Operations
Management
Managing Quality
Chapter 6
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Definitions of Quality
 ASC: Product characteristics & features that affect
customer satisfaction
 User-Based: What consumer says it is
 Manufacturing-Based: Degree to which a product
conforms to design specification
 Product-Based: Level of measurable product
characteristic
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Costs of Quality
 Prevention costs - reducing the potential for
defects
 Appraisal costs - evaluating products
 Internal failure - of producing defective parts or
service
 External costs - occur after delivery
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TQM
Encompasses entire organization, from supplier to
customer
Stresses a commitment by management to have a
continuing, company-wide, drive toward
excellence in all aspects of products and services
that are important to the customer.
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Deming’s Fourteen 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
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Deming’s Points - continued
 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
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Quality Loss Function
High Loss
Unacceptable
Loss
Poor
Fair
Good
Best
Low Loss
Frequency
Target-oriented quality
yields more product in
the "best" category
Conformance-oriented
quality keeps products
within 3 standard
deviations
Lower
Target
Upper
Distribution of Specifications for Products Produced
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Target Specification Example
A study found U.S. consumers preferred Sony TV’s
made in Japan to those made in the U.S. Both factories
used the same designs & specifications. The difference
in quality goals made the difference in consumer
preferences.
Freq.
LSL
Japanese factory
(Target-oriented)
Target
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U.S. factory
(ConformanceX oriented)
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Quality Loss Function; Distribution of
Products Produced
Quality Loss Function (a)
High loss
Unacceptable
Loss (to
producing
organization,
customer, and
society)
Low loss
Target-oriented
quality yields more
product in the
“best” category
Poor
Fair
Good
Best
Target-oriented quality
brings products toward
the target value
Conformance-oriented
quality keeps product
within three standard
deviations
Frequency
Lower
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Upper
Distribution of
specifications for product
produced (b)
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PDCA Cycle
4.Act:
Implement the
plan
1.Plan:
3.Check:
Is the plan
working
2.Do:
Test the plan
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Identify the
improvement and
make a plan
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Tools of TQM
 Tools for generating ideas
Check sheet
 Scatter diagram
 Cause and effect diagram

 Tools to organize data
Pareto charts
 Process charts (Flow diagrams)

 Tools for identifying problems
Histograms
 Statistical process control chart

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Seven Tools for TQM
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Pareto Analysis of Wine Glass
Defects (Total Defects = 75)
60
88%
54
50
97%
93%
100% 100%
72%
80%
40
60%
30
40%
20
12
5
10
4
2
0
20%
Cumulative Percent
Frequency (Number)
70
0%
Scratches
72%
Porosity
Nicks
Contamination
16%
5%
4%
Causes, by percent total defects
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Misc.
3%
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Process Chart
 Shows sequence of events in process
 Depicts activity relationships
 Has many uses
Identify data collection points
 Find problem sources
 Identify places for improvement
 Identify where travel distances can be reduced

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Statistical Process Control (SPC)
 Uses statistics & control charts to tell when to adjust
process
 Developed by Shewhart in 1920’s
 Involves
Creating standards (upper & lower limits)
 Measuring sample output (e.g. mean wgt.)
 Taking corrective action (if necessary)

 Done while product is being produced
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TQM In Services
 Service quality is more difficult to measure than for
goods
 Service quality perceptions depend on
Expectations versus reality
 Process and outcome

 Types of service quality
Normal: Routine service delivery
 Exceptional: How problems are handled

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Goods versus Services
Good
 Can be resold
 Can be inventoried
Service
 Reselling unusual
 Difficult to inventory
 Quality difficult to
measure
 Selling is part of
service
 Some aspects of
quality measurable
 Selling is distinct from
production
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Goods versus Services continued
Good
Service
 Product is transportable
 Site of facility important
for cost
 Provider, not product is
transportable
 Site of facility important
for customer contact
 Often difficult to
automate
 Revenue generated
primarily from intangible
service.
 Often easy to automate
 Revenue generated
primarily from tangible
product
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Operations
Management
Statistical Process Control
Supplement 6
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Statistical Quality Control (SPC)
 Measures performance of a process
 Uses mathematics (i.e., statistics)
 Involves collecting, organizing, & interpreting data
 Objective: provide statistical signal when assignable
causes of variation are present
 Used to
Control the process as products are produced
 Inspect samples of finished products

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Quality Characteristics
Variables
 Characteristics that you
measure, e.g., weight, length
 May be in whole or in
fractional numbers
 Continuous random variables
Attributes
 Characteristics for which you
focus on defects
 Classify products as either
‘good’ or ‘bad’, or count #
defects

e.g., radio works or not
 Categorical or discrete
random variables
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Statistical Process Control (SPC)
 Statistical technique used to ensure process is
making product to standard
 All process are subject to variability
Natural causes: Random variations
 Assignable causes: Correctable problems


