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


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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.
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* Corresponding author.
E-mail address: (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
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
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
© 2018 by the authors; licensee Growing Science, Canada
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
(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-
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)
Fig. 2. DMAIC Methodology
 
  
Fig. 3. Process Map
Monitor the website and ensure that the key metrics are in check
Identify, Evaluate, Select and Implement the right improvement solutions
Analyze the current state and identify the opportunities for improvement
Identify and Measure the Critical Quality Factor
Understand the requirements and formulate the vision and mission
Raw Material Receipt
Final Ins
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.
Supplier Input Process Output Customer
Hood Latch
Lock Mfg.
Lock Rejec-
tion Data
Analysis of
Hood Latch
Lock Mfg.
Customer Satisfaction
and Relationship
Six Sigma
P. Rana and P. Kaushik / Management Science Letters 8 (2018)
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.
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
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
Initial sam
le 15
First trial 1 4
Second trial 24
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)
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-)
Fr e q ue n c y
M ean 0.973
StD ev 0.02544
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
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
.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
Table 11
Multi Variant Analysis for Washer
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
to Cavit
P. Rana and P. Kaushik / Management Science Letters 8 (2018)
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
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/
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.
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.
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)
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BY) license (
... There are many strategies or methods or approaches that can be used in an effort to improve quality, productivity, and customer satisfaction, one of which is the Six Sigma approach. Six Sigma is a systematic and structured approach to increase performance / productivity and quality in meeting customer satisfaction to gain increased company profits [15,16,17,18,19]. ...
... Rahman et al. [17] in his research, succeeded in reducing defects such as broken stitches and open seam by 35 % and increasing sigma levels from 1.7 to 3.4. Rana and Kaushik [19] in the Six Sigma implementation have proven to reduce defects and increase productivity. In addition to other benefits that did not materialize (initiative, competitiveness), the DMAIC results showed that the defective washer thickness declined from 1550 PPM to near to 100 PPM within four months. ...
... The Six Sigma method (DMAIC) is a structured method for identifying, analyzing cause and effect, as well as opportunities for improvement of an ongoing problem that aims to maintain the stability of the process to get product quality improvement and increase company profits [18]. The implementation of Six Sigma has been proven to reduce disability and increase productivity, in addition to other benefits that do not materialize, such as initiative and competitiveness [19]. Six Sigma is a scientific, systematic and superior method of responding to changes that occur in the business world and is able to improve quality and productivity through reducing the variety of processes and products [27,28,23]. ...
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... The tools of Six Sigma are most often applied within a simple performance improvement model known as DMAIC which is used when a project's goal can be accomplished by improving an existing product, process, or service [23]. The basic principle of the approach is a structured step with the following phases [24]: 1) Define phase, is the first phase of the process of identification or defining problems, setting problem issues, and targets to be achieved [24]. This phase is important and is considered not as easy as the identification of the inadequate problem will affect the analysis and the results to be obtained [4,25,26]. ...
... The tools of Six Sigma are most often applied within a simple performance improvement model known as DMAIC which is used when a project's goal can be accomplished by improving an existing product, process, or service [23]. The basic principle of the approach is a structured step with the following phases [24]: 1) Define phase, is the first phase of the process of identification or defining problems, setting problem issues, and targets to be achieved [24]. This phase is important and is considered not as easy as the identification of the inadequate problem will affect the analysis and the results to be obtained [4,25,26]. ...
... This phase is important and is considered not as easy as the identification of the inadequate problem will affect the analysis and the results to be obtained [4,25,26]. 2) Measure phase is the measurement of critical quality factors to follow-up performance measurement that causes problems found in the define phase [24]. 3) Analyze phase is the phase of identification of the current condition and identification of the improvement opportunity [24]. ...
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High business competition demands business players to improve quality. The Six Sigma with DMAIC phases is a strategy that has proven effective in improving product and service quality. This study aims to find the consistency of DMAIC phases implementation and analyze the objective value in Six Sigma research. By using a number of trusted article sources during 2005 until 2019, this research finds that 72% research in manufacturing industry consistently implemented DMAIC roadmap especially in case study research type for problem-solving, while service industry pointed out the fewer number (60%). The causes of variations and defective products in the manufacturing industry are largely caused by a 4M 1E factor, while in service industry are caused by human behavior, and it’s system poorness. Both manufacturing & service industry emphasized standardization & monitoring to control the process which aimed at enhancing process capability and organization performance to increase customer satisfaction.
