Quality Planning and Control: Achieving Consistent Excellence

1. Chapter 17 Quality planning and control Source: Archie Miles

2. Quality planning and control Quality planning and control Operations strategy The market requires …consistent quality of products and services Operations management Improvement Design The operation supplies … the consistent delivery of products and services at specification or above Planning and control

3. The transcendent approach views quality as synonymous with innate excellence. The manufacturing-based approach assumes quality is all about making or providing error-free products or services. The user-based approach assumes quality is all about providing products or services that are fit for their purpose. The product-based approach views quality as a precise and measurable set of characteristics. The value-based approach defines quality in terms of ‘value’. The various definitions of quality

4. Processing time down Rework and scrap costs down Image up Service costs down Inventory down Inspection and test costs down Sales volume up Capital costs down Complaint and warranty costs down Price competition down Scale economies up Productivity up Operation costs down Revenue up High quality puts costs down and revenue up Quality up Profits up

5. Gap Gap Perceived quality is governed by the gap between customers’ expectations and their perceptions of the product or service Customers’ expectations for the product or service Customers’ perceptions of the product or service Customers’ perceptions of the product or service Customers’ expectations for the product or service Customers’ perceptions of the product or service Customers’ expectations for the product or service Expectations > perceptions Expectations = perceptions Expectations < perceptions Perceived quality is poor Perceived quality is good Perceived quality is acceptable

6. Customer’s perceptions concerning the product or service Customer’s expectations concerning a product or service The customer’s domain Gap 4 Customer’s own specification of quality The actual product or service Gap 1 Organization’s specification of quality Management’s concept of the product or service Gap 3 The operation’s domain A ‘gap’ model of quality Word-of-mouth communications Previous experience Image of product or service Gap ? Gap 2

7. The perception–expectation gap Action required to ensure high Main organizational perceived quality responsibility Ensure consistency betweeninternal quality specification andthe expectations of customers Marketing, operations, product/service development Gap 1 Marketing, operations, product/service development Ensure internal specification meets its intended concept of design Gap 2 Ensure actual product or service conforms to internally specified quality level Gap 3 Operations Ensure that promises made to customers concerning the product or service can really be delivered Marketing Gap 4

8. Functionality – how well the product or service does the job for which it was intended Appearance – the aesthetic appeal, look, feel, sound and smell of the product or service Reliability – the consistency of performance of the product or service over time Durability – the total useful life of the product or service Recovery – the ease with which problems with the product or service can be rectified or resolved Contact – the nature of the person-to-person contacts that take place Quality characteristics of goods and services

9. Attribute and variable measures of quality Attributes Variables Measured on a continuous scale Defective or not defective? Light bulb works or does not work Diameter of bulb Number of defects in a turbine blade Length of bar

10. Quality fitness for purpose Reliability ability to continue working at accepted quality level Quality of design degree to which design achieves purpose Quality of conformance faithfulness with which the operation agrees with design Variables things you can measure Attributes things you can assess and accept or reject Quality

11. Some measure of operations performance Time Process control charting Some aspect of the performance of a process is often measured over time Question: “Why do we do this?”

12. Question: “How do we know if the variation in process performance is ‘natural’ in terms of being a result of random causes, or is indicative of some ‘assignable’ causes in the process?” Some measure of operations performance Time Process control charting Some aspect of the performance of a process is often measured over time

13. Elapsed time of call Time Process control charting The last point plotted on this chart seems to be unusually low. How do we know if this is just random variation or the result of some change in the process which we should investigate? Some kind of ‘guidelines’ or ‘control limits’ would be useful.

