Improvement of quality awareness using six sigma methodology for achieving higher cmmi level

International Journal of Advanced Research in Management (IJARM),
Volume 1, Issue 1, June 2010. pp. 20-41
Prabhuswamy & Mamatha. M
I J ARM
International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.
http://www.iaeme.com/ijarm.html
© IAEME
IMPROVEMENT OF QUALITY AWARENESS USING SIX SIGMA
METHODOLOGY FOR ACHIEVING HIGHER CMMI LEVEL
B.P. Mahesh
Assistant Professor, Department of Industrial Engineering and Management
M.S.Ramaiah Institute of Technology, Bangalore-560054, India
bpmahesh@gmail.com (+91-9448739040)

Dr. M.S. Prabhuswamy
Professor, Department of Mechanical Engineering
S.J. College of Engineering, Mysore-570006, India
msp_sjce@yahoo.com (+91-9886624627)

Mamatha. M
Project Manager, FINACLE
Infosys Technologies Limited, Electronics City, Bangalore- 560100, INDIA
mamatha_m@infosys.com (+91-9945529504)

ABSTRACT
Globalization and increased competition gives rise to new approaches to
managing Quality and Productivity. New approaches and frame works such as TQM,
Business Process Re-engineering (BPR), Capability Maturity Model (CMM), etc., have
been extensively deployed in organizations. Along with these approaches, in the face
of a complex dynamic environment, the organizational survival hinges on adaptation
and human competence also. Managing the creative and innovative ability of the
human capital would make a difference between success and failure of any
organization. Six Sigma methodologies provide a highly prescriptive cultural
infrastructure and an adaptive framework for obtaining sustainable results in
manufacturing as well as service organizations. In this article, the research scholar
presents the application of Six Sigma framework for achieving a higher CMMI level
through improvement of quality awareness among process users. The pilot
implementation of recommendations of the study showed improved awareness, better
involvement and enhanced commitment from the process users to follow the
standardized processes for achieving the organization’s goal of being a CMMI level 4
assessed organization.
KEYWORDS
Capability Maturity Model Integration; Six Sigma; Quality Function Deployment;
Failure Mode and Effect Analysis; Quality Management System; Critical to Quality.

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1. INTRODUCTION
Six Sigma methodology has been effectively implemented in many
manufacturing and service sectors. But there is a lot of scope for implementing Six
Sigma methodology in the various areas of Information Technology sector. Software
Engineering Institute – Capability Maturity Model Integration (SEI – CMMI) provides
a road map for organizations to achieve excellence in the Information Technology
sector. The present study was undertaken at a multinational Research and Development
center located in Bangalore. The organization is currently SEI – CMM level 3 assessed
and is striving to achieve CMMI (Capability Maturity Model – Integration) level 4
assessment. To achieve CMMI level 4 assessments, all process users must follow
standardized processes as specified in the Quality Management System (QMS) of the
organization. The initial observation by the research scholar revealed that the process
users were not strictly adhering to specified standardized processes, thus causing a
hindrance for the organization to achieve CMMI level 4.
The objective of the study was to increase the awareness, understanding and
perceived importance of QMS amongst the process users. The Six Sigma - DMAIC
(Define, Measure, Analyze, Improve and Control) methodology was applied to meet
the set objective. The various TQM tools and techniques used in the study were
Structured Survey, Process Mapping, Quality Function Deployment (QFD), Pareto
Analysis, Failure Modes and Effects analysis (FMEA) and Regression Analysis.
2. LITERATURE REVIEW
Six Sigma is a statistical concept that measures a process in terms of defects.
Achieving Six Sigma means processes are delivering 3.4 defects per million
opportunities (DPMO). In other words, they are working almost perfectly.
Sigma is a term in statistics that measures standard deviation. In its business
use, it indicates defects in the outputs of a process, and helps us to understand how far
the process deviates from perfection. One sigma represents 691462.5 DPMO, which
translates to a percentage of non-defective outputs of only 30.854%. That’s obviously
really poor performance. If we have processes functioning at a three sigma level, this
means we are allowing 66807.2 errors per million opportunities, or delivering 93.319%
non-defective outputs. That is much better, but we are still wasting money and
disappointing our customers. The central idea of Six Sigma management is that if we
can measure the defects in a process, we can systematically figure out ways to
eliminate them, to approach a quality level of zero defects, which is the ultimate goal
of TQM.
DMAIC refers to a data-driven quality strategy for improving processes, and is
an integral part of the company's Six Sigma Quality Initiative. This methodology can
be applied to the product or process that is in existence. DMAIC is an acronym for five
interconnected phases: Define, Measure, Analyze, Improve, and Control. Each step in
the cyclical DMAIC Process is required to ensure the best possible results (Figure 1).

