Total Quality Management
- Overview
Session 13
Compiled by
Prof. Sanjib Biswas
Is your mobile phone worthy to you?
Why?
Do you recommend it to your mother on the mothers’ day?
Why?
What else you require?
Less price?
More features?
Gift?
Convenience?
After sales support?
July 9 & 16, 2018
2
• What is “ Quality”?
• What is the meaning of “Total”?
• What is “Management”?
• What is the difference between “ TQC” and “TQM”?
• What is the difference between Little ‘Q’ & Big ‘Q’?
• What is the difference between “ Best-in-Class” & “
World Class” Quality?
July 9 & 16, 2018
3
Quality
 Customer satisfactions and loyalty (or fitness for use) as defined by Dr Joseph M.Juran
 Predictable degree of uniformity as defined by Dr Deming.
 Loss to society as defined by Taguchi
 Conformation to specifications as defined by Crosby
International Organisation for Standardization (ISO) defines quality as the “Totality of
Characteristics of an entity that bear on its ability to satisfy stated and implied needs”.
In the context of today’s business, quality is best defined as “customer satisfaction and
loyalty”.
July 9 & 16, 2018
4
TQM – Key Aspects
• Customer is the dominant resource.
• Profits follow Quality and not the other way round.
• View that Quality is composed of multi-dimensional
attributes.
• Stresses on quality, flexibility & service to create value for
customers.
• Multiskilling.
• Flat organization structure.
• Process oriented approach.
July 9 & 16, 2018
5
TQC or TQM?
• Generally total quality control relates to the specific act of checking that a
product (for example) is coming off the production line to the expected
tolerances and having processes to correct the manufacturing if something is
not right.
• Total quality management generally encompasses the quality of all business
processes ensuring that the company does everything as efficiently as possible.
July 9 & 16, 2018
6
Little ‘Q’ vs Big ‘Q’
Fig: Attractive/Charming Quality of
Prof. Kano
July 9 & 16, 2018
7
Best-in-class vs World-class
• Customers’ expectations of quality are not the same for
different classes of products or services.
• Best-in-class quality means being the best product or
service in a particular class of products or services.
• Being a world-class company means that each of its
products and services are considered best-in-class by its
customers.
July 9 & 16, 2018
8
Revenue lost through poor quality
$10,000,00
0
1,000
X 25%
250
X 75%
188
X $10,000
$1,880,000
Annual customer service
revenue
Number of customers
Percent dissatisfied
Number of dissatisfied
Percent of switchers (60-90%
of dissatisfied)
Hidden Cost of Poor
Quality:
Lost sales, Extra inventory,
Downtime, Loss of
goodwill, excess
paperwork, delay, Low
morale etc.
Number of switchers
Average revenue per customer
“ Good Quality is Cheap”
Revenue lost through poor
quality
Source : The University of Tampa (1990)
July 9 & 16, 2018
9
Expectations
July 9 & 16, 2018
10
Enterprise Quality
Enterprise Quality = meeting the needs and expectations of
all interested parties in a balanced way over the long term.
Phases:
Decide
Prepare
Launch
Expand
Sustain
“ I believe the distinction between a good company and a
great one is this; A good company delivers excellent products
and services; A great one delivers excellent products and
services and strives to make the world a better place”
- William Clay Ford Jr
July 9 & 16, 2018
11
Using the Road Map Maximizes the
Probability of Success and Avoids the
“Flavour of the Month” syndrome
Phase 5
• Integrate, Audit,
Measure, Assess,
Review, Inspect,
Focus
Phase 4
Phase 3
• Expand Training across
the organisation
• Transition Training from
Juran to Client
• Initial Training
• Pilot Projects
Phase 2
• Up Front Planning
• Establish Infrastructure
• Executive Onboard
Phase 1
Yes to
Deployment
Organisation / Partner
July 9 & 16, 2018
Time
12
Significant contributions by the Quality Gurus
July 9 & 16, 2018
13
Historic Milestones of TQM (
selected)
July 9 & 16, 2018
14
Evolution of Modern Quality concept
Source: “From Product Quality to Organization Quality” by Dr. Isaac Sheps
July 9 & 16, 2018
15
Deming’s 14 points
• Create constancy of purpose for improvement of product and service.
• Adopt the new philosophy.
• Cease dependence on mass inspection.
• End the practice of awarding business on the price tag alone.
• Improve constantly and forever the system of production and training.
• Institute training.
• Institute leadership.
• Drive out fear.
• Break down barriers between staff areas.
• Eliminate slogans, exhortations, and targets for the workforce.
• Eliminate numerical quotas.
• Remove barriers to pride in workmanship.
• Institute a vigorous program of education and retraining.
• Take action to accomplish the program.
July 9 & 16, 2018
16
What is the Plan Do Check Act (PDCA) Method?
PDCA:
– is a Scientific Method to solving problems
– requires facts, measurement, objective analysis and
critical thinking surrounding the problem
– requires data and numerical evidence of the problem
– is designed to be applied over and over again, not just one
time
• referred to as “Closed Loop Thinking”
– naturally increases knowledge of the individual(s)
evaluating the causes of a problem
“As long as the circle is rolling, the quality is providing. Once
the circle is interrupted the quality fails.” (Deming)
July 9 & 16, 2018
17
Standards, Process Improvement and the PDCA Method
The Current
Standard
serves as
“The Chock”
to PREVENT
BACKSLIDING”
Constant
Consistent &
Continuous
Change for
the Better
A
PROCESS
If we DON’T continuously improve we
will experience a NATURALLY occurring Reaction!
“CHAOS” WILL TAKE OVER > STANDARDS WILL BACKSLIDE
July 9 & 16, 2018
18
“A3” Proposal/Report Format
PLAN
PLAN
An A3 lays out
an entire plan, large or small, on
one sheet of paper.
It should be visual and extremely concise.
PlanleftIt should tell a story, laidImplementation
out from upper
hand side to lower right, which anyone can
understand.PLAN
PLAN
Do
PLAN
Check
July 9 & 16, 2018
19
Applying PDCA and One Page Report Writing
Exercise instructions:
July 9 & 16, 2018
1.
Break into teams
2.
Each team pick a topic from work or from school
3.
For each topic, work through as much of the A-3 format
as you can. Defining the business problem is a good
place to start.
4.
Use visuals if possible. Use 5 why analysis to
understand root cause. Note where you are making
assumptions vs using facts.
5.
Brainstorm recommendations that address root cause.
6.
Summarize your ideas in the A-3 format.
20
Quality Objectives
What are your organization’s quality objectives?
•
•
•
•
•
•
•
•
Customer Satisfaction?
Time to market?
On-Time Delivery?
Cost Savings?
ROI?
Productivity?
Performance?
Cycle time?
How fast does your organization want to improve?
How important is your budget and cost savings?
July 9 & 16, 2018
21
Juran’s Definition of Quality
“Fitness for Use”
Product Features that
Meet Customer Needs
Freedom from
Deficiencies
• Provide customer satisfaction
• Eliminate defects, errors,
& waste
• Create product salability
• Compete for market share
• Respond to customer needs
• Higher quality costs more
July 9 & 16, 2018
• Avoid product
dissatisfaction
• Effect is on costs
• Higher quality costs less
22
Juran’s Trilogy
July 9 & 16, 2018
23
The Five Erroneous Assumptions
• Quality means goodness,
elegance
• Quality is intangible, not
measurable
• The “economics of quality”
are prohibitive, not relevant
• Quality problems originate
with the workers
• Quality is the responsibility
of the quality department
• Quality is conformance to
requirements
• Quality is measured by the
cost of nonconformance
• It is cheaper to do things
right the first time
• Most problems start in
planning and development
• Quality is shared by every
function and department
“Quality is Free” since a quality program can save a company more money
than it costs to implement
Source: “ Quality is Free” – Philip Crosby (1979)
July 9 & 16, 2018
24
Cost of Poor Quality (CoQ)
Represents the difference between The
actual cost of production or service &
What the cost would be if the process
were
effective in manufacturing products that
• met customer needs and
• were defect free.
“In most companies the costs of
poor quality run at 20 to 40
percent...
In other words,
about 20 to 40 percent of the
companies’ efforts are spent in
redoing things that went wrong
because of poor quality”
(Juran on Planning for Quality,
1988)
July 9 & 16, 2018
25
Total Quality Cost
I want my
money
back!
Prevention
Internal
Failure
Appraisal
External
Failure
$
Cost of Quality (COQ)
July 9 & 16, 2018
26
Generic CoQ models and cost categories
July 9 & 16, 2018
27
CoQ Metrics
July 9 & 16, 2018
28
Example of CoQ Calculation
Scrap/Waste
July 9 & 16, 2018
29
Example of CoQ Calculation
Customer Returns
July 9 & 16, 2018
30
Example of CoQ Calculation
Rework
Downtime
July 9 & 16, 2018
31
Example of CoQ Calculation:
Investigation Time
Disposal Cost
July 9 & 16, 2018
32
Relating COQ to Business Measures: Example
Return on Asset = Profit Margin X Asset Turnover
( Dupont Financial Model)
Illustrative Example:
Suppose, COQ was 10% of Sales revenue. Profit margin was 7% & Asset
turnover was 3%. Now, after implementing a quality improvement effort
organization wide, the COQ becomes 6% now whereas asset turnover
remains the same. What will be the impact on Return on Asset ?
Since COQ directly reduces the cost, it will influence the profit
margin. Reduction in COQ is (10-6) = 4% => New profit Margin
will be (7+4) = 11%. Thus, new Return on Asset will be (11* 3) =
33% which is much higher as compared to the earlier one i.e. (7* 3)
= 21%.
July 9 & 16, 2018
33
TQM : Up-stream Quality in Purchasing Process
Manufacturer
Stages of
Progress
Production
Incoming
Inspection
Supplier
Outgoing
Inspection
Production
Additional
Cost
Passed
to
the
Customer ($)
1
2
3
4
5
6
Poka-Yoke /
Process Control
Note:
Circles indicate the importance of quality check-points for the material
produced by suppliers
Source : Techniques of continous improvement – Prof. Kiyoshi Suzaki
July 9 & 16, 2018
34
The components of organizational excellence
Business Excellence is “excellence” in strategies, business practices,
and stakeholder-related performance results that have been
validated by assessments using proven business excellence models.
Source:
Assessing
Business
Excellence - L. J. Porter & S. J.
Tanner (2Ed., Elsevier ButterworthHeinemann, 2004)
11 March 2023
Sanjib Biswas
35
The Excellence Maturity Model
Source: Assessing Business Excellence - L. J. Porter & S. J. Tanner
(2Ed., Elsevier Butterworth-Heinemann, 2004)
11 March 2023
Sanjib Biswas
36
TQM Model
(For examining the impact of business excellence practices in diverse
organizations)
Source: GAO (1991)
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Sanjib Biswas
37
TQM Business Excellence Models
Widely used models/frameworks:
oDeming Prize
oMalcolm Baldrige National Quality Award (MBNQA)
oEuropean Foundation for Quality Management (EFQM)
oPhilips Quality Award (PQA)
oMotorola Business Excellence Model
oAustralian Business Excellence Framework (ABEF)
oTata Business Excellence Model (TBEM)
oGolden Peacock National Quality Award
oCII-EXIM Model etc.
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Sanjib Biswas
39
Deming Prize
It was set up in 1952 by the Japanese Union of Scientists and Engineers (JUSE) to
recognize and encourage companies that do an outstanding work in the field of
Quality Management. It is the oldest and the most prestigious Quality Award in
the world. In comparison the MBNQA, USA was set up in 1987 and EFQM,
Europe was set up in 1992. Amongst these three, Deming Prize is the only one
that can be challenged by a company from any country.
11 March 2023
Sanjib Biswas
Criteria
40
Malcolm Baldrige Performance Excellence Framework
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Sanjib Biswas
41
EFQM Excellence Model
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Sanjib Biswas
42
ISO 9000 Series
• ISO 9000 (a guide)
• ISO 9001 (a set of requirements for the quality system of the
supplier)
• ISO 9002 (product standards)
• ISO 9003 (final inspection and testing)
• ISO 9004 (guidelines for developing and implementing quality
system principles, structure, auditing and review)
ISO 9000 Standards
• The implementation of the ISO 9000 standards does not imply
necessarily a higher level of quality but it forces a company to
assure its customers that the products are manufactured according
to the standards.
• The directives of standards cover mainly such areas as product
safety, and other quality considerations.
• The list of products (medical implants, gas appliances, toys, building
products, etc)
PROCESS :
Nonconformance
Print Offset
Types of Defect
Date
No of nonconformances
Date
Date
Date
Date
1-Mar
2-Mar
Dent
IIII III
Burr
III
Date
IIII
Total defects
IIII
No of Defects
3-Mar
4-Mar
5-Mar
6-Mar
Doc No : PV/TF1/PDN /FM/ XXX
Rev No : 00
PV TECHNOLOGIES INDIA LIMITED
PRODUCTION CHECKSHEET FOR GLASS SEAMING &
INSPECTION
Rev Date : DD / MM / YYYY
Date :
S.No
Parameter
Unit
1
DI water inlet pressure bar
2
CDA Pressure
3
Seaming belt condition
4
Rubber roller
5
Nozzle condition
Shift Technician
Shift Incharge
bar
A
7
9
B
11
13
15
17
C
19
21
23
1
3
5
SNO
CLASS
BOUNDARY
MID
POINT
FREQUENCY
TOTAL
1.
7.10 -
7.79
7.45
IIII
4
2.
7.80 –
8.49
8.15
IIII
5
3.
8.50 –
8.85
IIII IIII
10
4.
9.20
-
9.55
IIII IIII IIII IIII
5.
9.90
– 10.59
10.25
IIII IIII I
11
6.
10.60 – 11.29
10.95
IIII
5
7.
11.30 – 11.99
11.65
IIII I
6
9.19
9.89
III
23
25
Frequency
20
15
10
5
0
7.1~7.79
7.8~8.49
8.5~9.19
9.2~9.89
Thickness
9.9~10.59
10.6~11.29 11.3~11.99
Bell Shape
A special type of symmetric unimodal histogram is one that is bell shaped:
Symmetry
A histogram is said to be symmetric if, when we draw a vertical line down the center of
the histogram, the two sides are identical in shape and size:
Skewed Distribution
A skewed histogram is one with a long tail extending to either the right or the left:
• What is a Pareto?
• A data display tool that breaks down discrete observations into
separate categories for the purpose of identifying the “vital few”.
• Discovered by Vilfredo Pareto (1906)
• Why use it?
• To focus on the problems/issues that offer greatest potential for
improvement
• Identify “the vital few” Identify the relative importance of problems
and see them in a simple graphical way
• Prioritize our efforts and resources for improvements
• Where to use it?
• Where we would want to identify
• Cause or source of the problem
• Customer type
• Location (region, building)
Category
Number
Halm unit
1015
FP1
860
3M camera syatem
610
Rotary table
485
FSCC
300
RSCC
75
Cases
55
FP2 /3
30
Pointer
30
%
29
25
18
14
9
2
2
1
1
Cum%
29
54
72
86
95
97
98
99
100
100
90
80
70
60
50
40
30
20
10
0
1100
1000
900
800
700
600
500
400
300
200
100
0
Halm unit
FP1
3M
Rotary
camera table
syatem
Category
FSCC
RSCC
Cases
Cumulative%
Numbers of
Occurence
Pareto Chart of GBP CS downtime






