Quality Improvement – The Six Sigma Way
Mala Murugappan and Gargi Keeni
Tata Consultancy Services
Abstract
Six Sigma provides an effective mechanism to focus
on customer requirements, through improvement of
process quality. In the Global Engineering Development
Center of Tata Consultancy Services (TCS – GEDC) at
Chennai, India, Six Sigma projects are being carried
out with the objective of improving on time delivery,
product quality and in-process quality.
This paper describes the application of the Six
Sigma methodology comprising the five phases –
Define, Measure, Analyze, Improve and Control, by
taking the example of a project, and demonstrates the
benefits attained.
1. Background
The Global Engineering Development Center of
Tata Consultancy Services (TCS–GEDC) located at
Chennai, India, has been executing projects for various
businesses of General Electric Company (GE) since
1995. The projects span the areas of Engineering
Design, Computer Aided Design, Finite Element
Analysis, Software Development for Engineering
Automation and Implementation of Product Data
Management Solutions. TCS – GEDC has a number of
initiatives in quality and process management and is on
its journey to Level 5 of the Capability Maturity Model
(CMM) enunciated by the Software Engineering
Institute of Carnegie Mellon University [1]. TCS –
GEDC's long association with GE has influenced it to
adopt the Six Sigma Approach for making disciplined
and rigorous progress in quality improvement. TCS –
GEDC learns and leverages from the experience of GE
in the implementation of Six Sigma Initiatives.
Six Sigma Quality quantitatively means that
the average review process generates 3.4 defects per
million units – where a unit can be anything ranging
from a component to a line of code or an administrative
form. This implies that nearly flawless execution of key
processes is critical to achieve customer satisfaction and
productivity growth [2].
The Six Sigma Initiatives in TCS – GEDC started
in 1998, and, since then, 10 Six Sigma projects have
been completed. These projects include Improvement of
Schedule Compliance, Quality Compliance, Input
Quality, Error Reduction, Cycle Time Reduction and
Design Improvement.
At present the Six Sigma Program addresses
Productivity Improvement and Defect Prevention in the
projects carried out in the TCS – GEDC. The Six Sigma
projects have been identified in the areas of
improvement after analysis of the process and product
metrics against the center level specification limits
.Currently, TCS – GEDC has 6 Black Belt and 30
Green Belt ongoing projects in different Business
Groups. The Green Belt Projects are derived from the
analysis phase / improvement phase of the Black Belt
projects.
The approach, methodology and benefits of Six
Sigma is explained in this paper by taking the case of a
Six Sigma project. This project is on the improvement
of product quality compliance carried out in one of the
Business Groups of TCS – GEDC in 1998–1999.
2. The Six Sigma approach
The Six Sigma Approach is customer-driven. For a
business or a manufacturing process, the Sigma
Capability is a metric that indicates how well the
process is being performed. The higher the Sigma
Capability, the better, because it measures the capability
of the process to achieve defect-free-work (where a
defect is anything that results in customer
dissatisfaction).
A defect is anything that
results in customer
dissatisfaction
The Six Sigma Approach is also data-driven. It
focuses on reducing process variation, centering the
process and on optimizing the process. The emphasis is
on the improvement of process capability rather than the
control of product quality, which includes the
improvement of quality and reduction of cost of quality.
In short, the Six Sigma Approach focuses on:
Customer needs
Data-driven improvements
The inputs of the process
And this results in:
Reducing or eliminating defects
Reducing process variation
Increasing process capability
In this context, the customer requirements for our
To successfully execute these Six Sigma projects,
an organization structure as shown in Figure 1-1 has
been formulated. This structure consists of the following
roles:
Champion: The Center Manager is the Champion, who
facilitates the implementation / deployment of the Six
Sigma Program. The Champion creates the vision,
defines the path to Six Sigma Quality, measures the
progress and sustains improvements.
Master Black Belt: The Master Black Belt is a
Mentor, who develops a Six Sigma network, provides
C H A M P IO N
(CENTER MANAG ER)
M aster B lack B elt
B lack B elt
(G E -P S g rou p )
B lack B elt
(G E -TS g rou p )
B lack B elt
(G E -N P g rou p )
B lack B elt
(A U TO M A TIO N g rou p )
B lack B elt
(F E A g rou p )
G reen B elts
G reen B elts
G reen B elts
G reen B elts
G reen B elts
Figure 1-1 Organization structure for Six Sigma
centre are On-Time, Accurate and Complete Customer
Deliverables
Customer Responsiveness
Marketplace Competitiveness
To achieve these goals the Six Sigma approach was
adopted in all the projects to pinpoint sources of errors
and ways of eliminating them.
