Outline

1. Outline • Design of Quality Control Systems • Process Quality Control • Attribute Control • Variable Control • Using Control Charts • Continuous Improvement • Quality Control in Industry

2. Designing of Quality Control Systems • QC is the continuous improvement of a stable process • Processes and “internal customers.” • Customer is the next process that receives the work output • “Critical points” and guidelines in identifying them. • Operator evaluation is preferred

3. Steps in Designing QC Systems • Identify critical points • Decide on the type of measurement (variable versus attribute) • Decide on the amount of inspection to be used. • Decide who should do the inspection

4. Inspection before/after production Corrective action during Production (SPC) Quality built into the process Acceptance sampling Process control Continuous improvement The least progressive The most progressive Phases of Quality Assurance Better to prevent defects from occurring than to inspect (too late) and correct defects after production

5. Inputs Transformation Outputs Acceptance sampling Acceptance sampling Process control Inspection/Evaluation • How Much/How Often • Where/When • Who: prevention program along with worker responsibility for Q less expensive

6. Where to Evaluate in the Process (Critical Points) • Raw materials and purchased parts • Eliminate by certifying suppliers • Finished products • Before a costly operation • Before an irreversible process • Before a process that limits the capacity of the entire system - bottleneck

7. TOC

8. Examples of Evaluation Points

9. Operational Definition • A definition that converts a concept into measurable, objective units. • Adjectives like ‘good’, ‘reliable’, ‘uniform’, ‘round’, ‘safe’, have no communicable meaning until they are expressed in operational terms. • Example: small business? Big car?

10. Example: Surgical Start-Time • Definition: Surgical start time is the time the first incision is made • Process: For patients undergoing surgery from August 12-14, the actual start time of the surgery will be recorded by an RN on a check sheet.

11. Process Quality Control • Basic assumptions (tenets) of Process Quality Control: • Every process has random (expected) variation in it. • Production processes are not usually found in a state of control. • “State of Control”; what does it mean?

12. All work occurs in a complex system of interconnected processes • Performance results from the interaction of people and processes • All processes exhibit variation in their indicators of performance • The process is the source of most (85%) of the variation.

13. Total Process materials methods supervision measurement equipment 15% Individual effort training staffing

14. Mean -3 -2 -1 +1 +2+3 68.26% 95.44% 99.74% The Normal Distribution  = Standard deviation

15. Statistical Process Control • Control charts have two lines that indicate the limits (upper and lower) to the common cause variation inherent in the process. • These control limits are calculated from the measurements actually taken on the process.

16. Statistical Process Control • Control limits are not simply theories of what should be happening, nor are they indicators of what you would like to have happen • They simply tell you how much variation you must expect to see in today’s process, and they provide a basis for action when unusual patterns or amounts of variation appear.

17. The case for control charts • While every data set contains noise, some data sets may contain signals; therefore, before you can detect a signal with any given data set, you must filter out the noise. • The SPC chart filters out the noise of routine, expected variation by the construction of limits. Signals of exceptional, unexpected variation are indicated by points which fall outside the limits or by non-random patterns of variation around the center line.

18. The case for control charts • There are 2 mistakes that one can make when interpreting data: • To interpret noise as if it were a signal • To miss a signal when one is present • The three-sigma limits of a control chart minimize the economic consequences of both types of mistakes.

19. The case for control charts • Unless, and until, you make the distinction between signals and noise, you will remain unable to properly analyze and interpret data. Before you can use data to justify any action, you should be able to detect a potential signal within the data. • Process behavior charts (SPC) are the simplest method that has ever been invented for separating potential signals from probable noise.

20. Three-sigma limits • Control charts will work well even when the data are not normally distributed. • When it comes to the use of three-sigma limits, the Central Limit Theorem is irrelevant. Regardless of the distribution of the sample statistic, virtually all of the values will fall within three-sigma limits whenever the process displays statistical control. • The purpose of control charts is to detect the presence of uncontrolled variation. Uncontrolled variation will undermine any notion of a distribution which we may try to attach to data.

21. Three-sigma limits • Three sigma-limits are so far out in the tail of a distribution that the shape of that distribution is essentially irrelevant. • Reasonable limits may be computed even if you have small amounts of data. When more data become available, recalculate the limits.

22. Process Control Chart (Figure 9.1) y Time x

23. Control Charts UCL Nominal LCL

24. UCL CL Quality Measurement LCL 1 2 3 4 5 6 Sample Quality Control Chart (Figure 9.2) Stop the process; look for assignable cause Good? Bad? Stop the process; look for assignable cause

25. Control Charts UCL Good? Nominal Bad? LCL Assignable causes likely 1 2 3 Samples

26. Detecting a lack of control: 7-7-1 rule • Significant change 1 – Run: Seven data points in a row on one side of the average need to be investigated. • Significant change 2 – Trend: Seven data points in a row going steadily up or down need to be investigated. • Significant change 3 – Spike: One data point outside the UCL or LCL needs to be investigated.

27. When should you recalculate your control limits? • Judgment call. Limits should be used to characterize the process in a realistic manner. Consider the following questions: • Do the data display a distinctly different kind of behavior than in the past? • Is the reason for this change in behavior known? • Is the new process behavior desirable? • Is it intended and expected that the new behavior will continue?

