Initiatives of six-sigma in an automotive ancillary unit: A case study

Abstract

It is a commonly visible scenario in todays’ market, especially in small and medium sized enterpris-es (SMEs), where the focus is on quantity rather than quality. This paper explains the common high rejection problem of a SME and how the productivity levels were enhanced after the successful implementation of Six-Sigma DMAIC methodology. Once the project completion industry was able to acquire many tangible and intangible benefits, this paper offers a systematic step by step illustration of DMAIC methodology to help the other firms start similar productivity improvement initiatives.

Full text

* Corresponding author.
E-mail address: pardeep2206@gmail.com (P. Rana)
© 2018 by the authors; licensee Growing Science, Canada
doi: 10.5267/j.msl.2018.4.033




Management Science Letters 8 (2018) 569–580
Contents lists available at GrowingScience
Management Science Letters
homepage: www.GrowingScience.com/msl
Initiatives of six-sigma in an automotive ancillary unit: A case study
Pardeep Ranaa* and Prabhakar Kaushikb
aResearch Scholar, UIET, Maharshi Dayanand University, Rohtak, Haryana, India
bAssociate Professor, UIET, Maharshi Dayanand University, Rohtak, Haryana, India
C H R O N I C L E A B S T R A C T
Article history:
Received: November 26, 2017
Received in revised format: Janu-
ary 31, 2018
Accepted: April 26, 2018
Availabl e online:
April 27, 2018
It is a commonly visible scenario in todays’ market, especially in small and medium sized enter-
prises (SMEs), where the focus is on quantity rather than quality. This paper explains the common
high rejection problem of a SME and how the productivity levels were enhanced after the success-
ful implementation of Six-Sigma DMAIC methodology. Once the project completion industry was
able to acquire many tangible and intangible benefits, this paper offers a systematic step by step
illustration of DMAIC methodology to help the other firms start similar productivity improvement
initiatives.
© 2018 by the authors; licensee Growing Science, Canada
Keywords:
Six-Sigma
Productivity Improvement
Quality Engineering
DMAIC Methodology
1. Introduction
Six-Sigma was introduced by Motorola, and in very short time, due to its enormous benefits, it was
introduced in many large scale manufacturing organizations across the globe (Kaushik et al., 2012;
Kaushik & Mittal, 2015; Kaushik, 2016a,b; Srinivasan et al., 2016; Uluskan, 2016). But the problem still
exists on how to apply it in SMEs. The evidences of Six-Sigma application in small and medium scaled
industries are very little. In large industries, Six-Sigma is an emerging and one of the most effective
business strategies all over the world. As for big manufacturing industries Six-Sigma achieved positive
results over different productivity problems, so it can also provide useful results for small scale industries
as well (Sreedharan & Raju, 2016). For SME sector to become successful in the present competitive
scenario, the strategy needs to be innovative (Biswas & Chowdhury 2016). SME sector needs immediate
attention due to its participation in global progress in form of breakthrough strategy other than Statistical
Process Control (SPC) (Kaushik et al., 2017a). In order to achieve customer satisfaction, researchers and
industrialists around the world have worked over various tools (Kaushik et al., 2017b,c) and techniques
like Total Quality Management (TQM), Quality circles (Mittal & Prajapati 2014), APQP (Mittal,
Kaushik, & Khanduja 2012; Mittal et al., 2011, 2012), Shainin system (Mittal et al., 2017b), Quality
Function Deployment (QFD) (Mittal & Kaushik 2011; Tewari et al., 2017), Decision Tree Analysis


