【導(dǎo)讀】46. R,XContinuous. p=proportion. c=count. 47. Start. Type. ofdata. Equal. sample. sizes. Equal. opportunity. Individuals. chart. Individuals. chart. EWMA. chart. chart. Continuous. Yes. No. Yes. Rational. Subgroups. Discrete. Yes. No. No. Dolimits. lookright?Needto. detectsmallshifts. quickly?Individual. measurements. orsubgroups. Dolimits. lookright?YesNo. Either/Or. No. Yes. Individual. measurements. Occurrences. X,Rchart,art. Itemswith. attribute. Counting. itemswithan. occurrences?C. PNP. U. Constantvariable. Discrete-. count. Discrete-. attribute. Exponentially. WeightedMoving. AverageChart. 48. Time:10mins.49. TypeofChart. Predictinghotel. occupancy. Monitoring. plantsafety. Predicting. Process. Yield. A)Percentoccupancy. B)Numberofrooms. occupied. A)Percentage. ofgoodproduct. B)Numberofgood. unitsper100units. sampled. Daily. Daily. Monthly. Monthly. Weekly. Weekly. 1.2.3.B)Numberofrecordable. workedpermonth. A)Numberofrecordable. injuries,when16,000. 50. p. np. c. u. p. np. Exercise4:Answers. TypeofChart. Predictinghotel. occupancy. Monitoring. plantsafety. Predicting. Process. Yield. A)Percentoccupancy. B)Numberofrooms. occupied. A)Percentage. ofgoodproduct. B)Numberofgood. unitsper100units. sampled. Daily. Daily. Monthly. Monthly. Weekly. Weekly. 1.2.3.B)Numberofrecordable. workedpermonth. A)Numberofrecordable. injuries,when16,000. 51. Poisson. c=countsofoccurrence. stdc=. Binomial. Situationnitems. kwithattribute. p==proportion. withattribute. Control. Chart. stdp=. DiscreteData. n. k. n. )p-(1pc. P. P. C. C. ProportionCount. 52. DiscreteData. distribution:. Twoattributesonly(.,defectivevs.non-defective). (thesame)foreachsample. distribution:

　　

【正文】 erator made an adjustment ? Test 4 could be caused by different process streams, different lots, operators (running high amp。 low), or different cavities ? Test 7 shows a reduction in variation, which is good. d. What actions will you take? Determine what the special cause is specifically and address that issue. 92 Summary: X, R Charts ? For continuous data ? For highvolume processes where rational subgroups, usually related to time, can be defined and sampled ? Underlying assumption: moncause variation within subgroups is equal to the moncause variation between subgroups ? If this assumption does not hold, the X limits will either be too wide or too narrow ? Think carefully about how the subgroups are chosen and the implications it will have on the assumption ? You can check the assumption by making both the X, R chart and the IMRR chart and paring them Summary: Control Charts 94 Procedure for Using Control Charts 1. Decide what type of control chart to use. ? What type of data are you plotting? ? How is it collected—individually or in subgroups? 2. Construct the control chart (use Minitab). 3. Assess the control limits. ? Do they look ―right?‖ If not, – Try an individuals chart. – Try a transformation. ? Omit special causes from calculation of limits. 4. Interpret the control chart. ? Look for signals of special causes. ? Determine appropriate actions. 5. Maintain the control chart. ? Update the plotted points as they occur. ? Determine appropriate actions immediately. ? Recalculate limits when appropriate (mark ?temporary‘ on control limits until you have enough data points). 95 Summary of Assumptions for Control Charts Distribution Related Control Charts Assumptions Normal distribution Used for Individuals Charts, X, R Charts Data distributed symmetrically around a mean。 peak of curve at the mean Binomial distribution Used for p Charts p is constant across subgroups。 Occurrences are independent Poisson distribution Used for c Charts Probability of occurrence is constant。 Occurrences are independent and rare 96 Exercise 10: Selecting a Control Chart Objective: Practice deciding which type of chart to try first for various situations. Time: 20 mins. Instructions: Work in small groups and fill in the right column. What You Are Measuring Appropriate Chart(s) and Possible Considerations 1. Cycle time from order until customer delivery 2. Monthly utility expenses 3. Patient‘s blood sugar level 4. Assembly time 5. Daily employee absenteeism percentage 6. Productivity ratio 7. Number of rings before phone is answered 8. Scrap percentage 9. Number of product returns 10. Days inventory on hand 11. Sales dollars (monthly or weekly) 12. Sales (units) 13. Water purity sampled 4 times each day 14. Yield 15. Number of machine Breakdowns (weekly) 16. Capacity utilization by line (percentage) 97 Exercise 10: Answers I Data may need transformation I I I Data may need transformation p I c or u p or I If n 1000, then use I c, u or I If c is not rare, then use I I I I X, R I (or if binomial) I (if is not rare) c or p By line (not time order), so don‘t connect dots with line What You Are Measuring Appropriate Chart(s) and Possible Considerations 1. Cycle time from order until customer delivery 2. Monthly utility expenses 3. Patient‘s blood sugar level 4. Assembly time 5. Daily employee absenteeism percentage 6. Productivity ratio 7. Number of rings before phone is answered 8. Scrap percentage 9. Number of product returns 10. Days inventory on hand 11. Sales dollars (monthly or weekly) 12. Sales (units) 13. Water purity sampled 4 times each day 14. Yield 15. Number of machine Breakdowns (weekly) 16. Capacity utilization by line (percentage) p c 98 Summary This module has covered: ? How to select an appropriate control chart ? How to use Minitab to construct control charts ? How to transform data ? How to spot control charts that don‘t look right and how to fix them ? How to interpret control charts ? How to maintain control charts Appendix: Discrete Data and Control Charts 100 Summary Table of Control Charts S i t u a t i o n C h a r t U s e d C o n t r o l L i m i t C a l c u l a t i o n s C o m m e n t s C o u n t i n g d e f e c t s h e N u m b e r o f d e f e c t s , a c c i d e n t s , o r f l a w s : o f a c c i d e n t s / m o n t o f b r e a k d o w n s / w e e k o f t i m e s t h e p h o n i s n o t a n s w e r e d w i t h i n t h r e e r i n g s o f f l a w s o n a n a u t o m o b i l e F r a c t i o n o f “ de f e c t i v e s ” F r a c t i o n o f r e q u e s t s n o t p r o c e s s e d w i t h i n 1 5 m i n u t e s f r a c t i o n o f o r d e r s n o t p r o c e s s e d p e r f e c t l y t h e f i r s t t i m e t h r o u g h ( f i r s t p a s s y i e l d ) V a r i a b l e s d a t a , o n e f i g u r e a t a t i m e S a l e s , c o s t s , v a r i a n c e s , c u s t o m e r s a t i s f a c t i o n s c o r e , t o t a l V a r i a b l e s d a t a , s e t s o f m e a s u r e m e n t s c c h a r t u c h a r t n p c h a r t I n d i v i d u a l s c h a r t X 177。 A R u 177。 3 p 177。 3 p (1 p ) n p 3 n p (1 p )A l w a y s p l o t d a t a i n t i m e o r d e r i f t h e r e i s a n a t u r a l c h r o n o l o g i c a l s e q u e n c e 。 b u t m a y a l s o u s e a o r c h a r t o n n o n