1.  By
Genichi Taguchi in Japan in 1950’s
 80% of the quality gains of Japan
 International Acceptance

2.  Quality
means different things to different
people
 Following Taguchi, the quality of a product is
measured in terms of the total loss to society
due to functional variation and harmful
effects. The loss would be zero for the ideal
quality.

3. LSL
mBAD
L(y)
m
0
GOOD
GOAL POST MENTALITY
USL
m + BAD
0

4. After process improvement and shifting the mean.
Initial distribution
m
m
m
Resistance ( Ohms / Kilometre)
Distribution of telephone cable resistance.

5. L(y)
LSL
m-
USL
m
0
m+
0

6. Average quality loss
Q = k [( m - )2 + 2 ]
It consists of two components:
•Shift of process average ( ) from the
target value (m)
•Spread of the process ( 2)
S/N ratios are a log-modified form of
Average quality loss function

7. 1.
2.
3.
Quality should be designed into the product
and not inspected into it.
Quality is best achieved by minimizing the
deviation from a target. The product should
be so designed that it is immune to
uncontrollable environmental factors.
The cost of quality should be measured as a
function of deviation from the standard and
the losses should be measured system-wide.

8. S/N ratio
Maximizing S/N ratio is equivalent to reducing
variance due to various noise factors and hence
improves quality during manufacturing, customer
usage and aging and simultaneously reducing cost
substantially.

9.  Identify
various causes, known as noise factors,
that degrade the product (process) performance

variations in raw materials and components,
machinery, workmanship, temperature, humidity,
loading, etc.
 Eliminate
the noise factors one by one

10. Eliminating
noise factors always leads to
increased costs
Reduction in profitability or loss of
market share in the face of global
competition

11. New
method of design optimization for
performance, quality, & cost
For existing processes, emphasis is on
parameter design
Smallest, affordable development cost

12.  All
engineering designs involve setting
values of a large number of decision
variables.
 Common approach is to study one variable
at a time or by trial and error

Either long and expensive time span for
completing the design / premature termination
of the design process

13.  MATHEMATICAL

Orthogonal arrays to study large number of
decision variables with a small number of
experiments
 NEW

TOOL
MEASURE OF QUALITY
Signal to noise (s/n) ratio to predict the quality
from the customer perspective

14. 1.
To establish the best or the optimum condition
for a product or a process.
2.
To estimate the contribution of individual
factors.
3.
To estimate the response under the optimum
conditions.

15. X
NOISE FACTORS
M
Y
SIGNAL
FACTORS
RESPONSE
A1
A2
CONTROL
Z FACTORS
BLOCK DIAGRAM OF A PRODUCT/ PROCESS

16. Size

17.  Many
cos. Big or small, high-tech and lowtech have found the method valuable
 High
quality at a low competitive price
while maintaining profit margin

18. 


Upfront improvement of quality by
design and process development.
Measurement of quality in terms of
deviation from the target (loss
function).
Problem solution by team approach
and brainstorming.

19. 




Consistency in experimental design and
analysis.
Reduction of time and cost of experiments.
Design of robustness into product/process.
Reduction of variation without removing its
causes.
Reduction of product warranty and service
costs by addressing them with the loss
function.