570
(Mittal et al., 2017; Mittal et al., 2017a), Fuzzy logic (Mittal et al. 2016a), Total Preventive Maintenance
(TPM), Business Process Reengineering (Mittal et al., 2016b), Lean and Six Sigma (Kaushik et al., 2012;
Kaushik et al., 2016a). The most popular of the techniques named above is Six-Sigma. It aims to find out
the basic causes in process and eliminates them to achieve business excellence (Kaushik et al., 2016b).
Various aspects of DMAIC strategy have been analyzed by researchers in great details in their own terms
and literature suggests that so far, Six Sigma has been mostly thought of quality management tool for
large manufacturing industries alone. With this in mind, an attempt has been made to visualize the appli-
cation of Six-Sigma in a SME explained in form of a case study.
The organization under consideration is a SME manufacturing automobiles components such as valves,
locks, carburetor repair parts, float needle, main jet, slow jet, throttle needle, chock piston, jet holder,
gasket kit, slide screw, air screw, etc. The company was established in the year 1987 with a vision to
deliver quality automobile components to fulfill the requirements of the Overall Equipment manufactur-
ers (OEMs). It owns a sophisticated manufacturing unit and is equipped with the latest technology and
tools to fabricate quality products which render long term service. The name of the company is withheld
at its behest, to maintain confidentiality of the company records.
The main product of company is locks and one variety of lock is hood latch lock as shown in Fig 1. The
company was facing high rejection rates due to tight movement of lock’s hood which ultimately results
in “Hook not return” complaint in long stay. Hence, it became essential to validate the design of the
product without changing the riveting specification due to high rejection rate and willingness of staff and
management to improve quality.

Fig. 1. Hood Latch Lock
Lock is the main component of any automobile vehicle and it should not cause any problem to any cus-
tomer while making it lock-unlock. Six-Sigma DMAIC methodology (Fig. 2) was selected to solve prob-
lem and to reduce effort while locking-unlocking. Various phases and their implementation are as fol-
lows:
1.1 Define
This is the first phase of any Six-Sigma project and mainly deals with following the voice of customer.
Customers’ provided specifications are refolded and relooked in this phase. Various brainstorming ses-
sions were held and process flow diagram and SIPOC diagram were drawn. Process flow diagram is a
representation of the activities performed on the raw material till the final product is manufactured. It is
a systematic flow or step by step procedure that will be followed on the raw material until the final
required product is manufactured. Process flow diagram for making hood latch lock is shown in Fig. 3.

P. Rana and P. Kaushik / Management
Science Letters 8 (2018)
571
Fig. 2. DMAIC Methodology



 


   





Fig. 3. Process Map
Control
Monitor the website and ensure that the key metrics are in check
Improve
Identify, Evaluate, Select and Implement the right improvement solutions
Analyze
Analyze the current state and identify the opportunities for improvement
Measure
Identify and Measure the Critical Quality Factor
Define
Understand the requirements and formulate the vision and mission
No
No
No
Yes
Y
es

Yes
Raw Material Receipt
Ins
p
ection

Manufacturin
g
Ins
p
ection
Assembl
y
Final Ins
p
ection
Di
sp
at
c
h
R
ejec
t

R
ejec
t
Re
j
ect


572
SIPOC
is a high level process map and a Six Sigma tool. It is used to obtain a descripton of the process
at hand, as well as define the boundaries of the project. General way of drawing a SIPOC starts from
cutomer (right) and working towards supplier (left) as shown in Fig. 4. Parts used for making hood latch
locks are hood base plate, washers, hook, spring and rivet as shown in Fig. 5.







Fig. 4. SIPOC Diagram

Fig. 5. Parts Used for Making Hood Latch Locks
1.2 Measure
This phase generally involves measuring the extent of problem and recording the results of process.
Firstly, in this phase factors which are critical to quality were listed and after that Gauge R&R study
(Mittal et al., 2018; Mittal & Kaushik, 2018) was performed to determine whether the tool used for
measuring the diameter of spring is working properly or not.
Gauge R&R study:
The aim behind this study is to categorize variation due to appraisers/operators and
measuring instruments. In the current study, sample size of 20 was taken over two operators taking two
readings on each sample, making a total of 40 readings as shown in Table 1 and Table 2. The instrument
used for measuring spring diameter is Screw Gauge. Result of Gauge R&R showed Repeatability at 25.60
and Reproducibility at 0.00 percent, putting average percentage study variation at 25.60 percent < 30
percent. Hence, it indicates that Screw Gauge was correct.
Flow
Supplier Input Process Output Customer
Hood Latch
Lock Mfg.
unit
Lock Rejec-
tion Data
Critical
Analysis of
Rejection
Decreased
DPMO
Hood Latch
Lock Mfg.
Management
Customer Satisfaction
and Relationship
Six Sigma
Methodolo
g
y
Thinking