... The purpose of M for Measure, also known as the assessment of project improvement, is to identify the measurement indicators that demand improvement with a focus on detecting issues. To assist the reflection of shortcomings and process obstacles, it is necessary to gather a sizable amount of data from the project's operational phases [7]. In order to accurately reveal the project's causality, A for Analysis is the key to identifying problems that require transformation and examining their internal logical relationships in light of feedback returns from the measured data. ...
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Water-efficient products, a key component of water-saving technology, are widely installed and utilized in all sectors of society. Due to China’s extensive and varied use of this product, advancements in effectiveness and quality will significantly enhance people’s standard of living. In recent years, manufacturers, corporate purchasers, and individual customers have given more attention to the quality of these items due to the spike in local market and export demands for water-efficient products in China. It has been a pressing problem to find a practical solution for increasing product quality in a reasonable and scientific manner. In order to build a DECIA quality improvement model for water-efficient product quality that is quantifiable and technically practical, this paper investigates how to improve the quality of smart water closets based on six-sigma management. Thus, the development of a water-efficient industry can be green and sustainable.
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Variation exists in all processes. There is not even a single process that is completely true. Measuring the trueness of the process is itself a process which can also imitate the process variation. Therefore, measurement system should be strong enough to wager on the trueness of the process. This paper is an attempt to indicate the true method and substantiate the use of measurement system analysis (MSA) by using it in two different environments i.e. in manufacturing as well as process industry. Also, a comparison among various analyzing techniques has been drawn for authenticating the candid method followed by an evaluation using fuzzy TOPSIS for authenticating the results of comparison. The organization’s type, also, strongly influences the performance of MSA as revealed in the conclusion of the article. The results calculated by various methods and in both environments were discussed and as a result ANOVA comes out to be the best method. The application of correct MSA is highly required which ultimately results in increased organizations’ performance. The study is one of its type and will motivate the researchers and industrialists to use and explore the new and efficient ways of MSA.
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Decision making is a regular exercise in our daily life. One has to make decisions in their personal as well as professional life on number of occasions but professional decisions affects whole organization, both in terms of future of organization and achieving the goals that have been embarked. One bad decision can ruin whole planning and preparation that have been made in realizing the targets. That's why decision making is termed as a tedious task. Thanks to our great researchers who have explored some techniques as an aid to this challenging yet essential task. One of those technique is "Decision Tree Analysis". A decision tree is a graphical representation of decisions and their corresponding effects both qualitatively and quantitatively. The structure of the methodology is in the form of a tree and hence named as decision tree analysis. In this paper authors describes the theory and history behind evolution of decision tree analysis along with its application, advantages and disadvantages. Some examples have also been listed that shows the positive effects of using decision tree analysis on productivity improvement under industrial environment.
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The scope of advance product quality planning (APQP) is still restricted to new product development (NPD) only but it lays down a series of such procedures which enhances the productivity of the component with higher customer satisfaction index. APQP is a notion that forces the employee to heed upon their work. It fills up all the lacunae between the production and productivity. This paper is an attempt to justify the highly useful role of quality management techniques like APQP for quality improvement which are normally presumed to be in the domain of new product development only, taking a specific case of a die casting unit. The study could be a paradigm initiative towards high quality products and services at low cost for every small and medium sized enterprise (SME).
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Six-Sigma is a statistical attitude – not judgment or sentiment. It is about statistical facts that as-sist the authorities to spotlight their concentration on the progression arrangement rather than on individuals and get assessments based on reality and unbiased information but not on impracti-cable prospects. Six-Sigma is an integrated approach to involve everything within the organiza-tion for reducing defects and variation. It is a combination of all efforts in an association for quality enhancement, quality progress and quality protection to reach the optimum clients satis-faction. The primary objective of Six-Sigma methodology is always directed towards the perfec-tion at any level whether financial or non-financial. Putting efforts rightly and maintaining con-sistency are the basic concepts behind successful implementation of Six-Sigma. The present work provides a way to examine the Six-Sigma practice in a manufacturing firm with the use of statistical thinking in getting high quality products by reducing the variation and ultimately in-creasing profit.
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In recent years, efforts have been made to produce advanced composite materials in order to lessen environmental impact and to extent sustainability. Traditional materials are largely substituted by composites due to their greater properties like flexural strength, low thermal expansion and high strength. Numerous studies are present that show the process of composite materials reinforcement with natural fiber to improve mechanical and thermal properties. The vital aspect of exploitation of natural fiber in composites is associated with biodegradability. An extensive range of different natural fibers has been used for reinforcement till now. In present work, mechanical properties of jute fiber reinforced epoxy and polyester composites manufactured using Taguchi optimization method are investigated, experimentally. It was found that jute reinforced epoxy composite had better mechanical properties than jute polyester composite. Also, Epoxy-jute composite had lower erosion wear rate than polyester jute composites.
