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Chapter 3
Controlling Processes
Copyright 2013 John Wiley & Sons, Inc.
Overview
3-2
Introduction
• Each year the Columbus air mail processing
center (AMC) processes in excess of 50
million letters
• Unfortunately, 8.7 percent did not meet ontime delivery commitments
• A Six Sigma improvement project was
initiated
• Late deliveries were reduced by 14.3 percent
and $15,000 was saved annually
• On time delivery is monitored using a p-chart
3-3
Monitoring and Control
• Monitoring system is a direct connection
between planning and control
• Monitor processes, output, and
environment to make sure that strategy is
appropriate to achieve goals
– First, identify the key factors to be
controlled
– Second, identify the relevant information to
be collected
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Process Monitoring
• There are a wide variety of elements to
potentially monitor
• Too much data can be worse than too
little data
– It can obscure important information
• Want to monitor our various processes
• Also need to monitor the environment
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Balanced Scorecard
• In the past, it was common to rely on financial
measures
• When inadequacies discovered, managers
either tried to improve or switched to
operational measures
• Many organizations now realize no one type of
measurement is best
• The purpose of the balanced scorecard it to
provide a comprehensive view
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Benefits of Balanced
Scorecard
• An effective way to clarify and gain consensus
of the strategy
• A mechanism for communicating the strategy
throughout the entire organization
• A mechanism for aligning departmental and
personal goals to the strategy
• A way to ensure that strategic objectives are
linked to annual budgets
• Timely feedback related to improving the
strategy
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Areas Measured by Balanced
Scorecard
1.
2.
3.
4.
Financial performance
Customer performance
Internal business process performance
Organizational learning and growth
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ISO 9000
• ISO 9000 was developed by International
Organization for Standardization
• ISO 9000 was developed as a guideline for
designing, manufacturing, selling, and
servicing products
• It is a “checklist” of good business practices
• Selecting an ISO 9000 certified supplier
provides some assurance that supplier follows
accepted business practices in areas covered
by the standard
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ISO 14000 and 14001
• Series of standards covering environmental
management systems, environmental auditing,
evaluation of environmental performance,
environmental labeling, and life-cycle
assessment
• Focus of ISO 14001 is on an organization’s
environment management system
– A standard on which organizations can become
certified
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Failure Mode and Effect Analysis
(FMEA)
• Developed as a structured approach to
identify and prioritize for close
monitoring
• Uses a scoring model approach set up in
six steps
• One way to identify items for inclusion
is by evaluating their process capability
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FMEA Steps
1.
2.
3.
4.
List ways a production system might fail
Evaluate the severity (S) of each failure
Estimate the likelihood (L) of each failure
Estimate the ability to detect (D) for each
cause
5. Find risk priority number RPN=S  L  D
6. Consider ways to reduce S, L, and D where
RPN is high
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Process Control
• Process control is the act of reducing
difference between plan and reality for each
process
– To no avail if corrections not made
• Control is one of the manager’s most difficult
tasks involving mechanistic and human
elements
• Control brings performance back in line with
the plan
3-13
Characteristics of a
Good Control System
•
•
•
•
•
•
•
Flexible
• Capable of being
extended
Cost effective
• Fully documented
Simple
• Ethical
Timely
Sufficiently precise
Easy to maintain
Signal if out of order
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Statistical Process Control
1. Inspection for variables
–
Measuring a variable that can be scaled
such as weight, length, temperature, and
diameter
2. Inspection of characteristic (attribute)
–
Determining the existence of a
characteristic such as acceptabledefective, timely-late, and right-wrong
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Chance Versus Assignable
Variation
• Chance variation is variability built into
the system
• Assignable variation occurs because
some element of the system or some
operating condition is out of control
• Quality control seeks to identify when
assignable variation is present so that
corrective action can be taken
3-16
Control Charts
• Developed to distinguish between chance
variation and assignable variation
• Measurements from the continued repetition of
some process are a population
– Normally distributed with some mean and standard
deviation
• As long as distribution stays the same, the
process is in control
• If it changes, the process is out of control
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Control Chart with Limits Set
at Three Standard Deviations
Figure 3.2
3-18
Two Control Charts for Variables
1. Sample Means Chart
2. Range Chart
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Sample Data of Process Times
Table 3.3
3-20
Sample Calculations
• Scenario 1
– Sample 1: mean = 5, range = 2
– Sample 2: mean = 7, range = 2
– Sample 3: mean = 8, range = 2
• Scenario 2
– Sample 1: mean = 5, range = 2
– Sample 2: mean = 5, range = 4
– Sample 3: mean = 5, range = 6
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Patterns of Change in Process
Distributions
Figure 3.3
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Control Limits for Sample Means
and Range Charts
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Constructing Control Charts
• A bank has 10 branches
• Wishes to monitor the age of
applications for home mortgages
• Branches selected at random and ages of
the applications noted
• Sample size of four out of the 10
branches each day
• Data summary on next slide
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Mean and Range of Ages of
Mortgage Applications
Table 3.5
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Finding Control Limits
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Statistical Process Control
(SPC)
• SPC provides a preventing approach to managing
quality by monitoring processes in a way that
identifies potential problems before defects are even
created.