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DEFINE MEASURE ANALYZE IMPROVE

CONTROL

Figure 1 Six Sigma – DMAIC Methodology

The DMAIC Methodology is explained in simple terms as follows.
Define the Customer, their critical to quality (CTQ) issues, and the core business
process involved.
Measure the performance of the Core Business Process involved.
Analyze the data collected and process map to determine root causes of defects and
opportunities for improvement.
Improve the target process by designing creative solutions to fix and prevent
problems.
Control the improvements to keep the process on the new course.
Doug Sanders and Cheryl R Hild [1] have stated that process knowledge is very
important in obtaining Six Sigma solutions. Also, the metrics associated need not
always be number of people trained in Six Sigma, or savings in cost, but defects per
unit, sigma level and rolled-throughput yield.
Cherly Hild, Doug Sanders and Tony Copper [2] have opined that to achieve
optimal outcomes in continuous process, non linear and complex relationships among
process factors must be managed. The data from continuous processes are often
plentiful in terms of processing variables and limited with regard to product
characteristics. With continuous processes, the variation in the main product stream
does not necessarily reflect the true level of variation exhibited by the process.
Goh T.N [3] has brought out an intuitive perspective on the fundamental
mechanics of design of experiments (DOE) in a way that would help enlighten a non-
statistician during the course of deployment of DOE related methodologies, regardless
of the context used. He has stated that in most of the experiments involving multifactor
processes, interactions of 3rd order and higher, often turn out to be insignificant and are
immaterial to subsequent process characterization and optimization.
Piere Bayle et al, [4] designed and optimized the braking subsystem for a new
product. They also stated that focus is placed on the factors that have the strongest
effect on the response, but there is as much information and insight provided about
direction of future work by considering the implications of factors with little or no
effect.
Spencer Graves [5] has used the tool of forecasted Pareto, which combined
Rolled Throughput Yield (RTY) and sales forecast. RTY estimates the probability
whether a product passes through a process defect free or not as recommended by Six
Sigma proponents, because it seems to be a highly correlated scrap rework, warranty
etc. It is relatively easy to compute from data obtainable from many processes.
22


Goh T.N [6] has explained, in a non mathematical language, the rationale and
mechanics of DOE as seen in its deployment in Six Sigma. He has stated the
advantages of DOE over process monitoring techniques. He has described about the
shifting emphasis in the deployment of DOE.
Dana Rasis et al [7] distinguished between black belt and green belt Six Sigma
projects on the basis of five criteria. A case study has been discussed presenting the
definition and measure phases of DMAIC method. The authors identified the CTQ and
performed gauge Repeatability and Reproducibility study on each CTQ.
Charles Ribardo and Theodore T Allen [8] have stated that desirability function
do not explicitly account for the combined effect of the mean and dispersion of quality.
The authors have proposed a desirability function that addresses these limitations and
estimates the effective yield. They have used an Arc welding application to illustrate
how the proposed desirability function can yield a substantially higher level of quality.
The proposed desirability function is based on the estimates of yield that is the fraction
of confirming units.
Goh T.N and M Sie [9] have described some alternative techniques for the
monitoring and control of a process that has been successfully implemented. The
techniques are particularly useful to Six Sigma black belts in dealing with high quality
processes. The methodology ensures a smooth transition from a low sigma process
management to maintenance of high sigma performance in the closing phase of a Six
Sigma project.
Rick L. Edgeman and David Bigio [10] have stated that the future Six Sigma
will be integrated with other tools, used in nontraditional sectors, more adapted and
strengthened. One can expect new concepts like lean Six Sigma, best Six Sigma, lean
best Six Sigma, Six Sigma in health care, lean design and macro Six Sigma to be
applied in manufacturing and service industries.
Mohammed Ramzan and Goyal [11] have stated that Six Sigma provides a
systematic, disciplined and quantitative approach to continuous improvement. Through
the application of statistical thinking, it uncovers the relationship between variation and
its effect on waste, operating cost, cycle time, profitability and customer satisfaction.
The scope of Six Sigma encompasses all aspects of the organization that is from
marketing to product and process designing to accounting to after sale service.
3. OBJECTIVE OF THE STUDY
The objective of the study is to measure the current process user’s awareness
about the organization’s QMS and to improve upon the average awareness level from
the existing 55% to around 70%. The increased awareness, understanding and
perceived importance of QMS enable to have more commitment from the process users
to follow the standardized processes and prepare the necessary documents for
achieving the organization’s goal of being a CMMI level 4 assessed organization.