3. Deployment
All employees are trained on Six Sigma Quality to
increase their awareness, understanding, and the day-today use of Six Sigma tools and processes, and their
application to projects.
Six Sigma projects (quality projects) are chosen,
based on customer feedback and analysis of the process
metrics. Projects that have a significant customer impact
and financial savings are given top priority.
training on strategies and tools, gives one-to-one
support on utilization and dissemination of Six Sigma
tools, and supervises the Six Sigma projects. The Master
Black Belt also facilitates sharing of best practices and
actively participates in the change process.
Black Belts / Green Belts: Black Belts / Green Belts lead
process improvement teams, demonstrate credible
application of Six Sigma tools, train their team members
and are accountable for Six Sigma project results. While
Black Belts work full-time on Six Sigma projects, the
Green Belts work only part-time on the Six Sigma
projects and devote the rest of their time to other
projects. The Black Belts report the progress of their
projects to the Master Black Belt and the Green Belts
report the progress of their projects to the Black Belts.
The Green Belts are expected to complete two Six
Sigma projects while the Black Belts are to complete
five.
The criteria for a successful Six Sigma project
could be any one of the following:
i. There is an improvement of one sigma in the
process capability, if the project has started the
process with less than one Sigma
ii.
iii.
There is a 50% reduction in defects, if the process
has started with more than three Sigma
There is a Return on Investment of 20%.
Recommended Action
Potential
Failure Modes
Potential
Causes
Current
Controls
Dispatch
incomplete
PDM structure
not followed
Non
conformance of
inputs with
Standards
Work
Pressure
Lack of
knowledge
Information
not given to
all team
members
against
blindly
following
SIMTOs
Lack of
knowledge
No Control
Risk
Priority
Number
200
No Control
180
No Control
160
No Control
140
Process Procedure to include all the
dispatch procedures.
QA tool
limitations
QA Tool
120
Limitations will be fixed by Jan ’99
end
Incomplete
dispatch
Non
compliance to
Standards
Development of tools for complete
delivery
PDM file handling procedure to be
evolved.
Maintain a text file in pkg. directory,
where a complete track of pkg. progress
is recorded to address any deviations /
non-conformances up front.
Table 5 -1 Failure Modes and Effects Analysis
4. Methodology
A Six Sigma Project consists of the following phases:
Define: The product or process to be improved is
identified. Customer needs are identified and translated
into Critical to Quality Characteristics (CTQs). The
problem/goal statement, the project scope, team roles
and milestones are developed. A high-level process is
mapped for the existing process.
Measure: The key internal processes that influence the
CTQs are identified and the defects generated relative to
the identified CTQs are measured.
Analyze: The objective of this phase is to understand
why defects are generated. Brainstorming and statistical
tools are used to identify key variables (X’s) that cause
defects. The output of this phase is the explanation of
the variables that are most likely to affect process
variation.
Improve: The objective of this phase is to confirm the
key variables and quantify the effect of these variables
on the CTQs. It also includes identifying the maximum
acceptable ranges of the key variables, validating the
measurement systems and modifying the existing
process to stay within these ranges.
Control: The objective of this phase is to ensure that the
modified process now enables the key variables to stay
within the maximum acceptable ranges, using tools like
Statistical Process Control (SPC) or simple checklists.
5. Six Sigma project on quality compliance
This section discusses a Six Sigma project on the
improvement of product quality compliance, which will
give a better understanding of the approach,
methodology and benefits of Six Sigma.
5.1. Define phase
The project teams had the problem of field errors
being reported in the deliverables. Based on the metrics
for 1997, the long-term process capability for product
quality was at 3.48σ, while the short-term capability
was at 4.98σ, and the Defects Per Million Opportunities
(DPMO) were at 253.
A Six Sigma approach was initiated to improve the
quality of deliverables. The goal of the project was to
improve the long-term process capability to more than
4σ and to reduce the DPMO by more than 50%.
Project
Initiation
Effort Estimation /
Scheduling
Project Study
ONSITE
Quality
Assurance
Resource
Allocation
Execution
CLIENT
STUDY
Delivery
Feedback
Figure 5-1: High-level process mapping of the existing process
The project started the Define Phase with the
identification of Product Quality as the CTQ. Team
characteristic that adds or deducts value from the
product, was defined, based on the opportunity
Measurements in Key Processes
12
Defects
10
8
6
4
2
0
Study
Execution
Delivery
Ke y Proce sse s
Figure 5-2: Measurement of defects in key processes
members from different levels namely Project Leaders,
Module Leaders, Team Members and the Quality Team
were identified for the Six Sigma project.
