1. Statistics for Managers Using Microsoft Excel 3rd Edition
Chapter 15
Statistical Applications in Quality and Productivity Management
© 2002 Prentice-Hall, Inc.

2. Chapter Topics Total quality management (TQM)
Theory of process management (Deming’s fourteen points)
The theory of control charts
Common cause variation vs. Special cause variation
Control charts for the proportion of nonconforming items
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3. Chapter Topics Process variability The c chart
(continued)
Process variability
The c chart
Control charts for the mean and the range
Process capability
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4. Themes of Quality Management
Primary focus is on process improvement
Most variations in process are due to systems
Teamwork is integral to quality management
Customer satisfaction is a primary goal
Organization transformation is necessary
It is important to remove fear
Higher quality costs less
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5. Deming’s 14 Points: Point 1
Point 1. Create Constancy of Purpose
Plan
Act Do
Study
The Shewhart-Deming Cycle Focuses on Constant Improvement
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6. Deming’s 14 Points: Points 2 and 3
Point 2. Adopt a New Philosophy
Better to be proactive and change before crisis occurs.
Point 3. Cease Dependence on mass inspection achieve quality.
Any inspection the purpose of which is to improve quality is too late.
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7. Deming’s 14 Points: Points 4 and 5
Point 4. End the practice of awarding business on the basis of price tag alone
Develop a long-term relationship between purchaser and supplier.
Point 5. Improve constantly and forever
Reinforce the importance of the Shewhart-Deming cycle.
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8. Deming’s 14 Points: Points 6 and 7
Point 6. Institute Training
Especially important for managers to understand the difference between special causes and common causes.
Point 7. Adopt and Institute Leadership
Differentiate between leadership and supervision. Leadership is to improve the system and achieve greater consistency of performance.
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9. Deming’s 14 Points: Points 8 to 12
8. Drive out fear
9. Break down barriers between staff areas
10. Eliminate slogans
11. Eliminate numerical quotas for workforce and numerical goals for management
12. Remove barriers to pride of workmanship
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10. Deming’s 14 Points: Points 13 and 14
Point 13. Encourage education and self improvement for everyone.
Improved knowledge of people will improve assets of organization.
Point 14. Take action to accomplish transformation.
Continually strive toward improvement.
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11. Control Charts Monitors variation in data
Exhibits trend -- make correction before process is out of control
A process -- A repeatable series of steps leading to a specific goal
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12. Control Charts Show when changes in data are due to:
(continued)
Show when changes in data are due to:
Special or assignable causes
Fluctuations not inherent to a process
Represents problems to be corrected
Data outside control limits or trend
Chance or common causes
Inherent random variations
Consist of numerous small causes of random variability
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13. Process Control Chart Graph of sample data plotted over time
Special Cause Variation
Process Average 
UCL
Mean
LCL
Common Cause Variation
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14. Control Limits UCL = Process Average + 3 Standard Deviations
LCL = Process Average - 3 Standard Deviations X
UCL
+ 3
Process Average
- 3
LCL
TIME
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15. Types of Error First Type: Second Type:
Belief that observed value represents special cause when in fact it is due to common cause
Second Type:
Treating special cause variation as if it is common cause variation
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16. Comparing Control Chart PatternsX X X
Common Cause Variation: No Points Outside Control Limits
Special Cause Variation: 2 Points Outside Control Limits
Downward Pattern: No Points Outside Control Limits but Trend Exists
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17. When to Take Corrective Action
Take corrective action when you observe points outside the control limits or when a trend has been detected
Eight consecutive points above the center line (or eight below)
Eight consecutive points that are increasing (decreasing)
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18. Out-of-control Processes
When the control chart indicates an out-of-control condition (a point outside the control limits or exhibiting trend)
Contains both common causes of variation and assignable causes of variation
The assignable causes of variation must be identified
If detrimental to the quality, assignable causes of variation must be removed
If increases quality, assignable causes must be incorporated into the process design
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19. In-control Process
When the control chart does not indicate any out-of-control condition
Contains only common causes of variation
Sometimes said to be in a state of statistical control
If the common causes of variation is small, then control chart can be used to monitor the process
If the common causes of variation is too large, you need to alter the process
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20. p Chart Control chart for proportions
Is an attribute chart
Shows proportion of nonconforming (success) items
e.g.: Count the number defective chairs and divide by total chairs inspected
Chair is either defective or not defective
Used with equal or unequal sample sizes over time
Unequal sizes should not differ by more than ±25% from average sample size
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21. p Chart Control Limits Average Proportion of Nonconforming Items
Average Group Size
# Defective Items in Sample i
# of Samples
Size of Sample i
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22. p Chart Example
You’re manager of a 500-room hotel. You want to achieve the highest level of service. For seven days, you collect data on the readiness of 200 rooms. Is the process in control?
