Heliyon 9 (2023) e14625
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Heliyon
journal homepage: www.cell.com/heliyon
Research article
The performance improvement analysis using Six Sigma DMAIC
methodology: A case study on Indian manufacturing company
Ankesh Mittal a, Pardeep Gupta a, Vimal Kumar b, *, Ali Al Owad c, Seema Mahlawat d,
Sumanjeet Singh e
a
Department of Mechanical Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, Punjab, India
Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan
c
Department of Industrial Engineering, Faculty of Engineering, Jazan University, Jazan, Saudi Arabia
d
Department of Commerce, Gurugram University, Gurugram, Haryana, 122413, India
e
Department of Commerce, Ramjas College, University of Delhi, New Delhi, 110007, India
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Six Sigma
DMAIC
Business improvement
Customer satisfaction
Productivity
The six-sigma methodology has been adopted by the industry as a business management tool to
improve operational capabilities and reduce defects in any process. This study aims to present a
case study on the implementation of the Six-Sigma DMAIC methodology with the purpose to
reduce the rejection rate of rubber weather strips manufactured by XYZ Ltd. (name changed)
situated in Gurugram, India. Weather strips are used in all four doors of cars for noise reduction,
waterproofing, dust proofing, soundproofing, windproofing, and for improving air conditioning
cooling, and heating effects. The overall rejection rate of both front and rear door rubber weather
strips was 5.5% which was causing a huge loss to the company. The average rejection rate of
rubber weather strips per day reduced from 5.5% to 3.08%. After implementing the Six-Sigma
project findings the rejection was reduced from 153 pieces to 68 pieces helped the industry in
saving the cost of a compound by Rs. 15,249 per month. The sigma level improved from 3.9 to
4.45 within three months with the implementation of one Six-Sigma project solution. The com­
pany was highly concerned about reducing the high rejection rate of rubber weather strips and
decided to deploy Six Sigma DMAIC as a quality improvement tool. The industry was keen to
reduce this high rejection rate to 2% and this target was materialized with the application of the
Six-Sigma DMAIC methodology. The novelty of this study is to analyze performance improvement
considering the Six Sigma DMAIC methodology to reduce the rejection rate of rubber weather
strip manufacturing companies.
1. Introduction
Six-Sigma is an organization-wide initiative to achieve zero defects and decrease variations in manufacturing/process industries.
This helps organizations relook at their processes, eliminate bottlenecks, and deliver consistent quality. By regularly reviewing and
fine-tuning any existing business process, the Six Sigma technique improves it. Six Sigma employs a five-step approach known as
* Corresponding author. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan.
E-mail addresses: ankeshmittal07@gmail.com (A. Mittal), pardeepgupta@sliet.ac.in (P. Gupta), vimaljss91@gmail.com, vimalkr@gm.cyut.edu.
tw (V. Kumar), aalowad@jazanu.edu.sa, ali.medawi@hotmail.com (A. Al Owad), seema.mahlawat@gurugramuniversity.ac.in, seema.aryan@gmail.
com (S. Mahlawat), dr.sumanjeet@ramjas.du.ac.in (S. Singh).
https://doi.org/10.1016/j.heliyon.2023.e14625
Received 12 September 2022; Received in revised form 12 March 2023; Accepted 13 March 2023
Available online 18 March 2023
2405-8440/© 2023 The Authors.
Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Heliyon 9 (2023) e14625
A. Mittal et al.
DMAIC to accomplish this (defines opportunities, measure performance, analyze opportunities, improve performance, and control
performance). DMAIC methodology of the Six-Sigma initiative is the roadmap for continuous improvement [1–3]. Six Sigma pro­
fessionals are used to assess a business process and find opportunities for improvement [4,5]. Six-Sigma reduces variation, enhances
process performance, and helps in maintaining consistency in the quality of the process output(s). This results in fewer defects, higher
earnings, higher product quality, and higher customer happiness. Many Business Process Management initiatives now use the Six
Sigma technique to improve quality, productivity, and other factors. Many of Six Sigma’s real-life benefits are still the subject of
wonder and more research in these areas is still required. In line with this, the main purpose of this paper is to realize the benefits
attained by Six Sigma and to study the bridge the existing gap between theory and practical applications in the field of implementation
of the Six Sigma methodology as a business improvement tool through an empirical study. XYZ Ltd. is a reputed and leading OEM
industry situated in Gurugram, Haryana, India manufacturing sealing products used in automobiles such as rubber weather-strips,
windshields, roof rails, body side moldings, etc. Maruti Suzuki, Indian railways, Honda, Nissan, Toyota, Fiat, Mahindra & Mahin­
dra, General Motors, and Swaraj Mazda are some of its key customers. In this paper, the application of Six-Sigma DMAIC as an
improvement tool has been presented to decrease the rejection rate of the weather strips manufactured by XYZ Ltd.
This paper is structured as follows. The next section offers a literature review discussing the Six-sigma methodology presented in
section 2. Section 3 outlines the research methodology followed by the description of a case study on the Six Sigma DMAIC imple­
mentation methodology in section 4. The discussion and results part is discussed in section 5. Section 6 explains the conclusions with
limitations and future scope.
2. Literature review
The Six Sigma methodology, which is utilized in the industry as a business improvement tool, is a product-driven management
approach that focuses on minimizing defects in goods, services, and processes [3]. Six-Sigma is a well-structured approach to
enhancing the quality of operations and products [6]. Through the successful full utilization of a project-driven methodology, Six
Sigma assists the organization in achieving its strategic goals [7]. Since Six Sigma is a project-driven methodology, picking the right
projects is essential that will give the firm the most financial advantage. Six Sigma refers to a statistical performance to achieve a
difficult target of 3.4 defective parts for every million opportunities [8]. In 1987, Motorola spent $170 million on Six Sigma training for
its employees, which resulted in USD 2.2 billion in savings [9]. Many businesses around the world have adopted Six Sigma to a large
extent and have emerged as a thrust area of research to give more benefits to the industry. For many businesses looking to cut costs and
boost efficiency, six-sigma is at the top of their priority list [10–12]. Six Sigma is a methodology that facilitates organizations to study
their present working condition and also help them in making improvements in processes for reducing variations [13]. Six Sigma is
applied in both the manufacturing and service industries [14]. Every year, thousands of Six Sigma projects are implemented in
manufacturing organizations, requiring a large commitment of capital and thorough analysis to ensure that the benefits received is far
more than the actual investment [15,16]. Although Six Sigma has more advantages than conventional quality management methods, it
also presents new obstacles for researchers and practitioners [17]. Six Sigma practices are treated as part of the total quality man­
agement (TQM) practices [18–20].
Three novel practices were discovered by Ref. [21] in a study that examined both traditional quality management and Six Sigma
literature. These practices are crucial for implementing the Six Sigma methodology in a firm. Examples of these techniques include the
Six-Sigma role structure, Six-Sigma structured improvement method, and Six-Sigma metrics focus. The current competitive market
places have a premium on manufacturing high-quality goods at the lowest possible price. Various quality improvement philosophies
have been proposed in recent years to attain this goal. Despite the widespread adoption of Six Sigma programs, according to some
research articles [22,23], there is growing worried about implementation difficulties. Many Six Sigma projects fail due to a lack of
guidance for organizations on how to implement them effectively [24]. As a result of the knowledge-creation techniques used in
Six-Sigma black belt projects. A conceptual model for predicting the effectiveness of process improvement projects was created by
Ref. [25]. There have been developed new scales for measuring explicit and tacit knowledge-generating processes in process
improvement. The findings back up the idea that knowledge-generation processes have an impact on the success of process
improvement efforts.