Start
Alarm Rings
Ready
to get
up
Climb out
of bed
End
Delay
Hit Snooze
Button
Fish-Bone diagram is a structured approach to exhaustively determine perceived
sources (causes) of a problem (effect)
Also known as Ishikawa Diagram or cause & effect diagram.
• Why use it?
• To help the team organize and graphically display all the knowledge it has
about the problem
• What does it do?
• It helps unearth all possible causes for the problem at hand by capturing views
of all members
• Creates a consensus around the problem and builds support for resulting
solutions
• Focuses the team on causes rather than symptoms
• Organizing data serves as a guide for discussion and inspires more ideas
Deposition Power Vs Film Uniformity
Film Uniformity(%)
50
40
30
20
10
0
0
2
4
6
8
10
12
14
16
Deposition Power(KW)
Deposition Power Vs Film Uniformity
Film Uniformity(%)
30
25
20
15
10
5
0
15
20
25
Deposition Power(KW)
30
35
Deposition Power(KW)
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
Film Uniformity(%)
40
28
20
35
18
24
20
28
14
30
18
25
35
26
17
27
20
28
21
32
16
28
14
35
30
19
24
Deposition Power(KW)
16
16.5
17
17.5
18
18.5
19
19.5
20
20.5
21
21.5
22
22.5
23
23.5
24
24.5
25
25.5
26
26.5
27
27.5
28
28.5
29
29.5
30
Film Uniformity(%)
22
24
20
21
20
21
18
20
19
17
18
16
19
16
17
16
15
17
14
15
16
14
15
11
14
12
11
13
10
Film Uniformity(%)
Deposition Power Vs Film Uniformity
60
50
40
30
20
10
0
25
30
35
40
45
50
Deposition Power(KW)
Film Uniformity(%)
Deposition Power Vs Film Uniformity
35
30
25
20
15
10
5
0
10
15
20
25
30
35
Deposition Power(KW)
40
45
Deposition Power(KW)
30
30.5
31
31.5
32
32.5
33
33.5
34
34.5
35
35.5
36
36.5
37
37.5
38
38.5
39
39.5
40
40.5
41
41.5
42
42.5
43
43.5
44
44.5
45
Film Uniformity(%)
10
13
14
12
14
15
13
14
16
15
14
20
18
19
17
24
19
21
25
24
30
28
26
27
29
33
34
31
36
44
50
De posi ti on Powe r( KW)
14
14.5
15
16
16.5
17
17.5
18
18.5
19
19.5
20
20
20.5
21
21.5
22
22.5
23
23.5
24
24.5
25
25.5
26
26.5
27
27.5
28
28.5
29
29.5
30
30.5
31
31.5
32
32.5
33
33.5
34
34.5
35
35.5
36
36.5
37
37.5
38
38.5
39
Fi l m Uni formi ty( %)
30
20
24
22
24
20
21
20
21
18
20
19
19
17
18
16
19
16
17
16
15
17
14
15
16
14
15
11
14
12
11
13
10
13
14
12
14
15
13
14
16
15
14
20
18
19
17
24
19
21
25