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23. p Chart Hotel Data # Not Day # Rooms Ready Proportion
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24. p Chart Control Limits Solution
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25. p Chart Control Chart Solution
0.15
UCL
0.10
Mean
0.05
LCL
0.00 1 2 3 4 5 6 7
Day
Individual points are distributed around without any pattern. Any improvement in the process must come from reduction of common-cause variation, which is the responsibility of the management.
© 2002 Prentice-Hall, Inc.

26. p Chart in PHStat PHStat | control charts | p chart …
Excel spreadsheet for the hotel room example
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27. Understanding Process Variability: Red Bead Example
Four Workers (A, B, C, D) spent three days to collect beads, at 50 beads per day. The expected number of red bead to be collected per day per worker is 10 or 20%.
Worker Day 1 Day 2 Day 3 All Days
A (18%) 11 (12%) (12%) (17.33%)
B (24%) 12 (24%) 8 (16%) (21.33%)
C (26%) 6 (12%) (24%) (20.67%)
D (14%) 9 (18%) 8 (16%) (16.0%)
Totals
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28. Understanding Process Variability: Example Calculations
Average Day 1 Day 2 Day 3 All Days X
p 20.5% 19% 17% % _
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29. Understanding Process Variability: Example Control Chart
UCL
.30
.20
.10 _ p
LCL
0 A1 B1 C1 D A2 B2 C2 D2 A3 B3 C3 D3
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30. Morals of the Example Variation is an inherent part of any process.
The system is primarily responsible for worker performance.
Only management can change the system.
Some workers will always be above average, and some will be below.
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31. The c Chart
Control chart for number of nonconformities (occurrences) in a unit (an area of opportunity)
Is an attribute chart
Shows total number of nonconforming items in a unit
e.g.: Count number of defective chairs manufactured per day
Assume that the size of each subgroup unit remains constant
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32. c Chart Control Limits Average Number of Occurrences
# of occurrences in sample i
# of Samples
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33. c Chart: Example
You’re manager of a 500-room hotel. You want to achieve the highest level of service. For seven days, you collect data on the readiness of 200 rooms. Is the process in control?
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34. c Chart: Hotel Data # Not Day # Rooms Ready
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35. c Chart: Control Limits Solution
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36. c Chart: Control Chart Solution30
UCL 20 10
LCL 1 2 3 4 5 6 7
Day
Individual points are distributed around without any pattern. Any improvement in the process must come from reduction of common-cause variation, which is the responsibility of the management.
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37. Variable Control Charts: R Chart
Monitors variability in process
Characteristic of interest is measured on numerical scale
Is a variables control chart
Shows sample range over time
Difference between smallest and largest values in inspection sample
e.g.: Amount of time required for luggage to be delivered to hotel room
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38. R Chart Control Limits From Table Sample Range at Time i or subgroup i
# Samples
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39. R Chart Example
You’re manager of a 500-room hotel. You want to analyze the time it takes to deliver luggage to the room. For 7 days, you collect data on 5 deliveries per day. Is the process in control?
