Business Moment

1. Business Moment Whether you think you can or whether you think you can't, you're right! Henry Ford

2. Fundamentals of Statistical Process Control. SPC

3. Statistical Process Control SPC is a tool for obtaining cost effective quality, achieved by process monitoring and forecasting techniques which are used to predict problems before they occur. (defect prevention)

4. Reliability Where should you spend your money? Increased Reliability - + $ $ Inspect Prevention Rework Corrective Action Rework Insp. Quality

5. Detection vs. Prevention Manufacture Manufacture Prevent $ Inspect & Sort Scrap or Rework SPC Feedback Ship Ship Old method New Method

6. Elements of Quality - Zero Defects (Implied or specified) - Conformance to Specification - Reliability (life & uptime) - Functionality (does what’s intended) - Price (competitive for performance) - Delivery (received on promise date) - Service and Support (incl. Warranty)

7. Elements of SPC • S – Statistical: Selective measurement of the product • Collect information on the Product • Size, weight, composition, etc • Organize data into a chart • Visual picture of process performance • Analyze data to make predictions about process performance over time • Process improvement can be achieved

8. Elements of SPC • P – Process: Any activity that produces a product or provides a service • A combination of equipment, manpower, material, method, and environment • Can be industrial, administrative, financial, or managerial

9. Elements of SPC • C – Control: The comparison of actual process performance with a target, or nominal, value • Taking appropriate corrective action when the process in not performing acceptably (out of control) • Analyzing effectiveness of corrective actions

10. Goals of SPC • Achieve consistency • Results in minimal waste and rejects • Quality built in at every stage of the process • Quality is satisfying the customer • Well made product with reduction of errors every step of the way • Ensures products and services are right the first time • Provides ultimate savings

11. Quality Costs • Four types of Quality Costs • External Failure Costs – after reaches the customer • Returns, replacements, warranty claims • Internal Failure Costs – after production but before it reaches the customer • Scrap, unusable raw material • Rework, refit, or repair

12. Quality Costs • Appraisal Costs – Related to inspection functions • As the process becomes more reliable, the need for inspection of the finished products decreases • Prevention Costs – Relate to measurements and inspections during the process • Permit timely adjustments to the process • Data collection • Proper handling and storage • Timely and regular equipment maintenance • Training of personnel

13. Process Data • No process can produce consecutive identical items over time. • Changes must monitored to detect shifts in performance • Corrective actions taken first • Then improvement actions • Requires collection of data • Measure components • Measured quantities are called variables • Dimensions, weight, % impurities • Count defects • Counted data is called attributes • Items conform to standards or don’t • Surface defects, color, misshaped

14. X Individual Measurements, the same values stack up. .5 .5 .5 .5 .4 .6 .5 .4 .6 .5 .6 .4 .5 .6 .4 .5 .6 .3 .4 .7 .5 .6 .3 .4 .7 .5 .6 .3 .4 .7 .2 .5 .6 .8 .3 .4 .7 -3 -2 -1 +1 +2 +3 Normal VariationVariable Data Variable Data is data that can be measured. 68% 95% 99.7% Six Sigma

15. Sample Quantity Sample Quantity 0 -1 -1 0+1 –1 0+1+2 0 -1 0 +1 -1 0+1+2 –1 0+1+2 -1 0 +2 -2 -1 0+1 +2 -1 0 +2 -1 0 +2 3 -2 -1 0 +1 +2 +3 3 -2 -1 0 +1 +2+3 -2 -1 0 +1 +2 -2 -1 0 +1 +2 3 -1 0 +2 3 -2 -1 0 +1 +2 +3 3 -2 -1 0 +1 +2 +3 3 -2 -1 0 +1 +2 +3 3 3 -2 -1 0 +1 +2 +3 5 20 23 40 x It takes approximately 30 samples to assure the normal bell shape. xxx xxxxx xxxxxxx xxxxxxxxx xxxxxxxxxxx xxxxxxxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx 102

