Principles of operations management 9th by heizer and render chapter 06s

6

SUPPLEMENT

Statistical
Process Control

PowerPoint presentation to accompany
Heizer and Render
Operations Management, Eleventh Edition
Principles of Operations Management, Ninth Edition
PowerPoint slides by Jeff Heyl
© 2014
© 2014
Pearson
Pearson
Education,
Education,
Inc.Inc.

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Outline
►
►
►

Statistical Process Control
Process Capability
Acceptance Sampling

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Learning Objectives
When you complete this supplement
you should be able to :
1. Explain the purpose of a control chart
2. Explain the role of the central limit theorem
in SPC
3. Build

x-charts and R-charts

4. List the five steps involved in building
control charts
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Learning Objectives
When you complete this supplement
you should be able to :
5. Build p-charts and c-charts
6. Explain process capability and compute Cp
and Cpk
7. Explain acceptance sampling

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Statistical Process Control
The objective of a process control
system is to provide a statistical
signal when assignable causes of
variation are present

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Statistical Process Control
(SPC)
►

Variability is inherent
in every process
►

Natural or common
causes

►

Special or assignable
causes

►

Provides a statistical signal when assignable
causes are present

►

Detect and eliminate assignable causes of
variation

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Natural Variations
►

Also called common causes

►

Affect virtually all production processes

►

Expected amount of variation

►

Output measures follow a probability
distribution

►

For any distribution there is a measure of
central tendency and dispersion

►

If the distribution of outputs falls within
acceptable limits, the process is said to be
“in control”

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Assignable Variations
►

Also called special causes of variation
►

Generally this is some change in the process

►

Variations that can be traced to a specific
reason

►

The objective is to discover when assignable
causes are present
►

Eliminate the bad causes

►

Incorporate the good causes

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Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps
Each of these
represents one
sample of five
boxes of cereal

(a) Samples of the product,
say five boxes of cereal
taken off the filling machine
line, vary from each other
in weight
Frequency

# #
# # #
# # # #
# # # # # # #
#

Figure S6.1
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# # # # # # # # #

Weight
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Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps

Frequency

(b) After enough samples
are taken from a stable
process, they form a
pattern called a
distribution

The solid line
represents the
distribution

Weight
Figure S6.1
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Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps
(c) There are many types of distributions, including the normal (bellshaped) distribution, but distributions do differ in terms of central
tendency (mean), standard deviation or variance, and shape

Frequency

Figure S6.1

Central tendency

Weight
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Variation

Weight

Shape

Weight
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Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps

Prediction

Frequency

(d) If only natural causes of
variation are present,
the output of a process
forms a distribution that
is stable over time and is
predictable

e

Tim

Weight
Figure S6.1
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Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps

Prediction

Frequency

(e) If assignable causes are
present, the process output
is not stable over time and
is not predicable

?
?? ??
?
?
?
?
?

?
?
?
?
??
??
?

e
Tim

Weight
Figure S6.1
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Control Charts
Constructed from historical data, the
purpose of control charts is to help
distinguish between natural variations
and variations due to assignable causes

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Process Control

Frequency
Lower control limit

(a) In statistical
control and capable
of producing within
control limits
Upper control limit

(b) In statistical control
but not capable of
producing within
control limits

(c) Out of control
Size
(weight, length, speed, etc.)
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Figure S6.2
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Control Charts for Variables
►

Characteristics that can take any real value

►

May be in whole or in fractional numbers

►

Continuous random variables

x-chart tracks changes in the central
tendency

s
R-chart indicates a gain or loss eoftwo chart
es
d
h
e
T
s
u
dispersion
e
b
t
s
u
m
r
e
h
t
e

to g
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Central Limit Theorem
Regardless of the distribution of the population,
the distribution of sample means drawn from the
population will tend to follow a normal curve
1) The mean of the sampling
distribution will be the same as the
population mean 
2) The standard deviation of the
sampling distribution (  x) will
equal the population standard
deviation () divided by the
square root of the sample size, n
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=

x

x 


n
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Population and Sampling
Distributions
Population
distributions

Distribution of
sample means
=

Mean of sample means = x

Beta

Standard
deviation of
the sample
means

Normal

x 


n

Uniform
|

|

|

3 x 2 x 1 x

|

=
x

|

|

1 x 2 x 3 x

95.45% fall within ± 2 x
99.73% of all x
fall within ± 3 x
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|

Figure S6.3

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Sampling Distribution
Figure S6.4

Sampling
distribution of
means
Process
distribution of
means

=

x= 
(mean)
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Setting Chart Limits
For x-Charts when we know 
=
Lower control limit (UCL)  x
 z x
=
Upper control limit (UCL)  x
 z x
Where

=
x

= mean of the sample means or a target value set
for the process
z = number of normal standard deviations
 / n
x = standard deviation of the sample means

 =
n =

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population (process) standard deviation
sample size

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Setting Control Limits
▶ Randomly select and weigh nine (n = 9) boxes
each hour
Average weight in 17 13 16 18 17 16 15 17 16

 16.1 ounces
the first sample
9
WEIGHT OF SAMPLE

WEIGHT OF SAMPLE

WEIGHT OF SAMPLE

HOUR

(AVG. OF 9
BOXES)

HOUR

(AVG. OF 9
BOXES)

HOUR

(AVG. OF 9
BOXES)

1

16.1

5

16.5

9

16.3

2

16.8

6

16.4

10

14.8

3

15.5

7

15.2

11

14.2

4

16.5

8

16.4

12

17.3

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Setting Control Limits
12

Avg of 9 boxes


Average mean  = i1
 x
of 12 samples

12





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









=
 16 ounces
x
n 9
z 3
  1 ounce

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Setting Control Limits
12

Avg of 9 boxes


Average mean  = i1
 x
of 12 samples

12














=
 16 ounces
x
n 9
z 3
  1 ounce

 1 
 1
UCL x  x  z x  16  3 
 16  3   17 ounces
3
 9
=

 1 
 1
LCL x  x z x  16  3 
 16  3   15 ounces

3
 9
=

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Setting Control Limits
Control Chart
for samples
of 9 boxes

Variation due
to assignable
causes

Out of
control

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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Out of
control

Variation due
to assignable
causes

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Setting Chart Limits
For x-Charts when we don’t know 
=
UCL x  x
 A2 R
=
LCL x  x
 A2 R
n

where

R

=

x

R

i

i1

n
samples
A2 =
S6.1
=

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

average range of the

control chart factor found in Table
mean of the sample means
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