1. Statistical Process Control04/03/961 What is Variation? Less Variation = Higher Quality

2. Statistical Process Control04/03/962 What is Variation? How the Loss Function Affects Product Both are within spec Which is more desirable? LSL USL

3. Statistical Process Control04/03/963 Examining Variation Definition A Stable Process has the same normal distribution at all times. A stable process is In Control A stable process still has variation

4. Statistical Process Control04/03/964 Examining Variation Stable Process Normal distribution at all times Prediction Time

5. Statistical Process Control04/03/965 Examining Variation Common Causes The cause of variations in a stable process is called a Common Cause. A common cause is a natural cause of variation in the system.

6. Statistical Process Control04/03/966 Examining Variation 3 Machine vibration 3 Temperature fluctuations 3 Slight variation in raw materials 3 Human variation in setting control dials Common Cause Examples

7. Statistical Process Control04/03/967 Examining Variation Tools for Examining Stability Trend Chart : A plot showing the behavior of a process over time. 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time

8. Statistical Process Control04/03/968 Examining Variation Tools for Examining Stability Histogram : A barchart showing the distribution of the process. Percentage 0 5 10 15 20 25 30 35 10203040506070 0 8090 100110120130140150160170180190200 Thickness

9. Statistical Process Control04/03/969 Examining Variation Thickness Activity: Comparing stable processes Which process has better quality? 150 140 130 120 110 100 90 80 70 60 50 150 140 130 120 110 100 90 80 70 60 50 Thickness Sequence 0510152025 Sequence 0510152025 A B

10. Statistical Process Control04/03/9610 Examining Variation Activity: Comparing stable processes (cont’d) Which process has better quality? Thickness 150 140 130 120 110 100 90 80 70 60 50 150 140 130 120 110 100 90 80 70 60 50 Thickness Sequence 0510152025 Sequence 0510152025 A B

11. Statistical Process Control04/03/9611 Examining Variation Unstable Process Any process that is not stable is called an unstable or out-of-control process. Prediction Time ??

12. Statistical Process Control04/03/9612 Examining Variation Kinds of Instability: Excursions 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time

13. Statistical Process Control04/03/9613 Examining Variation Kinds of Instability: Shifts 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time

14. Statistical Process Control04/03/9614 Examining Variation Kinds of Instability: Drifts 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time

15. Statistical Process Control04/03/9615 Examining Variation Kinds of Instability: Cycles 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time

16. Statistical Process Control04/03/9616 Examining Variation Kinds of Instability: Chaos 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time

17. Statistical Process Control04/03/9617 Examining Variation Special Causes Anything that causes variations that are not part of the stable process is called a special cause, assignable cause, or unnatural cause.

18. Statistical Process Control04/03/9618 Examining Variation 3 Batch of defective raw material 3 Faulty set-up 3 Human error 3 Incorrect recipe 3 Blown gasket 3 Earthquake Examples of Special Causes

19. Statistical Process Control04/03/9619 Reducing Variation 3 Centering at Target 3 Reducing Common Cause Variation Improving a Stable Process Two strategies for improving a stable process

20. Statistical Process Control04/03/9620 Reducing Variation Centering at Target 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time

21. Statistical Process Control04/03/9621 Reducing Variation Reducing Common Cause Variation 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time

22. Statistical Process Control04/03/9622 Reducing Variation 3 Structured problem solving 3 Planned experiments Reducing Variation in a Stable Process Make Permanent Changes Changes are based on the scientific approach Examples: new equipment, equipment upgrade, new procedure, new machine settings, better raw material

23. Statistical Process Control04/03/9623 Reducing Variation 3 Do not ignore special causes. 3 Do quickly detect special cause variations. 3 Do stop production until the process is fixed. (Reactive) 3 Do identify and permanently eliminate special causes. (Preventive) Reducing Variation in an Unstable Process

24. Statistical Process Control04/03/9624 Reducing Variation 3 Detect the special cause variation. 3 Identify the special cause. 3 Fix the process Remove the special cause, or Remove the special cause, or Compensate for the special cause. Compensate for the special cause. 3 Prevent the special cause from occurring again Improving an Unstable Process Four Step Process

25. Statistical Process Control04/03/9625 Reducing Variation Improving an Unstable Process Reactive 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time Detect Here Not Here Detect Here

26. Statistical Process Control04/03/9626 Detecting Variation How can we decide if variation is the result of common or special cause?