P. Rana and P. Kaushik / Management Science Letters 8 (2018)
573
1.3 Analysis
In this phase, the real root-cause analysis is performed using various statistical tools. After knowing the
extent of the problem in measure phase, various brainstorming sessions are held and a list of suspected
source and causes of rejection is prepared. In this case, one by one, components were analyzed to see the
main cause of problem in the lock movement.
Table 1
Minitab Data Sheet of Spring Diameter for Gauge R&R Study
Sequence
No.
Operation
Sequence no.
Operator
Trial Part No. Readings (Diameter, mm)
1 1 1 1 3 0.94
2 2 1 1 6 0.97
3 3 1 1 9 0.96
4 4 1 1 1 0.99
5 5 1 1 4 0.93
6 6 1 1 7 1.00
7 7 1 1 8 0.94
8 8 1 1 10 0.96
9 9 1 1 2 1.01
10 10 1 1 5 0.95
11 1 1 2 6 0.96
12 2 1 2 1 0.93
13 3 1 2 3 0.97
14 4 1 2 9 0.94
15 5 1 2 2 1.01
16 6 1 2 8 0.99
17 7 1 2 10 0.94
18 8 1 2 4 0.97
19 9 1 2 7 0.95
20 10 1 2 5 1.00
21 1 2 1 8 1.02
22 2 2 1 6 0.94
23 3 2 1 2 0.97
24 4 2 1 1 0.99
25 5 2 1 5 0.98
26 6 2 1 3 0.93
27 7 2 1 10 0.94
28 8 2 1 7 0.96
29 9 2 1 4 1.01
30 10 2 1 9 0.97
31 1 2 2 4 0.98
32 2 2 2 9 0.94
33 3 2 2 7 1.01
34 4 2 2 1 0.99
35 5 2 2 10 1.02
36 6 2 2 5 0.96
37 7 2 2 3 0.95
38 8 2 2 8 0.94
39 9 2 2 2 0.97
40 10 2 2 6 0.98
Suspected source of variations are categorized in two parts for further analysis. These are: -
 Process Variation
Assembly Riveting Process
 Input Product Variation
a) Hook Thickness


574
b) Washer Thickness
c) Hood Base Thickness
d) Rivet Height
e) Spring Diameter
Table 2
Result of Gauge R&R (Spring Diameter)
Source Std. Dev
Study Var. % Study Var.
(6* SD) (%SD)
Total gauge 0.0271712 0.163027 25.60
Repeatability 0.0271712 0.163027 25.60
Reproducibility 0.0000000 0.0000000 0.00
Part to Part 0.0024468 0.014681 98.97
Total Variation 0.0272812 0.16368 100.00
First suspected source of variation was Rivet height. Modified component search tool was selected for
its analysis. This tool is used when the problem is on an assembled product and parts will get damaged
during disassembling and rivet pin will get damaged during disassembly during analysis. Also, the com-
ponent was replaced with new pin for the First trial and Second trial run. A Best of Best (BOB) and
Worst of Worst (WOW) sample was collected based on Attribute Index. Both BOB & WOW assemblies
were disassembled two times and response is shown in Table 3.
Table 3
Response of Modified Component Search
Good (BOB) Bad (WOW)
Initial Value 1 5
First Disassembly and Reassembly 1 4
Second Disassembly and Reassembly 2 4
Based on the results obtained from Table 3, D/d ratio was calculated which tells whether rivet pin is
causing problem or not. Here, ‘D’ refers to difference of Medians of BOB and WOW, whereas ‘d’ indi-
cates the average sum of range of BOB and WOW. If D/d is equal to or more than 3 then it is concluded
that the component is not causing problem. Table 4 shows the results of the modified component search.
Since, in this search D/d ratio comes out to be equal to 3. Hence it can be stated that Assembly Process
or Replaced component is not causing any problem, the other parts are causing problem. Secondly, the
spring diameter was checked, whether it is causing problem or not. The dimension of spring diameter is
0.98mm +/- .5mm. A histogram (Fig. 6) for the 40 spring diameter readings was drawn. Histogram dis-
plays the large data that is difficult to interpret and also indicates process capability.
Table 4
Calculation of D/d Ratio
BOB
(
+
)
WOW
(
-
)
Initial sam
p
le 15
First trial 1 4
Second trial 24
Media
n
14
Range 11
D/d ratio 3/1=3
The Histogram clearly shows that the data for spring diameter is centric. Hence, it is not the reason for
the tightness of the hook of Hood Latch Lock. After the conclusion on spring diameter, three components
(Hook, Washer and Hook base plate) were left that might be causing problem. One by one component