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“Productivity is never an accident, it is always the result of a commitment to excellence, intelligent planning, and focused approach,” the phrase by Paul J. Meyer, an American businessman, has everything explained within it. Various quality improvement tools and techniques along with their integration have been attempted in the past for enhancing productivity levels in large-scale organizations across the globe. Similarly, new unification of these techniques can bring positive results even in a small- and medium-sized enterprise (SME). Authors, in this case study, use the synergy of two approaches, namely, “Shainin system” and “fuzzy analytical hierarchy process (AHP)” to enhance the productivity of a system. Shainin system stands close to a set of instruments that are clear to understand and easy to be applied, whereas AHP technique has a proven potential in decision-making and evaluation. Results, after successful implementation, indicate a monetary saving of $100,000, which is substantial for a SME. Emphasis is put on a coherent step-wise implementation of both the strategies and linking them together to inculcate the best potential outcomes.
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Industries, nowadays, are concerned about energy consumption and ever narrowing rules of emissions by the governments. Therefore, a race to clean; green and less energy consuming manufacturing is going on throughout the world. But in authors’ perspective, the major part of energy exploitation lies in the production of a rejected product. Therefore alongside the use of energy saving processes and machinery, industry should primarily look for rejection reduction. This, apart from energy saving and profitability, will add to the moral responsibility of every person toward nature. Here in this paper, authors describe a case study in which the increased rejection rate of a part of cycle chain assembly is controlled by the application of Six Sigma. Six Sigma, from many years has proved to be an ultimate solution when it comes to the application part in manufacturing industries. It’s very generic and easily applicable methodology has drawn tremendous positive results throughout the world. A financial gain of INR 0.267 million was yielded by implying six-sigma approach. In a move toward energy saving, the money saved by the project was used for green manufacturing to promote energy conservation.
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The manufacturing of plywood consists of simple procedural steps, but the range of problems associated with the plywood manufacturing industries, especially in the case of small-scale industries (SSI), is large. This paper describes the major problems faced by the plywood SSIs along with their cause and the ultimate effect, i.e. pruning the profits. Many cogent tools and techniques are present for the task, but an attempt has been made to apply multiple attribute decisionmaking (MADM) approach in ranking the problems in order of their extent on the basis of various parameters. Some suggestions for the improvement purposes have also been made to overcome the top-ranked problem. The study is the first of its type in a plywood industry, although same can be applied to other similar smallscale cluster industries like steel, textile, pharmaceutical, and automobile.
Purpose As opposed to general literature reviews, by narrowing down the context only around the resources related to Six Sigma tools, this study aims to offer a strong discussion about Six Sigma toolbox which has a vital role in the success of Six Sigma. Design/methodology/approach Based on a comprehensive literature research, the most used tools; classification of tools; flow of tools with respect to define, measure, analyze, improve and control (DMAIC) steps; tools as critical success factors and reasons of ineffective use of tools are reviewed. To stay focused and not to diverge from the research aim, 60 articles which are suitable to the context and flow of the discussion are selected during the construction of the study. Findings The study provides a detailed and integrated review of Six Sigma articles about tools. The most used tools are listed from different perspectives and resources, and the role of these tools has been discussed. After a broad review, a more practical and combined classification of Six Sigma tools is proposed. Next, the issue of using which tools during which steps of DMAIC is systematically addressed. Finally, emergence of tools as a critical success factor and the gaps in the literature related to tools of Six Sigma are pointed out. Practical implications Addressing important statistics and the facts related to the tools of Six Sigma helps new practitioners in particular to build a strategic filter to select the most proper tools throughout their projects. Originality/value This study is unique in investigating only Six Sigma toolbox and providing a literature review on this subject.
Purpose The purpose of this paper is to review Lean Six Sigma (LSS) literature and report different definitions, demographics, methodologies and industries. Design/methodology/approach This paper highlights various definitions by different researchers and practitioners. A total of 235 research papers has been reviewed for the LSS theme, research methodology adopted, type of industry, author profile, country of research and year of publication. Findings From the review, four significant LSS classifications were identified that deal with the spread of LSS in different industries followed by observation for classification. Practical implications LSS is a strategy for success, but it did not examine its presence in various Industries. From this paper, readers can understand the quantum of its spread before implementing LSS. For academicians, it will be a comprehensive list of papers for research. Originality/value This paper reviews 235 research papers for their year, author profile, research methodology and type of industry. Various characteristics of LSS definitions and their theme are also reviewed.