Variable 60 þ 0.030
Outer diameter of the bearing Variable 60 þ 0.036
Outer diameter of fan shaft Variable 3070.017
Inner diameter of the bearing Variable 30 þ 0.015
Clearance between the hub and shaft Variable 0.033-0.071
Table IV.
KPIVs
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12. The team collected data for KPIVs and performed capability analysis to study the
variation. Out of these five, the spread for two was found more, these were:
. inner diameter of bearing sleeve (Figure 8); and
. outer diameter of fan shaft (Figure 9).
Capability analysis for rest of KPIVs is attached as Appendix 3.
The root causes emerged out of the entire journey established the following root
causes contributing the failure of cooling fan assembly:
. use of extreme tolerance specifications; and
. cross-fitment of bearings.
3.1.4 Improve phase. This phase involves identification of possible solutions, their
implementation and verification of workability of the solutions (Benbow and Kubiak,
2010).
Identification of possible solutions. In order to find out the probable solutions,
the team brainstormed and constructed a counter measure matrix (Figure 10).
Impact (CTQs)
Sl. no. KPIV High Medium Low
1 Inner diameter of sleeve Impeller rubbing
Spline damage
Bracket damage
2 Outer diameter of the
bearing
Impeller rubbing
Spline damage
Less air flow Increased power
consumption
3 Outer diameter of fan shaft Impeller rubbing
Spline damage
Less air flow
Bracket damage
Increased power
consumption
4 Inner diameter of the
bearing
Impeller rubbing
Spline damage
Bracket damage
5 Clearance between the hub
and shaft
Impeller rubbing
Less air flow
Spline damage Increased power
consumption
Table V.
KPIV impact matrix
*
0
0.51
*
PPM USL
Observed Performance
Between/Within Capability of Inner Diameter of Sleeve
PPM Total
PPM USL
PPM LSL 0.00
0.00
0.00
7,947.19
68,323.22
76,270.41
PPM Total
PPM USL
PPM LSL
Exp. B/W Performance Exp. Overall Performance
PPM LSL
PPM Total 67,754.94
61,517.69
6,237.25
60.032
60.024
60.016
60.008
60.000
0.00742466
0.00769284
0.00769284
40
60.0186
60.03
60
Process Data
LSL
Target
USL
Sample Mean
Sample N
SD(Between)
SD(Within)
SD(B/W)
SD(Overall)
B/W
Overall
B/W Capability
Cp
CPL
CPU
Cpk 0.50
0.50
0.80
0.65
Overall Capability
Pp
PPL
PPU
Ppk
Cpm
0.51
0.83
0.67
USL
LSL
Figure 8.
Capability analysis
of inner diameter of
bearing sleeve
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13. 30.000
29.994
29.988
29.982
*
*
B/W
SD(Overall)
SD(B/W)
SD(Within)
SD(Between)
Sample N
Sample Mean
Target
USL
LSL
Process Data
29.983
30.017
29.9963
40
0.00194525
0.00615011
0.00645041
0.00673829
Between/Within Capability of Outer diameter of fan shaft
LSL USL
Overall
B/W Capability
Cp
CPL
CPU
Cpk 0.69
1.07
0.69
0.88
Overall Capability
PPL
PPU
Ppk
Cpm
Pp 0.84
0.66
1.02
0.66
30.012
30.006
PPM Total
PPM USL
PPM LSL
Observed Performance
0.00
0.00
0.00
PPM LSL
PPM USL
PPM Total
Exp. B/W Performance
19,425.60
674.79
20,100.40 PPM Total
PPM USL
PPM LSL
Exp. Overall Performance
23,992.23
1,076.44
25,068.67
Figure 9.
Capability analysis of
outer diameter of fan shaft
Cooling Fan
Failure
Extreme tolerance
specifications of shaft
and bearings
Ineffective cleaning
Cross fitment of
bearings
Reduce Specification
tolerances
Develop Bearing Matching
software
Manual Matching of spares for
close tolerances
Improved cleaning bath
Training operator with existing
processes
Replace manual jig with improved
power jig
Quenching shaft before fitment
Training operator with existing jig
Figure 10.
Counter measure matrix
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14. The team created an Impact/Effort Matrix for assessing the potential impact of
solutions against the estimated effort (Figure 11).
The following probable solutions emerged out of the Impact/Effort Matrix:
. creating a bearing matching software by utilizing the micrometry data;
. procuring an improved automated jig for fitment of bearings; and
. procuring an improved cleaning bath for effective cleaning.
