1. Lecture 29 Total Quality Management
Books
Introduction to Materials Management, Sixth Edition, J. R. Tony Arnold, P.E., CFPIM, CIRM, Fleming College, Emeritus, Stephen N. Chapman, Ph.D., CFPIM, North Carolina State University, Lloyd M. Clive, P.E., CFPIM, Fleming College
Operations Management for Competitive Advantage, 11th Edition, by Chase, Jacobs, and Aquilano, 2005, N.Y.: McGraw-Hill/Irwin.
Operations Management, 11/E, Jay Heizer, Texas Lutheran University, Barry Render, Graduate School of Business, Rollins College, Prentice Hall

2. Objectives Why quality is important What is quality
Dimensions of Quality
Why improve quality
Statistical quality control
Understanding variations
Statistical process control
Process capability
Three sigma vs. six sigma
Process control
Data types

3. Orlando Utilities Commission
Maintenance of power generating plants
Every year each plant is taken off-line for 1-3 weeks maintenance
Every three years each plant is taken off-line for 6-8 weeks for complete overhaul and turbine inspection
Each overhaul has 1,800 tasks and requires 72,000 labor hours
OUC performs over 12,000 maintenance tasks each year

4. Orlando Utilities Commission
Every day a plant is down costs OUC $110,000
Unexpected outages cost between $350,000 and $600,000 per day
Preventive maintenance discovered a cracked rotor blade which could have destroyed a $27 million piece of equipment

5. Why Quality is Important
Costs and market share
Company’s reputation
Product liability
International implications

6. What is Quality? Conformance to requirements? Zero defects?
Fitness for use?
Consistency?
“I can’t define it, but I know it when I see it”?

7. Garvin’s 8 Dimensions of Quality
Dimension Meaning
Performance Primary operating characteristics.
Features Secondary operating characteristics, added touches.
Reliability Extent of failure free operation.
Durability Amount of use before replacement is preferable to repair.
Consistency Uniformity around a target
Serviceability Resolution of problems and complaints.
Aesthetics Subjective characteristics that relate to senses.
Perceived Quality Indirect measures or inferences: reputation.

8. What is Quality?
Quality means user satisfaction: that goods and services satisfy the needs and expectations of the user.
Arnold

9. Why Improve Quality?

10. Quality Chain Reaction

11. Two Ways to Improve Quality

12. Sources of Improvement

13. Statistical Quality Control:Process Definition
Process: “A ‘process’ is any set of conditions, or set of causes, which work together to produce a given result. In its narrowest sense the term ‘process’ refers to the operation of a single cause. In its broadest sense it may refer to the operation of a very complicated ‘cause system.’
Reference: Statistical Quality Control Handbook, Western Electric

14. Statistical Quality Control
Statistical: With the help of numbers or data
Quality: we study the characteristics of our process
Control: In order to make it behave the way we want it to behave.
Reference: Statistical Quality Control Handbook, Western Electric

15. Understanding Variation
Variation exists in everything
Understanding variation is the key to improving quality
Two Kinds of Variation
Chance variation
Assignable variation

16. Cause and Effect Diagrams
Materials
Methods
Measurement
Process Doc.
Desired Effect
or Undesired
Effect PM
Motivation
Training
Machines
Manpower
Environment

17. SPC - Assignable Causes
The operational definition of assignable variation is variation that causes out-of-control points on a control chart.

18. Statistical Quality Control: Natural Patterns or Variations
Natural patterns exhibit the following characteristics:
Most of the points are near the centerline.
A few points spread out and approach the control limits.
None (or only on rare occasions) of the points exceeds the control limits.
Reference: Statistical Quality Control Handbook, Western Electric

19. Statistical Quality Control Unnatural Patterns or Variations
Unnatural patterns exhibit the following characteristics:
Absence of points near the centerline produces a pattern known as a “mixture.”
Absence of points near the control limits produces an unnatural pattern known as “stratification.”
Presence of points outside of the control limits produces an unnatural pattern known as “instability.”
Reference: Statistical Quality Control Handbook, Western Electric

20. Statistical Quality Control Tests for Unnatural Patterns
Instability
A single point falls outside of the 3 sigma control limits.
Two out of three successive points fall in the outer one third of the control limits.
Four out of five successive points fall in the outer two thirds of the control limits.
Eight successive points fall on one side of the centerline.
Systematic variable
A long series of points are high, low, high, low without interruption.
Reference: Statistical Quality Control Handbook, Western Electric

21. Statistical Process Control (SPC)
Chance variations are the many sources of variation within a process that is in statistical control. They behave like a constant system of random chance causes.
If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable.