27. Statistical Process Control04/03/9627 Detecting Variation Tool: Control Chart Benefit: Prevents tampering or ignoring 200 180 160 140 120 100 80 60 40 20 0 Thickness 200 180 160 140 120 100 80 60 40 20 0 Time

28. Statistical Process Control04/03/9628 Detecting Variation Control Chart for Detecting Variation Observe Variation Common Cause Detect Special Cause IdentifyFixPrevent Don’t Tamper Reduce Overall Variation ControlChart

29. Statistical Process Control04/03/9629 Detecting Variation Control Chart for Detecting Variation Control Chart Trend Chart Center Line Control Limits ++ Upper Control Limit Lower Control Limit Center Line

30. Statistical Process Control04/03/9630 Detecting Variation Control Limits Control limits tell us where the measurements in a stable process should fall Lower Control Limit Upper Control Limit

31. Statistical Process Control04/03/9631 Detecting Variation Creating a Control Chart Turn the distribution on its side Upper Control Limit Lower Control Limit Center Line

32. Statistical Process Control04/03/9632 Detecting Variation What is the Center Line? Process mean, based on historical data or Process Target Creating a Control Chart

33. Statistical Process Control04/03/9633 Detecting Variation Creating a Control Chart Selecting the Center Line The center line should be the target, unless we are unable or unwilling to control the process to target. Since the target is zero defects, the center line is the process mean. Measurements: Defects:

34. Statistical Process Control04/03/9634 Detecting Variation Control Limits 3 Based on performance of the process. 3 Tell us when to take action on the process. Spec Limits 3 Based on performance of the product. 3 Tell us when to disposition the product. Control Limits vs. Spec Limits

35. Statistical Process Control04/03/9635 Detecting Variation Focus On Control Limits Improve Process Quality Spec Limits Improve Product Quality Control Limits vs. Spec Limits

36. Statistical Process Control04/03/9636 Measurement Capability Have you ever been bitten by a measurement system? METROLOGY SYSTEM TrueData ObservedData black box

37. Statistical Process Control04/03/9637 Measurement Capability A Measurement Process 3 Measurement tools themselves –hardware –software 3 All the procedures for using the tools –which operators –set-up/handling procedures –off-line calculations and data entry –calibration frequency and technique

38. Statistical Process Control04/03/9638 Measurement Capability Why Do Measurements Vary? NOTE: Not all of these will necessarily be significant sources of variation for every measurement system. Measurement Variation Work Methods ease of data entry operator training calibration frequency operator technique maintenance of standards standard procedure sufficient time for work Environment line voltage variation temperature fluctuation vibration humidity fluctuation cleanliness Tool mechanical instability electrical instability wear algorithm instability

39. Statistical Process Control04/03/9639 Measurement Capability Assumptions We Often Make 3 Metrology tools are perfectly accurate 3 No day-to-day variation in performance 3 No operator-to-operator variation

40. Statistical Process Control04/03/9640 Measurement Capability MCA Tells Us: 3 How big is the measurement error? 3 What are the sources of measurement error? 3 Is the tool stable over time? 3 Is the tool capable of making the measurements for this project? 3 Is the tool capable of making the measurements for this process? 3 What needs to be done to improve the measurement process?

41. Statistical Process Control04/03/9641 Measurement Capability Capability vs. Calibration Calibration Procedure to compare readings from a tool with a standard and then correct for any deviations. Statistically: centering the mean of the distribution of readings on the “true value” (obtained from a standard).

42. Statistical Process Control04/03/9642 Measurement Capability Capability vs. Calibration (cont’d) Capability Procedure to identify and quantify sources of variation in readings and then eliminate them. Statistically: fitting the model to the readings so that the components of variance can be estimated. Both work together to keep measurement tool performing optimally.

43. Statistical Process Control04/03/9643 Concepts and Vocabulary Sources Of Variation Process Variation Measurement Variation Total Variation + =

44. Statistical Process Control04/03/9644 Introduction Total Variation Product MeasurementSystem Accuracy Precision

45. Statistical Process Control04/03/9645 Concepts and Vocabulary Accuracy The degree to which a process mean is on target Related Terms True Value Bias

46. Statistical Process Control04/03/9646 Concepts and Vocabulary Precision The degree of variability in a process Related Terms RepeatabilityReproducibility

47. Statistical Process Control04/03/9647 Concepts and Vocabulary Bias Distance between the average value of all the measurements and the true value. Can be positive or negative. Bias =  - True Value 3 Measures the amount by which a tool is consistently off target from the truth. 3 Bias is the numerical value we use to measure accuracy. 3 Synonyms: systematic error, offset.

48. Statistical Process Control04/03/9648 Concepts and Vocabulary Bias Observed Average True Value bias

49. Statistical Process Control04/03/9649 Concepts and Vocabulary Precision Says Nothing About How Close The Measurements Are To The Truth. Accuracy Says Nothing About How Close Measurements Are To Each Other.

50. Statistical Process Control04/03/9650 Concepts and Vocabulary Precision Can be separated into repeatability and reproducibility Total Variation Product Measurement System Accuracy Precision These characteristics have the relationship:  2 ms =  2 rpt +  2 rpd RepeatabilityReproducibility

51. Statistical Process Control04/03/9651 Concepts and Vocabulary Repeatability Variation that results when repeated measurements are made of the same parameter under absolutely identical conditions. 3 Same operator 3 Same set-up procedure 3 Same part 3 Same environmental conditions Repeatability (  2 rpt ) is usually much smaller (better) than the precision of the system.

52. Statistical Process Control04/03/9652 Concepts and Vocabulary Reproducibility The variation that results when different conditions are used to make the measurement. 3 Different Operators 3 Different Set-Up Procedures 3 Different Measurement Tools 3 Different Environmental Conditions 3 Different Days Reproducibility (  rpd ), is approximately the standard deviation of the averages of measurements from different measurement conditions.