Similarly [26,27], conducted a case study at an automotive part manufacturing company and applied the DMAIC methodology to
reduce process capability-related problems. As a result, process capability was substantially improved from the first pass yield from
94.86% to 99.48%. Su and Chou [28] conducted a study with the purpose to develop a novel approach to proposing Six Sigma projects
and prioritizing these projects. Using an event study technique, the effects of Six Sigma program adoptions were examined. Financial
data from 200 Six-Sigma-adopting organizations were contrasted with data from matched companies that acted as the studies’ control
groups [29]. In a furnace manufacturing company, Ref. [30] reported on the DMAIC phases of the Six Sigma program. Singh and Lal
[31] demonstrated a study of Six Sigma for an automobile manufacturing industry producing engine mufflers. The rejection rate of
muffler production decreased from 8.21 to 4.81% as a result of Six Sigma deployment, the industry’s Sigma level increased from 2.89
to 3.16, and process capability increased from 91.73% to 95.19%. In addition to reducing errors in the area of continuous improve­
ment, Six Sigma has also improved market share, cycle time optimization, customer happiness, and productivity [32–34]. The goal of
this study is to employ a DMAIC-based Six-Sigma approach to improve the radial forging operation variables.
Many companies find it difficult to implement Six Sigma due to two main reasons. The first one is having insufficient knowledge of
the five phases of the Six-Sigma and the second reason is the implementation of Six Sigma requires more time, resources, and effort
[35]. Six Sigma is a process improvement process that is based on a clear and well-defined methodology for obtaining the right so­
lutions [36]. The methodology allows for the identification of problems, the collection of relevant data, the identification of possible
2
Heliyon 9 (2023) e14625
A. Mittal et al.
causes, and the development of remedies. This process has proved to be very effective and many organizations have greatly benefitted.
In addition, a number of case studies have presented the Six-Sigma implementation methodology, although there is a shortage of
publications on the six-sigma DMAIC methodology in an Indian rubber weather strip manufacturing company. The author believes that
a case study on the application of Six-Sigma DMAIC methodology in an Indian rubber weather strip manufacturing company to develop
additional knowledge and lessons learned. The empirical study of this paper presents the application of the Six-Sigma DMAIC
methodology in an Indian rubber weather strip manufacturing company to reduce the defects in the production of rubber weather
strips used in cars. As a result, it’s very important to measure the number of defects and eliminate them in order to improve quality.
Defect per million opportunities (DPMO) is calculated as Eqs. (1)–(6), respectively.
Defect per unit =
Number of defects
Total number of units
(1)
Defects per unit
Number of defect opportunities per unit
(2)
Defects per million opportunity = Defects per opportunity ∗ 102
(3)
Yield = 1 − Defect per opportunity
(4)
Defect per opportunity =
Fig. 1. Research methodology Flowchart.
3
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A. Mittal et al.
(5)
Six − Sigma Level = Normsinv (in percentage Yield) + 1.5
DPMO = 100000 ∗
Total number of defects found in a sample
Defect opportunities in a sample
(6)
3. Research methodology
Six-Sigma methodology has been adopted by the industry as a business management tool to improve operational capabilities and
reduce defects in any process. To prepare for this study, the required information and primary data have been collected after having
discussions with industry officials associated with the quality team. The secondary data related to the project problem has been
collected from the websites of the industry. With the help of experts’ input, literature reviews, and primary and secondary data, this
study presents a case study on the implementation of the Six-Sigma DMAIC methodology with the purpose to reduce the rejection rate
of rubber weather strips manufactured by XYZ Ltd. (name changed) situated in Gurugram, India. A DMAIC analysis was performed to
reduce the rejection rate of rubber weather strips manufactured by the industry. The detailed flow diagram of the research meth­
odology for this paper is shown in Fig. 1.
4. Case study on six-sigma DMAIC implementation
The rejection rate of the rubber weather strips manufactured by XYZ Ltd. was significantly high and was about more than 5% which
was causing huge financial loss to the industry. Customer satisfaction was also affected because of the high rejection rate of weather
strips. The quality assurance section of the industry decided to minimize the high rejection rate of the rubber weather strips using the
Six Sigma DMAIC approach. The Six Sigma project was approved by the higher management and assigned to a quality improvement
team comprising two black belts and two green belt holders. The DMAIC methodology used in this project is categorized into the
following five basic phases:
Define: Define the problem and goal of the project.