A high-level process mapping for the existing
process was made as shown in Figure 5-2.
The next step was to define the measurement system.
Any attribute in the deliverable sent to the customer,
which does not meet the customer’s requirements, or
which is not as per the Customer’s Standards was
defined as a defect. For the purpose of calculating
DPMO, the opportunity, which is a product or process
definition by the client [3].
5.2. Measure phase
During this phase, the key processes in the project
lifecycle that affect the CTQ (in this case, product
quality), were identified to be project study, execution
and delivery. Measurements related to the CTQ were
made in these phases. The field errors reported by the
client were classified as defects that have occurred in
each of these processes as shown in Figure5-3.
In each of these processes, the input process
variables (controllable or critical-those that show
The Input Variable — Quality of Inputs, had various
attributes namely, clarity of scope definitions,
Project
Initiation
Quality
Assurance
CLIENT
Delivery
statistical significance) that affect the CTQ were
identified as shown in Figure 5-4.
Therefore, it was decided to analyse the effect of
quality of inputs separately. A Black Belt project named
Effort Estimation /
Scheduling
Project Study
ONSITE
Resource
Allocation
Execution
STUDY
Feedback
Figure 5-3 Input process variables in the Key
completeness of inputs, and conformance of inputs to
standards. This variable became a critical variable that
affected the product quality, since the inputs for the
projects were obtained from the customer. The projects
that were affected by poor quality of inputs were
measured. As depicted in Figure 5-5, a substantial
number of projects were affected by poor quality of
inputs in 1998.
'Analysis of Input Quality' was executed in 1998. As a
result of this project, process control in the form of an
input checklist was introduced at the project start-up
phase, to prevent defects occurring in the project
deliverables due to wrong inputs.
50
45
40
35
30
25
20
15
10
5
0
Total no.of projects
Projects affected
Jan Feb Mar Apr May Jun Jul Aug
Figure 5-4: Projects affected by Poor Input quality in 1998
The Input Variable — Quality of Inputs, had various
attributes namely, clarity of scope definitions,
completeness of inputs, and conformance of inputs to
standards. This variable became a critical variable that
affected the product quality, since the inputs for the
projects were obtained from the customer. The projects
that were affected by poor quality of inputs were
measured. As depicted in Figure 5-5, a substantial
number of projects were affected by poor quality of
inputs in 1998.
Therefore, it was decided to analyse the effect of
quality of inputs separately. A Black Belt project named
'Analysis of Input Quality' was executed in 1998. As a
result of this project, process control in the form of an
input checklist was introduced at the project start-up
phase, to prevent defects occurring in the project
deliverables due to wrong inputs.
5.3. Analyze phase
Process performance was assessed using Cause-andEffect diagrams, to isolate key problem areas, to study
the causes for the deviation from ideal performance, and
In iti a l s tu d y o f p k g
not do ne
Im p ro p ecauses
r e x e c u that
ti o n affect
Using FMEA, five most severe
the CTQs and the recommended actions were
T ig h t s c h e d u le
La ck of com m n.
Im p r o p e r p la n n in g
a m o n g te a m
m e m b e rs
A llo c a tio n b a s e d
P k g . S tu d y n o t
o n s k ill s e t
.
fo llo w e d s tric tly
in p k g p la n
S h iftin g p e o p le
T ig h t s c h e d u le
a c ro s s p k g .
fre q u e n tly
La ck of
k n o w le d g e
to identify if there is a relationship between the
variables. Extensive brain-storming sessions were held
with team members to evolve these diagrams. Figure 56 shows the Cause-and-Effect diagrams for one of the
project teams.
The probable causes that can lead to quality nonconformance in a project during different phases of a
project life cycle were listed
The Failure Modes and Effects Analysis (FMEA)
was subsequently carried out to arrive at a plan for
prevention of causes for failure.
FMEA is a tool that helps prevent the occurrence of
problems by:
Identifying the potential failure modes in which a
process or product may fail to meet specifications, and
rating the severity of the effect on the customer.
Providing an objective evaluation of the occurrence
of causes
Determining the ability of the current system to
detect when those causes or failure modes will occur.