16. Any Measurement! Range height 76-60 = 16 in. Range of weight could be 300-60 = 240 lb Mean (Center) height = 68 in. Mean weight = 180 lbs

17. .017 .016 .018 .017 .005 .019 .001 .018 .002 .016 .001 Increment of Measurement Takes more points to define bell shape. Fewer points make bell shape .00# .0# .005 range per block

18. Histogram 4.5 to 5.5 5.5 to 6.5 6.5 to 7.5

19. Process Control A process is “In Control” if products vary CONSISTENTLY within expected limits over TIME. Normal expectations when displayed by frequency of occurrence will present a curve or distribution showing a central peak and tapering off smoothly to tails on either side. .5 .5 .5 .5 .4 .6 .5 .4 .6 .5 .6 When the normal distribution exits the process is operating consistently and we can predict the process behavior. .4 .5 .6 .4 .5 .6 .3 .4 .7 .5 .6 .3 .4 .7 .5 .6 .3 .4 .7 .2 .5 .6 .8 .3 .4 .7

20. Controlling a Process 1. To control a process means to keep the bell shape normal. 2. Get the Process to a normal distribution. (Remove assignable causes) You don’t want to control a broken process. 3. Monitor the process property at an interval that can detect deterioration of the property and allow correction before rejections. Example: If the property was effected by a belt that wears over time, checking once a week may be adequate. If the property were to use a batch of epoxy in the working range before it cures to much, checking every 15 minutes would be appropriate. (The time interval is called “Frequency”) 4. Take corrective action before defects are produced.

21. Assignable Causes Easy to identify and correct. (low cost)

22. What is poor Quality? Not meeting customer expectations, implied or specified!

23. Reject No Question Perfection – Not Required Customer Dis-satisfaction A B Target Lower Spec Upper Spec A = more variation on target? or B = less variation off target?

24. What causes defects? 6 M’s Man (associated operator error) Machine (improper, misadjusted or broken) Material (wrong or defective) Measurement (wrong, bad or misread instrument) Method (improper, unclear or incomplete) Mother Nature (environment – temp – moisture …)

25. Bad (Out of Spec.) Customer Specification Good or Bad? LCL UCL LCL UCL 6 6 The Bell shape has nothing to do with good/bad only Normal.

26. When do you have rejects? When Specification Limits are imposed. (Customer or Company Spec.)

27. LSL USL LSL xxx USL LCL UCL xxxxx xxxxxxx xxxxxxxxx xxxxxxxxxxx xxxxxxxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx Not Capable Specification Limits Control limits 6 Capable Capability CP

28. Precise and Accurate Accurate and not Precise Not Precise and Not Accurate Precise and Accurate Precise and Not Accurate

29. Capability Improvement Variation Reduction

30. xxx xxxxx xxxxxxx xxx xxxxxxxxx xxxxx xxxxxxxxxxx Normal Shift xxxxxxx xxxxxxxxxxxxx USL USL xxxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxx xxxxxxxxxxxxxxxxx xxxxxxxxxxxx xxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx Avg. Avg-USL/3/2 Or LSL-Avg/ 3/2 Cpk=min Not Capable Capable Capability CPk Six Sigma

31. Variation of the Mean

32. Two Distributions Working Together (Movement: right, left or up, down or clockwise, counterclockwise …) (mixed materials, different lots of material, different operators…) (If parts tighter tolerance parts were selected out it would look like this.) xxx xxx xxxxx xxxxx xxxxxxx xxxxxxx xxxxxxxxx xxxxxxxxx xxxxxxxxxxx xxxxxxxxxxx xxxxxxxxxxxxx xxxxxxxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx Bimodal Six Sigma

33. Control Limits, the six sigma points LCL UCL - 3 +3 Mean (Average) 6 sigma

34. Making a time related distribution. Display the measurements in the order taken. Each point is an average from the samples measured (5 smpls)

35. Add Control Lines to charts UCL +3 Mean (Average) 6 sigma - 3 LCL

36. Xbar and Range Charts Xbar = X = The average of measured samples (5) Range = R = Highest reading – Lowest reading in sample (5)

38. Run Rules (normal no action required) Any point that crosses the upper or lower control limit on the Xbar Chart or the upper control limit on the R chart. Xbar (upper chart) Range – R (lower chart) * Note random points, some near control lines some near center.