Measure: To examine the current status of the problem.
Analyze: Analyze the current situation and find out the solution to achieve the goal.
Improve: Implementation of the solution to achieve the goal.
Control: Make sure that permanent improvement takes place.
4.1. Define phase
The define phase consists of three major steps such as identification of the problem, classify project objective and characterize
customer requirements as described below.
a) Problem: The rejection level of the Rubber Weather-strip of the Rear door Right Hand Side and Rear door Left Hand Side was 7.1%
while the overall rejection in both the front and rear door rubber weather-strip was 5.5%. Major defects occurring in the rubber
weather-strips were joint cracks and under-fill.
b) Objective: The objective of the project was to reduce the rejection rate of strips from 5.5% to 2%.
c) Identify Customer Requirement: Due to the high defective PPM (Parts per million) level, customer satisfaction was observed low
[1]. In order to improve customer satisfaction level, the customer requirements were identified based on the voice of customers
which are tabulated in Table 1.
4.2. Measure phase
The measure phase involves two major steps i.e. process mapping and data collection to find out the current level of process
performance [37].
a) Process Mapping: The process flow diagram as shown in Fig. 2 exhibits every step involved in the process of manufacturing weather
strips.
Table 1
Customer requirements.
CTQ (Critical to Quality) Issue
Key Customer complaints
Customer Requirements
Reduction in defects in manufacturing of weather-strips from 5.5% to 2%
Joint crack in RH and LH
Weather-strips
Under fill in RH and LH
Weather-strips
Press mark
Dent mark
No joint crack in Weather-strip
4
No under-filling in rubber weather- strips
Weather-strip should be free from Pressmarks
The weather strip should be free from Dent marks
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b) Data Collection: The data on defectives produced for the two months comprising different types of defects with their percentage
was collected as shown in Figs. 3 and 4. From this data, it was observed that the defects such as joint crack, under-fill, press marks,
overflow, and dent marks were the main defects that were responsible for the high rejection of rubber weather-strip. The per­
centage rejection rate (Average of two months) for the main defects was joint-crack = 25.10%, under-fill = 21.08%, press-mark =
16.25%, overflow = 9.46%, and dent-mark = 11.35%. Table 2 shows the defectives generated in the two consecutive months.
4.3. Analyze phase
In this phase, the data collected has been analyzed using Pareto Analysis Charts and Cause and Effect Diagram to identify major
defects and their causes for addressing them in order to improve the process. Twelve defects have been identified which were mainly
responsible for the high rejection level. To analyze the defects and their cumulative percentage responsible for the high rejection of the
product, Pareto charts were plotted as shown in Figs. 5 and 6.
It may be analyzed from the Pareto chart that the percentage of defective due to the first four defects (Joint crack, Underfill, Press
mark, and Overflow) was considerably high which is about 74% of the total number of defects.
Pareto Charts signified the five major defects as joint crack, under-fill, overflow, pressmark, and dent mark mainly responsible for
the high rejection rate of weather strips (Refer to Fig. 7). Therefore, the project team decided to address the causes responsible for
generating these five defects. To do this, the five significant faults’ primary causes were determined using a cause-and-effect diagram.
A cause-and-effect diagram is useful to identify the possible root causes so that corrective action could be taken up using a structured
approach. After studying the process, the Six Sigma team prepared the cause and effect diagram to identify the various causes,
responsible for the defects produced in rubber weather strips.
A total of eight causes were identified through the fishbone diagram which was responsible for poor delivery to customers. The next
step is to scrutinize each cause and decide the degree to which the cause is a potential cause of the problem. In this study, a straw vote’
has been used to decide the extent to which the cause is the potential cause of the problem. In this straw vote system, a scale of three
viz; very important (V), somewhat important (S) and not important (N) have been used to collect the information related to each
possible cause. Each project team member has to vote on each possible cause for how likely the listed cause is to be a potential cause of
the problem. Whichever possible cause gets the most votes related to ‘V’ or ‘S’ is distinguished next to the potential cause for the
problem. The collected information for every possible cause is presented in Table 3.
