Chapter 10 MIS 373 Basic Operations Management Additional content from Jeff Heyl Learning Objectives After this lecture students will be able to Explain the need for quality control List and briefly explain the elements of the control process ID: 578219
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Slide1
Quality Control
Chapter 10
MIS 373: Basic Operations Management
Additional content from Jeff HeylSlide2
Learning Objectives
After this lecture, students will be able to
Explain the need for quality control.
List and briefly explain the elements of the control process.Explain Type I and Type II errors
Explain how control charts are used to monitor a process and the concepts that underlie their use.
MIS 373: Basic Operations Management
2Slide3
Background Knowledge
How many of you have had at least one statistics course?
Normal distribution?
Standard deviation?
Z score?Slide4
Motivations
Making Beer Better With Quality and Statistics
http://
videos.asq.org/making-beer-better-with-quality-and-statistics
Quality for Life: Psychic Pizza
http://
videos.asq.org/quality-for-life-psychic-pizza
Slide5
What is Quality Control?
Quality Control
A process that evaluates output relative to a standard and takes corrective action when output doesn’t meet standardsIf results are acceptable no further action is requiredUnacceptable results call for correction
actionPhases of Quality Assurance
MIS 373: Basic Operations Management
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Inspection
Inspection
An appraisal activity that compares goods or services to a standardInspection issues:What to inspect
Count number of times defect occursMeasure the value of a characteristicHow much to inspect and how often
At what points in the process to
inspect
Raw materials and purchased parts
Finished productsBefore a costly operation
Before an irreversible process
Costly
, possibly destructive, and disruptive – non value-adding
Full inspection vs. Sampling
MIS 373: Basic Operations Management
6Slide7
How Much to Inspect
MIS 373: Basic Operations Management
7Slide8
How Much to Inspect
MIS 373: Basic Operations Management
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1 defect in 1 thousand unites
1 defect in 1 million unites
1 defect in 1 billion unites
Trying to catch:Slide9
Centralized vs. On-Site Inspection
Effects on cost and level of disruption are a major issue in selecting centralized vs. on-site inspection
CentralizedSpecialized tests that may best be completed in a labMore specialized testing equipment
More favorable testing environmentOn-SiteQuicker decisions are renderedAvoid introduction of extraneous factorsQuality at the source
MIS 373: Basic Operations Management
9Slide10
Statistical Process Control (SPC)
Quality control seeksQuality of Conformance
A product or service conforms to specificationsA tool used to help in this process:
SPC Statistical evaluation of the output of a processHelps us to decide if a process is “in control” or if corrective action is needed
“In control”
means that the variation in the provided products/services is tolerable
MIS 373: Basic Operations Management
10Slide11
Process Variability
Two basic questions: concerning variability:
Issue of Process ControlAre the variations random? If nonrandom variation is present, the process is said to be unstable.
Variations randomly distributed within control limits
Issue of Process Capability
Given a stable process, is the inherent variability of the process within a range that conforms to performance criteria
?
The control limits satisfy the design specification
MIS 373: Basic Operations Management
11Slide12
Variation
VariationRandom (common cause) variation:
Natural variation in the output of a process, created by countless minor factorsAssignable (special cause) variation: A variation whose cause can be identified.
A nonrandom variationIllustration: M&M’sSizeColor
MIS 373: Basic Operations Management
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Variation
Common cause
Inappropriate proceduresPoor designPoor maintenance of machinesLack of clearly defined
standard operating proceduresPoor working conditions, e.g. lighting, noise, dirt, temperature, ventilationSubstandard raw materialsMeasurement
error
Quality control
error
Vibration in industrial processesAmbient temperature and humidityNormal
wear and tear
Variability in
settings
Special cause
Poor adjustment of equipment
Operator
falls asleep
Faulty controllers
Machine malfunction
Fall of ground
Computer crash
Poor batch of raw material
Power surges
High healthcare demand from elderly people
Broken part
Abnormal traffic (
click fraud
) on web ads
Extremely long lab testing turnover time due to switching to a new computer system
Operator
absent
MIS 373: Basic Operations Management
13Slide14
Sampling and
Sample Distribution
SPC involves periodically taking samples of process output and computing sample statistics:Sample means
The number of occurrences of some outcomeSample statistics are used to judge the randomness of process variation
MIS 373: Basic Operations Management
14Slide15
Sampling and Sample Distribution
Sampling Distribution
A theoretical distribution that describes the random variability of sample statisticsThe normal distribution is commonly used for this purpose
Central Limit TheoremThe distribution of sample averages tends to be normal regardless of the shape of the underlying process distribution
MIS 373: Basic Operations Management
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Demo
Use simulation to test the Central
Limit
TheoremSlide17
The Normal Distribution
MIS 373: Basic Operations Management
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Control Process
Sampling and corrective action are only a part of the control process
Steps required for effective control:Define:
What is to be controlled?Measure: How will measurement be accomplished?Compare: There must be a standard of comparison
Evaluate:
Establish a definition of
out of control
Correct: Uncover the cause of nonrandom variability and fix itMonitor results: Verify that the problem has been eliminated
MIS 373: Basic Operations Management
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Control Charts:
The Voice of the Process
Control ChartA time ordered plot of representative sample statistics obtained from an ongoing process (e.g. sample means), used to distinguish between random and nonrandom
variabilityControl limitsThe dividing lines between random and nonrandom deviations from the mean of the distributionUpper and lower control limits define the range of acceptable variation
Upper control limit
= UCL = mean + z
σ
Lower control
limit
=
LCL
= mean + z
σ
MIS 373: Basic Operations Management
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UCL
LCL
Mean
Control
Chart Example
Each point on the control chart represents a sample of
n
observations
MIS 373: Basic Operations Management
Sample number
| | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12
Variation due to assignable causes
Variation due to assignable causes
Variation due to natural causes
Out of control
Out of control
20Slide21
Errors
Type I error
Narrow control limitsConcluding a process is not in control when it actually is.
