1. Quality and Education
Business has made progress toward quality over the past several years. But I don’t believe we can truly make quality a way of life … until we make quality a part of every student’s education
Edwin Artzt, Chairman and CEO, Proctor & Gamble Co., Quality Progress, October 1992, p. 25

2. Quality and Competitive Advantage
Better price
The better customers judge the quality of a product, the more they will pay for it
Lower production cost
It is cheaper to do a job right the first time than do it over
Faster response
A company with quality processes for handling orders, producing products, and delivering them can provide fast response to customer requests

3. Quality and Competitive Advantage
Reduced Inventory
When the production line runs smoothly with predictable results, inventory levels can be reduced
Improved competitive position in the marketplace
A customer who is satisfied with quality will tell 8 people about it; a dissatisfied customer will tell 22 (A.V. Feigenbaum, Quality Progress, February 1986, p. 27)

4. TQM Wheel Customer satisfaction
This slide presents the TQM Wheel from page This can be left on the screen while you discuss whatever aspects of the wheel you choose. The Employee Involvement section is presented in more detail on a subsequent slide. 2

5. Customer-Driven Definitions of Quality
Conformance to specifications
Conformance to advertised level of performance
Value
How well the purpose is served at a particular price.
For example, if a $2.00 plastic ballpoint pen lasts for six months, one may feel that the purchase was worth the price.
These are the five dimensions of quality as listed and defined on page We build on this slide as we advance so each definition can be discussed in depth.
Note: This list adds the important element of Value to what is essentially a simplified version of Garvin’s 8 dimensions of quality. 3

6. Customer-Driven Definitions of Quality
Fitness for use
Mechanical feature of a product, convenience of a service, appearance, style, durability, reliability, craftsmanship, serviceability
Support
Financial statements, warranty claims, advertising
Psychological Impressions
Atmosphere, image, aesthetics
“Thanks for shopping at Wal-Mart”

7. Defectives and Defect
In the popular sense, a defect is some characteristic that makes a product unsatisfactory for its intended purpose
Technically, a defect is a failure to conform to some specification e.g.,  in.
To avoid ambiguity, following words are suggested
Nonconformity or Nonconformance: defect
Nonconforming: defective

8. Quality Costs Prevention costs
Customer requirements/expectations market research
Product design/development reviews
Quality education programs
Equipment and preventive maintenance
Supplier-rating program administration

9. Quality Costs Appraisal costs Testing/inspection equipment
Inspection costs
Audits

10. Quality Costs Internal failure costs Rework, scrap, repair
External failure costs
Returned goods, warranty costs, liability costs, penalties
Intangible costs
Customer dissatisfaction, company image, lost sales, loss of customer goodwill

11. Process Final testing Customer When defect is detected
Costs of Detecting Defects
Cost of detection (dollars)
Process Final testing Customer
When defect is detected

12. Statistical Quality Control Introduction
Control charts and sampling
Simple and R charts
Variation
Common and assignable causes

13. Control Chart Viewpoint
Variation due to
Common or chance causes
Assignable causes
Control chart may be used to discover “assignable causes”

14. Scientific Sampling Inspection
Incoming materials, in-process products, finished goods
JIT inventory control makes formal sampling impractical except for quality audit purposes
The supplier performs sampling inspection and provides statistical evidence of conformance to specifications
100% inspection may be impractical or uneconomical

15. Some Terms Run chart - without any upper/lower limits
Specification/tolerance limits - not statistical
Control limits - statistical

16. Weakness of Plotting Individual Measurements against Specification/Tolerance Limits
If individual measurements are plotted against specification/tolerance limits, following problems may occur
If specification/tolerance limits are too wide, the systems may fail to detect some variations that are less likely to be caused by chance and more likely to be caused by some problems in the production system (see Example 1.1)
If specification/tolerance limits are too narrow, unavoidable random variations may be considered as defects and too many items may be rejected (see Example 1.2)

17. Control Charts Take periodic samples from a process
Plot the sample points on a control chart
Determine if the process is within limits
Correct the process before defects occur

18. Types of Data Variable data
Product characteristic that can be measured
Length, size, weight, height, time, velocity
Attribute data
Product characteristic evaluated with a discrete choice
Good/bad, yes/no

19. Process Control Chart Upper control limit Process average Lower 1 2 34 5 6 7 8 9 10
Sample number

20. Constructing a Control Chart
Decide what to measure or count
Collect the sample data
Plot the samples on a control chart
Calculate and plot the control limits on the control chart
Determine if the data is in-control
If non-random variation is present, discard the data (fix the problem) and recalculate the control limits

21. Control Charts For Variables
Mean chart (X-Bar Chart)
Measures central tendency of a sample
Range chart (R-Chart)
Measures amount of dispersion in a sample
Each chart measures the process differently. Both the process average and process variability must be in control for the process to be in control.
The mean or X-bar chart indicates how sample results relate to the process average or mean. The range or R chart reflects the amount of dispersion that is present in each sample. These charts are normally used together to determine if a process is in control.

