Transcript Document 7595330

Chapter 9
Managing Flow Variability:
Process Control and Capability
Amber Young
Sam Parduhn
Paresh Sinha
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Managing Flow Variability
§ 9.1 Performance Variability
§ 9.2 Analysis of Variability
§ 9.3 Process Control
§ 9.4 Process Capability
§ 9.5 Process Capability Improvement
§ 9.6 Product and Process Design
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Introduction ~ MBPF Example
MBPF, Inc. - High-tech manufacturer of steel doors
• This company prided themselves on:
– High Quality Products
– Professional after-sales service
– Solid reputation (15% share of market)
• Recently were celebrating their successes
during a holiday company party
– Numerous Speeches; Executives congratulating one
another on successes/accomplishments
• Company believed they were headed in the right
direction & that all was operating smoothly.
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MBPF Example (continued)
The celebration was short lived & had a quick change of
pace when a sales manager spoke up:
“Ladies & Gentlemen, I do not wish to spoil your mood, but
I have some disturbing news! Lately I have been talking
to some of our major customers, and I have found, much
to my surprise, that many of them are less than satisfied
with our products and service…”
“…Although we think our products are great & that our
service is unsurpassed, if what I’m hearing is right, it is
only a matter of time before we lose our loyal customer
base to the competition, which is working hard to provide
newer & better products, cheaper & faster.”
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MBPF Example (continued)
Wasn’t the case at MBPF as their CEO was a true leader &
was interested in these findings and asked for
elaboration:
CUSTOMER DISSATISFACTIONS/COMPLAINTS:
• Door quality in terms of safety, durability & ease of use
• Costs are much more than competitors
• Difficulty getting orders in on-time
• Customer Service when something went wrong with
installation/operation
*All very valid complaints for a company in their type of
business.
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MBPF Solution
CEO listened carefully to all complaints & decided it was
time to be PROACTIVE:
• Since the sales managers observations were primarily
subjective, the CEO recognized the need for something
solid as opposed to mere hearsay or intuition.
NEXT LOGICAL STEP:
• COLLECT & ANALYZE SOME HARD DATA
– Assigned a team to analyze the concrete data on critical
performance measures that drive customer satisfaction
GOAL:
• To IDENTIFY, CORRECT & PREVENT sources of future
problems
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Variability often = Customer Dissatisfaction
All Products & Services VARY in Terms Of:
Cost
Quality
Availability
Flow
Times
Variability often leads to Customer Dissatisfaction
• Chapter covers some geographical/statistical
methods for measuring, analyzing, controlling &
reducing variability in product & process
performance to improve customer satisfaction.
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§ 9.1 Performance Variability
• All measures of product & process performance (internal
& external) display Variability.
– External Measurements - customer satisfaction, relative product
rankings, customer complaints (vary from one market survey to
the next)
– Internally, flow units in all business processes vary with respect
to cost, quality & flow times
Example 1 ~ No 2 cars rolling off an assembly line are
identical. Even under identical circumstances, the time &
cost required to produce the same product could be
quite different.
Example 2 ~ Cost of operating a department within a
company can vary from one quarter to the next.
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§ 9.1 Performance Variability
• Sources of Variability
– Internal: imprecise equipment, untrained
workers, and lack of standard operating
procedures
– External: inconsistent raw materials, supplier
delivery delays, changing consumer tastes &
requirements, and changing economic
conditions
In general, variability refers to a discrepancy
between the actual and the expected
performance.
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§ 9.1 Performance Variability
A discrepancy between the actual and the
expected performance often leads to:
– higher costs, longer flow times, lower quality &
DISSATISFIED CUSTOMERS
• Processes with greater performance variability
are generally judged LESS satisfactory than
those with consistent, predictable performance.
• Variability in product & process performance, not
just its average, Matters to consumers!
• Customers perceive any variation in their
product or service from what they expected as a
LOSS IN VALUE.
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Quality Management Terms
• In general, a product is classified as defective if its cost,
quality, availability or flow time differ significantly from
their expected values, leading to dissatisfied customers.
**BOOK COVERS A FEW QUALITY MANAGEMENT TERMS:
• Quality of Design: how well product specifications aim
to meet customer requirements (what we promise
consumers ~ in terms of what the product can do)
• Quality Function Deployment (QFD): conceptual
framework for translating customers’ functional
requirements (such as ease of operation of a door or its
durability) into concrete design specifications (such as
the door weight should be between 75 and 85 kg.)
