Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology

ISSN No:-2456-2165

Reduction of Defect and Variation using

Six Sigma Methodology in an Instrument
Manufacturing Company
Department of Mechanical, Industrial, Aerospace
Engineering Concordia University Montreal Canada

Dhananjay Mrutunjay Nayak

Harsha Nagadasanahalli Suresh
Devanshu Rathore
Darpan Kumar
Karishma Raghu Shetty
Concordia University

Abstract:- Our Project centres around the II. LITERATURE REVIEW

implementation of the Six Sigma DMAIC methodology,
a structured approach utilized by organizations to  Six Sigma and its Methodology
effectively address challenges and achieve their Since its inception in the manufacturing sector, Six
objectives. The project's objective is to decrease the Sigma is a highly effective method of process improvement
rejection rate of valves and variation in the Instrument and quality management that has been adopted by several
Manufacturing Company process in Gujarat, India. industries. A process's flaws, variances, and errors must be
Following the systematic steps of Define, Measure, kept to a minimum to attain near-perfect levels of
Analysis, Improve, and Control (DMAIC) within the Six performance and customer satisfaction. The methodology
Sigma framework, the project study aims to identify the makes use of the DMAIC (Define, Measure, Analyze,
root cause(s) of defects and provide a reliable solution to Improve, Control) structured approach to identify the root
reduce or eliminate them thereby enhancing operational causes of problems, measure process performance, examine
capabilitiesmaking it a valuable tool for the organization. data for opportunities for improve ment, implement changes,
and establish control measures to maintain the
I. INTRODUCTION improvements.

 Company Overview  DMAIC

The instrument manufacturing industry is a diverse and DMAIC is a data-driven approach used for optimizing
dynamic industry that plays an important role in providing and improving existing business designs and processes. It is
precision instruments, equipment and instruments in various an effective method of controlled change management. The
fields including scientific research, medical, etc. healthcare, five phases of DMAIC are listed below, and each phase
engineering, electronics, aerospace, etc. This industry involves tools and tasks to help find the final solution.
designs, manufactures and distributes a wide range of tools
to support process measurement, analysis, control, and  Define the problem and the goals of the project.
monitoring.  Measure the different aspects of the existing process in
detail.
Valve manifolds are noted for their durability,  Analyze data to find the main flaw in a process.
dependability, and precision, making them excellent for use  Improve the given process.
in harsh settings. To satisfy the individual demands of the  Control the way the process is implemented in the future.
customers, they provide a wide choice of conventional and
custom-designed valve manifold alternatives. Six Sigma's guiding principles include a relentless
focus on customer needs, making decisions based on data
In addition to high-quality valve manifolds, the and facts rather than preconceptions, and allowing cross-
company offers customer service and technical assistance. functional teams to collaborate in the quest for process
The team of professionals is available to assist you in improvement. Organizations can improve productivity, cut
selecting the best valve manifold for your application as well costs, improve product quality, and develop a culture of
as with installation and maintenance. continuous improvement by implementing Six Sigma,
making it a powerful tool for attaining operational
excellence and providing superior value to consumers.

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 Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
III. DEFINE PHASE characteristics of the valve that are most important to
customers and the business.
The purpose of this phase is to clearly state the
problem and to establish measurable goals for reducing  Project Charter
defects and improving overall quality. A project charter is a concise, formal document
that outlines the project's goals, scope, schedule,
This information is usually documented to write down resources, and key stakeholders, providing a clear roadmap
what you currently know to determine the critical quality for successful project implementation labour.

Table 1 Project Charter

PROJECT CHARTER
Project Name: Reduction of Defect and Variation Using Six Sigma Methodology in an
Instrument Manufacturing Company
Problem Statement:
1. Reduce defects and rework during valve manifold production.
2. Decrease process variation, leading to a more consistent and reliable product.
Project Scope:
Critical parameters that are impacting the creation of defects and process variation shall beidentified and improved using the
DMAIC methodology.
Project Objective / Goal Statement:
Our main goal focuses on optimizing the manufacturing process of valve manifolds, aimingto streamline production, minimize
defects, and reduce variation in the final output. We will analyze the key stages of the manufacturing process, identify
bottlenecks, and implement data-driven solutions to enhance efficiency and quality.
Metrics Baseline Goal
 Defects Per Million Opportunities (DPMO) DPMO = (181/2500) *1000000 =72400 Reduce DPMO by25%.
 Process Sigma (σ)
Define Measure Tools used: - Check Analysis Improve Tools used: Control Tools used: -
Tools used: -Process Sheets Tools used: - -DOE SOP
MapCTQ Tree Data CollectionP Chart Pareto Analysis Fishbone
R&R Analysis DiagramANOVA
Team Members:
Dhananjay Mrutunjay Nayak, Harsha Nagadasanahalli Suresh, Devanshu Rathore, DarpanKumar, Karishma Raghu Shetty

