Transcript (Textbook) Behavior in Organizations, 8ed (A. B. Shani)

Chapter 7
Quality Tools:From
Process Performance to
Process Perfection
McGraw-Hill/Irwin
©The McGraw-Hill Companies, Inc. 2008
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Statistical Quality Control (SPC)
•
•
•
•
•
Measures performance of a process
Uses statistics
Involves collecting, organizing, & interpreting data
Objective: Regulate product quality
Used to
–
–
Control the process as products are produced
Inspect samples of finished products
7-5
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
7-6
Controlling Process Variability:
Statistical Process Control (SPC)
•
•
•
•
•
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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Types of
Statistical Quality Control
Statistical
Quality Control
Process
Control
Variables
Charts
Attributes
Charts
Acceptance
Sampling
Variables
Attributes
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Quality Characteristics
Variables
Attributes
¨ Characteristics that you
• Characteristics for which you
focus on defects
measure, e.g., weight, length
• Classify products as either ‘good’
¨ May be in whole or in
or ‘bad’, or count # defects
fractional numbers
– e.g., radio works or not
¨ Continuous random variables • Categorical or discrete random
variables
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Control Chart
Normal Distribution Chart
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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Control Chart Types
Continuous
Numerical Data
Control
Charts
Categorical or
Discrete Numerical
Data
Variables
Charts
R
Chart
Attributes
Charts
`X
Chart
P
Chart
C
Chart
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Statistical Process Control Steps
Start
Produce Good
Provide Service
Take Sample
No
Assign.
Causes?
Yes
Inspect Sample
Stop Process
Create
Control Chart
Find Out Why
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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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X-bar Chart Steps
• Measure a sample of the process output
– Four to five units of output for most applications
– Many (>25) samples
• Calculate sample means ( X-bar ), grand mean (X-double
bar), & ranges (R)
• Compare the “X-bars” being plotted to the upper and
lower control limits and look for “assignable cause”
variability.
• Assignable cause variability means that the process has
changed.
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`X Chart
Control Limits
UCL
xA R
x

LCL
xA R
x

k
x 
 xi
i 1
n
From
Table
k
Sample
Mean at
Time i
R 
 Ri
i 1
n
Sample Range
iSat Time 3.1
k

# Samples
x 
i 1 i
x
n
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Control Charts: X-bar
• Distinguishing between random fluctuation and fluctuation due to an
assignable cause.
– X-bar chart tracks the trend in sample means to see if any disturbing
patterns emerge.
•
Steps:
-Calculate Upper & Lower
Control Limits (UCL & LCL).
??
•Use special charts based on
sample size
-Plot X-bar value for each sample
-Investigate “Nonrandom”
patterns
??
Exhibit 7.18 X-bar Chart for Example 7.2
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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
LCL
 D 4R
R
From Table
 D 3R
R
Sample Range at Time
i
k
R 
 Ri
i 1
n
Transparency Masters to accompany Operations
Management, 5E (Heizer & Render)
# Samples
4S-18
© 1998 by Prentice Hall, Inc.
A Simon & Schuster Company
Upper Saddle River, N.J. 07458
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R-charts
• R-charts monitor variation within each sample.
• R-charts are always used with X-bar charts.
•
Steps
•
Calculate Upper & Lower
Control Limits (UCL & LCL).
•
??
Use special tables based on
sample size.
•
Plot the R value for each sample
•
Investigate “Nonrandom”
patterns
??
Exhibit 7.22 R-Chart for Example 7.4
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Control Chart Types
Continuous
Numerical Data
Control
Charts
Categorical or
Discrete Numerical
Data
Variables
Charts
R
Chart
Attributes
Charts
`X
Chart
P
Chart
C
Chart
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p Chart
• Type of attributes control chart
–
Nominally scaled categorical data
• e.g., good-bad
• Shows % of nonconforming items
• Example: Count # defective chairs & divide by total
chairs inspected; Plot
–
Chair is either defective or not defective
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p Chart
Control Limits
UCL
LCL
 p  z
p
n
 p  z
p
p (1  p )
n
k
p 
p (1  p )
 p
i 1
n
z = 2 for 95.5%
limits; z = 3 for
99.7% limits
# Defective
Items in
Sample i
Size of
sample i
i
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Process Control Charts for Attributes
• P-charts
– Used to monitor the proportion or percentage of items defective in a
given sample.
UCL= p  z
p
LCL= p  z
p
p 
p (1  p ) / n
n = the sample size
p = the long-run average and center line
Z is the number of normal standard deviations for the desired confidence
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c Chart
• Type of attributes control chart
–
Discrete quantitative data
• Shows number of nonconformities (defects) in a
unit
–
–
Unit may be chair, steel sheet, car etc.
Size of unit must be constant
• Example: Count # defects (scratches, chips etc.) in
each chair of a sample of 100 chairs; Plot
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c Chart
Control Limits
UCL
c
 c  z
c
LCL
c
 c  z
c
k
c 
 ci
# Defects in
Unit i
i 1
n
# Units
Sampled
©
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Process Control Charts for Attributes
• C-charts
– Used to monitor the counts of noncomformities
per unit.

UCL =
LCL =
2
c
c  3( )
c  3( )
 
c
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What Is
Acceptance Sampling?
• Form of quality testing used for incoming materials or
finished goods
–
e.g., purchased material & components
• Procedure
–
–
–
Take one or more samples at random from a lot (shipment)
of items
Inspect each of the items in the sample
Decide whether to reject the whole lot based on the
inspection results
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What Is an
Acceptance Plan?
• Set of procedures for inspecting incoming materials or
finished goods
• Identifies
–
–
–
Type of sample
Sample size (n)
Criteria (c) used to reject or accept a lot
• Producer (supplier) & consumer (buyer) must negotiate
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