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IPPTChap009.pdf
Chapter 9:
Quality Control and Improvement
Operations Management in the
Supply Chain: Decisions and Cases,
6th edition
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin
9-2
Chapter 9 Outline • Design of Quality Control Systems
• Process Quality Control
• Attribute Control Chart
• Variables Control Chart
• Using Control Charts
• Process Capability
• Continuous Improvement
• Six Sigma
• Lean and Six Sigma
• Quality Control and Improvement in Industry
9-3
Design of Quality Control Systems
• Break down production process into subprocesses and
identify internal customers.
• Identify critical control points where inspection or
measurement should take place.
• Use operator inspection when possible, placing
responsibility for quality on workers.
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Steps in Designing QC Systems
Identify critical points for inspection
• Incoming materials & services
• Work in process
• Finished product or service
Decide on the type of measurement
• Variables: continuous scale
• Attributes: discrete count, or good/bad
Decide on the amount of inspection to be used
Decide who should do the inspection
9-5
Types Of Measurement
• Variables measurement
• Product/service characteristic that can be measured
on a continuous scale: Length, size, weight, height, time,
velocity, temperature
• Attributes measurement
• Product/service characteristic evaluated with a discrete
choice: Good/bad, yes/no, count of defects
9-6
Process Quality Control
• Principles of Process Control:
• Every process has random variation.
• Production processes are not usually in a state of control.
• “State of Control” - What does it mean?
• Unnecessary variation has been eliminated.
• Remaining variation is due to random causes.
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Process Quality Control • Assignable (special) cause variation
• Can be identified and corrected
• Could be due to machine, worker, materials, etc.
• Common (random) cause variation
• Reasonable, acceptable variation
• Within 3 standard deviations ( 3) of mean
• Cannot be changed unless process is redesigned
9-8
Quality Control Chart
x
y
Time
Upper control limit (UCL)
Center line (CL)
Lower control limit (LCL)
Average + 3
standard
deviations
Quality
measurement
average
Average - 3
standard
deviations
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Normal Distribution on Control Chart
UCL
Mean
LCL
Samples
Assignable causes likely
1 2 3
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Quality Control Chart
Temperature & Humidity Control in a Museum
Monitor for unexpected readings
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Attribute Control (3)
(1 ) 3
p p p
n
• p-chart
• Calculate center line = mean proportion defective
across many samples
• Calculate upper and lower control limits
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Variables Control (3)
RDLCL 3
• x-chart • Calculate center line = mean of sample means
• Calculate upper and lower control limits
• R-chart • Calculate center line = mean of sample ranges
• Calculate upper and lower control limits
RDUCL 4
RAx 2
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Using Quality Control Charts
• If an observation (data point) is outside 3 and/or a
pattern is detected, the process is NOT in a state of
control.
• Very likely something is wrong.
• Conclude assignable cause of variation may exist.
• Signal to take action to eliminate assignable cause – find
it, understand its cause, fix it!
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Using Quality Control Charts
• How large should sample be?
• Large enough to detect defects
• Variables can use smaller sample sizes
• How often to sample?
• Depends upon cost, production rate
• Process control vs. Process capability
• Is the process capable of producing to specification?
• Are the specifications appropriate?
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Process Capability Index Examples (Figure 9.3)
F re
q u e n c y
Process measure Process measure
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Computation of Cpk (Figure 9.4) F
re q u e
n c y
Process measure Process measure
9-17
Continuous Improvement • When process is not meeting customer specifications.
• Work on processes with strategic importance and low
process capability first!
• Use seven tools of quality control.
9-18
Seven Tools of Quality Control (Figure 9.5)
• Flowchart
• Check Sheet
• Histogram
• Pareto Chart
• Cause-and- Effect (fishbone, Ishikawa) Diagram
• Scatter Diagram
• Control Chart
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Seven Tools of Quality Control
• A battery manufacturer in NW Ohio in
6 weeks, using only the 7 tools of
quality, decreased defectives from 7.2
per 100 to 2.6 per 100.
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Pareto Analysis
Table 9.4
Defect Items
# of
Defectives
Precent
Defective
Cumulative
Percentage
Loose connections 193 46.8% 46.8%
Cracked connectors 131 31.8% 78.6%
Fitting burrs 47 11.4% 90.0%
Improper torque 25 6.1% 96.1%
O-rings missing 16 3.9% 100.0%
Total 412 100.0%
Note: 40% (2) of the sources cause 78.6% of the defects
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Pareto Diagram (Figure 9.6)
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
120.0%
0
50
100
150
200
250
Loose connections
Cracked connectors
Fitting burrs Improper torque O-rings missing
P e r c e n
ta g
e
# o
f D
e fe
c ti
v e s
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Cause-and-Effect (fishbone, Ishikawa) Diagram (Figure 9.7)
Loose
connections
Workers
Material
connectors
Inspection Tools
Content Nuts
Knowledge Fatigue
Training
Hose
Size
Surface defect
SizeSmall
Large
Judgment
Measurement
Measuring
tools Errors
Inspector
Experience
Training
Wear
Adjustment
Torque
Air pressure
9-23
Six-Sigma Quality
• Pioneered by Motorola in 1980s
• 3.4 defects per million
• Most process are 4 sigma, e.g., payroll, prescriptions, baggage handling, restaurant bills
• Airline fatalities are 6.4 sigma
• IRS tax advice is less than 2 sigma
9-24
Six Sigma Quality
• Process Improvement steps (DMAIC):
1. Define – select process
2. Measure – measure relevant variables
3. Analyze – determine root causes and alternatives
4. Improve – change process
5. Control – ensure improvements not lost over time
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Six Sigma Quality • Uses project/team approach
• Strategic process is selected for improvement
• Cross-functional team is formed
• ‘Black belt’ leader is chosen
• The team uses the DMAIC method (and quality tools)
to find root causes and improving the process
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Lean and Six Sigma
• Complementary approaches to improvement:
• Lean seeks to eliminate waste
• Six sigma seeks to eliminate defects
• Six sigma organization is more formal and training
intensive
• Six sigma is longer-term project focused, with major
financial impacts
• Lean is more broad based, quick projects with less
impact
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Quality Control and Improvement in Industry
• 75% of U.S. firms use process control charts
• More use of variable (x-bar and R) charts than attribute
(p) charts (sample size requirements)
• Six Sigma has broad acceptance
• Quality control in services (SERVQUAL)
• Attention to quality is now pervasive outside of
operations function
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Chapter 9 Summary
• Design of Quality Control Systems
• Process Quality Control
• Attribute Control Chart
• Variables Control Chart
• Using Control Charts
• Process Capability
• Continuous Improvement
• Six Sigma
• Lean and Six Sigma
• Quality Control and Improvement in Industry