A solution prioritization matrix was created on the basis of the criteria of: likely defect
reduction, time and cost of implementation, and likelihood of future complaints and
safety (Table VI).
5
5
4
4
3
Impact
3
2
2
Effort
1
Training operator
Manual matching
of spares
Quenching shaft
before fitment
Improved
cleaning bath
Use of Bearing
Matching Software
Improved
automated jig
Reduce spec.
tolerances
0 1
Figure 11.
Impact/effort matrix
Prioritization criterion
Likely defect
reduction (10)
Time to
implement (6)
Cost to
implement (8)
Future complaints
and safety (6)
Solutions Score w/score Score w/score Score w/score Score w/score Total (30)
1. Reduce spec.
tolerances 9 90 2 12 1 8 1 6 116
2. Use of bearing
matching software 9 90 4 24 7 56 5 30 200
3. Manual matching
spares 3 30 5 30 7 56 2 12 128
4. Improved
automated jig 8 80 4 24 5 40 5 30 174
5. Quenching of shaft 7 70 3 18 2 16 2 12 116
6. Training of operator 3 30 5 30 6 48 2 12 120
7. Improved cleaning
bath 7 70 4 24 4 32 4 24 150
Table VI.
Solution prioritisation
matrix
9
=
;
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15. Implementation of solutions. The following solutions were implemented for the
improvement of reduction in failure of cooling fan assembly.
Bearing matching software: for improving the cross-fitment of bearings, software
was created in MS Access using the micrometry data collected for warranty claims.
This software helped find the best matching bearing available in the bay depending
upon the shaft and sleeve number. A screenshot of the software is shown in Figure 12.
Hydraulic jig with electronic display: a hydraulic jig with electronic display was used
in place of manual jig with torque meter in order to improve the fitment of the bearing
(Figure 13).
Modified cleaning bath: a modified cleaning bath was procured in place of old bath
in order to improve cleaning of the spares.
Process failure mode effect analysis (PFMEA): the PFMEA of the process was
conducted for critical steps (micrometry, assembly, testing and packing/transport).
All the failure modes and its effect were identified, controls were planned and
RPN numbers of before and after controls were analyzed and the effectiveness after
implementing the improvements was assessed. It was observed that the RPN numbers
for all the key process activities were significantly reduced after implementation of the
solutions. The PFMEA is shown in Table VII.
The team prepared an implementation plan to implement the proposed solutions
(Table VIII).
Cost benefit analysis: a cost benefit analysis was carried by the team to compare the
cost incurred in the project vs the benefits achieved (Table IX).
After implementing the recommended process changes and actions, the following
results were achieved:
. An estimated saving of INR 3,834,100/-, by the reduction in COPQ for the
current year, was recorded. It is interesting to note that the cost incurred for
implementation of solutions was one-time in nature and hence the organization is
expected to gain full amount in future years.
Figure 12.
Bearing matching
software
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Sigma DMAIC

16. . Reduction in cooling fan assembly defects from 9 percent to almost no defects.
. Opportunities of use of bearing matching software for aero-engine and other
component assemblies.
. Understanding the significance of database applications in assembly line
operations.
. Enhanced customer satisfaction.
3.1.5 Control phase. To sustain the improvement of the sigma level of the processes,
the following control measures were recommended by the project team (Table X):
. The impact of the improvements on the out of control KPIVs was monitored by
using control charts (Figure 14).
4. Conclusion and discussion
It is a well-known fact that quality is a prerequisite and is no longer a differentiator
in today’s intensely competitive business climate. However, the contribution of
COPQ toward total costs and to the bottom line can never be over-emphasized.
Since COPQ cannot be mapped by current accounting practices hence it remains
a hidden cost. This present case is an application of Six Sigma DMAIC methodology
with the objective of reducing COPQ in repair division of a helicopter company.
The project team reduced the rejection rate of cooling fan assembly from
9 percent to almost 0 percent by systematic application of problem solving
statistical tools.
Apart from the repair division of the company, conducting this project created
quality awareness in other areas of the company. Juran’s project by project approach of
quality improvement was well accepted by the employees and many quality
Figure 13.