From, the identified responsible cause’s project team identified five serious potential causes that required to be accomplished first.
The ‘a straw vote’ helped the project team in identifying the potential causes mentioned below to be responsible for the variation in the
process performance.
i. Inadequate work instructions: Lack of insufficient work instructions as per the SOPs available in the industry, the operators was
unable to operate the machines in producing the products with zero defects.
ii. Lack of training of mold operators: Knowledge and training in inculcating skills in the operators are predominantly essential to
operate the machines effectively and efficiently.
iii. Mold cleaning not done: Mold cleaning is a very crucial step in producing products with no defects.
iv. Slug weight variation: The uniformity in slug weight is the need to produce quality products with consistency.
v. High speed of the injection of compound: High speed of the injection of the compound during the molding process needs to be
controlled in producing rubber products without defects.
4.4. Improve phase
In improve phase, various solutions were proposed by the team members. After discussion with different stakeholders (project team
members, Customers, and Suppliers), the following solutions were implemented to address the dominant possible causes to improve
Table 2
Defectives Generated in the two consecutive months.
S. No.
1
2
3
4
5
6
7
8
9
10
11
12
Type of Defects Found in Rubber Weather-Strip
Joint Crack
Under-fill
Overflow
Pressmark
Gas Mark
Under Cure
Joint Bend
Blister
Dent Mark
Bubble
Lip Press
Push Back
First Month
Second Month
Rejection Rate
Percentage
Rejection Rate
Percentage
2.29
1.85
1.10
1.60
0.01
0.02
0.31
0.07
0.87
0.48
0.16
0.53
24.65
19.91
11.84
17.22
0.11
0.22
3.34
0.75
9.36
5.17
1.72
5.71
2.09
1.82
0.58
1.25
0.06
0.04
0.55
0.12
1.09
0.02
0.11
0.45
25.55
22.25
7.09
15.28
0.73
0.49
6.72
1.47
13.33
0.24
1.34
5.50
5
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A. Mittal et al.
Fig. 2. Process flow diagram of Weather-strip.
Fig. 3. Defectives generated data collected for the First month.
Fig. 4. Defectives generated data collected for the Second month.
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A. Mittal et al.
Fig. 5. Pareto chart on the basis of data of first month.
Fig. 6. Pareto chart on the basis of data of second month.
Fig. 7. Cause and effect diagram.
the process.
1.
2.
3.
4.
5.
Work instructions were reviewed and put into practice
Training to mold operators was imparted to improve their operational skills.
Mold cleaning schedules were formulated so that cleaning may be done at regular intervals of time
Slug weight variation eliminated
The speed of the injection of the compound got controlled or reduced by connecting the flow control valve unit in the molding
machine. Before installing the flow control valve, the frequency of injection of the compound was 5–10 s. After installing the flow
control valve, the frequency of injection of the compound was reduced to once in 15–17 s. The machines before and after installing
the flow control valve are shown below in Fig. 8.
4.5. Control phase
The purpose of the control phase is to attain sustainability in the improved and modified system which shall be robust and well7
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A. Mittal et al.
Fig. 8. Molding machine before and after application of a solution.
Table 3
Straw vote for each possible cause.
S. No.
Causes
X1
X2
X3
X4
X5
X6
X7
X8
Total Score
1.
2.
3.
4.
5.
6.
7.
8.