23


4. DMAIC METHODOLOGY ADOPTED IN THE PRESENT STUDY
4.1 DEFINE PHASE
The process users of the organization are only 55% aware of the uses/benefits of
the organization's QMS. This lack of awareness among the process users can lead to be
a hurdle for the organization in achieving CMMI Level 4 Assessment as per the set
deadlines. The process users who are well aware about the QMS & its benefits could
commit themselves to follow the standardized processes and prepare the relevant
documents which would result in having instances necessary for achieving the CMMI
Level 4 Assessment for the organization.
The Define Phase consists of Preparation of Project Charter, Collecting the Voice
of Customers (VOC), Identifying the Critical to Quality (CTQs) and Process Mapping.
• Preparation of Project Charter
The study starts with preparation of a document called Project Charter. This
document clarifies what is expected out of the research team. The major elements of
this document deals with the questions like,
What is the problem for which the study is being carried out?
What is the goal of the study?
Why the study is worth doing?
How the study's goal can be achieved?
When the study's goal is supposed to be met?
Who all are involved in the study?
What are the challenges/risks that are foreseen in the study?
Problem Statement
Process users are only 55% aware of the uses / benefits of QMS / QI Page as at
the starting of the study and are not fully following the standardized processes (as
available in the organization's QMS) in their projects.
All other issues have been dealt in the project charter in Figure 2.
• Collection of the VOC
The VOC was collected using a survey questionnaire. The customers for this
study are the process users who are the potential users of the organization's QMS. The
questions used for the purpose of collecting what the customers wanted were open
ended. Some of the questions included in the survey were like
What would you like to have added on the QMS?
How do you think Quality can be improved in the organization?
These questions were included in the questionnaire as well as were asked
verbally in the form of interviews. A standard template was used to collect all the
requirements and suggestions of the customers.
• Identification of the CTQs
The VOC, which was collected in the Define Phase with the help of the survey,
is used to identify the CTQs related to the process. These CTQs are used to carry out a

24


QFD. The outcome of this application can be used as the suggestions for improving the
process to make the process users at least 70% aware about the organization's QMS.
Goal Risks
To achieve SEI - CMMI level 4 assessment Getting time from the process users for the
from the existing SEI - CMM level 3. survey.
New resources joining the organization, if
surveyed, can give inaccurate results.
Objective Statement of Work
To increase the average awareness level of Modifying the process by which the
Quality / QMS among the process users Process users are made aware of QMS at
from the existing 55% to at least 70%. the organization.
Value of the study Methodology
It will ensure increased awareness level The methodology used for the project is Six
about organization's QMS among the Sigma DMAIC methodology.
process users and enable obtaining more
commitment from them to follow the
standardized processes that would result in Background Knowledge
having instances necessary for achieving The training used for making process users
the CMMI Level 4 Assessment for the aware of QMS in the organization.
organization.

Figure 2 Project Charter
• Process Mapping
The existing process for any process user / employee to be made aware about
the organization's QMS or the Quality related activities is mapped by studying the
system of induction trainings in the organization. This process is clearly depicted in
Figure 3. The shaded boxes on the process flow chart indicate where the improvements
in the process may take place.

4.2 MEASURE PHASE
The measure phase consists of Selecting CTQ characteristics using TQM tools
like QFD, FMEA & Process Mapping, Defining the performance standards and
Measurement system analysis.
• Selecting CTQ characteristics using Quality Function Deployment (QFD)
QFD may be defined as a systematic process used to integrate the customer
requirements with design, development, engineering, manufacturing and service
functions. The CTQs identified in the previous step are used to prepare the first House
of Quality. Figure 4 shows the VOC on the Y-axis and the requirements of the process
for quality awareness on the X-axis.
The Second House of Quality, as shown in the Figure 5 provides us with the
“HOWS” that tells us how the process can be more effective and efficient in making
the process users aware about the organization’s QMS.

25


New Employee
joins the organization

Is a batch of 5
Employee work on his / her respective No new employees
project until the batch size reaches 6 waiting for
QMS training?

Employees go through QMS training in Yes
batch of 6. (Induction)

Project Manager (PM) /Project Leader
(PL) fills up the Templates or just educate
the employee in filling template.

Software Quality Analyst (SQA)/ Project
Quality Analyst (PQA) reviews the
documents, checks whether the processes
are being followed once a week / fortnight
(mostly with PM / PL)

QMS Awareness
among the employees Figure 3 Existing flow process chart of induction process

The "Hows" obtained as the suggestions from the Houses of Quality are as
follows.
a) Training to be more frequent.
b) Instructor to be trained for training.
c) Conducting regular quality quiz to evaluate the process users' quality awareness.
d) Employee scoring below 70% in the quality quiz to be helped by SQA/PQA.
e) Search functionality to be added on the QI page.
f) QTM and QR of each dept. to come up with dept. specific examples.
g) Project knowledge sharing for best practices related to quality to be initiated.
h) Training invitee list to be compared with the Training attendee list.
From the Pareto Charts as shown in the Figures 6 & 7 for the two Houses of
Quality, we can conclude that Frequency of the QMS training, Conducting regular
Quality Quiz and Instructor to be trained for QMS training are the factors that can
largely satisfy the CTQs, and thus result in having higher awareness levels about
Quality / QMS among the process users.
26


Process Requirement

Experienced employees Refresher Quality training for their dept.

