Quality Management
Class 4: 2/9/11
OBJECTIVES
Total Quality Management
Defined
 Quality Specifications and Costs
 Six Sigma Quality and Tools
 External Benchmarking
 ISO 9000
 Service Quality Measurement
 Process Variation
 Process Capability
 Process Control Procedures

TOTAL QUALITY MANAGEMENT (TQM)
 Total
quality management is
defined as managing the entire
organization so that it excels on
all dimensions of products and
services that are important to the
customer


Careful design of product or service
Ensuring that the organization’s
systems can consistently produce
the design
 TQM
was a response to the
Japanese superiority in quality
QUALITY SPECIFICATIONS
 Design
quality: Inherent value
of the product in the
marketplace

Dimensions include: Performance,
Features, Reliability/Durability,
Serviceability, Aesthetics, and
Perceived Quality.
 Conformance
quality: Degree
to which the product or service
design specifications are met
Three Quality Gurus Define Quality
Crosby: conformance to requirements
Deming: A predictable degree of uniformity
and dependability at low cost and suited to
the market
Juran: fitness for use (satisfies customer’s
needs)
Deming’s 14 Points
Create consistency of purpose
 Lead to promote change
 Build quality into the products
 Build long term relationships
 Continuously improve product, quality, and service
 Start training
 Emphasize leadership

Deming’s 14 Points
Drive out fear
 Break down barriers between departments
 Stop haranguing workers
 Support, help, improve
 Remove barriers to pride in work
 Institute a vigorous program of education and selfimprovement
 Put everybody in the company to work on the
transformation

Shewhart’s PDCA Model
4.Act
1.Plan
3.Check
2.Do
Implement Identify the
improvement and
the plan
make a plan
Is the plan
working
Test the plan
COSTS OF QUALITY
Appraisal Costs
External Failure
Costs
Costs of
Quality
Internal Failure
Costs
Prevention Costs
SIX SIGMA QUALITY
A philosophy and set of methods
companies use to eliminate defects in
their products and processes
 Seeks to reduce variation in the processes
that lead to product defects

SIX SIGMA QUALITY (CONTINUED)
 Six
Sigma allows managers to readily
describe process performance using a
common metric: Defects Per Million
Opportunities (DPMO)
DPMO 
Numberof defects
 Numberof 
 opportunit
ies  x No.of units
 for error per 
 unit



x 1,000,000
SIX SIGMA QUALITY (CONTINUED)
Example of Defects Per Million
Opportunities (DPMO)
calculation. Suppose we observe
200 letters delivered incorrectly
to the wrong addresses in a small
city during a single day when a
total of 200,000 letters were
delivered. What is the DPMO in
this situation?
DPMO 
200
1 
So, for every one
million letters
delivered this
city’s postal
managers can
expect to have
1,000 letters
incorrectly sent
to the wrong
address.
x 1,000,000 1, 000
x 200,000
Cost of Quality: What might that DPMO mean in terms
of over-time employment to correct the errors?
SIX SIGMA QUALITY: DMAIC CYCLE
 Define,
Measure, Analyze, Improve,
and Control (DMAIC)
 Developed by General Electric as a
means of focusing effort on quality
using a methodological approach
 Overall focus of the methodology is
to understand and achieve what
the customer wants
 A 6-sigma program seeks to reduce
the variation in the processes that
lead to these defects
 DMAIC consists of five steps….
SIX SIGMA QUALITY: DMAIC CYCLE (CONTINUED)
1. Define (D)
Customers and their priorities
2. Measure (M)
Process and its performance
3. Analyze (A)
Causes of defects
4. Improve (I)
Remove causes of defects
5. Control (C)
Maintain quality
EXAMPLE TO ILLUSTRATE THE PROCESS…
We are the maker of this cereal. Consumer
reports has just published an article that shows
that we frequently have less than 16 ounces of
cereal in a box.
 What should we do?

STEP 1 - DEFINE
What is the critical-to-quality characteristic?
 The CTQ (critical-to-quality) characteristic in
this case is the weight of the cereal in the box.