P. Rana and P. Kaushik / Management Science Letters 8 (2018)
575
was disassembled from Good and assembled in the bad and the response is taken as shown in Table 5,
Table 6 and Table 7.
List of suspected components are
1) Hook – A (A-R+, A+R-)
2) Washer – B (B-R+, B+R-)
3) Hook Base Plate – C (C-R+, C+R-)
1.021.000.980.960.940.92
7
6
5
4
3
2
1
0
readings
Fr e q ue n c y
M ean 0.973
StD ev 0.02544
N40
Histogram of readings
Norm a l
Fig. 6. Histogram for Spring Diameter
A conclusion table for the above analysis was drawn as Table 8.
Table 5
Response for Hook (A-R+, A+R-)
Good Assembly (+) Response Bad Assembly (-) Response
A-R+ 1 A+R- 4
Table 6
Response for Washer (B-R+, B+R-)
Good Assembly (+) Response Bad Assembly (-) Response
B-R+ 5 B+R- 1
Table 7: Response for Hook Base Plate (C-R+, C+R-)
Good Assembly (+) Response Bad Assembly (-) Response
C-R+ 1 C+R- 5
Table 8
Conclusion of Hook, Washer and Base Plate Response
A-R+, A+R- Replacing of Hook from Good to Bad & Bad to
Good No Reversal in Response
B-R+, B+R- Replacing of Hook from Good to Bad & Bad to
Good (Both Washers were replaced) Complete Reversal of Response
C-R+,C+R- Replacing of Hook from Good to Bad & Bad to
Good No Reversal in Response


576
Finally, it is evident form the Table 8 that washer was the component causing problem. In the next step,
validation of the results obtained from analysis was done. The component identified i.e. Washer was
swapped to the original assemblies and checked for complete reversal as a part of validation as shown in
Table 9, which validated the root cause.
Table 9
Initial Assembly Response
Good (BOB) Bad (WOW)
Initial Value 1 4
After the validation process, for finding the optimum value of washer thickness, Paired Comparison was
performed. In Paired Comparison, 8 BOB and WOW assembly parts were selected based on the attribute
Response of tightness. All the assemblies were disassembled & tabled in ascending order.
Specification of Washer: 1.00 mm & Tolerance: +/-0.25 mm
Table 10
Paired Comparison
Thickness Res
p
onse
.90 G
.91 G
.91 G
.91 G
.93 G
.95 G
1.03 B
1.03 B
1.03 B
1.05 B
1.05 B
1.05 B
Conclusions based on Paired Comparison
 From the response Table 10, it can be definitely concluded that Washer more than 1.02mm diameter
is creating the problem.
 The washer should be less than .95mm and washer tolerance should also be considered for revision.
 Considering Washer Thickness as the response Multi Variant Analysis for the 10 Cavity Mold Tool
was conducted. Multi Variant Analysis is used only when the problem is generated from a manufac-
turing process. Table 11 shows the multi variant analysis done for washer thickness as response. Cav-
ity to Cavity variation is found to be more than part to part, so the corrective action is required for the
cavities.
Table 11
Multi Variant Analysis for Washer
SAMPLE CAVITY
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
1 0.92 0.88 0.9 0.92 1.02 0.92 0.91 1.03 0.92 0.91
2 0.92 0.89 0.88 0.91 1.03 0.93 0.91 1.03 0.93 0.92
3 0.92 0.88 0.9 0.92 1.02 0.92 0.92 1.02 0.93 0.91
Range 0 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Average 0.9 0.9 0.9 0.9 10.9 0.9 1 0.9 0.9
Part to Part 0.02
Cavit
y
to Cavit
y
0.1

