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QUALITY MANAGEMENT
Kano’s Model of (NonLinear) Customer
Customer
Satisfaction
Satisfied
Delighter (D)
Linear Satisfier (L) or Performance
Requirement
Not Fulfilled
Indifferent (I)
Requirement
Fulfilled
Time
Customer
Not
Satisfied
Must Have (M) or basic Needs
Prof. Indrajit Mukherjee, School of Management, IIT Bombay
1
QUALITY MANAGEMENT
Market Dynamics and the Kano Model
Customer Satisfaction
“Delighters”
move to “Must
Be” features
over time!
Execution Excellence
Prof. Indrajit Mukherjee, School of Management, IIT Bombay
2
QUALITY MANAGEMENT
Quality
Function
Deployment
“House of the Quality”
Voice of the
Engineer
Voice of the
customer
correlations
Competitive
Analysis
Technical
Comparison
Prof. Indrajit Mukherjee, School of Management, IIT Bombay
3
QUALITY MANAGEMENT
Designing for the Customer:
The House of Quality
Car door Design
Engineering
Correlation:
∆
Strong Positive
o
Positive
X
Negative
*
Strong Negative
X
X
X
X
X
Characteristics
X=Us
A=Comp. A
B=Comp. B
(5 is Best)
1 2 3 4
Customer
Requirements
Easy to close
7
Can open on a hill
5
Easy to open
3
Doesn’t leak in rain
3
x
5
AB
x AB
x AB
A xB
Customer
requirements
information forms
the basis for this
matrix, used to
translate them into
operating or
engineering goals
No road noise
2
x
Importance Weighting
10
6
6
9
2
3
Target Values
Technical Evaluation
(5 is Best)
5
4
3
2
1
B
A
BA
x
B
A
x
B
x
B XA
BA
A
B
Relationships:
∆ Strong=9
o Medium=3
* Small=1
X
A
x
Prof. Indrajit Mukherjee, School of Management, IIT Bombay
4
QUALITY MANAGEMENT
Customer-Driven Quality cycle
Customer needs and expectations
(expected Quality)
Identification of customer needs
Translation into product/service specifications
(design Quality)
Performance/Output (actual quality)
Customer perceptions (Perceived quality)
Measurement and feedback
PERCEIVED QUALITY = ACTUAL - EXPECTED
Prof. Indrajit Mukherjee, School of Management, IIT Bombay
5
QUALITY MANAGEMENT
The Engineering Method and Statistical Thinking
Develop a
dear
description
Identify the
important
factors
Propose
or refine a
model
Manipulat
e the
model
Confirm
the
solution
Conclusion and
recommendations
Conduct
experime
nts
Prof. Indrajit Mukherjee, School of Management, IIT Bombay
6
X  13
QUALITY MANAGEMENT
Median
• Not affected by extreme values
0 1 2 3 4 5 6 7 8 9 10
Median = 3
0 1 2 3 4 5 6 7 8 9 10
Median = 3
• In an ordered array, the median is the
“middle” number
– If n or N is odd, the median is the middle number
– If n or N is even, the median is the average of
the two middle numbers
Prof. Indrajit Mukherjee, School of Management, IIT Bombay
7
X  13
QUALITY MANAGEMENT
Variation
Measures of variation give information
on the spread or variability of the
data values.
Same center,
different variation
Prof. Indrajit Mukherjee, School of Management, IIT Bombay
8
X  13
QUALITY MANAGEMENT
Data Summary and Display
How Does the Sample Variance Measure Variability?
x
12
13
14
x2
x8
x1
x7
x4
15
x3
x6
x5
Figure How the sample Variance measures variability through the deviations
xi  x
Prof. Indrajit Mukherjee, School of Management, IIT Bombay
9