Team
members
Grievance
handling
Appraisal
system
Canteen
Transport
US
2
2
1
1
NG
1
3
1
1
DS
1
2
2
1
NB
3
1
1
1
PK
1
2
2
1
Total
8
10
7
5
P
Process
3
S
I
Suppliers
Inputs
1
2
5
4
Process Boundary
O
C
Outputs
Customers
5 – WHY ANALYSIS
The 5-Why analysis method is used to move past symptoms and
understand the true root cause of a problem. It is said that only
by asking "Why?" five times, successively, you can delve into a
problem deeply enough to understand the ultimate root cause.
Problem
1st Why
2nd Why
3rd Why
Rotary
table
throw ing
aw ay the
Cells
Rotary
Table
throw ing
aw ay Cells
from Som e
heads
Cell is
falling
dow n
from
head no.
1&7
during
rotation
of Rotary
table
Less
Vacuum
level to
hold the
cell on
the head
no 1 & 7
of Rotary
table
4th Why
5th Why
Vacuum
leakage in
the supply
of head no
1&7
Vacuum
seal
found
dam aged
in head
no 1 & 7
BACK
BACK
79
What is a Process ?
Sequence of interdependent and linked procedures which,
at every stage, consume one or more resources (employee
time, energy, machines, money) to convert inputs (data,
material, parts, etc.) into outputs. These outputs then serve
as inputs for the next stage until a known goal or end
result is reached.
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DISTRIBUTION
While individual measured values may all be different, as
a group they tend to exhibit a pattern. This is called
distribution which can be described by:
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11 March 2023

