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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

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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.

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Example: p-chart (Cont.) Sample Number Wrong Account Numbers Sample Number Wrong Account Numbers 115724 21287 319910 42 17 5191115 64123 Total147

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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.

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Example: Lumber yard Lumber yard expects 4 knotholes per eight-foot board. Then,

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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