22. Statistical Process Control (SPC)
Assignable variation in a process can be traced to a specific reason.
Machine wear
Misadjusted equipment
Fatigued or untrained workers
If assignable causes of variation are present, the process output is not stable over time and is not predictable.

23. Statistical Process Control Why use averages?
To create a normal distribution
Averages are more sensitive to change than individuals

24. The Process (2 of 2)
The distribution of a process’ output has a mean, , and a standard deviation, ; it can have a wide variety of shapes
Process distribution
Mean

25. Process Capability (1 of 3)
When selecting a process to perform an operation, the inherent variability of process output should be compared to the range or tolerances allowed by the designer’s specifications

26. Process Capability (2 of 3)
process distribution
Lower Specification
Upper Specification
Much of the process output fits within specification width
In other words, is the process capable of producing the item
within specifications?
Almost all of the process output fits within the specification width
A significant portion of the process output falls outside of the specification width

27. Process Capability (3 of 3)
The process capability index (cp) compares the design specifications with a measure of process variability

28. Three-Sigma Quality Lower Upper design specification 1350 ppm 1350 ppm
mean

29. Three-Sigma Quality vs. Six-Sigma Quality
Lower
design specification
Upper
1350 ppm
1350 ppm
+3 Sigma
-3 Sigma
mean
1.7 ppm
1.7 ppm
+6 Sigma
-6 Sigma
mean

30. Normal Distribution Standard deviation     Mean 95.5%
99.7%
Standard deviation

31. Process Control (1 of 6)
Once a process is in operation, a goal is to maintain the status quo, i.e., keep the process “in control”
What can make the process no longer be in control, i.e., go “out of control”?
The presence of an assignable cause
The presence of an assignable cause may cause the process distribution to
shift to the left or right, and/or
increase the variability (flatten out)

32. Process Control (2 of 6)
If the process mean shifts, more of output falls outside the specifications
upper design
specification
Time
lower design

33. Process Control (3 of 6)
If the process mean shifts, more of output falls outside the specifications
If process variance increases, more of the output falls outside of the specifications
Time
upper design
specification
lower design

34. Process Control (4 of 6)
In either case, the process is considered to be out of control
It should be stopped, investigated (the assignable cause found if present) and corrected (the process brought back to the status quo)

35. Process Control (5 of 6) Examples of assignable causes include
operator
raw material
equipment
environment

36. Process Control (6 of 6)
How does management detect the presence of an assignable cause?
Process output is monitored to detect any changes by inspecting the output of the process
Inspection means assessing some characteristic of a unit of output

37. Central Limit Theorem Simulation
The distribution of a sample approaches normal even when the parent population is not normally distributed.

38. Statistical Process Control
Tolerance or specification limits
Defined by an engineer
Related to product design requirements
Control limits
Defined by the process
Related to the variation in the process
Unrelated to product needs

39. Much of the process output fits within specification width
Lower Specification
Upper Specification
Process Capability
Lower Specification
Upper Specification
Almost all of the process output fits within the specification width
Lower Specification
Upper Specification
A significant portion of the process output falls outside of the specification width
In other words, is the process capable of producing the item?

40. Type I Error
Mean
LCL
UCL
/2
Probability of Type I error

41. Types of Sampling Errors

42. Control Chart 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UCL
LCL
Sample number
Mean
Out of control
Normal variation due to chance
Abnormal variation due to assignable sources

43. Observations from Sample Distribution
UCL
LCL 1 2 3 4
Sample number

44. Data Types
Attribute - characteristic evaluated generates data that are counted (good or bad, yes or no).
Variable - characteristic evaluated can be measured within a range of values

45. Mean and Range Charts Detects shift x-Chart Does not detect shift
(process mean is
shifting upward)
Sampling
Distribution
UCL
LCL
x-Chart
Detects shift
UCL
LCL
Does not detect shift
R-chart

46. Mean and Range Charts Does not reveal increase x-Chart R-chart
Sampling
Distribution
(process variability is increasing)
UCL
LCL
x-Chart
UCL
Does not reveal increase
R-chart
Reveals increase
LCL

47. End of Lecture 29