Revamping of QI page (training material, search functionality).

Department wise categorization of processes on the QI Page.
Knowledge sharing related to quality by the projects.
Dept. specific examples in the QMS training.
Customer Expectation

Department-wise QMS training.
QMS Training Attendee list.
QMS Training Efficiency.
Importance.

Total
Frequency of QMS Training 5 H L 50
QMS training for everyone 5 M M H 75
Search Functionality on the QI page 5 M H 60
Different links for different departments 4 H L 40
Guidance for the usage of templates 4 L H 40
Relevance of the training topic 4 H L 40
Time lag between joining the org and QMS 4 L L 8
training
Accessibility of QMS training material 2 M L 8
More examples in the QMS training 2 L H 20
material
Total 64 57 56 51 45 38 26 4

Figure 4 First House of quality
H : High relationship between customer expectation and process requirement.
M : Medium relationship between customer expectation and process requirement.
L : Low relationship between customer expectation and process requirement.
Numerical equivalent of these variables are H = 9, M = 3 and L = 1.

27


How’s

Reward the Project Team following the best quality practices.
Invite employees scoring low in quiz for QMS training.

Reward experienced PM / PL for training.
Instructor to be trained for QMS training.
Support from QTM and QR of the dept.
QMS training week every 2 months.
Process Requirement

Conduct regular quality quiz.
Importance

Total
Experienced employees-refresher Quality 5 H M 60
trainings for their dept.
Revamping of QI page (training material, search 5 M 15
functionality).
Department-wise QMS training. 4 L L 8
Dept. specific examples in the QMS training. 4 H M 48
Knowledge sharing related to quality by the 4 H 36
projects.
QMS Training Attendee list. 4 H 36
QMS Training Efficiency. 4 M H H L 88
Department-wise categorization of processes on 3 M 9
the QI page.
Total 61 51 49 48 40 36 15

Figure 5 Second House of Quality

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1st House – Pareto

80
70 19%
17% 16%
60 15%
50 13%
11%
40
30 08%
20
10 01%
0
Legend 1 2 3 4 5 6 7
1 : Experienced employee – refresher quality trainings for their department.
2 : Revamping of QI page (training material, search functionality).
3 : QMS Training Efficiency.
4 : Department-wise QMS training.
5 : QMS Training Attendance list.
6 : Department specific examples in the QMS training.
7 : Knowledge sharing related to quality by the projects.
8 : Department-wise categorization of processes on the QI page.

2nd House - Pareto

80
70 21%
60 18% 16% 15%
50
13%
40 12%
30
20
10
0
1 2 3 4 5 6

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Legend
1 : QMS training week every 2 months.
2 : Conduct regular quality quiz.
3 : Support from QTM and QR of the department.
4 : Instructor to be trained for QMS training.
5 : Employees scoring low in quiz for QMS training.
6 : Reward the Project Team which follows the best quality practices.
7 : Reward experienced PM / PL for training.
• Failure Modes and Effects Analysis (FMEA)
FMEA is a structured approach to identify the ways in which a process can fail
to meet critical customer requirements. In this study, FMEA is performed to identify
the potential failure modes in the Quality / QMS awareness process. The potential
failure effects of these failure modes, the causes for these failures and the controls that
currently exist over the causes are identified. The severity of the effects of the failure is
rated on a scale of 1 to 10, with 1 being the case when the failure has no effect on the
customer requirements and 10 being the case when the failure largely affects the
customer requirements. The probability of occurrence of the causes of these failures is
also on the same scale, with 1 being the case when these causes are unlikely to occur
and 10 being the case when the probability of occurrence of the causes are very high.
The detection certainty of the causes is rated on a scale of 1 to 10, with 1 being the case
when the cause can be easily detectable and 10 being the case when the causes usually
are not detectable. The performed FMEA is shown in the Figure 8.
• Definition of Performance Standards
The operational definition for the study is that process users are expected to be
at least 55% aware about the organization's QMS. Anyone having an awareness level
below 55% is considered as a defect for the current process. The data collection
methodology that was used for this study is survey. This survey was conducted in a
form of questionnaire consisting of QMS-related questions. The data obtained from the
survey was used for calculating the current Sigma level for the awareness level of the
process users about the organization's QMS.
• Measurement System Analysis -Data Collection Plan
The measures used for this study are the scores in the questionnaire. A survey
was conducted in the form of a questionnaire consisting of QMS-related questions.
Each question had four options, out of which only one was correct. Each question
carried different weights, which were arrived at in a discussion with the Quality Team
members. The designing of the questionnaire involved a brainstorming session with the
Quality Team members. The measurement system tool used is MINITAB®Release
14.12.0, Statistical software.

