1. “It costs a lot to produce a bad product.” Norman Augustine
Quality Management
“It costs a lot to produce a bad product.” Norman Augustine

2. Cost of quality Prevention costs Appraisal costs
Internal failure costs
External failure costs
Opportunity costs

3. What is quality management all about?
Try to manage all aspects of the organization in order to excel in all dimensions that are important to “customers”
Two aspects of quality:
features: more features that meet customer needs = higher quality
freedom from trouble: fewer defects = higher quality

4. The Quality Gurus – Edward Deming
Quality is “uniformity and dependability”
Focus on SPC and statistical tools
“14 Points” for management
PDCA method
1986

5. The Quality Gurus – Joseph Juran
Quality is “fitness for use”
Pareto Principle
Cost of Quality
General management approach as well as statistics
1951

6. Defining Quality
The totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs
American Society for Quality

7. MNBQA
Leadership: How upper management leads the organization, and how the organization leads within the community.
Strategic planning: How the organization establishes and plans to implement strategic directions.
Customer and market focus: How the organization builds and maintains strong, lasting relationships with customers.
Measurement, analysis, and knowledge management: How the organization uses data to support key processes and manage performance.
Human resource focus: How the organization empowers and involves its workforce.
Process management: How the organization designs, manages and improves key processes.
Business/organizational performance results: How the organization performs in terms of customer satisfaction, finances, human resources, supplier and partner performance, operations, governance and social responsibility, and how the organization compares to its competitors.

8. (Process Analysis, SPC, QFD)
What does Total Quality Management encompass?
TQM is a management philosophy:
continuous improvement
leadership development
partnership development
Cultural
Alignment
Technical
Tools
(Process Analysis, SPC, QFD)
Customer

9. Developing quality specifications
Design
Design quality
Input
Process
Output
Dimensions of quality
Conformance quality

10. Quality Improvement
Continuous Improvement
Quality
Traditional
Time

11. Continuous improvement philosophy
Kaizen: Japanese term for continuous improvement. A step-by-step improvement of business processes.
PDCA: Plan-do-check-act as defined by Deming.
Plan Do
Act
Check
Benchmarking : what do top performers do?

12. Tools used for continuous improvement
1. Process flowchart

13. Tools used for continuous improvement
2. Run Chart
Performance
Time

14. Tools used for continuous improvement
3. Control Charts
Performance Metric
Time

15. Tools used for continuous improvement
4. Cause and effect diagram (fishbone)
Environment
Machine
Man
Method
Material

16. Tools used for continuous improvement
5. Check sheet
Item A B C D E F G
√ √ √
√ √ √

17. Tools used for continuous improvement
6. Histogram
Frequency

18. Tools used for continuous improvement
7. Pareto Analysis
100% 60
75% 50 40
Frequency
50% 30
Percentage 20
25% 10 0% A B C D E F

19. Six Sigma Quality
A philosophy and set of methods companies use to eliminate defects in their products and processes
Seeks to reduce variation in the processes that lead to product defects
The name “six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs

20. Six Sigma Quality

21. Absent receiving party Working system of operators
Fishbone diagram analysis
Makes customer wait
Absent receiving party
Working system of operators
Customer
Operator
Absent
Out of office
Not at desk
Lunchtime
Too many phone calls
Absent
Not giving receiving party’s coordinates
Complaining
Leaving a message
Lengthy talk
Does not know organization well
Takes too much time to explain
Does not understand customer

22. Reasons why customers have to wait (12-day analysis with check sheet)
Daily average
Total number A
One operator (partner out of office)
14.3
172 B
Receiving party not present
6.1 73 C
No one present in the section receiving call
5.1 61 D
Section and name of the party not given
1.6 19 E
Inquiry about branch office locations
1.3 16 F
Other reasons
0.8 10
29.2
351

23. Pareto Analysis: reasons why customers have to waitB C D E F
Frequency
Percentage 0%
49%
71.2%
100
200
300
87.1%
150
250