 Process Map
A process map is a visual representation of a workflow, illustrating the sequence of activities, decision points, and
interactions within a process, allowing for better understanding, analysis, and process improvement.

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Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
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 High-Level Manufacturing Process Map

Fig 1 High-Level Manufacturing Process Map

 CTQ Tree
The CTQ (Critical-to-Quality) tree is a powerful tool in the Six Sigma methodology that helps identify andprioritize the most
important quality characteristics (CTQs) of a product or process, tailoring them to the requirements customers to drive targeted
improvement efforts.

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Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165

Fig 2 CTQ Tree

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Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IV. MEASURE PHASE

The measure phase is about the baseline of the current process, data collection, validating the measurement system, and
determining the process capability

 Check Sheet

Table 2 Check Sheet

Inspection Check Sheet

 Data Collection
Data collection is the systematic process of collecting and recording relevant information and observations from a variety of
sources, providing the basis for analysis, decision-making, and information generation, research, business, and other fields.

We have collected data in the month of April 2023 where there were 10 lots each of 250 valves manufactured. From the
check sheets from the In Process Inspection and Final Dispatch Inspection, we collected the.

Table 3 Data on defects

Sr. No Lot No of Machining Dimension Body Alignment Marking Leakage
Size defects Error Error Finish test
1 250 11 8 2 1 0 0 0
2 250 23 16 4 1 1 1 0
3 250 15 9 3 3 0 0 0
4 250 18 12 4 1 1 0 0
5 250 7 2 1 2 1 0 1
6 250 23 15 4 2 0 2 0
7 250 28 21 5 1 0 0 1
8 250 19 11 5 3 0 0 0
9 250 16 7 3 3 0 3 0
10 250 21 14 5 2 0 0 0
Total 2500 181 115 36 19 3 6 2

 KPI Identification  Process Sigma

From the above we have identified the following two Our current process sigma level is shown below.
KPIs: -
 P Chart
 Defects Per Million Opportunities A p-chart is a statistical control chart used in quality
Our current Defects Per Million Opportunities, DPMO management to track the percentage or percentage of
= (181/2500) *1000000 = 72400 nonconforming items in a sample or process, allowing
organizations to identify variations and take corrective
action to maintain process stability and quality.

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Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
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Fig 3 P-Chart

 R&R Analysis
R&R analysis, also known as instrument repeatability and reproducibility analysis, is a statistical method used to evaluate the
variability and reliability of a measurement system, allowing the organization to determine whether a measurement tool or process
can produce the consistent and accurate results required for quality, process assurance and improvement.

Table 4 Operator Readings of Five Valves - Valve Body Length Dimension

Part Reading-01 Reading-02 Mean Range
Operator-01
1 119.98 119.97 119.975 0.01
2 120.12 120.15 120.135 0.03
3 120.26 120.29 120.275 0.03
4 119.91 119.93 119.92 0.02
5 120.28 120.29 120.285 0.01
Operator-02
1 119.91 119.98 119.945 0.07
2 120.11 120.12 120.115 0.01
3 120.21 120.26 120.235 0.05
4 119.91 119.88 119.895 0.03
5 120.26 120.29 120.275 0.03
Operator-03
1 119.99 120.01 120 0.02
2 120.13 120.18 120.155 0.05
3 120.11 120.19 120.15 0.08
4 119.92 119.88 119.9 0.04
5 120.32 120.31 120.315 0.01
Average 120.105 0.033

LCL Mean UCL

Range 0.000 0.033 0.107
Average 120.044 120.105 120.166

Sigma (e) 0.028

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Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
Constants
D3 0
D4 3.267
A2 1.88
D2* 1.15