30

International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S. Prabhuswamy & Mamatha. M
Potential Potential S O D

Occurrences
Failure Failure RPN Responsibi-

Detection
Severity

RPN
Modes Effects Potential Current Action lity
Causes Control Recommended and Target
Date

QMS No 10 Trainer busy with 1 Stand by trainer 2 20
induction awareness other project
training not about QMS Trainee not attending 4 None 4 160 Get non-attendee for HR dept. 10 3 2 60
happened next training
Frequency of QMS 8 Training only 4 320 QMS training week Quality team 10 3 4 120
training very low when batch size every 2 months
reaches 6
members
Training Lack of 9 Poor instructor’s 2 None 6 252 Instructor to be trained Quality team 9 1 6 90
not QMS presentation skills for QMS training
effective awareness Examples not 4 4 9 1 4
among included
attendees Lack of attendee’s 6 None 3 162 Reward highest scorer Quality team 9 3 4 108
interest for quality in quiz
Topic irrelevant to 2 Department wise 5 90 Training requested by QRs, QTMs 9 2 3 54
the attendees trainings QR, PM / PL
Process Lack of 9 PM/PL fills all the 8 None 3 216 Initiate project SQAs 9 5 3 135
users not QMS templates knowledge sharing for
filling the awareness best practices related to
templates among quality.
process
users
Process Lack of 8 QI page structure not 7 None 4 224 Add search EPG 8 5 3 120
users not QMS user friendly functionality to QI page
visiting QI awareness
page for among Too much data 5 None 3 120 Include and elaborate Instructor 8 4 3 96
searching process the QI page during
the users QMS training
processes
or
Poor process users 8 None 4 256 Conduct regular quality SQAs 8 7 2 112
templates
motivation for quality quiz
available in
QMS

Figure 8 FMEA Table

31

Even if one person repeatedly measures the awareness level of process
users using the survey questionnaire, there will be no variation in the result and
even if two or more people evaluates the process users' awareness revel using
this questionnaire, there will be no variation. Thus, the questionnaire used as
the measurement system satisfies the Repeatability and Reproducibility (R&R)
conditions.
The survey is conducted over a number of process users spread through
various departments of the organization. This sample size is to be sufficient
enough as the organization consists of around 150 process users out of which
around 30 are students who are not directly involved in the projects.
4.3 ANALYZE PHASE
The Analyze Phase consists of Establishing Process Capability,
Defining the Performance Objectives and Identifying Variation Sources.
• Establishment of Process Capability
The scores obtained by the process users from the survey which was
conducted during the Define phase is plotted (Figure 9). This graph shows
pictorially the score obtained by the process users. The red bars are the defects.
These bars show the process users scoring below the average score, i.e. below
55%.
Figure 10 shows the summary of statistics for the score obtained. The
histogram is shown along with the normal curve fitted to it. The box plot shows
that there are no Outliers. The P-value calculated is 0.038, which is below 0.05
(i.e. 5%). This result signifies that the scores are normally distributed. Thus the
process capability calcu1ations are performed.
The current average awareness level of the process users as per the
survey conducted is found to be only 55%. The defect definition for the process
is decided to be "an employee scoring less than the mean score, i e. less than
55%". Thus, for the current process, the defects in the process are the process
users scoring below 55%.

Score obtained (%) v/s
100
90
80
Score obtained (%)

70
60
50
40
30
20
10
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65

Emp. No.

Figure 9 Plot of score obtained vs. Emp. No.

32


Anderson-Darling normality test

A- Squared 0.79
P- Value 0.038

Mean 55.477
St. Dev. 22.456
Variance 504.253
Skewness -0.05419
Kurtosis -1.13341
N 65

Minimum 13.000
1st Quartile 36.500
Median 56.000
3rd Quartile 76.000
Maximum 95.000

95% Confidence Interval for
Mean
49.913 61.041
95% Confidence Interval for Median
45.121 66.000
95% Confidence Interval for St.
Dev.
19.150 27.152

Figure 10 Summary of Statistics for the Quality Awareness Score
The calculations of the process capability of the current process are shown
below.
Total number of process users surveyed (o - opportunities) = 65
Average Score of the process users = 55%
Number of process users on or above the average score (c) = 33
Number of employee below the average score (d -defects)= (o)-(c) = 65-33= 32
Defects per opportunity (dpo) = (d / o) = (32/65) = 0.49230769
Defects per million opportunities (dpmo) = (d/o)*1000000 = 492307.6
For the calculated dpmo, the current Sigma Rating† =1.52σ
Process Capability of the current process = 1.52σ
• Definition of Performance Objectives
The goal of the study can be defined statistically as follows.
“To increase the average awareness level of process users (process target)
from 55% to 70% and the process capability from 1.52σ to 2.1σ”

†
= The Sigma Rating is obtained from the standard Sigma and DPMO Conversion Table.