24. In general, how can we monitor quality…?
By observing
variation in
output measures!
Assignable variation: we can assess the cause
Common variation: variation that may not be possible to correct (random variation, random noise)

25. SPC – suppl ch. 6 Statistical Process Control (SPC)
Control Charts for Variables
The Central Limit Theorem
Setting Mean Chart Limits (x-Charts)
Setting Range Chart Limits (R-Charts)
Using Mean and Range Charts
Control Charts for Attributes
Managerial Issues and Control Charts

26. Statistical Process Control (SPC)
Variability is inherent in every process
Natural or common causes
Special or assignable causes
Provides a statistical signal when assignable causes are present
Detect and eliminate assignable causes of variation
Points which might be emphasized include:
- Statistical process control measures the performance of a process, it does not help to identify a particular specimen produced as being “good” or “bad,” in or out of tolerance.
- Statistical process control requires the collection and analysis of data - therefore it is not helpful when total production consists of a small number of units
- While statistical process control can not help identify a “good” or “bad” unit, it can enable one to decide whether or not to accept an entire production lot. If a sample of a production lot contains more than a specified number of defective items, statistical process control can give us a basis for rejecting the entire lot. The issue of rejecting a lot which was actually good can be raised here, but is probably better left to later.

27. Each of these represents one sample of five boxes of cereal
Samples
To measure the process, we take samples and analyze the sample statistics following these steps
Each of these represents one sample of five boxes of cereal
(a) Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weight
Frequency
Weight #
Figure S6.1

28. The solid line represents the distribution
Samples
To measure the process, we take samples and analyze the sample statistics following these steps
The solid line represents the distribution
(b) After enough samples are taken from a stable process, they form a pattern called a distribution
Frequency
Weight
Figure S6.1

29. Samples
To measure the process, we take samples and analyze the sample statistics following these steps
(c) There are many types of distributions, including the normal (bell-shaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape
Figure S6.1
Weight
Central tendency
Variation
Shape
Frequency

30. Samples
To measure the process, we take samples and analyze the sample statistics following these steps
(d) If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable
Prediction
Weight
Time
Frequency
Figure S6.1

31. Samples
To measure the process, we take samples and analyze the sample statistics following these steps
Prediction ?
(e) If assignable causes are present, the process output is not stable over time and is not predicable
Weight
Time
Frequency
Figure S6.1

32. Control Charts
Constructed from historical data, the purpose of control charts is to help distinguish between natural variations and variations due to assignable causes
Students should understand both the concepts of natural and assignable variation, and the nature of the efforts required to deal with them.

33. Types of Data Variables Attributes
Characteristics that can take any real value
May be in whole or in fractional numbers
Continuous random variables
Defect-related characteristics
Classify products as either good or bad or count defects
Categorical or discrete random variables
Once the categories are outlined, students may be asked to provide examples of items for which variable or attribute inspection might be appropriate. They might also be asked to provide examples of products for which both characteristics might be important at different stages of the production process.

34. Process Control
(a) In statistical control and capable of producing within control limits
Frequency
Lower control limit
Upper control limit
(b) In statistical control but not capable of producing within control limits
This slide helps introduce different process outputs.
It can also be used to illustrate natural and assignable variation.
(c) Out of control
(weight, length, speed, etc.)
Size
Figure S6.2

35. Population and Sampling Distributions
Three population distributions
Beta
Normal
Uniform
Distribution of sample means
Standard deviation of the sample means
= sx = s n
Mean of sample means = x
| | | | | | |
-3sx -2sx -1sx x +1sx +2sx +3sx
99.73% of all x
fall within ± 3sx
95.45% fall within ± 2sx
Figure S6.3

36. Sampling Distribution
Sampling distribution of means
Process distribution of means
x = m
(mean)
It may be useful to spend some time explicitly discussing the difference between the sampling distribution of the means and the mean of the process population.
Figure S6.4