Lack of Commitment
Poor Training
Mold cleaning not done
Improper maintenance
Poor Quality
Slug weight variation
No proper cycle time
High speed of injection
V
V
V
N
N
S
N
V
V
S
S
S
N
V
S
V
S
V
V
N
V
V
V
S
V
V
S
N
S
S
N
V
V
S
S
N
V
V
N
V
S
V
S
S
S
V
N
V
V
V
S
S
S
V
S
S
S
S
V
S
V
V
S
S
(V
(V
(V
(V
(V
(V
(V
(V
=
=
=
=
=
=
=
=
5, S
5, S
3, S
0, S
3, S
6, S
1, S
5, S
= 3,
= 3,
= 5,
= 4,
= 3,
= 2,
= 3,
= 3,
N
N
N
N
N
N
N
N
= 0)
= 0)
= 0)
= 4)
= 2)
= 0)
= 4)
= 0)
maintained to keep the process in control. It has been observed that if no restrictions are introduced, the old practices will gradually
return, wiping away all of the advances made thus far. Major initiatives performed in this phase are.
• Imparting regular training to workers
• Identify the existing documents which are to be revised and replaced with new documentation.
5. Results and discussion
5.1. Improvements after implementation of Six Sigma project findings
The countermeasures taken about the major causes responsible for the high rejection rate remained helpful in improving the
process performance and subsequently in decreasing the rejection rate of the weather strips. The installation of a flow control valve
unit in the molding machines brought a significant improvement in reducing the rejection rate of weather strips. The three days’ data
on the rejection percentage of weather strips pertaining before and after the Six Sigma project implementation has been collected and
analyzed. A comparison of the Six Sigma project’s before and after state is presented in Table 4 and Table 5 which highlight that a
considerable improvement in the rejection rate has taken place.
5.2. Saving on rejection cost
The installation of flow control valve units in the molding machines meant for producing weather strips reduced the rejection
percentage from 5.5% to 3.08%. Thus, it has been observed that a significant saving in rejection cost by up to 58% has taken place, and
based upon this gain, horizontal deployment of this modification i.e. installation of flow control valve units in all the 24 molding
machines used for producing weather strips in the industry was done.
5.3. Cost-benefit analysis
Initially, the rejection level of rubber weather strips of the rear door right-hand side and the rear door left-hand side was 7.1% while
the overall rejection in both front and rear door rubber weather-strip was 5.5% which was causing a huge loss to the company. On
average 153 pieces of weather strips rubber used were rejected per day before the Six-Sigma project. After implementing the Six-Sigma
project findings the rejection was reduced from 153 pieces to 68 pieces. Thus, saving in the rejection of the weather strip of 85 units
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Table 4
Rejection data of rear left & right-hand weather-strip rubber before Six-Sigma Project implementation.
Days
Rejection Percentage of Rear Spool Type Joint weather-strips
Day 1
Day 2
Day 3
Joint X Left Hand
Joint X Right Hand
Joint Y Left Hand
Joint Y Right Hand
7.21
7.36
6.96
6.07
5.77
7.04
5.83
7.24
7.94
6.07
5.38
8.41
Table 5
Rejection data of rear left & right-hand weather-strip rubber after Six-Sigma Project implementation.
Days
Rejection Percentage of Rear Spool Type Joint weather-strips
Day 5
Day 6
Day 7
Joint X Left Hand
Joint X Right Hand
Joint Y Left Hand
Joint Y Right Hand
4.08
5.70
3.72
2.65
0.60
0.82
4.41
6.33
4.05
4.12
2.94
2.71
took place as presented in Table 6.
5.4. Improvement in sigma level after remedial action
The sigma level of the process used for producing weather strips was enhanced after implementing one Six-Sigma project from 3.9
to 4.45.
The empirical study on the implementation of the Six Sigma project methodology for improving the manufacturing process used for
manufacturing rubber weather strips reveals that the Six Sigma project truly helped the industry in reducing the rejection rate of the
rubber strips. The case study presented in this present research paper showed the achievement of a sigma level from 3.9 to 4.45 within
three months with the implementation of one Six Sigma project solution. Improvement in employee participative work culture
enhanced the morale of the employees, increases customer and employee satisfactions, gained a good reputation, etc are some of the
intangible benefits gained by the industry [14,38]. The top managerial officials are supported highly in the line of repeated
enhancement towards getting the organizational objective of Six Sigma-level qualities [39–41]. The results of this study may help Six
Sigma practitioners encourage comparable strategies for raising productivity, lowering rejection rates, and raising product quality.