Machine wear, unskilled workers, poor material
 Objective: Identify assignable causes
 Uses process control charts
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Sampling Distribution of Means,
and Process Distribution
Sampling
distribution of the
means
Process
distribution of
the sample
xm
( mean )
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Process Control Charts
Sample Value
Plot of Sample Data Over Time
80
Sample
Value
UCL
60
40
Average
20
LCL
0
1
5
9
13
17
21
Time
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Control Chart Purposes
 Show changes in data pattern

e.g., trends

Make corrections before process is out of control
 Show causes of changes in data

Assignable causes


Data outside control limits or trend in data
Natural causes

Random variations around average
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Theoretical Basis
of Control Charts
Central Limit Theorem
As sample size
gets
large
enough,
sampling distribution
becomes almost
normal regardless of
population
distribution.
X
X
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Control Chart Types
Continuous
Numerical Data
Control
Charts
Categorical or Discrete
Numerical Data
Variables
Charts
R
Chart
Attributes
Charts
P
Chart
X
Chart
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C
Chart
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X Chart
 Type of variables control chart

Interval or ratio scaled numerical data
 Shows sample means over time
 Monitors process average
 Example: Weigh samples of coffee & compute
means of samples; Plot
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Control Chart for Samples of 9 Boxes
Variation due to
assignable causes
17=UCL
Variation due to
natural causes
16=Mean
15=LCL
1 2
3 4
5
6
7
8 9 10 11 12
Sample Number
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Variation due to
assignable causes
Out of control
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X Chart
Control Limits
UCL x  x  A R
From
Table S6.1
LCLx  x  A R
n
x 
 xi
Mean for
sample i
n
 Ri
i 
n
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Range for
sample i
R  i 1
n
# Samples
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Factors for Computing Control
Chart Limits
Sample
Size, n
2
Mean
Upper
Lower
Factor, A2 Range, D4 Range, D3
1.880
3.268
0
3
1.023
2.574
0
4
0.729
2.282
0
5
0.577
2.115
0
6
0.483
2.004
0
7
0.419
1.924
0.076
8
0.373
1.864
0.136
9
0.337
1.816
0.184
10
0.308
1.777
0.223
12
0.266
1.716
0.284
0.184
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R Chart
 Type of variables control chart

Interval or ratio scaled numerical data
 Shows sample ranges over time

Difference between smallest & largest values in
inspection sample
 Monitors variability in process
 Example: Weigh samples of coffee & compute
ranges of samples; Plot
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R Chart
Control Limits
UCL R  D 4 R
From Table S6.1
LCL R  D 3R
Range for Sample i
n
 Ri
R  i 1
n
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Steps to Follow When Using
Control Charts
1. Collect 20 to 25 samples of n=4 or n=5 from a
stable process and compute the mean.
2. Compute the overall means, set approximate
control limits,and calculate the preliminary
upper and lower control limits.If the process
is not currently stable, use the desired mean
instead of the overall mean to calculate limits.
3. Graph the sample means and ranges on their
respective control charts and determine
whether they fall outside the acceptable
limits.
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Steps to Follow When Using
Control Charts - continued
4. Investigate points or patterns that indicate the
process is out of control. Assign causes for the
variations.
5. Collect additional samples and revalidate the
control limits.
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Mean and Range Charts
Complement Each Other
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Patterns to Look for in Control
Charts
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Deciding Which Control Chart to Use
 Using an X and R chart:
Observations are variables
Collect 20-25 samples of n=4, or n=5, or more each
from a stable process and compute the mean for the X
chart and range for the R chart.
 Track samples of n observations each.


 Using the P-Chart:
We deal with fraction, proportion, or percent defectives
 Observations are attributes that can be categorized in
two states
 Have several samples, each with many observations
 Assume a binomial distribution unless the number of
samples is very large – then assume a normal
distribution.

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Deciding Which Control Chart to Use
 Using a C-Chart:
Observations are attributes whose defects per unit of
output can be counted
 The number counted is often a small part of the
possible occurrences
 Assume a Poisson distribution
 Defects such as: number of blemishes on a desk,
number of typos in a page of text, flaws in a bolt of
cloth

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Process Capability Ratio, Cp
Upper Specificat ion  Lower Specificat ion
Cp 
6σ
  standard deviation of the process
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Process Capability Cpk
 Upper Specificat ion Limit  x
C pk  minimum of 
, or
3

x  Lower Specificat ion Limit 

3

where x  process mean
  standard deviation of the process population
Assumes that the process is:
• under control
• normally distributed
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Meanings of Cpk Measures
Cpk = negative number
Cpk = zero
Cpk = between 0 and 1
Cpk = 1
Cpk > 1
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