Table 5 Operator-01 Operator-02 Operator-03

Part Reading-01 Reading-02 Reading-01 Reading-02 Reading-01 Reading-02 Mean Range
1 119.98 119.97 119.91 119.98 119.99 120.01 119.973 0.1
2 120.12 120.15 120.11 120.12 120.13 120.18 120.135 0.07
3 120.26 120.29 120.21 120.26 120.11 120.19 120.220 0.18
4 119.91 119.93 119.91 119.88 119.92 119.88 119.905 0.05
5 120.28 120.29 120.26 120.29 120.32 120.31 120.292 0.06
Average 120.105 0.092

LCL Mean UCL Constants

Range 0 0.092 0.184368 D3 0
Average 120.061 120.105 120.149 D4 2.004
A2 0.483
D2* 2.56

Sigma (m) 0.0359

Sigma(repr) 0.0220

Part-to-part variation

R(p) 0.387 D2* 2.67

Sigma(p) 0.145

Sigma(t) 0.149

%EV 19.04%
%AV 14.75%
%R&R 24.08%

Table 6 Go and No-go Thread Plug Readings

Part Standard A B C Date Time Reproducibility Accuracy

1 1 1 1 1 Morning 1 1
1 1 0 1 1 Evening 0 0
2 0 0 0 0 Morning 1 1
2 0 0 0 0 Evening 1 1
3 1 1 1 1 Morning 1 1
3 1 1 1 0 Evening 0 0
1 1 1 1 1 Morning 1 1
1 1 1 1 1 Evening 1 1
2 0 0 0 0 Morning 1 1
2 0 1 1 1 Evening 1 0
3 1 1 1 1 Morning 1 1
3 1 1 1 1 Evening 1 1
83.33% 75%

Overall Week-01 Week-02

A B C A B C A B C
A 66.66 74.995 74.995 66.66 83.33 66.66 66.66 66.66 66.66
B 83.33 83.33 100 83.33 66.66 66.66
C 83.33 66.66 66.66

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Inspector A B C
Accuracy 83.33 83.33 75

 Repeatability - 66.66+83.33+83.33/3=77.75 measurements can be generated and validated. It is at this

 Reproducibility-83.33 stage that issues are analyzed using statistical methods and
 Accuracy-74.99Bias-19.44 further inquiry.

 Remarks  Pareto Analysis

With the above calculation of Part-to-Part Variation, Pareto analysis, based on the Pareto principle (80/20
Repeatability and Reproducibility analysis, we conclude that rule), is a problem-solving technique that helps identify and
the Company has a healthy measurement system. prioritize the most important factors that contribute to a
problem or outcomeorganizations focus their efforts on some
V. ANALYZE PHASE of the most important areas with significant impact.

In the Analyze phase, data is collected so that  Error Analysis

hypotheses about the root causes of variations in the process

Table 7 Error Analysis

Machining Error Dimension Error Body Finish Alignment Marking Leakage test
115 36 19 3 6 2

Fig 4 Pareto Chart on Machining Error Fig 5 Pareto Chart on Machining Error

Further, we will analyse what are the different types of  Fishbone Diagram
machining errors and which machining error is the most With fishbone analysis, we investigate the possible
significant. cause and effect of the errors.

Fig 6 Fishbone Diagram

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Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
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 Analyze Phase Analysis  Null Hypothesis (H0):
From the above Analysis, we identified that the
following factors are responsible for Threading Errors.  H01: The feed rate does not have a significant effect on
the pitch diameter. H02: The speed does not have a
 Speed of the Machines significant effect on the pitch diameter.
 Feed Rate  H03: There is no interaction effect between feed rate and
 Tool Material speed on the pitch diameter.
 Coolant Type
 Coolant Pressure  Alternative Hypotheses (Ha):

After further investigation, causal factors like tool  Ha1: The feed rate has a significant effect on the pitch
material, coolant type and coolant pressure are either diameter. Ha2: The speed has a significant effect on the
customer-specified or fixed during product development. pitch diameter.
 Ha3: There is an interaction effect between feed rate and
Thus, our focus will be on two main factors, the Speed speed on the pitch diameter. Following are the results of
of the Machines and Feed Rate as they areoperator controlled ANOVA from Minitab: -
and we can see variability in these factors during Production.

 ANOVA
Using ANOVA, we see that there is a relationship
between the Feed Rate and Speed of the machines on the
defects.