6. Conclusions
In today’s era, the world started to eyewitness the materialization of improvement tools under various designations like total
quality management, total productive maintenance, 5S, Lean, and Six Sigma. Six Sigma is one of the emerging approaches which were
first executed by the company name Motorola in the 1980s. Six Sigma is an organization-wide initiative to achieve zero defects and
decrease variations in manufacturing/process industries. In line with the target of achieving zero defects, researchers and practitioners
started to implement the Six Sigma methodology for allowing organizations to progress toward achieving Six Sigma-level quality. In
this research paper, a case study on the implementation of the Six-Sigma DMAIC methodology with the purpose to reduce the rejection
rate of rubber weather strips manufactured by XYZ Ltd. (name changed) situated in Gurugram, India has been presented. In this
research case study, the defects that occur in the case of weather strips (Used in all four doors of automobile cars) have been measured.
Initially, the rejection level of rubber weather strips of the rear door right-hand side and the rear door left-hand side was 7.1% while the
overall rejection in both front and rear door rubber weather-strip was 5.5% which was causing a huge loss to the company. To
overcome the rejection rate of weather, strip top management of the case taken empirical study has initiated the DMAIC project under
the Six-Sigma methodology. After implementing the Six Sigma project findings the average rejection rate of rubber weather strips per
day reduced from 5.5% to 3.08%. The rejection was reduced from 153 pieces to 68 pieces helped the industry in saving the cost of a
compound by Rs. 15,249 per month. The sigma level improved from 3.9 to 4.45 within three months with the implementation of one
Six-Sigma project solution. The company was highly concerned about reducing the high rejection rate of rubber weather strips and
Table 6
Cost-benefit analysis.
Cost of compound used in S & T joint of weather strip
Total saving in the cost of a compound due to a decrease in rejection rate
Saving in cost of compound per month
Annual Saving in cost of compound
Cost of Flow adjustment valve per machine in rupees
Above for all 24 machines in YP8 HB in rupees
The payback period for the same in months
9
Rs. 6.90/unit
Rs.6.90 × 85 = Rs. 586.50
Rs. 586.50 × 26 = Rs. 15,249
Rs. 15,249 × 12 = Rs. 1,82,988
12,000
288,000
18.9
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A. Mittal et al.
decided to deploy Six Sigma DMAIC as a quality improvement tool. The industry was keen to reduce this high rejection rate to 2% and
this target was materialized with the application of the Six-Sigma DMAIC methodology. The empirical study on the implementation of
the Six Sigma project methodology for improving the manufacturing process used for manufacturing rubber weather strips reveals that
the Six Sigma project truly helped the industry in reducing the rejection rate of the rubber strips. The present research work has definite
limitations. The present study is an industry-specific study and precise to an exacting data set with one case study example. In the
future as per the application of DMAIC, it can be implemented in other sections/departments of the same different industry. Future
research can also examine the mediating interactions between the Six Sigma concepts and various performance measurements.
Author contribution statement
Ankesh Mittal: Wrote the paper, Conceived and designed the experiments, Analyzed and interpreted the data. Pardeep Gupta:
Contributed reagents, materials, analysis tools or data. Vimal Kumar: Wrote the paper, Conceived and designed the experiments,
Analyzed and interpreted the data. Ali Al Owad: Performed the experiments. Seema Mahalwat: Performed the experiments. Sumanjeet
Singh: Performed the experiments.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability statement
Data will be made available on request.
Declaration of interest’s statement
The authors declare no conflict of interest.
Appendix A. Abbreviations (Complete explanations of abbreviations used in this study)
DMAIC
Define opportunities, Measure performance, Analyze opportunity, Improve performance, Control
CTQ
PPM
LH
RH
TQM
Critical to Quality
Parts per million
Left Hand
Right Hand
Total Quality Management
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