28. If the answer to all four questions is yes, then revise the limits based on data collected since the change in the process. • If the answer to question 1 is no, then there should be no need for new limits. • If the answer to any of the questions 2-4 is no, then you should be looking for special causes instead of tinkering with the limits. • Remember, the objective is to take the right action on the process, not to find the “right numbers”.

29. Using Control Charts for Process Improvement • Measure the process • When problems are indicated, find the assignable cause • Eliminate problems, incorporate improvements (system) • Repeat the cycle (PDSA)

30. Measurement and Data Collection • The goal of data collection is to gain an objective view of the process under investigation and to understand how it is performing over time with respect to the operational definition of one key quality characteristic.

31. Extra notes on SPC • Review the extra notes for chapter 9. • V.O.P. • Tampering: making random changes to a stable process • Common/special causes of variation • Stable and unstable processes • SPC • V.O.C. • Improving stable/unstable processes

32. Extra Notes: Joiner • Voice of the Process (VOP): data gathered through measurement is telling us what the process is actually capable of producing. This helps us to understand and reduce the variation in the process. • Failure to understand variation is the central problem of management

33. Variation • Controlled variation is characterized by a stable and consistent pattern of variation over time. Such variation is due to common causes. • Uncontrolled variation is characterized by a pattern of variation that changes over time in an unpredictable manner. These unpredictable changes in the pattern of variation are due to special causes.

34. Variation • Two types • Common cause: the variation a process would exhibit if it were behaving normally. • Special cause: results from sources external to the process • A process can be off-target if its average is not at the desired level

35. Common cause variation • Causes that are inherent in the process. They affect everyone working in the process and all outputs of the process. Every process has common causes. Common cause variation is typically due to a large number of small sources of variation. Working to reduce or eliminate common causes requires making fundamental changes to the process itself.

36. Special cause variation • Those causes that are not a part of the process all the time or do not affect everyone working in the process. Special causes arise because of a specific circumstance, for example, from differences in: equipment, workers (training), materials (suppliers), methods, and each factor over time.

37. Stable vs unstable process • A stable process is: consistent, predictable, affected by only common causes. • An unstable process is: inconsistent, unpredictable, out-of-control, affected by both common and special causes.

38. Using SPC, managers can determine whether variation is a system is within expected parameters (in which case they should not tamper with the system) or whether the variation is beyond expected parameters and in need of action. • Tampering: making random changes to a stable system • SPC answers the question “is this a stable process?” A stable process is not necessarily a good process. A stable process can produce 100% off-spec product. Likewise, it can be unstable and produce 100% in-spec product.

39. Control charts (SPC) are needed because they: • •Operationally define common versus special cause variation • •They work. They are based on statistical theory, not anecdotal evidence. They help establish management by fact, as opposed to "seat of the pants" management. • They are used to: • Bring a process into control (remove special causes) • Maintain a process in control • 3. Improve a stable process (remove common causes) • 4. Help determine process capability (to meet specifications)

40. Voice of the customer: External customers are the most important judges of quality. To deliver world-class quality to customers, we must understand their perceptions of value. We must help employees understand the customer's target. We must develop methods for reliably getting closer and closer to the target, reducing variation around the target.

41. When people are pressured to meet a target value, there are three ways they can proceed: • They can work to improve the system • They can distort the system • They can distort the data • (e.g. Andersen consulting & Enron, Worldcom) • Comparing numbers to specifications will not lead to improvement of the process. Specifications reflect the voice of the customer, not the voice of the process. If we only compare data to specifications, we are unable to improve the system and are left with only the last two ways of meeting a goal. Distortion is always easier than working to improve the system.

42. Improving an unstable process: • •Get timely data so special causes are signaled quickly. • •Put in place an immediate remedy to contain any damage (angry customers, defective products, blown schedules). • •Search for the cause - see what was different. Keep asking "why, why, why?" until you get the deepest cause. • •Develop a long-term remedy.

43. Improvement Recall that: Quality is continuous improvement of a stable process/system (Deming)

44. Improvement • Improvement of a stable process • Make the process more predictable by reducing the variability in the process • Shift the average toward a target value expressed by the customer • Both require fundamental changes to the process, which first requires fundamental understanding of the process. • PTSA

45. Improving a stable process • To reduce common cause variation, we need strategies that help us deeply understand the workings and interactions of factors that are present all the time in the system: • Stratify • Disaggregate • Experiment

46. Stratify: sort the data into groups or categories based on different factors; look for patterns in the way the data points cluster or do not cluster; identify contradiction that we can study and explain. • Must have information on conditions related to the data: e.g. shift, day of the week, floor or department, equipment, employee, etc. • Will probably need to collect new data.

47. Improving a stable process • 2. Disaggregate: divide the process into component pieces and investigate the pieces. This is not contrary to system thinking if the disaggregation is temporary and done for the purpose of improving the system. Only works when each piece of the process has an aim tied to serving the next step in the process and is consistent with the overall aim of the process. Patient food service process (timeliness) Order Preparation Delivery Clean-up

48. Improving a stable process • 3. Experiment: Using knowledge gained from stratification, disaggregation, and other quality improvement tools (e.g. cause-effect diagrams, why?/why? Charts, brainstorming) make planned changes to a process and learn from the effects. This requires a deep understanding of customer needs and an in-depth knowledge of how a process does or should work. Use PTSA framework.

49. Q.C. and Variation Understand Control Reduce VOP

50. Quality Improvement: Alignment of the Voice of the Process (VOP) with the Voice of the Customer (VOC) VOP VOC