33

• Identification of Variation Sources
The P–value calculated signifies that the scores obtained are normally
distributed (for 95% confidence level). P-value may be formally defined as the
probability of being wrong if the alternative hypothesis is selected. The P-value
is calculated here by considering the null hypothesis as “the data follows
normal distribution”. Thus, P-value of less than 0.05 indicates that this null
hypothesis is true. The graphs as shown in Figure 11 show the effects of the
critical ‘X’ on the ‘Y’. This ‘Y’ is the Quality / QMS awareness level of the
process users. These are the critical ‘X’s which were obtained as a result of
QFD and FMEA.
The ‘X’s are:
Frequency of training
Instructor to be trained for training
Conducting regular quality quiz
Happening of Project knowledge sharing
Search functionality on the QI Page
Null Hypothesis statement
The present process is better than the new proposed process.
4.4 IMPROVE PHASE
The Improve Phase consists of Screening the Potential Causes,
Discovering Variable Relationships and Establishing Operating Tolerances.
• Screening the Potential Causes
This step involves determination of the vital few ‘X’s that affect the ‘Y’.
In this study, the screening of the potential causes identified in the Measure and
Analyze Phases, using basic tools like QFD and FMEA, is being done in the
Improve Phase. Five major factors or ‘X’s that affect the Quality Awareness
among the process users of the organization have been identified.
The Main Effects Plot is used when one have multiple factors. The
points in the plot are the means of the Quality / QMS Awareness at various
levels of each factor (i.e ‘X’s). The plot in Figure 11 is used for comparing the
magnitude of effect, various factors have on the Quality / QMS Awareness (i.e
‘Y’). The slope of the lines depicts the effect of the factors on the ‘Y’. The
higher the slope of the line, higher is the effect of the particular ‘X’ on the ‘Y’.
In the Figure 11, it can be clearly seen that the slope of the line for
‘Frequency of Training’ is highest. Thus it can be concluded that the Quality /
QMS Awareness among the process users is largely affected by the ‘Frequency
of Training’. The factor ‘Conducting Quality Quiz’ has the second highest
slope, i.e Quality / QMS Awareness among the process users can also be highly
affected by ‘Conducting Quality Quiz’. The factor ‘Instructor Training’ also
affects the Quality / QMS Awareness among the process users. However,
adding a ‘QI Page-Search’ and ‘Project Knowledge Sharing’ would not affect
the awareness level among the process users as much as the other 3 factors.

34

Table 1 Data for Regression Analysis
Frequency Instructor Regular Project QI Page Quality /
of Training Training Quality Knowledge Search QMS
Quiz Sharing Awareness
1 1 1 1 1 1.00
0 1 1 1 1 0.75
1 0 1 1 1 0.80
1 1 0 1 1 0.79
1 1 1 0 1 0.83
1 1 1 1 0 0.83
0 0 0 0 0 0.00
0 0 1 1 1 0.55
1 0 0 1 1 0.59
1 1 0 0 1 0.62
1 1 1 0 0 0.66
0 1 1 1 0 0.58
0 0 0 1 1 0.34
1 0 0 0 1 0.42
1 1 0 0 0 0.45
0 1 1 0 0 0.41
0 0 1 1 0 0.38
0 0 1 0 1 0.38
1 0 0 1 0 0.42
0 1 0 0 1 0.37
0 1 0 1 0 0.37
1 0 1 0 0 0.46
0 0 0 0 1 0.17
0 0 0 1 0 0.17
0 0 1 0 0 0.21
0 1 0 0 0 0.20
1 0 0 0 0 0.25

Figure 11 Main Effects Plot

35

Interaction plot (data means) for Quality / QMS Awareness

Figure 12 Interaction Plots
• Discovering Variable relationships
The variable relationships were discovered using the main effects plot
and the interaction plots. Interaction plots are useful for judging the presence of
interaction among the factors. Interaction is present when the response at a
factor level depends upon the level(s) of other factors. Parallel lines in an
interactions plot indicate no interaction. The greater the departure of the lines
from the parallel stage, higher the degree of interaction.
Figure 12 shows a matrix of interaction plots for the five factors. It is a
plot of means for each level of a factor with the level of a second factor held
constant. In the full matrix, the transpose of each plot in the upper right is
displayed in the lower left portion of the matrix.
Figure 12 clearly shows that the ‘Frequency of Training’ is not affected
by the factors ‘Conducting Quality Quiz’ and ‘Project Knowledge Sharing’.
However, there is an interaction between the ‘Frequency of Training’ with the
‘Search functionality on the QI Page’ and ‘Instructor’s training’. Similarly it
can be seen that ‘Project Knowledge Sharing’ has an interaction with the
‘Search functionality’ on the ‘QI Page’. From the interaction plots as shown in
Figure 12, the variables or the factors affecting the quality awareness do not
have much effect on each other.
The prioritization of the factors that affect the awareness of
Quality/QMS among the process users as obtained from the Main Effects Plot
is shown in Table 2.