37. Control Charts for Variables
For variables that have continuous dimensions
Weight, speed, length, strength, etc.
x-charts are to control the central tendency of the process
R-charts are to control the dispersion of the process
These two charts must be used together

38. Setting Chart Limits For x-Charts when we know s
Upper control limit (UCL) = x + zsx
Lower control limit (LCL) = x - zsx
where x = mean of the sample means or a target value set for the process
z = number of normal standard deviations
sx = standard deviation of the sample means
= s/ n
s = population standard deviation
n = sample size

39. Setting Control Limits
Hour 1
Sample Weight of Number Oat Flakes
1 17
2 13
3 16
4 18
5 17
6 16
7 15
8 17
9 16
Mean 16.1
s = 1
Hour Mean Hour Mean
n = 9
For 99.73% control limits, z = 3
UCLx = x + zsx = (1/3) = 17 ozs
LCLx = x - zsx = (1/3) = 15 ozs

40. Setting Control Limits
Control Chart for sample of 9 boxes
Variation due to assignable causes
Out of control
Sample number
| | | | | | | | | | | |
17 = UCL
15 = LCL
16 = Mean
Variation due to natural causes
Out of control

41. Setting Chart Limits For x-Charts when we don’t know s
Upper control limit (UCL) = x + A2R
Lower control limit (LCL) = x - A2R
where R = average range of the samples
A2 = control chart factor found in Table S6.1
x = mean of the sample means

42. Control Chart Factors Sample Size Mean Factor Upper Range Lower Range
n A2 D4 D3
Table S6.1

43. Setting Control Limits
Process average x = ounces
Average range R = .25
Sample size n = 5

44. Setting Control Limits
Process average x = ounces
Average range R = .25
Sample size n = 5
UCLx = x + A2R
= (.577)(.25) =
= ounces
From Table S6.1

45. Setting Control Limits
Process average x = ounces
Average range R = .25
Sample size n = 5
UCL =
Mean = 16.01
LCL =
UCLx = x + A2R
= (.577)(.25) =
= ounces
LCLx = x - A2R =
= ounces

46. R – Chart Type of variables control chart
Shows sample ranges over time
Difference between smallest and largest values in sample
Monitors process variability
Independent from process mean

47. Setting Chart Limits For R-Charts Upper control limit (UCLR) = D4R
Lower control limit (LCLR) = D3R
where
R = average range of the samples
D3 and D4 = control chart factors from Table S6.1

48. Setting Control Limits
Average range R = 5.3 pounds
Sample size n = 5
From Table S6.1 D4 = 2.115, D3 = 0
UCL = 11.2
Mean = 5.3
LCL = 0
UCLR = D4R
= (2.115)(5.3)
= 11.2 pounds
LCLR = D3R
= (0)(5.3)
= 0 pounds

49. Mean and Range Charts (a)
These sampling distributions result in the charts below
(Sampling mean is shifting upward but range is consistent)
x-chart
(x-chart detects shift in central tendency)
UCL
LCL
R-chart
(R-chart does not detect change in mean)
UCL
LCL
Figure S6.5

50. Mean and Range Charts (b)
These sampling distributions result in the charts below
(Sampling mean is constant but dispersion is increasing)
x-chart
(x-chart does not detect the increase in dispersion)
UCL
LCL
R-chart
(R-chart detects increase in dispersion)
UCL
LCL
Figure S6.5

51. Automated Control Charts

52. Control Charts for Attributes
For variables that are categorical
Good/bad, yes/no, acceptable/unacceptable
Measurement is typically counting defectives
Charts may measure
Percent defective (p-chart)
Number of defects (c-chart)

53. Control Limits for p-Charts
Population will be a binomial distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics
UCLp = p + zsp ^
p(1 - p) n
sp = ^
Instructors may wish to point out the calculation of the standard deviation reflects the binomial distribution of the population
LCLp = p - zsp ^
where p = mean fraction defective in the sample
z = number of standard deviations
sp = standard deviation of the sampling distribution
n = sample size ^