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Quality Management Terms
• Quality of conformance: how closely the actual
product conforms to the chosen design
specifications (how well we keep our promise in
terms of how it actually performs)
– Measures: # defects per car, fraction of output that
meets specifications
• Example: Airline conformance can be measured
in terms of the percentage of flights delayed for
more than 15 minutes OR the number of
reservation errors made in a specific period of
time.
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§ 9.2 Analysis of Variability
•
To analyze and improve variability there are
diagnostic tools to help us:
1.
2.
3.
4.
5.
Monitor the actual process performance over time
Analyze variability in the process
Uncover root causes
Eliminate those causes
Prevent them from recurring in the future
*Again we will use MBPF Inc. as an example and
look at how their customers perceive the
experience of doing business with the company
& how it can be improved.
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§ 9.2 Analysis of Variability
• Need to present raw data in a way to
make sense of the numbers, track change
over time, or identify key characteristics of
the data set.
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§ 9.2.1 Check Sheets
• A check sheet is simply a tally of the types
and frequency of problems with a product
or a service experienced by customers.
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Example 9.1
Type of Complaint
Number of Complaints
Cost
IIII IIII
Response Time
IIII
Customization
IIII
Service Quality
IIII IIII IIII
Door Quality
IIII IIII IIII IIII IIII
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Check Sheets
Good
• Easy to collect data
Bad
• Not very enlightening
• No numerical characteristics
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§ 9.2.2 Pareto Charts
• A Pareto chart is simply a bar chart that
plots frequencies of occurrences of
problem types in decreasing order.
• The 80-20 Pareto principle states that 20%
of problem types account for 80% of all
occurrences.
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Example 9.2
25
20
15
10
5
0
Door Quality
Service Quality
Cost
Response Time
Customization
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Pareto Charts
Good
• Ranks problems
• Shows relative size of quantities
Bad
• No numerical characteristics
• Only categorizes data
• No comparison process information
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§ 9.2.3 Histograms
• A histogram is a bar plot that displays the
frequency distribution of an observed
performance characteristic.
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Example 9.3
14
Frequency
12
10
8
6
4
2
0
72
74
76
78
80
82
84
86
88
90
92
Weight (kg)
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Histograms
Good
• Visualizes data distribution
• Shows relative size of quantities
Bad
• No numerical characteristics
• Dependant on category size
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Table 9.1
Day
Time
1
2
3
4
5
6
7
8
9
10
9:00 AM
81
82
80
74
75
81
83
86
88
82
11:00 AM
73
87
83
81
86
86
82
83
79
84
1:00 PM
85
88
76
91
82
83
76
82
86
89
3:00 PM
90
78
84
75
84
88
77
79
84
84
5:00 PM
80
84
82
83
75
81
78
85
85
80
Day
Time
11
12
13
14
15
16
17
18
19
20
9:00 AM
86
86
88
72
84
76
74
85
82
89
11:00 AM
84
83
79
86
85
82
86
85
84
80
1:00 PM
81
78
83
80
81
83
83
82
83
90
3:00 PM
81
80
83
79
88
84
89
77
92
83
5:00 PM
87
83
82
87
81
79
83
77
84
77
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Raw Data
Good
• Actual information
• Specific numbers
Bad
• Not intuitive
• Does not help with understanding of
relationships
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§ 9.2.4 Run Charts
• A run chart is a plot of some measure of
process performance monitored over time.
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Example 9.4
95
90
85
80
75
70
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97
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Run Charts
Good
• Shows data in chronological order
• Displays relative change over time
Bad
• Erratic graph
• No numerical characteristics
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§ 9.2.5 Multi-Vari Charts
• A multi-vari chart is a plot of high-averagelow values of performance measurement
sampled over time.