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7-
Controlling Process
Variability
•
•
•
•
•
Common cause variability versus assignable cause variability
Common cause variability comes from random fluctuation
inherent to the process.
Assignable cause variability is avoidable and not part of the
process.
SPC takes advantage of our knowledge about the standardized
distribution of these measures.
Process Control
– Identifies potential problems before defects are created by
watching the process unfold
– It uses X-bar Charts, R-Charts, P-charts, and C-charts
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7-
Process Capability
Answers the Question:
Can the process provide
acceptable quality consistently?
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Control Charts
 Control Chart – A specialized run chart
that helps an organization track changes
in key measures over time.
 Continuous variable – A variable that can
be measured along a continuous scale.
 Attribute – The presence or absence of a
particular characteristic.
3-30
Control Charts
• X chart - A specific type of control chart for a
continuous variable that is used to track the
average value for future samples.
• R chart – A specific type of control chart for a
continuous variable that is used to track how
much the individual observations within each
sample vary.
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Continuous Variable
Measurements
3-32
Mean Distribution
3-33
Process Capability Ratio (Cp)
Process Capability Ratio (Cp) – Measures whether or not a process is potentially
capable of
meeting certain quality standards
Cp =
Upper Tolerance Limit – Lower Tolerance Limit
6σ
Where σ is the estimated
standard deviation
for the individual observations
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Normal Distribution
3-35
Process Capability Values
3-36
Six Sigma Quality
To achieve Six Sigma quality, the
variability
of a process must be reduced to the point
that the process capability ratio is greater
than or equal to 2.
3-37
Six Sigma Quality
3-38
Six Sigma Quality level
Process Capability Ratio (Cp) – Measures whether or not a process is potentially
capable of
meeting certain quality standards
Cp =
Upper Tolerance Limit – Lower Tolerance Limit >= 2
6σ
Where σ is the estimated
standard deviation
for the individual observations
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Step 1 – Sampling the Process
Observation
Sample
1
2
3
4
5
1
136
137
144
141
138
2
143
138
140
140
139
3
140
141
144
137
135
4
139
140
141
139
141
5
137
138
143
140
138
6
142
141
140
139
138
7
143
141
143
140
140
8
139
139
141
140
136
9
140
138
143
141
139
10
139
141
142
140
136
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Step 2 – Calculate the Mean
and Range for each sample
Sample
R
1
139.2
8
2
140
5
3
139.4
9
4
140
2
5
139.2
6
6
140
4
7
141.4
3
8
139
5
9
140.2
5
10
139.6
6
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Step 3 –
Calculate control limits
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Step 4 – Plot the Data
Sample
R
11
141.2
8
12
142
9
13
144
12
14
140
5
15
139.6
4
16
140.8
5
3-43
Step 2 – Calculate the Mean
and Range for each sample
Sample
R
1
139.2
8
2
140
5
3
139.4
9
4
140
2
5
139.2
6
6
140
4
7
141.4
3
8
139
5
9
140.2
5
10
139.6
6
3-44
Step 3 –
Calculate control limits
3-45
Mean Mortgage Application Age
Figure 3.4
3-46
Range in Mortgage Application
Age
Figure 3.4
3-47
Fraction-Defective (ρ) Charts
total number of defects
p
total number of units sampled
p 
p (1  p )
n
UCL p  p  z p
LCL p  p  z p
3-48
Number-of-Defects (c) Charts
number of incidents observed
c
number of units sampled
c  c
UCL c  c  z c
LCL c  c  z c
3-49
Controlling Service Quality
• Process control, strategy maps, and control
charts can also be used for quality control in
services
• Measuring service quality is more difficult
– Service is abstract, transient, and psychological
• Can cope using customer satisfaction surveys
• Training employees in standard procedures
can improve quality
3-50
Service Defections
• Feedback from defecting customers can
be used to identify problem areas
• Can determine what is needed to win
them back
• Changes in defection rate can be used as
early warning signal
3-51