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40. R Chart and Mean Chart Hotel Data
Sample Sample Day Average Range
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41. R Chart Control Limits Solution
From Table E.11 (n = 5)
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42. R Chart Control Chart Solution
Minutes
UCL 8 6 _ 4 R 2
LCL 1 2 3 4 5 6 7
Day
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43. Variables Control Charts: Mean Chart (The Chart)
Shows sample mean over time
Compute mean of inspection sample over time
e.g.: Average luggage delivery time in hotel
Monitors process average
Must be preceded by examination of the R chart to make sure that the process is in-control
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44. Mean Chart Computed From Table Sample Mean at Time i
Sample Range at Time i
# Samples
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45. Mean Chart Example
You’re manager of a 500-room hotel. You want to analyze the time it takes to deliver luggage to the room. For seven days, you collect data on five deliveries per day. Is the process in control?
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46. R Chart and Mean Chart Hotel Data
Sample Sample Day Average Range
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47. Mean Chart Control Limits Solution
From Table E.9
(n = 5)
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48. Mean Chart Control Chart Solution
Minutes
UCL 8 _ _ 6 X 4 2
LCL 1 2 3 4 5 6 7
Day
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49. R Chart and Mean Chart in PHStat
PHStat | control charts | R & Xbar charts …
Excel spreadsheet for the hotel room example
© 2002 Prentice-Hall, Inc.

50. Process Capability
Process capability is the ability of a process to consistently meet specified customer-driven requirement
Specification limits are set by management in response to customers’ expectations
The upper specification limit (USL) is the largest value that can be obtained and still conform to customers’ expectations
The lower specification limit (LSL) is the smallest value that is still conforming
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51. Estimating Process Capability
Must first have an in-control process
Estimate the percentage of product or service within specification
Assume the population of X values is approximately normally distributed with mean estimated by and standard deviation estimated by
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52. Estimating Process Capability
(continued)
For a characteristic with an LSL and a USL
Where Z is a standardized normal random variable
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53. Estimating Process Capability
(continued)
For a characteristic with only a LSL
Where Z is a standardized normal random variable
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54. Estimating Process Capability
(continued)
For a characteristic with only a USL
Where Z is a standardized normal random variable
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55. Process Capability Example
You’re manager of a 500-room hotel. You have instituted a policy that 99% of all luggage deliveries must be completed within ten minutes or less. For seven days, you collect data on five deliveries per day. Is the process capable?
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56. Process Capability: Hotel Data
Sample Sample Day Average Range
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57. Process Capability: Hotel Example Solution
Therefore, we estimate that 99.38% of the luggage deliveries will be made within the ten minutes or less specification. The process is capable of meeting the 99% goal.
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58. Capability Indices
Aggregate measures of a process’s ability to meet specification limits.
The larger (>1) the values, the more capable a process is of meeting requirements
Measure of process potential performance
Cp>1 implies a process has the potential of having more than 99.73% of outcomes within specifications
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59. Capability Indices Measures of actual process performance
(continued)
Measures of actual process performance
For one-sided specification limits
CPL (CPU) >1 implies that the process mean is more than 3 standard deviation away from the lower (upper) specification limit
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60. Capability Indices For two-sided specification limits
(continued)
For two-sided specification limits
Cpk = 1 indicates that the process average is 3 standard deviation away from the closest specification limit.
Larger Cpk indicates larger capability of meeting the requirements
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61. Process Capability Example
You’re manager of a 500-room hotel. You have instituted a policy that all luggage deliveries must be completed within ten minutes or less. For seven days, you collect data on five deliveries per day. Compute an appropriate capability index for the delivery process.
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62. Process Capability: Hotel Data
Sample Sample Day Average Range
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63. Process Capability: Hotel Example Solution
Since there is only the upper specification limit, we need to only compute CPU. The capability index for the luggage delivery process is .8337, which is less than 1. The upper specification limit is less than 3 standard deviation above the mean.
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64. Chapter Summary Described total quality management (TQM)
Addressed the theory of process management
Deming’s fourteen points
Discussed the theory of control charts
Common cause variation vs. special cause variation
© 2002 Prentice-Hall, Inc.

65. Chapter Summary
(continued)
Computed control charts for the proportion of nonconforming items
Described process variability
Described c chart
Computed control charts for the mean and the range
Discussed process capability
© 2002 Prentice-Hall, Inc.