36

Table 2 Prioritization of factors affecting Quality awareness
Factors Priority
Frequency of QMS training 1
Conducting regular Quality Quiz 2
QMS training instructor’s presentation skills 3
Search functionality on QI page 4
Project knowledge sharing for best practices related to quality 5
This prioritization is used for arriving at an equation relating various
factors with the Quality / QMS Awareness among the process users. These
magnitudes of effect that the various factors have on the Quality / QMS
Awareness (i.e. ‘Y’) can be seen in the Main Effects Plot (Figure 11). The
slope of the lines depicts the effect of the factors on the ‘Y’. The higher the
slope of the line, higher is the effect of the particular ‘X’ on the ‘Y’.
Regression Analysis was executed for arriving at the equation. (Table 1)
Transfer Function between ‘Y’ and the vital few ‘X’s is

Y = 0.25X1 + 0.21 X2 + 0.20X3 + 0.17X4 + 0.17X5
Where, Y Quality / QMS Awareness among the process users.
X1 Frequency of the QMS training.
X2 Regular Quality Quiz.
X3 Instructor to be trained for QMS training.
X4 Project Knowledge Sharing for best practices related to quality.
X5 Search functionality on the QI page.
• Proposed Process
Based on the results of the steps performed above, the proposed process
of making the employees aware of the organization’s QMS / Quality related
activities, is shown in the Figure 13.
4.5 CONTROL PHASE
The Control Phase consists of Definition and Validation of Measurement
System for the 'X's in actual implementation, Determination of Process
Capability (i.e. Short Term Sigma or σST) and Controlling Long Term Sigma
(σLT).
• Definition and Validation of Measurement System for the 'X's' in actual
implementation
The proposed process needs a pilot study. The need for a pilot study is
to better understand the effects of the proposed solution and plan for a
successful full-scale implementation and to lower the risk of failing to meet
improvement goals when the solution is fully implemented. The measures for
the pilot study stage remains the same as were during the Measure Phase, i.e.
scores obtained in the questionnaire. This data collection plan is used to
confirm that the suggested solution meets the improvement goals.

37


New Employee
Joins the Instructor is trained for
organization QMS training

Mention about URL
Employee to undergo QMS
for QI Page and EPG
induction training, which will
especially
happen bi-monthly and as per
need-basis
Department specific
examples are included in
consultation with the
experienced PMs / PLs and
QR.

Is the score of the
employee above Yes
70% in the quiz Employee continues to
conducted with work on his / her
the QMS training? project and prepare
necessary documents
Yes

No

The employee’s name is noted in the Is the employee
invitee list of the next QMS training / No scoring > 70% in
special attention to be given by the the regular
SQA / PQA in the project he / she is quality quiz (by
working. SQA / PQA)?

Figure 13 Proposed Process
• Determination of Process Capability
During the first few trials, in any process, the variability is small and
mean is centered at the target. It is called Short Term Sigma (σST). This is the
best the process is capable of. The survey used for measuring the Quality
Awareness levels of the process users again after implementing the suggested
improvements is the data for calculating the process capability of the new
process.

38

The defect definition for the process is modified as "employee scoring
less than the mean score, i. e. less than 70%". This change in defect definition
is due to the goal of this study, which aims at having an average score of 70%
in the questionnaire used for survey. Thus, the number of process users scoring
below 70% is the number of defects for the new process and the number of
process users being surveyed is the number of opportunities. Every possibility
of making an error is called an opportunity and in this process, an opportunity
is an employee who is being surveyed.
The number of defects and the number of opportunities are used to
calculate defects per million opportunities (dpmo). The process capability (σST)
of the new process is obtained using the "Sigma and DPMO Conversion Table"
corresponding to the calculated dpmo. If this sigma rating is around 2.1σ, the
new process is successful. The new process is then to be documented and
followed.
• Controlling the Long Term Sigma (σLT)
Over a period of time, assignable causes creep in and the capability of
the process to meet the requirements diminishes. This sigma which represents
the capability of the process to meet the requirements over a period of time
considering those extraneous conditions causes process shifts from that at
which it was set is called the Long Term Sigma. Normally, the short term
sigma is higher than long term sigma. Unless otherwise specified, long term
sigma is calculated as σLT = σST – 1.5.
There are various mechanisms that can be used to control a process
namely, Risk Management, Mistake Proofing, Statistical Process Control
(SPC) and Control Plans.
The key to controlling the process is frequent interval monitoring. The
ongoing measurements of the process variation and/or process capability are to
be used for monitoring. The ongoing measurements in this study are the regular
quality quizzes that need to be conducted by the Quality Team. Even random
auditing of the documents prepared by the process users for their projects can
give an idea of how much the process users are aware of the organization's
QMS. The responses obtained by these measurement systems indicate the
success of the new process.
5. SOLUTIONS FOR IMPROVING QUALITY AWARENESS
The first four phases -Define, Measure, Analyze, and Improve -of the
DMAIC methodology have been applied successfully to this study. The
improvements suggested were planned for implementation, which essentially
forms the Control Phase. Rigorous efforts were made to get the required
approvals from the top management and co-operation from the process users
themselves to improve the Quality Awareness levels in the organization.
Some of the improvements suggested were
• To have QMS trainings every 2 months or on the need basis.
• To conduct regular Quality Quiz for all the process users of the
organization.
• To train the instructor who conducts QMS training.