Location (Process level or centering)

Spread or dispersion (Range of values from
smallest to largest)

Shape (Pattern of variation, whether symmetrical or
skewed etc.)
Distribution of Data

82
Normal distributions
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
Skewed distribution
Variation



83
There is no two natural items in any category are the same.
Variation may be quite large or very small.
If variation very small, it may appear that items are identical, but precision
instruments will show differences.
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3 Categories of variation



84
Within-piece variation
◦ One portion of surface is rougher than another portion.
Apiece-to-piece variation
◦ Variation among pieces produced at the same time.
Time-to-time variation
◦ Service given early would be different from that given later in the day.
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Source of variation




85
Equipment
◦ Tool wear, machine vibration, …
Material
◦ Raw material quality
Environment
◦ Temperature, pressure, humadity
Operator
◦ Operator performs- physical & emotional
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A
Spread
A - Original Process
B - Increase in spread
with same location
B
Change in process variation
B – Pattern is skewed
Shape
A - Original symmetrical
pattern
Change in pattern of variation
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11 March 2023
In the figure Change in pattern of
variation the Original pattern (A) is
symmetrical but the new pattern (B) is
skewed. Even though the centering is the
same, the shapes or patterns are different.
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STABILITY
If the process characterised by distribution remains unchanged over a period of
time, then the process is said to be Stable and Repeatable. This can be understood
from the following depiction of process over a period of time, see the figure
below:
Target
Time
Stable and repeatable process
This pattern results when only common causes are present in the process.
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11 March 2023