P. Rana and P. Kaushik / Management Science Letters 8 (2018)
577
1.4 Improvement phase
This is the fourth phase of Six Sigma DMAIC methodology and in this phase, improvement is made in
the cause of problem by implementing the corrective measures recommended by the team in analysis
phase. As in current case study, washer thickness is found to be the source of problem so the improvement
action for the same is taken into account. The washer is manufactured by molding process, so as a first
corrective step, mold tool was improved at the manufacturing end by revising tolerance. Improvement
required in size for various parts is shown in Table 12.
Table 12
Corrective Actions for Revising Tolerance in Mold Design
COMPONENT RESPONSE
Hook Thickness 2.6 mm, Tolerance Not Specified In The Drawing
( UT Working Tolerance=+/-0.05 (2.55 ~2.65mm) No Change in Specification
Rivet Pin :4.7,+0.15/+0.05, 4.75~4.85mm (Drawing Tolerance) No Change in Specification
Washer : 1.0mm Tolerance As Per (Engineering Specification) =+/-
0.25(0.75 ~1.25mm)
Tolerance Revised ( 0.90 -0.0/
+0.05)
Fig. 7 and Fig. 8 show the picture of molded component and 10 Cavity mold for washer. Various tests
including flexure strength testing (Kaushik et al., 2017; Kaushik et al., 2017) were also performed for
changes made in the thickness of washer.
Fig. 7. Molded Component Fig. 8. 10 Cavity Mold Tool
1.5 Control Phase
This is the final phase of Six Sigma DMAIC Methodology. In this phase results of the improvement
phase are checked. The true aim of this phase is to cross check the implementation and raise a feedback
system if deviation is visualized. In present case study, as the mold tool is the only component which
required improvement. After the implementation of recommended actions, defects in hood latch locks
were reduced to a great extent. Results shows the decrease in PPM in four months after the improvement
done in the mold tool used for making washer. Initially, PPM was about 1550 which has been reduced
to nearly 100 PPM in a short period of four months. Additionally, a control plan for the mold tool has
been prepared to keep a check on the variation in washer thickness.


578
2. Results and Discussion
The results showed a huge monetary gains when calculated. Successful implementation of SIX-Sigma
DMAIC methodology brought a financial benefit of Rs. 104000 per month. Similar measures were ap-
plied to the products of same part family which raise the extrapolated annual benefit to around Rs.
1500000, which is a huge amount for SME. The calculation for the same is as follows:
Before Improvement,
Cost of Poor Quality = Rs 1, 20,000 per month
Rejections per month =300 per month on average
Cost of poor quality per rejection = Rs 400 per rejection
After Improvement,
Rejections per month = 40 per month (reducing)
Cost of poor quality per rejection = Rs 16,000 per month
Savings in cost after improvement = Rs (1, 20,000 – 16000) = Rs 1, 04,000
One can understand and gain profit from Six Sigma strategy by its project by project application in small
sized enterprise. For the upliftment of the enterprise in the global market and strengthening of the bottom
line in small sized enterprise, Six Sigma can play a vital role and is much awaited strategy. To extract
the benefits from Six Sigma one should believe in it and prepare the road map and implement it into the
industry and proceed earnestly. So for the observation of impact of Six Sigma in SME’s, an attempt has
been made to implement it in car lock manufacturing organization. The study was an attempt to allay
myths and fears of Six Sigma implementation in small scale industries. Since small industries have their
own constraints and resource limitations, so efficacy of Six Sigma to improve productivity, without major
investments, has been highlighted by the results of the study.
3. Conclusions
It can be concluded that Six Sigma is not only a strategic tool, but it can be used as a process improvement
tool as well. In present work, an effort has been made to implement Six Sigma on a small hood latch lock
manufacturing industry. The results have shown an impressive reduction in rejection rates. The main
reason identified for the rejection was washer thickness. After the application of paired comparison and
multi vary analysis, it has been found that the thickness of washer was varying from cavity to cavity
which is causing problem. During the improvement phase, the tolerance of the washer thickness has been
revised from 1mm +/-0.25mm to 0.90mm -0.00/ +0.05mm and accordingly the mold has been corrected.
After the improvement phase, the results have shown a high improvement and reduce the cost of poor
quality from Rs 1, 20,000 per month to Rs 16,000 per month making the savings of Rs 1, 04,000 per
month which is indeed a great achievement for industry of such stature. Apart from tangible benefits,
intangible savings such as reduction in consumer complaints and inspection, personnel development of
employees, organization culture improvement etc. were also noticed. This case study clearly challenges
the saying that Six Sigma has the domain of only large companies.
Acknowledgement
The authors would like to thank the anonymous referees for constructive comments on earlier version of
this paper.