39

• To add a search functionality on the QI Page on the organization's intranet.
• To initiate regular project knowledge sharing sessions by the SQAs/PQAs
highlighting the best practices related to quality.
• To involve QRs and experienced PMs/PLs of all the departments to suggest
good examples that can be included in the QMS training material.
• To involve experienced PMs/PLs to conduct refresher QMS/Quality-
related trainings for their departments.
• To welcome constructive comments, so that the Quality Awareness process
can be improved continuously.
6. POST IMPLEMENTATION RESULTS
In a span of three months, all solutions recommended were
implemented. Then, the research scholar repeated the Measure and Analyze
phases. The scores obtained by the process users in the post implementation
study are plotted (Figure 14). The red bars are the defects. These bars show the
process users scoring below the average score, i.e. below 70%.
In the improved process, for 17 defects out of 65 opportunities, the
dpmo is found out to be 261538. i.e. the sigma rating or the process capability
of the improved process is found to be 2.13σ.

Score obtained (%) v/s Emp.No.
100
90
80
Score obtained (%)

70
60
50
40
30
20
10
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65

Emp. No.

Figure 14 Plot of score obtained vs. Emp. No.
7. CONCLUSION
All the phases - Define, Measure, Analyze, Improve and Control - of the
DMAIC methodology have been successfully applied to the study. The
solutions implemented resulted in increasing the awareness level of the process
user’s form 55% to 70% and increasing the sigma level from 1.52σ to 2.13σ
about the organization's QMS. Similarly, efforts can be put for achieving
higher and higher level of Sigma, until the organization reaches Six Sigma
level.

40

REFERENCES
1. Doug Sanders and Cheryl R Hild (2000-01), “Common Myths about Six
Sigma”, Quality Engineering, Vol 13, No 2, pp 269-276.
2. Cheryl Hild, Doug Sanders and Tony Cooper (2000-02), “Six Sigma on
continuous processes: How & why it differs?” Quality Engineering, Vol
13, No1, pp 1-9.
3. Goh T.N (2001), “Information Transformation Perspective on
Experimental Design in Six Sigma”, Quality Engineering, Vol 13, No 3, pp
349-355.
4. Piere Bayle, Mike Farrington, Brenner Sharp, Cheryl Hild & Doug Sanders
(2001), “Illustration of Six Sigma Assistance on a Design Project”, Quality
Engineering, Vol 13, No 3, pp 341-348.
5. Spencer Graves (2001-02), “Six Sigma Rolled Throughput Yield”, Quality
Engineering, Vol 14, No. 2, pp 257-266.
6. Goh T.N (2002), “The role of Statistical Design of Experiments in Six
Sigma: Perspectives of a Practitioner”, Quality Engineering, Vol 14, No 4,
pp 659 – 671.
7. Dana Rasis, Howard. S. Gitlow & Edward Popouich (2002-2003), “A
fictitious Six Sigma Green Belt, case study”, Quality Engg; Vol 15, No 1,
127-145.
8. Charles Ribardo and Theodore T Allen (2003), “An Alternative
Desirability Function for achieving Six Sigma Quality”, Quality and
Reliability Engineering International, Vol 19, pp 227-240.
9. Goh T N and M Sie (2003), “Statistical control of a Six Sigma Process”,
Quality Engineering, Vol 15, No 4, pp 587-592.
10. Rick L. Edgeman & David Bigio (Jan 2004), “Six Sigma in Metaphor:
Heresy or Holy Writ?” Quality Progress, pp 25 -31.
11. Mohammed Ramzan and Goyal (Jan 2006), “Six Sigma: An introduction
for Industrial Engineers”, IIIE Journal, Vol 35, No. 1, pp 13-15.
12. Peter S. Pande & Larry Holpp (2001), “What is Six Sigma?” Tata Mc Graw
Hill Company Limited 1st edition.
13. Peter S. Pande, Robert P. Neuman, Roland R. Cavanagh (2000), “The Six
Sigma Way: How GE, Motorola, and Other Top Companies are Honing
Their Performance”, McGraw- Hill Companies.
14. Greg Brue (2002), “Six Sigma for Managers” Tata McGraw-Hill.

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