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Quality Control

Quality Control. Chapter 6. Transformation Process. Inputs Facilities Equipment Materials Energy. Outputs Goods & Services. Transformation Process. Variation in inputs create variation in outputs Variations in the transformation process create variation in outputs.

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Quality Control

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  1. Quality Control Chapter 6

  2. Transformation Process • Inputs • Facilities • Equipment • Materials • Energy Outputs Goods & Services Transformation Process • Variation in inputs create variation in outputs • Variations in the transformation process • create variation in outputs

  3. Variation • All processes have variation. • Common cause variation is random variation that is always present in a process. • Assignable cause variation results from changes in the inputs or the process. The cause can and should be identified. • A process is in control if it has no assignable cause variation. • The process is consistent

  4. Statistical Process Control (SPC) • Distinguishes between common cause and assignable cause variation • Measure characteristics of goods or services that are important to customers • Make a control chart for each characteristic • The chart is used to determine whether the process is in control

  5. Capability and Conformance Quality (1) • A process is capable if • It is in control and • It consistently produces outputs that meet specifications. • A capable process produces outputs that have conformance quality (outputs that meet specifications).

  6. Capable Transformation Process • Inputs • Facilities • Equipment • Materials • Energy Outputs Goods & Services that meet specifications CapableTransformation Process

  7. Capability and Conformance Quality (2) • If the process is capable and the product specification is based on current customer requirements, outputs will meet customer expectations.

  8. Product specification that meets current customer requirements Capable Transformation Process + = Customer satisfaction Customer Satisfaction

  9. Objectives of SPC • To determine if the process is in control (predictable) • To determine if the process is capable (in control and meets specifications)

  10. Variable Measures • Continuous random variables • Measure does not have to be a whole number. • Examples: time, weight, miles per gallon, length, diameter

  11. Attribute Measures • Discrete random variables – finite number of possibilities • Also called categorical variables • Different types of control charts are used for variable and attribute measures

  12. Examples of Attribute Measures • Good/bad evaluations • Good or defective • Correct or incorrect • Number of defects per unit • Number of scratches on a table • Opinion surveys of quality • Customer satisfaction surveys • Teacher evaluations

  13. The Mean- measure of central tendency The Range- difference between largest/smallest observations in a set of data Standard Deviation measures the amount of data dispersion around mean Descriptive StatisticsDescribe Results from a Random Sample

  14. Important Figures and Charts • Figures 6.1, 6.2, and 6.3, page 176 • Figure 6.4 page 177 • Control charts, pages 180 and 183 • Figure 6.6, page 184

  15. Control chart for the mean of a product characteristic • Random samples are taken from process output • A process characteristic is measured • Sample means are plotted • Control limits are based on a confidence interval for • the mean • CL = center line (mean line) • LCL = lower control limit UCL = upper control limit

  16. Percentage of values under normal curve m = population mean s = population standard deviation 95.4% of the population is within 2s of the mean 99.74% of the population is within 3s of the mean 99.74% of the population is within the interval from m - 3sto m + 3s We will compute 3s confidence intervals for sample means

  17. Specification Limits • The target is the ideal value • Example: if the amount of beverage in a bottle should be 16 ounces, the target is 16 ounces • Specification limits are the acceptable range of values for a variable • Example: the amount of beverage in a bottle must be at least 15.8 ounces and no more than 16.2 ounces. • Range is 15.8 – 16.2 ounces. • Lower specification limit = 15.8 ounces or LSPEC = 15.8 ounces • Upper specification limit = 16.2 ounces or USPEC = 16.2 ounces

  18. Test for Process Capability(with respect to x ) • The process is in control with respect to x AND • The control limits (LCL and UCL) for x are within the specification limits • Capability index, Cpk is used to determine whether a process is capable

  19. Process is Capable Upper specification limit UCL X LCL Lower specification limit

  20. Process is Not Capable UCL outside specification limits  not capable UCL Upper specification limit X LCL Lower specification limit

  21. Cpk Index • m = process mean (or estimated mean) • LSPEC = lower specification limit • USPEC = upper specification limit Cpk = Smaller {(USPEC- m)/3s, (m – LSPEC)/ 3s} • If Cpk >= 1, process meets customer requirements 99.74% of the time. • To allow for changes in the mean, many firms set a requirement that Cpk >= 1.33.

  22. 3-Sigma Quality • Uses 3-s control limits for x. • Corresponds to 3 defects per 1,000 units. • If a product has 250 parts and each has 3-s control limits, P[at least 1 bad part] = 0.528

  23. 6-Sigma Quality • Use 6-s control limits for x. • Control limits are (X- 2A2R, X + 2A2R). • Corresponds to 3.4 defects per million • If a product has 250 parts and each has 6-s control limits, P[at least 1 bad part] <0.001

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