Based on the above factors, a Risk Priority Number
(RPN) for each failure mode is calculated [3].
m e eshows
t in g
identified. Figure 5-7
the results of FMEA
conducted in one of the project groups.
tig h t s c h e d u le
La ck of
k n o w le d g e
.
N o p r io rity
M is in te r p r e ta tio n
o f in s tr u c tio n s
N e w a v a ila b ility
o f re s o u r c e s
5.4 Improve phase
La ck of
k n o w le d g e
N o c a u s a l a n a ly s is
N o IQ A
C h k lis ts /IQ A
n o t fo llo w e d
C la r if. n o t
ra is e d in tim e
R e p e titiv e
e r r o rs
N o s h a r in g o f
le s s o n s le a rn t
Q u a lit y -n o n
c o m p li a n c e
N o p r io rity
La ck of
k n o w le d g e
U n p a ra m e tric
fe a tu re s
tig h t
s c h e d u le
N ew
p e o p le
N o in d u c tio n
tra in in g
P o o r q u a lity o f
in p u ts /in s tn s .
Lack of
tr a in in g
No EQA
M is in te r p r e ta tio n
o f in s tr u c tio n s
N o t g iv in g re le v a n t
d o c . fo r E Q A
N o n a v a ila b ility
o f re s o u r c e s
In itia to r s
p r e fe r e n c e s
U p d a te o f
d a ta file s
N o t u p d a tin g
c h e c k lis ts /
g u id e lin e s
In fo . n o t
g iv e n to a ll
te a m m e m b e r s
C h a n g e in G E S T D s
C & E D&ia geffect
ra m - P ip
in g Q u a lity for
C o mproduct
p lia n c e
Figure 5-5 Cause
diagram
quality
.
s /w to o ls n o t
ru n n in g o n th e
m a c h in e s
Q A T o o l, P ip e
m a s te r
Project Study
•
•
•
Project Start-up meetings
Input Checklist
Traceability Matrix
Execution
Checklists (Process / Product)
•
Guidelines, Procedures
•
Methodologies
•
Training
•
Error Proofing Tools
•
Standardisation - Templates
•
Regular Team Meetings for
•
Defect Prevention
Log files for usage of Tools
•
Delivery
•
•
Error Proofing Tools
Guidelines, Procedures
Figure 5-6 Process Improvements
Based on the recommended actions from FMEA,
several process improvements were introduced in the
phases - Project Study, Execution and Delivery. These
improvements include developing process control and
error proofing tools amongst others. Figure 5-7 shows
some of the improvements carried out in different
phases.
In addtion to this, a customer feedback form was
introduced. The various quality attributes of the
deliverables are rated by the customer on a 1 to 5 scale.
Causal analysis is carried out by teams when the rating
is below 4 and preventive actions is initiated.
5.5 Control phase
The process improvements that were introduced resulted
in the reduction of field errors. The process capability
for quality of deliverables improved from 3.48σ to
Trend in Quality Compliance
101%
Quality Compliance
100%
99%
98%
97%
96%
95%
94%
3rd Qtr '98
4th Qtr '98 1st Qtr '99 2nd Qtr'99 3rd Qtr '99 4th Qtr '98
Period
Figure 5-7: Trends in quality compliance
1st Qtr '00
3.98σ in the long-term and from 4.98σ to 5.48σ in the
short-term by the end of 1998, and the DPMO reduced
from 253 to 34. This trend continued in 1999 and 2000,
resulting in the improved trend in the quality
compliance as shown in Figure 5-8. The best practices
and lessons learnt in this Six Sigma project for
engineering design were applied in other project teams
and other types of projects.
Since the field errors reduced and the process
capability for quality deliverables increased to more
than five sigma, the emphasis shifted to improvement of
in-process quality. This is to be achieved through
reduction of in-process quality cost as explained below.
The project life cycle has a phase for Quality
Assurance (QA). As a continuous improvement
initiative, Green Belt projects have been initiated in
different project teams, to reduce the rework cost after
QA. These Green Belt projects used the results of the
Cause and Effect, and FMEA of the Six Sigma project
on Quality Compliance.
A number of Defect Prevention practices were
identified in these GB projects and were built into the
processes of the project life cycle. To measure the
quality cost, a metric called "rework" index was used.
This metric is calculated as percentage project effort
spent in rework. Control charts (I-MR) were drawn to
track the process level (process characteristic within
projects) and process variation (process characteristic
between projects) simultaneously, and also to detect the
presence of special causes.