14. 2.2 2.2 2.2 2.2 2.2 3.6 3.6 3.6 3.6 3.6 0.8 0.8 0.8 0.8 0.8 After the first sample After the second sample Fitting a normal distribution to the histogram of sampled call times By the end of the first day By the end of the second day Process control charting

15. 99.7% of points –3 standard deviations +3 standard deviations 95.4% of points –2 standard deviations +2 standard deviations –1 standard deviation +1 standard deviation 68% of points A standard deviation  = sigma Process control charting Frequency 40 100 160 Elapsed time of call (seconds) The chances of measurement points deviating from the averageare predictable in a normal distribution

16. Process control charting If we understand the normal distribution, which describes random variationwhen the process is operating normally, then we can use the distributionto draw the control limits. In this case the final point is very likely to be caused by an ‘assignable’ cause,i.e. the process is likely to be out of control. Elapsed time of call Time

17. X X Process variability X X A P A P On/off target – accuracy: A Scatter – precision: P A P A P

18. Process control charting In addition to points falling outside the control limits, other unlikely sequences of points should be investigated. UCL C/L LCL Alternating and erratic behaviour – investigate!

19. Process control charting In addition to points falling outside the control limits, other unlikely sequences of points should be investigated. UCL C/L LCL Suspiciously average behaviour – investigate!

20. Process control charting In addition to points falling outside the control limits, other unlikely sequences of points should be investigated. UCL C/L LCL Two points near control limit – investigate!

21. Process control charting In addition to points falling outside the control limits, other unlikely sequences of points should be investigated. UCL C/L LCL Five points on one side of centre line – investigate!

22. Process control charting In addition to points falling outside the control limits, other unlikely sequences of points should be investigated. UCL C/L LCL Apparent trend in one direction – investigate!

23. Process control charting In addition to points falling outside the control limits, other unlikely sequences of points should be investigated. UCL C/L LCL Sudden change in level – investigate!

24. Process distribution A Process distribution B Process distribution A Process distribution B A A B B Time Low process variation allows changes in process performance to be readily detected Time

25. Process variation and its effect on process defects per million opportunities (DPMO) Process variation Process variation Process variation Process variation LSL LSL LSL LSL USL USL USL USL 3 sigma process variation = 66800 defects per million opportunities 4 sigma process variation = 6200 defects per million opportunities 5 sigma process variation = 230 defects per million opportunities 6 sigma process variation = 3.4 defects per million opportunities

26. Type 1 error Type 2 error Ideal and real operating characteristics In this ideal operating characteristic,the probability of accepting the batchif it contains more than 0.04% defective items is zero, and the probability of accepting the batch if it containsless than 0.04% defective items is 1 Producer’s risk (0.05) 1.0 0.9 0.8 0.7 In this real operating characteristic (where n = 250 and c = 1), bothtype 1 and type 2 errors will occur 0.6 Probability of accepting the batch 0.5 0.4 0.3 0.2 0.1 AQL LTPD Consumer’s risk (1.0) 0 0 0.01 0.02 0.03 0.04 0.05 0.06 0.08 0.07 Percentage actual defective in the batch

27. Key Terms Test Quality Consistent conformance to customers’ expectations. Quality characteristics The various elements within the concept of quality, such as functionality, appearance, reliability, durability, recovery, etc. Quality sampling The practice of inspecting only a sample of products or services produced rather than every single one.

28. Key Terms Test Statistical process control (SPC) A technique that monitors processes as they produce products or services and attempts to distinguish between normal or natural variation in process performance and unusual or ‘assignable’ causes of variation. Acceptance sampling A technique of quality sampling that is used to decide whether to accept a whole batch of products (and occasionally services) on the basis of a sample; it is based on the operation’s willingness to risk rejecting a ‘good’ batch and accepting a ‘bad’ batch. Control charts The charts used within statistical process control to record process performance.

29. Key Terms Test Process capability An arithmetic measure of the acceptability of the variation of a process. Control limits The lines on a control chart used in statistical process control to indicate the extent of natural or common-cause variations; any points lying outside these control limits are deemed to indicate that the process is likely to be out of control. Quality loss function (QLF) A mathematical function devised by Genichi Taguchi that includes all the costs of deviating from a target performance.

30. Key Terms Test Six Sigma An approach to improvement and quality management that originated in the Motorola Company but was widely popularized by its adoption in the GE Company in America. Although based on traditional statistical process control, it is now a far broader ‘philosophy of improvement’ that recommends a particular approach to measuring, improving and managing quality and operations performance generally. Zero defect The idea that quality management should strive for perfection as its ultimate objective, even though in practice this will never be reached.