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Chapter 6 Part 3

Chapter 6 Part 3. X-bar and R Control Charts. Attribute Data. Data that is discrete Discrete data is based on “counts.” Assumes integer values Number of defective units Number of customers who are “very satisfied” Number of defects. Variables Data.

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Chapter 6 Part 3

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  1. Chapter 6 Part 3 X-bar and R Control Charts

  2. Attribute Data • Data that is discrete • Discrete data is based on “counts.” • Assumes integer values • Number of defective units • Number of customers who are “very satisfied” • Number of defects

  3. Variables Data • X-bar and R chart is used to monitor mean and variance of a process when quality characteristic is continuous. • Continuous values (variables data) can theoretically assume an infinite number of values in some interval. • Time • Weight • Ounces • Diameter

  4. X-bar and R Chart • X-bar chart monitors the process mean by using the means of small samples taken frequently • R chart monitors the process variation by using the sample ranges as the measure of variability • Range = Maximum value – Minimum value

  5. Notation

  6. Notation

  7. Notation

  8. Example of Notation • A company monitors the time (in minutes) it takes to assemble a product. • The company decides to sample 3 units of the product at three different times tomorrow: • 9 AM • 12 Noon • 3 PM • What is the sample size, n? • What is k, the number of samples?

  9. Suppose the following data are obtained. How would you compute X-bar? R R-bar X double bar

  10. Example of Notation

  11. Sample Means First Sample

  12. Sample Means Second Sample Third Sample

  13. Estimated Process Mean

  14. Sample Ranges

  15. Mean of R

  16. Underlying Distributions • When constructing an X-bar chart, we actually have two distributions to consider: • The distribution of the sample means , and • The process distribution, the distribution of the quality characteristic itself, X. • The distribution of is a distribution of averages. • The distribution of X is a distribution of ???

  17. Underlying Distributions These distributions have the same mean Their variances (or standard deviations) are different. Which distribution has the bigger variance? Would you expect more variability among averages or among individual values? The variability among the individual values is ???

  18. Underlying Distributions The standard deviation among the sample means is smaller by a factor of Therefore,

  19. Underlying Distributions Samplingdistribution of Distribution of X

  20. X Distribution of X M M m M M m LCL UCL M M m The distribution of X is assumed to be normal. This assumption needs to be tested in practice.

  21. Distribution of X-bar M M m M M m LCL UCL M M m If the distribution of X is normal, the distribution of X-bar will be normal for any sample size.

  22. Control Limits for X-bar Chart Since we are plotting sample means on the X-bar chart, the control limits are based on the distribution of the sample means. The control limits are therefore

  23. Control Limits for X-bar Chart Distribution of LCL UCL

  24. Control Limits for X-bar Chart

  25. Control Limits for X-bar Chart • A2 is a factor that depends on the n, the sample size, • and will be given in a table.

  26. Example of X-bar Chart • A company that makes soft drinks wants to monitor the sugar content of its drinks. • The sugar content (X) is normally distributed, but the means and variances are unknown. • The target sugar level for one of its drinks is 15 grams. • The lower spec limit is 10 grams. • The upper spec limit is 20 grams.

  27. Example of X-bar Chart • The company wants to know how much sugar on average is being put into this soft drink and how much variability there is in the sugar content in each bottle. • The company also wants to know if the mean sugar content is on target. • Lastly, the company wants to know the percentage of drinks that are too sweet and the percentage that are not sweet enough. (Next section)

  28. Example of X-bar Chart • To obtain this information, the company decides to sample 3 bottles of the soft drink at 3 different time each day: • 10 A.M, • 1:00 P.M. and • 4:00 P.M. • The company will use this data to construct an X-bar and R chart. (In practice, you need 25-30 samples to construct the control limits.) • For the past two days, the following data were collected:

  29. Example of X-bar Chart What is n? What is the k? What is the next step?

  30. X Example of X-bar Chart

  31. X-bar Chart Control Limits

  32. Table A: X-bar Chart Factor, A2

  33. X-bar Chart Control Limits

  34. X-bar Chart Control Limits

  35. Interpretation of X-bar Chart • The X-bar chart is in control because ???? • This means that the only source of variation among the sample mean is due to random causes. • The process mean is therefore stable and predictable and, consequently, we can estimate it.

  36. Interpretation of X-bar Chart • Our best estimate of the mean is the center line on the control chart, which is the overall mean (X-double bar) of 15.33 grams. • If the process remains in control, the company can predict that all bottles of this soft drink produced in the future will have a sugar content of, on average, 15.33 grams.

  37. Interpretation of X-bar Chart • This prediction, however, indicates that there is a problem with the location of the mean. • The process mean is off target by 0.33 grams (15.33 -15.00). • The process mean, although stable and predictable, is at the wrong level and should be corrected to the target.

  38. Interpretation of X-bar Chart • Since the process mean is in control, there are no special causes of variation that may be responsible for the mean being off target. • Since the operators are responsible for correcting problems due to special causes and managementis responsible for correcting problems due to random causes of variation, management action is required to fix this problem.

  39. Interpretation of X-bar Chart • The reason is that, because the process is in control, the filling machines require more than a simple adjustments (typically due to special causes) which can be made by the operators. • The machines may require • new parts, • a complete overhaul, or • they may simply not be capable of operating on target, in which case a new machine is required.

  40. Interpretation of X-bar Chart • Expecting the operators to adjust the mean to the target when the process is in control is analogous to requiring that you produce zero heads (head = defective unit) if you are hired to toss a fair coin 100 times each day. • Why?

  41. R Chart • Monitors the process variability (the variability of X) • Tells us when the process variability has changed or is about to change. • R chart must be in control before we can use the X-bar chart.

  42. R Chart • Rules for detecting changes in variance: • If at least one sample range falls above the upper control limit, or there is an upward trend within the control limits, process variability has increased. • If at least one sample range falls on or below the lower control limit, or there is a downward trend within the control limits, process variability has decreased.

  43. R Chart Control Limits

  44. Table B: Factors for R Chart

  45. R Chart Control Limits

  46. Interpretation of R Chart • Since all of the sample ranges fall within the control limits, the R chart is in control. • The standard deviation is stable and predictable and can be estimated—done in next section. • This does not necessarily mean that the amount of variation in the process is acceptable.

  47. Interpretation of R Chart • Continuous improvement means the company should continuously reduce the variance. • Since the process variation is in control, management action is required to reduce the variation.

  48. X-bar Chart Expected Pattern in a Stable Process UCL LCL Time Expected pattern is a normal distribution

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