2 - MEASURE
How would we measure to evaluate the
extent of the problem?
 What are acceptable limits on this
measure?

2 – MEASURE (CONTINUED)
Let’s assume that the government says
that we must be within ± 5 percent of
the weight advertised on the box.
 Upper Tolerance Limit = 16 + .05(16) =
16.8 ounces
 Lower Tolerance Limit = 16 – .05(16) =
15.2 ounces

2 – MEASURE (CONTINUED)
We go out and buy 1,000 boxes of cereal
and find that they weight an average of
15.875 ounces with a standard
deviation of .529 ounces.
 What percentage of boxes are outside
the tolerance limits?

Lower Tolerance
= 15.2
Process
Mean = 15.875
Std. Dev. = .529
Upper Tolerance
= 16.8
What percentage of boxes are defective (i.e. less than 15.2 oz)?
Z = (x – Mean)/Std. Dev. = (15.2 – 15.875)/.529 = -1.276
NORMSDIST(Z) = NORMSDIST(-1.276) = .100978
Approximately, 10 percent of the boxes have less than 15.2
Ounces of cereal in them!
STEP 3 - ANALYZE - HOW CAN WE IMPROVE THE
CAPABILITY OF OUR CEREAL BOX FILLING
PROCESS?
 Decrease
Variation
 Center Process
 Increase Specifications
STEP 4 – IMPROVE – HOW GOOD IS GOOD
ENOUGH? MOTOROLA’S “SIX SIGMA”
 6s
minimum from
process center to nearest
spec
12s
6s
3
2
1
0
1
2
3
MOTOROLA’S “SIX SIGMA”
 Implies
2 ppB “bad” with no
process shift.
 With
1.5s shift in either direction
from center (process will move),
implies 3.4 ppm “bad”.
12s
3
2
1
0
1
2
3
STEP 5 – CONTROL
Statistical
(SPC)
 Use
Process Control
data from the actual
process
 Estimate distributions
 Look at capability - is good
quality possible
 Statistically monitor the
process over time
ANALYTICAL TOOLS FOR SIX SIGMA AND CONTINUOUS
IMPROVEMENT: FLOW CHART
Material
Received
from
Supplier
No,
Continue…
Inspect
Material for
Defects
Defects
found?
Yes
Can be used to
find quality
problems
Return to
Supplier
for Credit
Diameter
ANALYTICAL TOOLS FOR SIX SIGMA AND CONTINUOUS IMPROVEMENT:
RUN CHART
Can be used to identify
when equipment or
processes are not
behaving according to
specifications
0.58
0.56
0.54
0.52
0.5
0.48
0.46
0.44
1
2
3
4
5
6
7
8
Time (Hours)
9
10 11 12
ANALYTICAL TOOLS FOR SIX SIGMA AND CONTINUOUS IMPROVEMENT:
PARETO ANALYSIS
80%
Frequency
Can be used
to find when
80% of the
problems
may be
attributed to
20% of the
causes
Design
Assy.
Instruct.
Purch.
Training
ANALYTICAL TOOLS FOR SIX SIGMA AND CONTINUOUS
IMPROVEMENT: CHECKSHEET
Monday
Billing Errors
Wrong Account
Wrong Amount
A/R Errors
Wrong Account
Wrong Amount
Can be used to keep track of
defects or used to make sure
people collect data in a
correct manner
Number of Lots
ANALYTICAL TOOLS FOR SIX SIGMA AND CONTINUOUS
IMPROVEMENT: HISTOGRAM
Can be used to identify the frequency of quality
defect occurrence and display quality
performance
0
1
2
Data Ranges
3
4 Defects
in lot
ANALYTICAL TOOLS FOR SIX SIGMA AND CONTINUOUS
IMPROVEMENT: CAUSE & EFFECT DIAGRAM
Possible causes:
Machine
Man
The results
or effect
Effect
Environment
Method
Material
Can be used to systematically track backwards to
find a possible cause of a quality problem (or
effect)
ANALYTICAL TOOLS FOR SIX SIGMA AND CONTINUOUS IMPROVEMENT:
CONTROL CHARTS
Can be used to monitor ongoing production process
quality and quality conformance to stated standards of
quality
1020
UCL
1010
1000
990
LCL
980
970
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15
SIX SIGMA ROLES AND RESPONSIBILITIES
1.
2.
3.
4.
Executive leaders must
champion the process of
improvement
Corporation-wide training in Six
Sigma concepts and tools
Setting stretch objectives for
improvement
Continuous reinforcement and
rewards
THE SHINGO SYSTEM: FAIL-SAFE DESIGN
 Shingo’s