I-MR Chart for Rework Index
1
Individual Value
8
3
1
4
UCL=2.735
Mean=0.9435
0
Subgroup
Moving Range
2
1
8
7
6
5
4
3
2
1
0
LCL=-0.8475
0
10
20
1
30
2
40
50
3
1
1
UCL=2.200
R=0.6734
LCL=0
April 2000
May 2000
June 2000
Figure 5-8 I-MR chart for rework index
Figure 5-9shows the Control charts drawn over a
period of time for rework index for one of the project
teams. The assignable causes for variations from the
target values were analyzed, and corrective actions were
initiated to remove the causes and to prevent them from
occurring again. From the control charts, it is evident
that the process improvement initiatives have been
effective in reducing the rework index. This is indicated
by the converging control limits in the individual's chart
and the reduction in process variation as seen in the
moving range chart.
Using these values, process capability studies are
also undertaken. Figure 5-10 shows the process
capability study for rework index in one of the project
teams. This study can be used to assess whether the
process is centered on the target and whether it is
capable of consistently meeting process specifications.
For the metric — rework index, the specification limits
were set at 4% to –2% of the project effort as rework
after QA. As seen from the chart, the expected overall
performance has a DPMO of 23480, which is equal to
3.47σ.
Process Capability Study
Process Data
4.00000
*
-2.00000
1.20203
Mean
47
Sample N
0.99351
StDev (Within)
1.30883
StDev (Overall)
LSL
USL
USL
Target
LSL
Within
Overall
Potential (Within) Capability
1.01
Cp
0.94
CPU
1.07
CPL
0.94
Cpk
*
Cpm
Overall Capability
Pp
PPU
PPL
Ppk
0.76
0.71
0.82
0.71
-2
0
Observed Performance
0.00
PPM < LSL
21276.60
PPM > USL
21276.60
PPM Total
2
4
Exp. "Within" Performance
634.38
PPM < LSL
2429.42
PPM > USL
3063.80
PPM Total
6
8
Exp. "Overall" Performance
7212.66
PPM < LSL
16267.78
PPM > USL
23480.44
PPM Total
Figure 5-9 Process capability study
6. Conclusion
The thrust on Six Sigma Quality has helped in
creating and sustaining customer focus in the TCS —
GEDC, leading to improved customer satisfaction as
indicated in the feedback from the customer. At the
same time, active participation of the team members
from all levels in the Six Sigma projects has evolved a
culture of effective and creative team work.
The goal is to achieve Six Sigma level not only in
product quality, which is currently at 5.85σ, but also in
the other client specified metrics of on-time delivery
and estimate compliance. To achieve this goal, TCS —
GEDC plans to have about 60 Six Sigma projects
completed by the second quarter of 2001.
7. References
[1] Paulk M, Weber C, Curtis B, Chrissis M.B. (1995)
The Capability Maturity Model: Guidelines for
Improving the Software Process. Addison Wesley
Publishing Company, Reading, MA.
[2] Forrest W. Breyfogle Implementing Six Sigma:
Smarter Solutions Using Statistical Methods :
[3]. 6σ OJT Six Sigma Application – GEPS Playbook,
GE Power Systems
8. Author information
Mala Murugappan (mmala@chennai.tcs.co.in) is the
SEPG
coordinator
at
Offshore
Engineering
Development Center, Chennai, India. A Certified
Quality Analyst, her interests include process and
quality improvement and Six Sigma Quality. She has
undergone Six Sigma Training for Green/Black Belt
conducted by GE, India. Mala received her Bachelor of
Engineering in Mechanical Engineering from College of
Engineering, Guindy, Chennai and Master of
Technology in Machine Design from the Indian Institute
of Technology, Chennai.
Dr. Gargi Keeni (gkeeni@mumbai.tcs.co.in) is the
Corporate Quality Head at Tata Consultancy Services,
responsible for managing process improvement
activities. A Certified Quality Analyst and CMM Lead
Assessor, she has extensive experience in software
project management, software tools development,
managing training activities, managing human resource
allocation
and
managing
quality
processes
implementation. She has conducted several courses and
workshops on Quality Management, Software CMM,
People CMM, ISO 9000 and JRD Quality Value Award
(the Malcolm Baldridge National Quality Award criteria
adapted by the Tata Group of companies). A Doctorate
in Nuclear Physics from Tohoku University of Japan,
she was a Research Fellow at Saha Institute of Nuclear
Physics, Calcutta, and a Systems Engineer at Fujitsu,
Japan. Dr. Keeni is a member of Computer Society of
India and IEEE.