Measure Phase
Objective: Identify the characteristic to
be measured and quantify the current level.
The process was mapped and flow-charted to clearly
establish project boundaries. Rejections at the
customer end for April, May, and June 2002 were analyzed
in detail and the current level of Project Y (Figure
5) was accurately quantified. The actual customer
ppm levels are not shown for business confidentiality
reasons; however, we can clearly see the gap between
where we were in the May-June 2002 time frame and
where we wanted to go (our target, based on world-class
benchmarks).
Analyze Phase
Objective: Identify the input variables (key
Xs) that affect the Ys the most; i.e., identify input
factors for successful performance of Y.
The team felt that in order to reduce the rejections
at the customer end, our painting processes had to
be improved. Defects were prioritized based on
rejections at the customer end for two months. This
helped the team to transition from Project Y to lower-level
Ys. A sample analysis for one of the three components
(rear bumpers) selected for improvement is displayed
in Figure
6.
The team realized that the defect "spot touch-up
not OK" was the result of improper reworking caused
by defects like "dust" and "paint run-down." The
team spent many hours brainstorming causes for these
lower-level defects as preventing these would eliminate
"spot touch-up" permanently. Fifteen separate cause
and effect diagrams were prepared to look for potential
root causes. A separate task force was also formed
for investigating the defect "assembly not OK."
Based on the cause and effect analysis, extensive
investigations for verification of causes were undertaken
to identify the real causes. For example, a 24
full factorial designed experiment was conducted to
investigate if color of paint (light or dark shade),
fixture condition (clean or un-clean), time sequence
in a painting run (beginning or end), and type of
bumper (front or rear) had any statistical impact
on dust levels on bumpers. Figure
7 shows the main factor plots for the DOE.
We learned that rear bumpers had statistically more
dust than front bumpers. This led to a series of experiments
to find the root cause for this observation. For instance,
the team found that the filler particles which got
deposited on the backside of front bumpers were falling
down and appearing as dust on rear bumpers. Similar
probing was done for other defects.
Apart from these defects, the team also collectively
agreed to focus their efforts on "final inspection"
efficiency before shipping to the customer. As
plastic painting is a complex process with very stringent
aesthetic/cosmetic requirements, the team identified
measures for effectively and reliably discerning defects
at the final inspection stage:
The result of our analysis phase was the identification
of statistically validated Key Process Input Variables
(KPIV's), as shown in Figure
8.
Continue
On: Improve Phase
Acknowledgements
We acknowledge the management support of Mr. Bakshi,
CEO; Mr. Babu, VP Operations; Mr. Vamburkar, VP Finance
of TAPS; and Mr. Asokan, VP Supply Chain Business Group,
TACO.
Valuable contributions from team members Sanjeev Pandit
and Prashant Wadekar are also very much appreciated.
Without their hard work and enthusiasm this project
could not have been completed.
About the Authors
M. M. Kapadia is Head of the Quality Engineering Group
and a Six Sigma Master Black Belt at Tata Auto Comp
Systems, Pune, India. He has a doctorate in Mechanical
Engineering with specialization in Quality Engineering
from the University of Mumbai, India. He also has
a master's degree in Mechanical Engineering from
Pennsylvania State University, USA, and a master's
degree in Business Administration from St. Joseph's
University in Philadelphia, USA. He has over eighteen
years of experience in quality control and engineering
in various industries in the USA and India. Dr.
Kapadia is also an American Society for Quality
(ASQ) certified quality and reliability engineer.
A. Mishra is Head of the Paint Shop at Tata Auto Plastic
Systems, Pune, India. He has bachelor's degrees
in science and in chemical technology, both from
Harcourt Butler Technological Institute, Kanpur,
India. He has a total of fourteen years of experience
in painting processes in scooter, motorcycle, and
automobile component manufacturing with specialization
in process control and mass scale production.
S. Hemanth is a member of the Quality Engineering Group
and a Six Sigma Black Belt at Tata Auto Comp Systems,
Pune, India. He has a bachelor's degree in Mechanical
Engineering from Regional Engineering College, Surathkal,
India. He also has a diploma in Total Quality Management
from the University of Pune, India, and is a member
of ASQ. Mr. Hemanth has been working in the field
of quality for the last three years and has successfully
completed several quality improvement projects.
V. Limaye is Head of the Customer Service Group at Tata
Auto Plastic Systems, Pune, India. He has a diploma
in Mechanical Engineering and a bachelor's degree
in Production Engineering, both from Shivaji University,
Kolhapur, India. He has a total of fifteen years
of industrial experience in the field of materials
and logistics. In his current assignment at Tata
Auto Plastic Systems, Mr. Limaye is responsible
for various aspects of quality, cost, and delivery
for customers.
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