argument:
SQC methods do not prevent defects
Defects arise when people make
errors
Defects can be prevented by
providing workers with feedback on
errors
 Poka-Yoke


includes:
Checklists
Special tooling that prevents workers
from making errors
ISO 9000 AND ISO 14000
 Series
of standards agreed upon by
the International Organization for
Standardization (ISO)
 Adopted
 More
A
in 1987
than 160 countries
prerequisite for global competition?
 ISO
9000 an international reference
for quality, ISO 14000 is primarily
concerned with environmental
management
THREE FORMS OF ISO CERTIFICATION
1. First party: A firm audits itself
against ISO 9000 standards
2. Second party: A customer audits its
supplier
3. Third party: A "qualified" national
or international standards or
certifying agency serves as auditor
EXTERNAL BENCHMARKING STEPS
1. Identify those processes needing
improvement
2. Identify a firm that is the world
leader in performing the process
3. Contact the managers of that
company and make a personal
visit to interview managers and
workers
4. Analyze data
Process Control
Process Variation
 Process Capability
 Process Control Procedures



Variable data
Attribute data
BASIC FORMS OF VARIATION
Assignable variation is
caused by factors that
can be clearly
identified and possibly
managed
Example: A poorly trained
employee that creates
variation in finished
product output.
Common variation is
inherent in the
production process
Example: A molding
process that always leaves
“burrs” or flaws on a
molded item.
TAGUCHI’S VIEW OF VARIATION
Traditional view is that quality within the LS and US is good
and that the cost of quality outside this range is constant, where
Taguchi views costs as increasing as variability increases, so seek
to achieve zero defects and that will truly minimize quality costs.
High
High
Incremental
Cost of
Variability
Incremental
Cost of
Variability
Zero
Zero
Lower Target
Spec
Spec
Upper
Spec
Traditional View
Lower
Spec
Target
Spec
Upper
Spec
Taguchi’s View
PROCESS CAPABILITY
 Process
limits
 Specification
 How
limits
do the limits relate to one
another?
PROCESS CAPABILITY INDEX, CPK
Capability Index shows
how well parts being
produced fit into design
limit specifications.
 X  LTL
UTL - X 

C pk = min 
or
3s 
 3s
As a production process
produces items small
shifts in equipment or
systems can cause
differences in
production
performance from
differing samples.
Shifts in Process Mean
PROCESS CAPABILITY – A STANDARD MEASURE
OF HOW GOOD A PROCESS IS.
A simple ratio:
Specification Width
_________________________________________________________
Actual “Process Width”
Generally, the bigger the better.
PROCESS CAPABILITY
C pk
 X  LTL UTL  X 
 Min 
;

3s 
 3s
This is a “one-sided” Capability Index
Concentration on the side which is closest to the
specification - closest to being “bad”
THE CEREAL BOX EXAMPLE





We are the maker of this cereal. Consumer reports
has just published an article that shows that we
frequently have less than 16 ounces of cereal in a
box.
Let’s assume that the government says that we must
be within ± 5 percent of the weight advertised on
the box.
Upper Tolerance Limit = 16 + .05(16) = 16.8 ounces
Lower Tolerance Limit = 16 – .05(16) = 15.2 ounces
We go out and buy 1,000 boxes of cereal and find
that they weight an average of 15.875 ounces with a
standard deviation of .529 ounces.
CEREAL BOX PROCESS CAPABILITY