P. Rana and P. Kaushik / Management Science Letters 8 (2018)
579
References
Biswas, S., & Chowdhury, B. (2016). Industrial Applications and Practices of Six Sigma–A Literature
Review. Sigma, 3(3), 176-79.
Kaushik, P., Khanduja, D., Mittal, K., & Jaglan, P. (2012). A case study: Application of Six Sigma meth-
odology in a small and medium-sized manufacturing enterprise. The TQM Journal, 24(1), 4-16.
Kaushik, P., & Mittal, K. (2015). Significance of six sigma basics in a manufacturing unit: A case
study. Progress in Management Sciences, 3(1).
Kaushik, P., Mittal, K., & Rana, P. (2016a). Energy paybacks of six-sigma: A case study of manufactur-
ing industry in India. Management Science Letters, 6(11), 691-700.
Kaushik, P., Prikshit, & Mittal, K., (2016b). Gratitude of six-sigma: A case study of manufacturing
environment Prabhakar. Journal of Mechanical Engineering, 126, 223–31.
Kaushik, P., Dahiya, V., & Mittal, K. (2017a). Statistics for industries: A sophisticated approach. Man-
agement Science Letters, 7(8), 397-406.
Kaushik, P., Jaivir, J., & Mittal, K. (2017b). Analysis of mechanical properties of jute fiber strengthened
epoxy/polyester composites. Engineering Solid Mechanics, 5(2), 103-112.
Kaushik, P., Kumar, S., Siwach, R., & Mittal, K., (2017c). Mechanical behavior of short & long bamboo
fiber based polyester : A case study. International Journal of Research in Engineering & Management
1(2), 23–31.
Mittal, K., & Kaushik, P. (2011). Quality function deployment: A way to total quality. Yamuna Journal
of Business Management & Technology, 1(1), 11–18.
Mittal, K., Khanduja, D & Kaushik, P. (2011). Leveraging APQP methodology to drive improvement in
SME : A case study. International Journal of Contemporary Practices, 1(1), 38–51.
Mittal, K., Khanduja, D. & Kaushik, P. (2012). Evidence of APQP in quality improvement : An SME
case study. International Journal of Management Science and Engineering Management, 7(1), 20–
28.
Mittal, K., & Prajapati, R. K. (2014). Quality councils: an overview. International Journal of Manage-
ment, IT and Engineering, 4(12), 200.
Mittal, K., Tewari, P. C., & Khanduja, D. (2016a). Refurbishing business processes: An insight. Journal
of Industrial Engineering and Advances, 1(1).
Mittal, K., Chandra Tewari, P., Khanduja, D., & Kaushik, P. (2016b). Application of Fuzzy TOPSIS
MADM approach in ranking & underlining the problems of plywood industry in India. Cogent Engi-
neering, 3(1), 1155839.
Mittal, K., Tewari, P. C., & Khanduja, D. (2017a). An insight into ‘Decision Tree Analysis’”. World
Wide Journal of Multidisciplinary Research and Development, 3(12), 111–115.
Mittal, K., Tewari, P. C., & Khanduja, D. (2017b). Productivity improvement under manufacturing en-
vironment using Shainin system and fuzzy analytical hierarchy process: a case study. The Interna-
tional Journal of Advanced Manufacturing Technology, 92(1-4), 407-421.
Mittal, K., Tewari, P., & Khanduja, D. (2018). On the fuzzy evaluation of measurement system analysis
in a manufacturing and process industry environment: A comparative study. Management Science
Letters, 8(4), 201-216.
Mittal, K., & Kaushik, P. (2018). A general model for problem solving in manufacturing or service or-
ganizations.
Sreedharan, V. R., & Raju, R. (2016). A systematic literature review of Lean Six Sigma in different
industries. International Journal of Lean Six Sigma, 7(4), 430-466.
Srinivasan, K., Muthu, S., Devadasan, S. R., & Sugumaran, C. (2016). Six Sigma through DMAIC
phases: a literature review. International Journal of Productivity and Quality Management, 17(2),
236-257.
Tewari, P. C., Mittal, K., & Khanduja, D. (2017). On the right approach to selecting a quality improve-
ment project in manufacturing industries. Operations Research and Decisions, 27(1), 105-124.


580
Uluskan, M. (2016). A comprehensive insight into the Six Sigma DMAIC toolbox.
International Journal
of Lean Six Sigma
,
7
(4), 406-429.
© 2018 by the authors; licensee Growing Science, Canada. This is an open access article
distributed under the terms and conditions of the Creative Commons Attribution (CC-
BY) license (http://creativecommons.org/licenses/by/4.0/).