 Level
PPM
% *(Yield)
6
3.4
99.99966
5
233
99.9767
4
6220
99.379
3
66820
93.32
2
308700
69.13
1
697700
60.23


Type of
Data
Continuous
Attribute
Control Chart
- Individual measurement
- X - R, X-s charts
- p, c, u charts
- Individual measurement
The common causes are minute and many and are
individually not measurable. The pattern resulting from
the influence of common causes is called “State of
statistical control” or sometimes, just “In control”.
It is called statistical because the variation can be
described by statistical laws. It only common causes are
present and do not change, the output of a process is
predictable.
They are known to be “Chance Causes”
9
The advantages of maintaining a state of statistical
control are:



However, process level and variation may change due to
influence of causes additional to common causes. Such
causes are called special causes.
9
Examples of special causes are changes in setting, operator, material input, etc.
When they occur, they make the (overall) process distribution change. Unless
they are arrested, they will continue to affect the process output in unpredictable
ways as shown below:
Increase in variation
Shift in process level
Time
Original
process
9
Shift in process
level and variation
Unstable Process
They are also called
“assignable causes”
Changes in process pattern due to special
causes can be either detrimental or
beneficial. When detrimental, they need
to be identified and eliminated. When
beneficial, they need to be perpetuated
by making them a permanent part of the
process.
9
11 March
Date
Present employee
•
•
31
29
27
25
23
21
19
17
15
13
11
9
7
5
3
2030
2020
2010
2000
1990
1980
1970
1960
1950
1
Nos
Trend Chart for Present employee




Variable vs. Attribute



104
Control Charts show sample data plotted on a graph with CL, UCL, and LCL
Control chart for variables are used to monitor characteristics that can be measured, e.g.
length, weight, diameter, time
Control charts for attributes are used to monitor characteristics that have discrete values
and can be counted, e.g. % defective, number of flaws in a shirt, number of broken eggs in a
box
11 March 2023
Types of Control Charts





105
X-bar-R: Continuous values measuring product or service attributes
X: similar, but subgroups contain one value
Nonconforming Units (based on the Binomial distribution): p chart, np chart.
Nonconformities (based on the Poisson distribution): c chart, u chart.
Special Control Charts: Cusum, Trend, Moving average, Multivariate etc.
11 March 2023
Control Chart Selection
Quality Characteristic
Variable
Attribute
Defective
n>1?
no
x and MR
yes
no
n>=10?
Defect
x and R
constant
sample
size?
yes
no
yes
x and s
106
11 March 2023
p-chart with
variable sample
size
constant
sampling
unit?
p or
np
yes
no
c
u
Control Charts for Variables




107
Use X-bar and R-bar
charts together
Used
to
monitor
different variables
X-bar & R-bar Charts
reveal different problems
In statistical control on
one chart, out of control
on the other chart? OK?
11 March 2023
Control Charts for Variables
Use X-bar charts to monitor the changes in
the mean of a process (central tendencies)
 Use R-bar charts to monitor the dispersion or
variability of the process
 System
can show acceptable central
tendencies but unacceptable variability or
 System can show acceptable variability but
unacceptable central tendencies

108
11 March 2023
Constructing a X-bar Chart: A quality control inspector at the Cocoa Fizz soft drink
company has taken three samples with four observations each of the volume of
bottles filled. If the standard deviation of the bottling operation is .2 ounces, use the
below data to develop control charts with limits of 3 standard deviations for the 16
oz. bottling operation.
Time 1
Time 2
Time 3
Observation 1
15.8
16.1
16.0
Observation 2
16.0
16.0
15.9
Observation 3
15.8
15.8
15.9
Observation 4
15.9
15.9
15.8
Sample means
(X-bar)
15.875
15.975
15.9
0.2
0.3
0.2
Sample
ranges (R)
109
11 March 2023

Center line and control limit
formulas
x 1  x 2  ...x n
σ
, σx 
k
n
where (k ) is the # of sample means and (n)
x
is the # of observations w/in each sample
UCL x  x  zσ x
LCL x  x  zσ x
Solution and Control Chart (X-bar)

Center line (X-double bar):
15.875  15.975  15.9
x
 15.92
3

Control limits for±3σ limits:
 .2 
UCL x  x  zσ x  15.92  3
  16.22
 4
 .2 
LCL x  x  zσ x  15.92  3
  15.62
 4
110
11 March 2023
X-Bar Control Chart
111
11 March 2023
Control Chart for Range (R)

Center Line and Control Limit
formulas:
R
0.2  0.3  0.2
 .233
3
UCLR  D4 R  2.28(.233)  .53
LCLR  D3 R  0.0(.233)  0.0
112
11 March 2023

Factors for three sigma control limits
Factor for x-Chart
Sample Size
(n)
2
3
4
5
6
7
8
9
10
11
12
13
14
15
A2
1.88
1.02
0.73
0.58
0.48
0.42
0.37
0.34
0.31
0.29
0.27
0.25
0.24
0.22
Factors for R-Chart
D3
0.00
0.00
0.00
0.00
0.00
0.08
0.14
0.18
0.22
0.26
0.28
0.31
0.33
0.35
D4
3.27
2.57
2.28
2.11
2.00
1.92
1.86
1.82
1.78
1.74
1.72
1.69
1.67
1.65
R-Bar Control Chart
113
11 March 2023
Second Method for the X-bar Chart Using
R-bar and the A2 Factor