22. Example: Control Charts for Variable Data
Slip Ring Diameter (cm)
Sample X R
Example4.3

23. Normal Distribution Review
If the diameters are normally distributed with a mean of 5.01 cm and a standard deviation of 0.05 cm, find the probability that the sample means are smaller than 4.98 cm or bigger than 5.02 cm.

24. Normal Distribution Review
If the diameters are normally distributed with a mean of 5.01 cm and a standard deviation of 0.05 cm, find a lower value and an upper value of the sample means such that 97% sample means are between the lower and upper values.

25. Normal Distribution Review
Define the 3-sigma limits for sample means as follows:
What is the probability that the sample means will lie outside 3-sigma limits?

26. Normal Distribution Review
Note that the 3-sigma limits for sample means are different from natural tolerances which are at

27. Constructing a Range Chart
Note: The control limits are only preliminary with 10 samples.
It is desirable to have at least 25 samples.

28. Constructing A Mean Chart

29. 3-Sigma Control Chart Factors
Sample size X-chart R-chart
n A2 D3 D4

30. Common Causes
The first set of slides presents the various aspects of common and assignable causes. This slide and the two following show a normal process distribution and how it allows for expected variability which is termed common causes. 2

31. Assignable Causes (a) Mean Average Grams
The new distribution will look as shown in this slide.
Grams
(a) Mean 7

32. Assignable Causes (b) Spread Average Grams
The new distribution has a much greater spread (higher standard deviation).
Grams
(b) Spread 9

33. Assignable Causes (c) Shape Average Grams
A skewed (non-normal) distribution will result in a different pattern of variability.
Grams
(c) Shape 11

34. The Normal Distribution  = Standard deviation Mean
-3 -2 -1  +2 +3
Mean
68.26%
95.44%
99.74%
 = Standard deviation
And at +/- 3 sigma, the most common choice of confidence/control limits in quality control application, the area is 99.97%. 20

35. Control Charts Assignable causes likely UCL Nominal LCL 1 2 3 Samples
However, the third sample plots outside the original distribution, indicating the likely presence of an assignable cause.
LCL
Samples 24

36. Control Chart Examples
UCL
Nominal
Variations
LCL
And finally, this chart shows a process with two points actually outside the control limits, an easy indicator to detect but not the only one.
These rules, there are a few more, are commonly referred to as the Western Electric Rules and can be found in any advanced quality reference.
Sample number 30

37. Control Limits and Errors
Type I error:
Probability of searching for
a cause when none exists
UCL
Process
average
As long as the area encompassed by the control limits is less than 100% of the area under the distribution, there will be a probability of a Type I error. A Type I error occurs when it is concluded that a process is out of control when in fact pure randomness is present. Given +/- 3 sigma, the probability of a Type I error is = , a very small probability.
LCL
(a) Three-sigma limits 32

38. Control Limits and Errors
Type I error:
Probability of searching for
a cause when none exists
UCL
Process
average
When the control limits are changed to +/- 2 sigma, the probability of a Type I error goes up considerably, from to = While this is still a small probability, it is a change that should be carefully considered. In practice, this would mean more samples would be identified inappropriately as out-of-control. Even if they were subsequently ‘Okd’, there would be increased costs due to many more cycles through the four step improvement process shown previously.
LCL
(b) Two-sigma limits 33

39. Control Limits and Errors
Type II error:
Probability of concluding
that nothing has changed
UCL
Shift in process
average
Process
average
Returning to the 3 sigma limits, we can see the probability of making a Type II error, in this case failing to detect a shift in the process mean.
LCL
(a) Three-sigma limits 34

40. Control Limits and Errors
Type II error:
Probability of concluding
that nothing has changed
UCL
Shift in process
average
Process
average
By reducing the control limits to +/- 2 sigma, we see the probability of failing to detect the shift has been reduced.
LCL
(b) Two-sigma limits 35

41. Process Capability Range of natural variability in process
Measured with control charts
Process cannot meet specifications if natural variability exceeds tolerances
3-sigma quality
specifications equal the process control limits.
6-sigma quality
specifications twice as large as control limits

42. Process Capability Natural Natural control Design control limits specs
Process can meet specifications
Process cannot meet specifications
Natural
control
limits
Design
specs
PROCESS
Process capability exceeds specifications

43. Process Capability
If the R chart shows control, estimate the standard deviation of items as
If the R chart does not show control, remove the ones that showed lack of control, calculate a revised and new control limits for R. Repeat the process as long as it is needed. Estimate standard deviation of items as shown above.
Process capability

44. Process Capability
By computing we can conclude whether the mean has shifted towards upper/lower specification limit and if it has shifted at all. If both the numbers are equal, the mean is at the center. If the first number is smaller, the mean has shifted towards LSLx. If the second number is smaller, the mean has shifted towards USLx.
If then the process is capable of producing 99.74% items within the specification limits. Else, either the process needs improvement or the specification limits must be widened.

45. Text Exercise 2.2: Is the following process capable?:

46. Reading and Exercises Chapter 1: pp. 3-24 Chapter 2: pp. 37-54
Problems 2.5, 2.6, 2.10