Operations and Supply Chain Management MGMT 3306 Lecture 04
Instructor: Dr. Yan Qin
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Outline Managing Quality What is Quality Cost of Quality (COQ)
International Quality Standards 7 Concepts of Total Quality
Management Statistical Process Control Variations in processes
Process capability Process control charts
Slide 3
What is Quality? David Garvin, in his book Managing Quality,
summarized five principal approaches to defining quality:
Transcendental view: I cant define it, but I know when I see it;
Product-based view: Quality is viewed as quantifiable and
measurable characteristics or attributes; (Design quality)
User-based view: Quality is an individual matter, and products that
best satisfy their preferences are those with the highest
quality;
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What is Quality? Five principal approaches to defining quality
(Cont.) Manufacturing-based view: conformance to requirements
(Conformance quality) Value-based view: Quality is defined in terms
of costs and prices as well as a number of other attributes.
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Two Ways Quality Improves Profitability Improved Quality
Increased Profits Increased productivity Lower rework and scrap
costs Lower warranty costs Reduced Costs via Improved response
Flexible pricing Improved reputation Sales Gains via
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Quality Program Fundamental to any quality program is 1.the
determination of quality specifications, and 2.the costs of
achieving (or not achieving) those specifications.
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Quality Specification Design Quality measures how well a
product meets customer expectation. Specifications of Design
Quality Functions/features intended to deliver
Reliability/durability Serviceability Aesthetics Conformance
Quality measures how well design specifications are met in
production.
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Cost of Quality Cost of Quality refers to all of the costs
attributable to the production of quality that is not 100% perfect.
It is estimated that the cost of quality is between 15% and 20% of
every sales dollar. (Philip Crosby:
Process Control Charts Sampling by Attributes (Go or no-go
information) Defectives refers to the acceptability of product
across a range of characteristics. Defects refers to the number of
defects per unit which may be higher than the number of defectives.
Tools: p-chart (1 defect for each unit), c-chart (>1 defect each
unit) Sampling by Variable (Continuous) Amount of deviation from a
set standard for a single variable. Tools: X-bar chart and R
chart
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p-charts In p-charts, we create the control limits for the
proportion of defects. We call the limits Upper Control Limit (UCL)
and Lower Control Limit (LCL). Plot the sample points and see if
they fall within the control limits. If a sample point falls within
the control limits, it means that the sample is under statistical
control. We usually use 3-sigma control limits.
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Using p-charts Let z be the number of standard deviations. For
99.7% confidence z =3. For 99% confidence, z = 2.58. We usually
just set z = 3. Fraction defective
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Example: p-chart Hometown Bank is concerned about the number of
wrong customer account numbers recorded. Each week a random sample
of 2,500 deposits is taken and the number of incorrect account
numbers is recorded The results for the past 12 weeks are shown in
the table on the next slide.(We therefore have 12 samples in this
case.) Is the booking process out of statistical control? Use
three-sigma control limits.
Example: Solution Step 1 147 12(2,500) = = 0.0049 p = Total
defectives Total number of observations p = p (1 p)/n = 0.0049(1
0.0049)/2,500 = 0.0014 UCL = p + z p LCL= p z p = 0.0049 +
3(0.0014) = 0.0091 = 0.0049 3(0.0014) = 0.0007 Step 1:Using this
sample data to compute parameters
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Solution Step 2 & 3 Step 2:Calculate the sample proportion
defective. Step 3:Plot each sample proportion defective on the
chart,
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Solution Step 3 Fraction Defective Sample Mean UCL
LCL.0091.0049.0007 |||||||||||| 123456789101112 X X X X X X X X X X
X X The p-Chart Showing sample defective 7 is Out of Control
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Using c-chart With p charts, each item can only have one
defect. With a c chart, each item can have multiple defects.
Control chart factors Sample Size Mean Factor Upper Range Lower
Range n A 2 D 4 D 3 21.8803.2680 31.0232.5740 4.7292.2820
5.5772.1150 6.4832.0040 7.4191.9240.076 8.3731.8640.136
9.3371.8160.184 10.3081.7770.223 12.2661.7160.284
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Example: X-bar and R charts The Watson Electric Company
produces incandescent light bulbs. The following data on the number
of lumens for 40-watt light bulbs were collected when the process
was in control. a. Calculate control limits for an R-chart and an
X-chart. b. Since these data were collected, some new employees
were hired. A new sample obtained the following readings: 570, 603,
623, and 583. Is the process still in control? Observation
Sample1234 1604612588600 2597601607603 3581570585592 4620605595588
5590614608604
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Example: Solution SampleR 160124 260210 358222 460232 560424
Total2,991112 Average
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Example: Solution (Cont.) The R-chart b.The range is 53 (or 623
570), which is outside the UCL for the R-chart. A search for
assignable causes inducing excessive variability must be conducted.
2.282*22.4 = 51.12 0*22.4 = 0 598.2 + 0.729*22.4 = 614.53 598.2
0.729*22.4 = 581.87
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Sample Control charts Nominal UCL LCL Variations Sample number
Normal No action
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Sample control chart Nominal UCL LCL Variations Sample number
Run Take action
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Sample control chart Nominal UCL LCL Variations Sample number
Sudden change Monitor
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Sample control charts Nominal UCL LCL Variations Sample number
Exceeds control limits Take action