Use this method when sigma for the process
distribution is not know
Control limits solution:
0.2  0.3  0.2
R
 .233
3
UCL x  X  A 2 R  15.92  0.73.233  16.09
LCLx  X  A 2 R  15.92  0.73.233  15.75
114
11 March 2023
Another Example
A pharmaceutical Mfg. needs to control the concentration of
active ingredient in a formula used to restore hair to bald people.
The concentration should be around 10%. Accordingly, in a shift,
total 30 observations taken from 3 different lots are as follow:
10.22, 10.46, 10.82, 9.88, 9.92, 10.15, 10.69, 10.12, 10.31, 10.07,
10.25, 10.06, 10.52, 10.31, 9.94, 10.85, 10.32, 10.8, 10.23, 10.15,
10.37, 10.59, 10.13, 10.33, 9.39, 10.14, 9.79, 10.26, 10.2, 10.31.
Comment on the stability of the process.
115
11 March 2023
Control Charts for Attributes –p-Charts & c-Charts

Attributes are discrete events; yes/no, pass/fail
◦ Use p-Charts for quality characteristics that are discrete and
involve yes/no or good/bad decisions
 Number of leaking caulking tubes in a box of 48
 Number of broken eggs in a carton
◦ Use c-Charts for discrete defects when there can be more than
one defect per unit
 Number of flaws or stains in a carpet sample cut from a production run
 Number of complaints per customer at a hotel
116
11 March 2023
Defect vs. Defective
117

‘Defect’ – a single nonconforming quality characteristic.

‘Defective’ – items having one or more defects.
11 March 2023
P-Chart
118

The P Chart is used for data that consist of the proportion of the number of
occurrences of an event to the total number of occurrences.

It is used in quality to report the fraction or percent nonconforming in a
product, quality characteristic, or group of quality characteristics.
11 March 2023
P-Chart
Formula:
119
11 March 2023
p 
np
n

The fraction nonconforming, p, is usually small,
say, 0.10 or less.

Because the fraction nonconforming is very small,
the subgroup sizes must be quite large to produce a
meaningful chart.
Formula
p 
 np
n
UCL  p  3
LCL  p  3
120
11 March 2023
p (1  p )
n
p (1  p )
n
p-Chart Example: A Production manager for a tire company has inspected the number of
defective tires in five random samples with 20 tires in each sample. The table below shows the
number of defective tires in each sample of 20 tires. Calculate the control limits.
121
Sample
Number
of
Defective
Tires
Number of
Tires in
each
Sample
Proportion
Defective
1
3
20
.15
2
2
20
.10
3
1
20
.05
4
2
20
.10
5
2
20
.05
Total
9
100
.09
11 March 2023

CL  p 
σp 
Solution:
# Defectives
9

 .09
Total Inspected 100
p(1  p )
(.09)(.91)

 0.64
n
20
UCLp  p  z σ   .09  3(.064)  .282
LCLp  p  z σ   .09  3(.064)  .102  0
p- Control Chart
122
11 March 2023
Another Example
One French Tire Mfg. company randomly samples 40
tires at the end of each shift to test for tires that are
defective. The number of defectives in 12 shifts is as
follows: 4,2, 0, 5, 2, 3, 14, 2, 3, 4, 12, 3. Construct a
control chart for this process. Is the production process
under control?
123
11 March 2023
Control Charts for Count of Nonconformities




124
11 March 2023
The nonconformities chart controls the count of nonconformities within the
product or service.
An item is classified as a nonconforming unit whether it has one or many
nonconformities.
Count of nonconformities (c) chart.
Count of nonconformities per unit (u) chart.
Control Charts for Count of Nonconformities

125
11 March 2023
Since these charts are based on the Poisson
distribution, two conditions must be met:
1. The average count of nonconformities must be
much less than the total possible count of
nonconformities.
2. The occurrences are independent.
Formula
c
c
g
UCL  c  3 c
LCL  c  3 c
126
11 March 2023
c-Chart Example: The number of weekly customer complaints are monitored
in
a
large
hotel
using
a
c-chart. Develop three sigma control limits using the data table below.
127
Week
Number of
Complaints
1
3
2
2
3
3
4
1
5
3
6
3
7
2
8
1
9
3
10
1
Total
22
11 March 2023

Solution:
# complaints 22
CL 

 2.2
# of samples 10
UCLc  c  z c  2.2  3 2.2  6.65
LCLc  c  z c  2.2  3 2.2  2.25  0
c - Control Chart
128
11 March 2023
Another Example
The following data are the number of nonconformities
in bolts for use in cars made by the Ford Motor
Company: 9, 15, 11, 8, 17, 11, 5, 11, 13, 7, 10, 12, 4, 3,
7, 2, 3, 3, 6, 2, 7, 9, 1, 5, 8. Is there evidence that the
process is out of control?
129
11 March 2023
Summary
130
11 March 2023
The Cusum Control Chart for Monitoring the Process Mean
• The cusum chart incorporates all information in the sequence of sample values by plotting
the cumulative sums of the deviations of the sample values from a target value.
• If 0 is the target for the process mean, is the average of the jth sample, then the cumulative
xj
sum control chart is formed by plotting the quantity
i
Example: Say target 0 = 10
If
the
process
remains in-control,
Ci remains near 0
131
11 March 2023
Ci   ( x j   0 )
j1
MA Control Chart
(Non-Shewhart Control Chart)