For the ANOVA, the following is the Null Hypothesis

Hypotheses Formulation: Fig 7 Factor Information

Fig 8 Anova Analysis

In conclusion, based on the analysis results and interpretation, you can reject the null hypotheses for feed rate and speed,
suggesting that both factors have a significant effect on pitch diameter. Additionally, the interaction effect between feed rate and
speed is also significant. The model appears to be a good fit for the data, explaining a substantial portion of the variability in pitch
diameter

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Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
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VI. IMPROVE PHASE

 Design of Experiments
DOE is a statistical technique used in quality control for designing, controlling, evaluating, and interpreting groups of
experiments to make sound decisions at a low cost and in a short amountof time.

DOE is concerned with understanding the impacts of certain variables on other variables. The goal is to create a cause-and-
effect link between a variety of independent factors and relevant dependent variables. In the DOE competition, the dependent
variable is known as the response, while the independent variables are known as factors. Experiments are carried out at various
factor values known as levels. Each experiment run involves a different level of the researched factors.

Table 8 Doe Parameters M16 X 1.5

Experiment X1 (RPM) X2 (Feed Rate) Observation (Y)
1 1000 0.5 15.061
2 1000 0.5 15.059
3 1000 0.5 15.069
4 1000 0.5 15.058
5 1000 0.8 15.205
6 1000 0.8 15.189
7 1000 0.8 15.2
8 1000 0.8 15.07
9 1000 1 15.01
10 1000 1 15.034
11 1000 1 15.11
12 1000 1 15.143
13 1000 1.2 15.15
14 1000 1.2 15.123
15 1000 1.2 15.045
16 1000 1.2 15.118
17 1100 0.5 15.11
18 1100 0.5 15.132
19 1100 0.5 15.128
20 1100 0.5 15.121
21 1100 0.8 15.085
22 1100 0.8 15.092
23 1100 0.8 15.098
24 1100 0.8 15.079
25 1100 1 15.156
26 1100 1 15.168
27 1100 1 15.148
28 1100 1 15.152
29 1100 1.2 15.103
30 1100 1.2 15.118
31 1100 1.2 15.109
32 1100 1.2 15.099
33 1200 0.5 15.063
34 1200 0.5 15.078
35 1200 0.5 15.1
36 1200 0.5 15.088
37 1200 0.8 15.182
38 1200 0.8 15.191
39 1200 0.8 15.209
40 1200 0.8 15.179
41 1200 1 15.036
42 1200 1 15.001
43 1200 1 15.044
44 1200 1 15.034
45 1200 1.2 15.126
46 1200 1.2 15.117

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47 1200 1.2 15.134
48 1200 1.2 15.129
49 1250 0.5 15.204
50 1250 0.5 15.228
51 1250 0.5 15.213
52 1250 0.5 15.195
53 1250 0.8 15.077
54 1250 0.8 15.065
55 1250 0.8 15.08
56 1250 0.8 15.063
57 1250 1 15.157
58 1250 1 15.139
59 1250 1 15.161
60 1250 1 15.169
61 1250 1.2 15.024
62 1250 1.2 15.048
63 1250 1.2 15.037
64 1250 1.2 15.03

The above table shows the variations in the RPM and Feed Rate with respect to the variation in Pitch Diameter. We carried
out four trials for each with four levels of variation in the attributes. Each trial gave us a different pitch dimension, all the obtained
data are to be evaluated so that we can conclude which among the variation is suitable to obtain good dimension stability in future
work also.

 ANOVA ON DOE

Fig 9 ANOVA ON DOE

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Fig 10 Fisher Pairwise Comparison

