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

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

The various definitions of quality 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’.

High quality puts costs down and revenue up Quality up Image up Processing time down Inspection and test costs down Rework and scrap costs down Inventory down Service costs down Complaint and warranty costs down Capital costs down Price competition down Sales volume up Productivity up Revenue up Scale economies up Operation costs down The net effect of all these consequences is that revenues increase and costs reduce. In other words, the overall effect of raising quality levels is to increase profitability. Profits up

Perceived quality is governed by the gap between customers’ expectations and their perceptions of the product or service Gap Gap Customers’ expectations for 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’ perceptions of the product or service Customers’ expectations for the product or service Expectations > perceptions Expectations = perceptions Expectations < perceptions So the continuum between quality being perceived as poor through to it being perceived as good is primarily a function of the nature and extent of any gaps between customers’ expectations and their perceptions. The implication of this is that in order to manage customers’ perceived quality levels, both their expectations and their perceptions must be managed. Perceived quality is poor Perceived quality is acceptable Perceived quality is good

The operation’s domain A ‘gap’ model of quality Management’s concept of the product or service The customer’s domain Previous experience Word-of-mouth communications Image of product or service Gap 4 Customer’s expectations concerning a product or service Customer’s perceptions concerning the product or service Gap ? Customer’s own specification of quality The actual product or service Gap 1 Finally, the organization may be influencing the customers’ image of the product or service, for example through its advertising or other promotional activity, in such a way as to conflict with its actual reality. This is called gap 4. The operation’s domain Organization’s specification of quality Gap 3 Gap 2

The perception–expectation gap Action required to ensure high Main organizational perceived quality responsibility Ensure consistency between internal quality specification and the 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 Gap 4 Marketing

Quality characteristics of goods and services 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

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

Quality of conformance 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

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

Process control charting Some aspect of the performance of a process is often measured over time 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?” Time Some measure of operations performance

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. Time Elapsed time of call

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

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

Process control charting If we understand the normal distribution, which describes random variation when the process is operating normally, then we can use the distribution to 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

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

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!

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!

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!

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!

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!

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!

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

Process variation and its effect on process defects per million opportunities (DPMO) LSL USL LSL USL LSL USL LSL 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

Ideal and real operating characteristics In this ideal operating characteristic, the probability of accepting the batch if it contains more than 0.04% defective items is zero, and the probability of accepting the batch if it contains less 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), both type 1 and type 2 errors will occur 0.6 Probability of accepting the batch 0.5 0.4 Type 1 error Type 2 error 0.3 0.2 0.1 AQL LTPD Consumer’s risk (1.0) 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Percentage actual defective in the batch

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.

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.

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.

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.