54. p-Chart for Data Entry p = = .04 sp = = .02 1 6 .06 11 6 .06
Sample Number Fraction Sample Number Fraction
Number of Errors Defective Number of Errors Defective
Total = 80
p = = .04 80
(100)(20)
(.04)( )
100
sp = = .02 ^

55. p-Chart for Data Entry UCLp = p + zsp = .04 + 3(.02) = .10^
LCLp = p - zsp = (.02) = 0 ^
.11 –
.10 –
.09 –
.08 –
.07 –
.06 –
.05 –
.04 –
.03 –
.02 –
.01 –
.00 –
Sample number
Fraction defective
| | | | | | | | | |
UCLp = 0.10
LCLp = 0.00
p = 0.04

56. Possible assignable causes present
p-Chart for Data Entry
.11 –
.10 –
.09 –
.08 –
.07 –
.06 –
.05 –
.04 –
.03 –
.02 –
.01 –
.00 –
Sample number
Fraction defective
| | | | | | | | | |
UCLp = p + zsp = (.02) = .10 ^
LCLp = p - zsp = (.02) = 0
UCLp = 0.10
LCLp = 0.00
p = 0.04
Possible assignable causes present
There is always a focus on finding and eliminating problems. But control charts find any process changed, good or bad. The clever company will be looking at Operator 3 and 19 as they reported no errors during this period. The company should find out why (find the assignable cause) and see if there are skills or processes that can be applied to the other operators.

57. Control Limits for c-Charts
Population will be a Poisson distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics
UCLc = c + 3 c
LCLc = c - 3 c
Instructors may wish to point out the calculation of the standard deviation reflects the Poisson distribution of the population where the standard deviation equals the square root of the mean
where c = mean number defective in the sample

58. c-Chart for Cab Company
c = 54 complaints/9 days = 6 complaints/day
UCLc = c + 3 c =
= 13.35 | 1 2 3 4 5 6 7 8 9
Day
Number defective
14 –
12 –
10 –
8 –
6 –
4 –
2 –
0 –
UCLc = 13.35
LCLc = 0
c = 6
LCLc = c - 3 c =
= 0

59. Patterns in Control Charts
Upper control limit
Target
Lower control limit
Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
Normal behavior. Process is “in control.”
Figure S6.7

60. Patterns in Control Charts
Upper control limit
Target
Lower control limit
Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
One plot out above (or below). Investigate for cause. Process is “out of control.”
Figure S6.7

61. Patterns in Control Charts
Upper control limit
Target
Lower control limit
Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
Trends in either direction, 5 plots. Investigate for cause of progressive change.
Figure S6.7

62. Patterns in Control Charts
Upper control limit
Target
Lower control limit
Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
Two plots very near lower (or upper) control. Investigate for cause.
Figure S6.7

63. Patterns in Control Charts
Upper control limit
Target
Lower control limit
Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
Run of 5 above (or below) central line. Investigate for cause.
Figure S6.7

64. Patterns in Control Charts
Upper control limit
Target
Lower control limit
Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
Erratic behavior. Investigate.
Figure S6.7

65. Which Control Chart to Use
Variables Data
Using an x-chart and R-chart:
Observations are variables
Collect samples of n = 4, or n = 5, or more, each from a stable process and compute the mean for the x-chart and range for the R-chart
Track samples of n observations each

66. Which Control Chart to Use
Attribute Data
Using the p-chart:
Observations are attributes that can be categorized in two states
We deal with fraction, proportion, or percent defectives
Have several samples, each with many observations

67. Which Control Chart to Use
Attribute Data
Using a c-Chart:
Observations are attributes whose defects per unit of output can be counted
The number counted is often a small part of the possible occurrences
Defects such as number of blemishes on a desk, number of typos in a page of text, flaws in a bolt of cloth