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Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
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The Fisher Pairwise Comparisons analysis for the Variation Using Six Sigma Methodology" initiative,
interaction between RPM and Feed Rate reveals significant strong emphasis should be placed on introducing process
differences in pitch diameter among different combinations. controls and standardization measures. Crucial parameters
Specifically, the Pitch Diameter at 1250 RPM with a Feed identified in earlier DMAIC phases should be closely
Rate of 0.5 and Pitch Diameter at 1200 RPM with a Feed Rate monitored to ensure they remain within specified limits.
of 0.8 is significantly better than other combinations. These Standardized work instructions, visual aids, and
findings emphasize the intricate interplay between RPM and comprehensive process documentation should be put in
Feed Rate on the pitch diameter outcomes in the needle valve place to guide operators and uphold consistency.
manufacturing process.  Utilization of Statistical Process Control (SPC): To
maintain the achieved reductions in defects and process
 Control Phase variation, Statistical Process Control techniques should be
The Control Phase of the "Reduction of Defect and harnessed. Continuous monitoring of control charts should
Variation Using Six Sigma Methodology" endeavour focuses be conducted to swiftly detect any shifts or trends in
on sustaining the advancements made in minimizing defects process performance. This proactive approach facilitates
and process variation. Through the deployment of a range of prompt corrective actions, safeguarding stable production.
tools, the initiative ensures the optimization, efficiency, and  Continual Training and Skill Enhancement: Ongoing
uniformity of the valve manifold manufacturing process. training initiatives should be executed to elevate the
Precise process controls are methodically introduced, guiding competencies and understanding of operators and
operators in adhering to standardized procedures and pivotal pertinent staff members. The periodic training should
parameters. Statistical Process Control methods provide real- ensure that the workforce is well-equipped to adhere to
time surveillance, enabling swift actions to counter potential established protocols, effectively manage unforeseen
deviations. Ongoing training initiatives continually enhance situations, and proficiently sustain the improved
the workforce's skills, ensuring precise execution of the manufacturing process.
enhanced process. Consistent monitoring and audits ascertain  Regular Monitoring and Auditing: Consistent audits and
alignment with established standards. Key performance process evaluations should be performed to assess
indicators are vigilantly monitored and communicated, adherence to established process controls and standards.
providing valuable insights into the effectiveness of control This continuous monitoring mechanism shall aid in the
measures and overall project triumph. By leveraging these early detection of potential issues or deviations, enabling
Control Phase tools, the overarching objective of timely interventions and corrective measures.
streamlining production, reducing defects, and lessening  Measurement of Performance and Reporting: Essential
process variation is accomplished, culminating in heightened performance indicators (KPIs) should be diligently
efficiency and product quality. tracked to gauge the effectiveness of process controls and
enhancements. Progress and accomplishments should be
 Control Phase Report: communicated through periodic performance reports,
offering stakeholders valuable insights into project
 Implementation of Process Controls and Standardization: success.
During the Control Phase of the "Reduction of Defect and

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Volume 8, Issue 9, September – 2023 International Journal of Innovative Science and Research Technology
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VII. CONCLUSION

To measure the above-chosen improvement decision, we have initiated the data of possible defects with the same volume and
number of lots of valves manufactured. Using the P Chart, wemeasure the.

Fig 11 Possible Process Sigma Level

 From the Identified two KPIs: - REFERENCES

 Defects Per Million Opportunities [1]. Pressure Regulator Valves Manufacturer, Instrument
Our New Defects Per Million Opportunities = Ball Valve Supplier, Exporter, Gujarat, India. (n.d.).
(121/2500) *1000000 = 48400 Improvement on DPMO = Pressure Regulator Valves Manufacturer, Instrument
(Initial DPMO - Final DPMO) / Initial DPMO Ball Valve Supplier, Exporter, Gujarat, India.
http://www.smiplvapi.com/ In-Text Citation:
Improvement on DPMO = (72400 - 48400) / 72400 (Pressure Regulator Valves Manufacturer, Instrument
Improvement on DPMO = 33.15% Ball Valve Supplier, Exporter, Gujarat, India, n.d.)
[2]. The Six Sigma Handbook, 5th edition, McGraw Hill
 Process Sigma Level [3]. Excellence through quality | ASQ. (n.d.).
Our new process sigma level is 3.161 https://asq.org/
[4]. James Roughton, Nathan Crutchfield."Effectively
Improvement on Process Sigma Level = (Updated Managing a JHA Process using Six Sigma", Elsevier
Process sigma level - Initial process sigma level) / Initial BV, 2016
process sigma level [5]. www.invensislearning.com
[6]. www.iss-foundation.org
Improvement on Process Sigma Level = (3.161 – [7]. ieomsociety.org
2.958) / 2.958 Improvement on Process Sigma Level = [8]. www.ssu.ac.in
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reduce the defects by atleast25%. Journal of Six Sigma and Competitive Advantage,
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[10]. ROUGHTON. "Six Sigma as a Management
System:A Tool for Effectively Managing a JHA
Process", Job Hazard Analysis, 2008

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