Plot sample statistic: average of last w data points (Mi )

Computing point to plot ( Mi ) for the chart:
xi  xi 1  ...  xi  w1
Mi 
w

Estimate for μ (to find center line):
1 n
μ0   xi
n i 1

Estimate for  (to find control limits, changes with each point):
σ
132
3/11/2023
σx
w
MA Control Chart
(Non-Shewhart Control Chart)

General model for MA control chart
UCL  μ0  Lσ  μ0  L
σx
w
1 n
CL  μ0   xi
n i 1
UCL  μ0  Lσ  μ0  L

σx
w
Notes:
◦ Picking w larger makes chart faster to detect to smaller shifts
◦ Picking w smaller makes chart more sensitive to larger shifts
◦ MA is better at detecting smaller shifts than a Shewhart chart,
but not as effective as a EWMA or CUSUM chart
133
3/11/2023
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Normal behavior. Process is “in
control.”
134
11 March 2023
Patterns in Control Charts
Upper control limit
Target
Lower control limit
One plot out above (or below).
Investigate for cause. Process is “out
of control.”
135
11 March 2023
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Trends in either direction, 5 plots.
Investigate for cause of progressive
change.
136
11 March 2023
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Two plots very near lower (or upper)
control. Investigate for cause.
137
11 March 2023
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Run of 5 above (or below) central
line. Investigate for cause.
138
11 March 2023
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Erratic behavior. Investigate.
139
11 March 2023
Control Limits and Errors
Type I error:
Probability of searching for
a cause when none exists
(a) Three-sigma limits
UCL
Process
average
LCL
140
11 March 2023
Control Limits and Errors
Type I error:
Probability of searching for
a cause when none exists
(b) Two-sigma limits
UCL
Process
average
LCL
141
11 March 2023
Control Limits and Errors
(a) Three-sigma limits
Type II error:
Probability of concluding
that nothing has changed
UCL
Shift in process
average
Process
average
LCL
142
11 March 2023
Control Limits and Errors
(b) Two-sigma limits
Type II error:
Probability of concluding
that nothing has changed
UCL
Shift in process
average
Process
average
LCL
143
11 March 2023
PROCESS CONTROL
This is the state where only common causes are
present. The proof of this situation is when the
pattern of variation conforms to the statistical
normal distribution.
It involves continuous monitoring of the process
for special causes and eliminating them. Evidence
of special causes is provided by systematic
patterns in process variability.
144
11 March 2023
Statistical Process Control
SPC (Statistical Process Control) is a group of tools and techniques
used to determine the stability and predictability of a process.
Graphical depictions of process output are plotted on Control
Charts.
The first Control Charts were developed by Walter Shewhart at Bell
Labs in the 1920’s. At this time, telephone technology was in its
infancy with poor reliability. Shewhart used SPC to study variation and
reduce special causes of failure. Quality and reliability in phone
service increased dramatically as a result of SPC. W. Edwards Deming
is credited for introducing SPC to the Japanese after World War II. The
resulting rise in Japanese quality and reliability is well documented.
145
11 March 2023
Implementation of SPC:
An Example
146
11 March 2023
Cpk| 1.0 Background
Control Charts show sample data plotted on a graph with CL, UCL, and
LCL
Cpk| 2.0 Terminologies
•
Definition: “ Process Capability is the measured, inherent variation of the
product turned out by a process”
•
What is Process: To some unique combination of machines, tools, methods,
materials & people engaged in production.
•
Capability: An ability, based on tested performance, to achieve measurable
results
•
Process Capability = +3σ or -3σ ( a total of 6σ) Where, σ shows the standard
deviation of Process under a state of statistical control.
•
Cp: process capability index
•
Cpk: minimum process capability index
•
Pp: process performance index
•
Ppk: minimum process performance index
Cont..
Cpk| 2.0 Terminologies
Cpk is:
• Process Capability measure
• Simple statistical measure estimate level  process output which will
within specified limit
• Provides comparison between output of process Vs Process Specification
• Process Improvement:
Statistical Process Control tool
monitors process
tells whether capable or not meeting desired level of performance
action to be taken
To investigate concerns
Helps in process improvement and to achieve desired capability levels Cont..
Cpk| 2.0 Terminologies
Cp, Cpk Vs Pp, Ppk
Cp Cpk
Process
Capability
Pp Ppk
Process Performance
Aims
Process Verification
Cp- Potential Capability
What process can do under
certain condition
i.e. variation in short run for
process in state of statistical
control
Cpk- Actual Capability
Estimate of capability what
process is doing over extended
period of time
-Usage process Sigma for
-For Process Performance
-Process in too new (At
development stage)
- No historical Data
- Sample size is larger from
process
- Usage sample sigma for
calculation
- Cpk > Ppk
- Anomalies in case of: sample
size is small or data represents
Cont..
short amount of time only in
Cpk 3.0 Relationship between Process
variability and Specification width
•
Three possible ranges for Cp
– Cp = 1, as in Fig. (a), process
variability just meets
specifications
– Cp ≤ 1, as in Fig. (b), process
not capable of producing
within specifications
– Cp ≥ 1, as in Fig. (c), process
exceeds minimal
specifications
•
One shortcoming, Cp assumes
that the process is centered on
the specification range
•
Cp=Cpk when process is
centered
Cpk| 4.0 Calculations
USL: upper specification limit;
LSL: lower specification limit;
𝑪𝒑𝒌 =
𝑻𝒐𝒕𝒂𝒍 𝑻𝒐𝒍𝒆𝒓𝒂𝒏𝒄𝒆
𝑷𝒓𝒐𝒄𝒆𝒔𝒔 𝑺𝒑𝒓𝒆𝒂𝒅
Mean: grand average of all the data
Sigma hat: estimated inherent variability (noise) of a stable process
SD: overall variability
Cpk| 4.0 Calculation
Process Capability analysis Process:
- Take representative sample of process output
- Statistical analysis of samples (Mathematical tools, Scattered Plot, Pareto
Chart, Histogram, (Minitab/Excel/QI Macros for exel)
- Calculate mean and Standard Deviation form the sample size
- Calculate Cp Value as well as Cpkl and Cpku
- Minimum value from Cpkl and Cpku is the value of Cpk
- By observing results of statistical analysis one can be explain or determine
future expected process capabilities (ex. In response to Productivity or
Quality Attribute)
- Process Capability provides a single number which has ability to provide
details of process consistency output.
Requirement:
- Stable Process
Cpk| 5.0 Interpretation of Values
Sigm
a
Leve
l
Defect
Rate
(DPMO)
Yield
%Goods
Cpk
Sigm
a
Level
Defect
Rate
(DPMO)
Yield
%Goods
Cpk
1σ
691462
30.9
0.33
4σ
6210
99.40%
1.33
2σ
308770
69.10%
0.67
5σ
233
99.98%
1.67
Cpk| 6.0 Cpk Value Ranges
Red (Bad)
Yellow (OK)
Green (Good)
Cp
Cpk
Pp
Ppk
Sigma
< 1.00
< 1.00
< 1.33
< 1.33
< 4.5
1.00 - 1.33
1.00 - 1.33
1.33 - 1.67
1.33 - 1.67
4.5 - 5.5
> 1.33
> 1.33
> 1.67
> 1.67
> 5.5
Cpk| 7.0 What if process not capable
-Initial action - increase the inspection level and ensure that confidence with
respect to the quality of output product is increased.
-Clearly, quality cannot be inspected into a product or process, therefore, the
net steps will be to look at how to improve the capability of the process.
-Reviewing the product specifications, as by widening the specifications, the
capability can be increased. (This can only be performed, if any proposed
specification changes are acceptable per customer needs.)
-Then looking at the process/Actual Operations itself, there will be a need
to identify the sources of variation
Measurement, Mother Nature)
(Ex. Fish Bone i.e. 6M_Man, Material, Machine, Method,