Specification or Tolerance
Limits



Upper Spec = 16.8 oz
Lower Spec = 15.2 oz
Observed Weight


Mean = 15.875 oz
Std Dev = .529 oz
C pk
 X  LTL UTL  X 
 Min 
;

3s 
 3s
15.875  15.2 16.8  15.875 
C pk  Min 
;

3(.529) 
 3(.529)
C pk  Min.4253; .5829
C pk  .4253
WHAT DOES A CPK OF .4253 MEAN?
An index that shows how well the units being
produced fit within the specification limits.
 This is a process that will produce a relatively
high number of defects.
 Many companies look for a Cpk of 1.3 or better…
6-Sigma company wants 2.0!

TYPES OF STATISTICAL SAMPLING
 Attribute
(Go or no-go
information)



Defectives refers to the acceptability
of product across a range of
characteristics.
Defects refers to the number of
defects per unit which may be higher
than the number of defectives.
p-chart application
 Variable


(Continuous)
Usually measured by the mean and
the standard deviation.
X-bar and R chart applications
Statistical
Process Normal Behavior
Control
(SPC) Charts
UCL
LCL
1
2
3
4
5
6
Samples
over time
UCL
Possible problem, investigate
LCL
1
2
3
4
5
6
Samples
over time
UCL
Possible problem, investigate
LCL
1
2
3
4
5
6
Samples
over time
CONTROL LIMITS ARE BASED ON THE NORMAL CURVE
x
m
-3
-2
-1
Standard
deviation
units or “z”
units.
0
1
2
3
z
CONTROL LIMITS
We establish the Upper Control
Limits (UCL) and the Lower Control
Limits (LCL) with plus or minus 3
standard deviations from some xbar or mean value. Based on this we
can expect 99.7% of our sample
observations to fall within these
limits.
99.7%
LCL
UCL
x
EXAMPLE OF CONSTRUCTING A P-CHART:
REQUIRED DATA
Sample
No. of
No.
Samples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
Number of
defects found
in each sample
4
2
5
3
6
4
3
7
1
2
3
2
2
8
3
STATISTICAL PROCESS CONTROL FORMULAS:
ATTRIBUTE MEASUREMENTS (P-CHART)
T o ta l N u m b e r o f D e fe c tiv e s
Given: p =
T o ta l N u m b e r o f O b s e rv a tio n s
sp =
p (1 - p)
n
Compute control limits:
UCL = p + z sp
LCL = p - z sp
EXAMPLE OF CONSTRUCTING A P-CHART: STEP 1
1. Calculate the
sample proportions,
p (these are what
can be plotted on the
p-chart) for each
sample
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
n Defectives
100
4
100
2
100
5
100
3
100
6
100
4
100
3
100
7
100
1
100
2
100
3
100
2
100
2
100
8
100
3
p
0.04
0.02
0.05
0.03
0.06
0.04
0.03
0.07
0.01
0.02
0.03
0.02
0.02
0.08
0.03
EXAMPLE OF CONSTRUCTING A P-CHART: STEPS 2&3
2. Calculate the average of the sample proportions
55
p =
1500
= 0.036
3. Calculate the standard deviation of the
sample proportion
sp =
p (1 - p)
=
n
.036(1- .036)
= .0188
100
EXAMPLE OF CONSTRUCTING A P-CHART: STEP 4
4. Calculate the control limits
UCL = p + z sp
LCL = p - z sp
.036  3(.0188)
UCL = 0.0924