39. Run Rules (when to take action) Any point that crosses the upper or lower control limit on the Xbar Chart or the upper control limit on the R chart. Xbar (upper chart) Range – R (lower chart) * Check for possible bad measurement or data entry error.

40. Run Rules (when to take action - Run) 7 points in a row on one side of the center line on the upper chart. Xbar (upper chart) Or *Check material, lot, operator, machine adjustment…

41. Run Rules (when to take action - trends) 7 points in a continuing downward or upward direction on X chart Xbar (upper chart) Or * Check machine wear, material, method or operator fatigue…

42. Run Rule (when to take action - trends) 7 points in a continuing downward or upward direction on R chart Range R (lower chart) Improving – capture or speed up Or Getting Worse - correct * Check machine wear, material, method or operator fatigue…

43. Run Rule (clustered points) “Over controlled” If bell was normal points would be near the edges. 7 points in a row clustered around the center line on the Xbar chart or clustered around zero (bottom line) on the R Range chart Xbar (upper chart) Improved – capture or speed up Range – R (lower chart) Improved – capture or speed up

44. Run Rules (when to take action – Unusual pattern) No points near the center (possibly bimodal) Xbar (upper chart) *Two lots of material, two operators, selected parts removed…

45. Attribute Data Non-measurable data Good, Bad; Pass, Fail; True, False; Go, NoGo; Present, Missing… Charts: P (Percent Defective), NP (Number Defective) C (Defects per unit), U (Defects per Subgroup) Simple if sample size is constant and equated easily to 100. Ex: Number defective from sample 50 multiply by 2, 25x4, 10x10, 5x20, 2x50 EXAMPLE: 5 bad in sample of 25 (5x4) =20% The software will do the math but keeping a fixed sample size is preferred. Fixed sample size will keep the control lines straight and understood.

47. Plotting Procedure 1. Count out the sample size and note the number defective. 2. If you are using the current date and time skip step 3. 3. Enter your earlier date and time in military format 10/18/03 14:15 in the Override Date/Time cell 4. Enter your data into the cells provided. Sample Size, Qty Defective (Yellow cells) (Note: Click on the yellow Cell to start entering data, use the Tab key and or arrow keys to move to the next yellow cell.) 5. On the last entry use the arrow keys to enter and procede to the Initials cell (otherwise click the Initials cell). Enter your Initials, Press Tab. 6. Press the "plot button" in the lower right corner and observe that your data point has been plotted. 7. If there appears to be an error in the data, you can click on the defective data and reenter it. Press Tab, then click the Replot Button. 8. Once the point is correctly plotted, Observe if any Run Rules have been violated. If not, enter "none" in the action cell, then click the "Tab then Action Button." (lower right corner). Otherwise, take and log the corrective action taken. 9. Observe that the data cells are empty, indicating a successful transaction. The lower scroll pane (two rows at the bottom of screen) can be used to observe the calculations and action entered. (Just click the down scroll arrow until the last row is displayed. (80 rows maximum can be entered.) Run Rules 1. Any point that crosses the upper or lower control limit on the P Chart 2. 7 points in a row on one side of the center line. 3. 7 points in a continuing upward or downward direction on the P chart (Trend). 4. 7 points in a row at the upper and lower extremes (no points on or near the centerline) 5. 7 points in a row clustered near bottom of R Chart or trending toward bottom (Capture improvement or speed up the process)

48. The End