Project Title
Business Case
Currently the breakage at Last section of Cell Line (Laser
to Shrink Wrap) is approx. 5%, which is leading to major
line Yield loss & is a major hurdle in achieving the ABP of
MBPV because the target of line yield to achieve the ABP
is 94%.
Metric
Breakage % at
Laser to Shrink
wrap section
Goal statement
Current Goal /
level
Target
5%
With in 2.5%
Target
date
28.02.09
Project plan
Phase
Define
Measure
Analyze
Improve
Control
Start
27.09.08
25.10.08
16.11.08
11.12.08
01.01.09
End
25.10.08
15.11.08
10.12.08
31.12.08
25.03.09
Opportunity Statement
Pain: Cells are breaking at final stage of
classification, which is a loss of finished good
costing 7.23$ per cell & the amount of loss is
5% of input qty.
Impact of pain in Rs. (or soft) : 11.7
Crore/year
Sigma Level: 3.1
Project scope
Process under improvement: Laser ,Cell
Sorting & Shrink Wrapping
Starts with: Cell entering in the Laser
Ends with: FG Handover to stores
Team Selection
Remarks
Champion:
BB
:
Member: (Maintenance)
Member: (Production)
Member: ( Production)
Member: ( Production)
Member: ( Production)
Service Quality
Customers also form perceptions of quality during
the service transaction - how effectively and
efficiently the service was delivered and the speed
and convenience of completing the transaction .
Finally, customers evaluate support activities that
occur after the transaction, that is post-sale
services.
3-163
Qualities of services
Search qualities
Experience qualities
Credence qualities
11-Mar-23
164
The Service Profit Chain
Internal Service
Delivery -Employees
Employee
Value
-- Workplace design
-- Process Tools
-- Rewards/Recognition
Service
Concept
External --Customers
Service
Value
-- Higher
reliability
-- Lower costs
Service Value
Outcomes –
C/S, Loyalty
--- Lifetime Value
--- Retention
--- Referrals
Adapted from Heskett, Sasser, and Schlesinger (1997).
11-Mar-23
Profits
Growth
165
Service Quality Gap Model
Service
Quality
Gap
Model
Customer
Customer
Perceptions
Managing the
Evidence
Customer Satisfaction
GAP 5
Customer /
Marketing Research
GAP 1
Communication
GAP 4
Understanding
the Customer
Management
Perceptions
of Customer
Expectations
Service
Delivery
Conformance
GAP 3
Design GAP 2
Conformance
Service
Standards
11-Mar-23
Expectations
Service Design
166
Service Process Control
11-Mar-23
167