Hydraulic jig
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17. Scores after
improvement
Process step Potential failures Potential effects S Potential causes O Current controls D RPN Actions S O D RPN
Micrometry Wrong measure-
ment
Rubbing of
impeller
Damaged spline
9 Instrument error
Operator error
Data entry error
1 Supervisor checks
QA checks
Periodic
calibration
1 9 Periodic training 9 1 1 9
Assembly Wrong matching
Cross fitment
Rubbing of
impeller
Damaged spline
Lesser air flow
9 Operator error
Wrong spares
Use of extreme
tolerances
Faulty jig and
fixtures
5 Supervisor checks
QA checks
3 135 Bearing matching
software
Automated jig
Modified grease gun
Training
9 1 2 18
Testing Wrong results Lesser air flow
Rework
7 Ineffective
cleaning
Test rig faulty
New operator
2 Supervisor checks
QA checks
2 28 Improved cleaning
bath
Regular training
Preventive maint
7 1 1 7
Packing/
transport
Damage Damaged spline
Damaged
bracket
8 Ineffective
packing
Careless
handling
4 Final check 2 48 Improved packaging
Training
New spline blanking
7 1 1 7
Table
VII.
Process
failure
mode
effect
analysis
(PFMEA)
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Six
Sigma
DMAIC

18. improvement projects were proposed and approved by the top management after
successful implementation of the present project. Involvement of leadership proved to
be the major success factor of the project. This project unfastened the path for a new
culture and shared aims in the company.
The Six Sigma DMAIC methodology is appropriate fit for the reduction in COPQ
in manufacturing processes such machining, fabrication, repair and maintenance,
Plan Activity Responsibility Target date Completion date
Communication
plan
Develop and implement:
Training needs
Communication needs
In charge
Fan Bay
20 April 15 April
Resource plan Estimate manpower
requirement
Identify resources
Develop job responsibility
In charge
Testing Bay
20 April 18 April
Budget Develop cost benefit
analysis
In charge
Admin
20 April 20 April
Process
implementation
Data collection plan
Amend work packages
Pilot testing
In charge
Assembly Bay
20 April 20 April
Control plan Identify control measures
Develop and implement
control plan
In charge
Assembly Bay
30 April 30 April
Table VIII.
Implementation plan
(a) Bearing matching software INR 5,000/-
(b) Modified cleaning bath INR 98,000/-
(c) Hydraulic jig with electronic display INR 280,000/-
(d) Modified grease gun INR 900/-
(e) Total expense (a þb þ c þ d) INR 383,900/-
(f) Benefit per annum INR 42,00,000/-
(g) Benefit for the current year (fe) INR 38,34,100/-
Table IX.
Cost benefit analysis
Root cause Control measure Responsibility
Extreme tolerance specifications
of shaft and bearings
Use bearing matching software for
assembly of bearings
Incorporate in work package
Training to operators
Control chart of bearing fitment
Technical officer
Supervisor
Operators
Ineffective cleaning Use of improved cleaning bath
Incorporate in work package
Training of operators
Technical officer
Supervisor
Operators
Cross fitment of bearings Replace manual jig with improved
and powered jig
Incorporate in work package
Training of operators
Technical officer
Supervisor
Operators
Table X.
Control plan
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19. supply chain, etc. (Kumar et al., 2007; Li et al., 2008; Tong et al., 2004; Kumar
and Sosnoski, 2009). This approach can benefit different industries for solving
quality-related problems. However, the companies will be most likely to succeed
if their top-level management is supportive of a continuous improvement culture.
Another key to success is to choose projects that can be measured and that
potentially have a good return on quality.
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I-MR Chart of Difference Between Outer Dias
0.000
–0.002
Individual
Value
–0.004
–0.006
–0.008
1 2 3 4 5 6
Observation
7 8 9 10 11 12 13
LCL = –0.007392
x
–
= –0.003846
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Range
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0.003
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1 2 3 4 5 6
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MR = 0.001333
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Figure 14.
Control chart
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Sigma DMAIC

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21. Further reading
Kumar, M. (2007), “Critical success factors and hurdles to Six Sigma implementation: the case of
a UK manufacturing SME”, International Journal of Six Sigma and Competitive
Advantage, Vol. 3 No. 4, pp. 333-351.
Wessel, G. and Burcher, P. (2004), “Six Sigma for small and medium-sized enterprises”, The TQM
Magazine, Vol. 16 No. 4, pp. 264-272.
Appendix 1. Project Charter
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22. Appendix 2. Data for CTQs
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23. Appendix III
Figure A1.
Capability analysis
of KPIV’s
125
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24. About the author
Dr Anupama Prashar is a Lean Six Sigma Black Belt certified trainer and has guided
over 30 green belt projects. She has 13 years of experience in the area of research, consultancy
and management education. Her primary teaching interests are in the area of quantitative
analysis and operations management. Her research areas include operational efficiency
and business improvement. She has published a number of papers in various international
and national journals. She has authored books on industrial safety and environment.
Dr Anupama Prashar can be contacted at: prasharanu@gmail.com
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