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Example 9.5
95
90
85
High
Low
Average
80
75
70
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
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Table 9.2
Day
1
2
3
4
5
6
7
8
9
10
High
90
88
84
91
86
88
83
86
88
89
Low
73
78
76
74
75
81
76
79
79
80
81.8
83.8
81.0
80.8
80.4
83.8
79.2
83.0
84.4
83.8
Average
Day
11
12
13
14
15
16
17
18
19
20
High
87
86
88
87
88
84
89
85
92
90
Low
81
78
79
72
81
76
74
77
82
77
83.8
82.0
83.0
80.8
83.8
80.8
83.0
81.2
85.0
83.8
Average
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Multi-Vari Charts
Good
• Shows numerical range and average
• Displays relative change over time
Bad
• Erratic graph
• No numerical characteristics
• Lacks distribution information
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Process Management
Two aspects to process management
• Process planning
• Process control
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§ 9.3 Process Planning
It involves
• Structuring the process
• Designing operating procedures and
• Developing key competencies such as
process capability, flexibility, capacity, and
cost efficiency.
Its goal is to produce and deliver products
that satisfy targeted customer needs.
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§ 9.3 Process Control
Involves:
• Tracking deviations between the actual
and the planned performance and taking
corrective actions to identify and eliminate
sources of these variations.
• There could be various reasons behind
variation in performance.
• Its goal is to ensure that actual
performance conforms to the planned
performance.
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§ 9.3.1 The Feedback Control Principle
•
Process performance management is based
on the general principle of feedback control
of dynamical systems.
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The Feedback Control Principle
Applying the feedback control principle to
process control..
“involves periodically monitoring the actual
process performance (in terms of cost,
quality, availability, and response time),
comparing it to the planned levels of
performance, identifying causes of the
observed discrepancy between the two, and
taking corrective actions to eliminate those
causes.”
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Plan-Do-Check-Act (PDCA)
• Process planning and process control are
similar to the Plan-Do-Check-Act (PDCA)
cycle.
– PDCA cycle…
“involves planning the process, operating it
inspecting its output, and adjusting it in light of the
observation.”
• Performed continuously to monitor and
improve the process performance.
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Problems in Process Control
• Performance variances are determined by
comparison of the current and previous
period’s performances.
• Decisions are based on results of this
comparison.
• Some variances may be due to factors
beyond a worker’s control.
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Process Control
•
•
According to W. Edward Deming, incentives
based on factors that are beyond a worker’s
control is like rewarding or punishing workers
according to a lottery.
Two categories of performance variability
– Variability due to factors within a worker’s control.
– Variability due to factors beyond a worker’s control.
•
Two types of variability
1. Normal variability
2. Abnormal variability
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§ 9.3.2 Types and Cause of Variability
Two types of variability
• Normal variability is statistically predictable
and includes both structural variability and
stochastic variability.
• Abnormal variability is unpredictable and
disturbs the state of statistical equilibrium
of the process by changing parameters of
its distribution in an unexpected way.
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Normal Variability
• Statistically predictable.
• Contains structural variability & stochastic
variability.
• Random causes have unpredictable effect,
and cannot be removed easily.
• Not in worker’s control.
• Can be removed only by process redesign, more precise equipment, skilled
workers, better quality material etc.
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Abnormal Variability
• Unpredictable
• Disturbs statistical equilibrium in
unexpected way.
• Implies that one or more performance
affecting factors may have changed.
• Due to causes superimposed externally or
process tampering.
• Within worker’s control.
• Can be identified and removed easily
therefore worker’s responsibility.
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Process Control
•
If observed performance variability is
– Normal - due to random causes - process is in
control
– Abnormal - due to assignable causes - process is
out of control
•
The short run goal is:
1.
2.
3.
4.
•
Estimate normal stochastic variability.
Accept it as an inevitable and avoid tampering
Detect presence of abnormal variability
Identify and eliminate its sources
The long run goal is to reduce normal variability
by improving process.
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§ 9.3.3 Control Limit Policy
• How to decide whether observed
variability is normal or abnormal?
• Control Limit Policy
– Control band - A range within which any
variation in performance is interpreted as
normal due to causes that cannot be identified
or eliminated in short run.
– Variability outside this range is abnormal.
– Lower limit of acceptable mileage, control
band for house temperature.
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Process Control
• Process control is useful to control any type of
process.
• Application of control limit policy
– Managing inventory, process capacity and flow time.
– Cash management - liquidate some assets if cash
falls below a certain level.
– Stock trading - purchase a stock if and when its price
drops to a specific level.
• Control limit policy has usage in a wide variety of
business in form of critical threshold for taking
action
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Questions?
(Applause)
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