LCL = -0.0204 (or 0)
9A-56
EXAMPLE OF CONSTRUCTING A P-CHART: STEP 5
5. Plot the individual sample proportions, the average
of the proportions, and the control limits
EXAMPLE OF X-BAR AND R CHARTS:
REQUIRED DATA
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Obs 1
10.682
10.787
10.78
10.591
10.693
10.749
10.791
10.744
10.769
10.718
10.787
10.622
10.657
10.806
10.66
Obs 2
10.689
10.86
10.667
10.727
10.708
10.714
10.713
10.779
10.773
10.671
10.821
10.802
10.822
10.749
10.681
Obs 3
10.776
10.601
10.838
10.812
10.79
10.738
10.689
10.11
10.641
10.708
10.764
10.818
10.893
10.859
10.644
Obs 4
10.798
10.746
10.785
10.775
10.758
10.719
10.877
10.737
10.644
10.85
10.658
10.872
10.544
10.801
10.747
Obs 5
10.714
10.779
10.723
10.73
10.671
10.606
10.603
10.75
10.725
10.712
10.708
10.727
10.75
10.701
10.728
EXAMPLE OF X-BAR AND R CHARTS: STEP 1. CALCULATE SAMPLE MEANS,
SAMPLE RANGES, MEAN OF MEANS, AND MEAN OF RANGES.
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Obs 1
10.682
10.787
10.78
10.591
10.693
10.749
10.791
10.744
10.769
10.718
10.787
10.622
10.657
10.806
10.66
Obs 2
10.689
10.86
10.667
10.727
10.708
10.714
10.713
10.779
10.773
10.671
10.821
10.802
10.822
10.749
10.681
Obs 3
10.776
10.601
10.838
10.812
10.79
10.738
10.689
10.11
10.641
10.708
10.764
10.818
10.893
10.859
10.644
Obs 4
10.798
10.746
10.785
10.775
10.758
10.719
10.877
10.737
10.644
10.85
10.658
10.872
10.544
10.801
10.747
Obs 5
10.714
10.779
10.723
10.73
10.671
10.606
10.603
10.75
10.725
10.712
10.708
10.727
10.75
10.701
10.728
Averages
Avg
10.732
10.755
10.759
10.727
10.724
10.705
10.735
10.624
10.710
10.732
10.748
10.768
10.733
10.783
10.692
Range
0.116
0.259
0.171
0.221
0.119
0.143
0.274
0.669
0.132
0.179
0.163
0.250
0.349
0.158
0.103
10.728 0.220400
EXAMPLE OF X-BAR AND R CHARTS: STEP 2. DETERMINE CONTROL LIMIT
FORMULAS AND NECESSARY TABLED VALUES
x Chart Control Limits
UCL = x + A 2 R
LCL = x - A 2 R
R Chart Control Limits
UCL = D 4 R
LCL = D 3 R
From Exhibit TN 8.7
n
2
3
4
5
6
7
8
9
10
11
A2
1.88
1.02
0.73
0.58
0.48
0.42
0.37
0.34
0.31
0.29
D3
0
0
0
0
0
0.08
0.14
0.18
0.22
0.26
D4
3.27
2.57
2.28
2.11
2.00
1.92
1.86
1.82
1.78
1.74
EXAMPLE OF X-BAR AND R CHARTS: STEPS 3&4. CALCULATE X-BAR CHART
AND PLOT VALUES
UCL = x + A 2 R  10.728 - .58(0.2204 ) = 10.856
LCL = x - A 2 R  10.728 - .58(0.2204 ) = 10.601
1 0 .9 0 0
UCL
1 0 .8 5 0
M ea n s
1 0 .8 0 0
1 0 .7 5 0
1 0 .7 0 0
1 0 .6 5 0
1 0 .6 0 0
LCL
1 0 .5 5 0
1
2
3
4
5
6
7
8
S am p le
9
10
11
12
13
14
15
EXAMPLE OF X-BAR AND R CHARTS: STEPS 5&6. CALCULATE R-CHART AND PLOT
VALUES
UCL = D 4 R  ( 2.11)(0.2204)  0.46504
LCL = D3 R  (0)(0.2204)  0
0.800
0.700
0.600
0.500
R 0.400
UCL
0.300
0.200
0.100
LCL
0.000
1
2
3
4
5
6
7 8 9
Sample
10 11 12 13 14 15