Manufacturer’s RiskType II errorWide control limits
Concluding a process is in control when it is not.
Consumer’s Risk
MIS 373: Basic Operations Management
Alarm
No Alarm
Process
In-Control
Process
Out-of-Control
Type I
Type II
n
o-error
n
o-error
21Slide22
Errors Illustration
Q: I always get confused about Type I and II errors. Can you show me something to help me remember the difference?
Source:
Effect
Size FAQs
by
Paul EllisSlide23
Control Charts
MIS 373: Basic Operations Management
Every process displays variation in performance: normal or abnormal
Control charts monitor process to identify abnormal variation
Do not tamper with a process that is “in control” with normal variation
Correct an “out of control” process with abnormal variation
Control charts may cause false alarms – too narrow - (or missed signals – too wide) by mistaking normal (abnormal) variation for abnormal (normal) variation
Out
of Control
In
Control Improved
LCL
UCL
23Slide24
Control
Charts
Data that are measured
“x-bar” charts (Mean)
Used to monitor the central tendency of a process.
R charts
(Range)
Used to monitor the process dispersion
MIS 373: Basic Operations Management
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x
-bar
(sample average) chart Control Limits
MIS 373: Basic Operations Management
)
k = number of samples
n = sample size
commonly: z = 3
25Slide26
X-bar Chart
Mean = 5.5.
STD = 0.4 ft
99.74% within ± 3 STD
(random) 9 students
{6.5, 6.4, 6.6, 6.3, 6.7, 6.5, 6.6, 6.4, 6.5}
each within “normal”
average = 6.5
ft
Sample control limits
tighter than population
UCL=
=
=5.9 ft.
GROUP above “normal” (outside control limits)
MIS 373: Basic Operations Management
5.5
6.7
4.3
5.1
5.9
5.5
6.5
26Slide27
R
-Chart: Control Limits
Range charts or R-charts are used to monitor process dispersion
MIS 373: Basic Operations Management
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Mean and range
chartsMIS 373: Basic Operations Management
(a)
These
sampling distributions result in the charts below
(Sampling mean is shifting upward but range is consistent)
R-chart
(
R
-chart does not detect change in mean)
UCL
LCL
x
-chart
(
x
-chart detects shift in central tendency)
UCL
LCL
28Slide29
Mean and range charts
MIS 373: Basic Operations Management
R
-chart
(
R
-chart detects increase in dispersion)
UCL
LCL
(b)
These sampling distributions result in the charts below
(Sampling mean is constant but dispersion is increasing)
x
-chart
(
x
-chart does not detect the increase in dispersion)
UCL
LCL
29Slide30
Run Tests
Even if a process appears to be in control, the data may still not reflect a random process
Analysts often supplement control charts with a run testRun testA test for patterns in a sequence
RunSequence of observations with a certain characteristicMIS 373: Basic Operations Management
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Run Tests
MIS 373: Basic Operations Management
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A: Above
B: Below
U: Upward
D: DownwardSlide32
Patterns in Control Charts
MIS 373: Basic Operations Management
UCL
Target
LCL
Erratic behavior.
UCL
Target
LCL
Run of 5 above (or below) central line.
UCL
Target
LCL
Two plots very near lower (or upper) control
.
Normal behavior. Process is “in control.”
UCL
Target
LCL
UCL
Target
LCL
One plot out above (or below).
Process
is “out of control.”
UCL
Target
LCL
Trends in either direction, 5 plots.
Progressive
change.
32Slide33
Demo
ASQ Control chart template
http://
asq.org/learn-about-quality/data-collection-analysis-tools/overview/asq-control-chart.xls
Slide34
Key Points
All processes exhibit random variation. Quality control's purpose is to identify a process that also exhibits nonrandom (correctable) variation on the basis of sample statistics (e.g., sample means) obtained from the process.
Control charts and run tests can be used to detect nonrandom variation in sample statistics. It is also advisable to plot the data to visually check for patterns.
MIS 373: Basic Operations Management
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