1. Stevenson 9
Management of Quality

2. Learning Objectives Define the term quality.
Explain why quality is important and the consequences of poor quality.
Identify the determinants of quality.
Describe the costs associated with quality.
Describe TQM.
Describe Lean Production.
Give an overview of problem solving.
Describe and use various quality tools.

3. Key Contributors to Quality Management

4. Quality Management What does the term quality mean?
Quality is the ability of a product or service to consistently meet or exceed customer expectations.

5. Quality Assurance vs. Strategic Approach
Emphasis on finding and correcting defects before reaching market
Strategic Approach
Proactive, focusing on preventing mistakes from occurring
Greater emphasis on customer satisfaction

6. Dimensions of Quality
Performance - main characteristics of the product/service
Aesthetics - appearance, feel, smell, taste
Special Features - extra characteristics
Conformance - how well product/service conforms to customer’s expectations
Reliability - consistency of performance

7. Dimensions of Quality (Cont’d)
Durability - useful life of the product/service
Perceived Quality - indirect evaluation of quality (e.g. reputation)
Serviceability - service after sale

8. Examples of Quality Dimensions

9. Examples of Quality Dimensions (Cont’d)

10. Service Quality Convenience Reliability Responsiveness Time Assurance
Courtesy
Tangibles

11. Examples of Service Quality
Dimension
Examples
1. Convenience
Was the service center conveniently located?
2. Reliability
Was the problem fixed?
3. Responsiveness
Were customer service personnel willing and able to answer questions?
4. Time
How long did the customer wait?
5. Assurance
Did the customer service personnel seem knowledgeable about the repair?
6. Courtesy
Were customer service personnel and the cashier friendly and courteous?
7. Tangibles
Were the facilities clean, personnel neat?

12. Challenges with Service Quality
Customer expectations often change
Different customers have different expectations
Each customer contact is a “moment of truth”
Customer participation can affect perception of quality
“Fail-safe” must be designed into the system (for customer self-service)

13. Determinants of Quality
Quality of design
Intention of designers to include or exclude features in a product or service
Quality of conformance
The degree to which goods or services conform to the intent of the designers

14. The Consequences of Poor Quality
Loss of business
Liability
Productivity
Costs

15. Responsibility for Quality
Top management
Design
Procurement
Production/operations
Quality assurance
Packaging and shipping
Marketing and sales
Customer service
IDEALLY, EVERYONE IS RESPONSIBLE FOR QUALITY

16. Costs of Quality
Failure Costs - costs incurred by defective parts/products or faulty services.
Internal Failure Costs
Costs incurred to fix problems that are detected before the product/service is delivered to the customer.
External Failure Costs
All costs incurred to fix problems that are detected after the product/service is delivered to the customer.

17. Costs of Quality (continued)
Appraisal Costs
Costs of activities designed to ensure quality or uncover defects
Prevention Costs
All TQ training, TQ planning, customer assessment, process control, and quality improvement costs to prevent defects from occurring

18. Ethics and Quality Substandard work Defective products
Substandard service
Poor designs
Shoddy workmanship
Substandard parts and materials
Having knowledge of this and failing to correct
and report it in a timely manner is unethical.

19. Quality Certification
ISO 9000
Set of international standards on quality management and quality assurance, critical to international business
ISO 14000
A set of international standards for assessing a company’s environmental performance

20. ISO 9000 Quality Management Principles
Customer focus
Leadership
People involvement
Process approach
A systems approach to management
Continual improvement
Factual approach to decision making
Mutually beneficial supplier relationships

21. ISO 14000 Management systems Operations Environmental systems
Systems development and integration of environmental responsibilities into business planning
Operations
Consumption of natural resources and energy
Environmental systems
Measuring, assessing and managing emissions, effluents, and other waste

22. Total Quality Management
A philosophy that involves everyone in an organization in a continual effort to improve quality and achieve customer satisfaction. T Q M

23. The TQM Approach Find out what the customer wants
Design a product or service that meets or exceeds customer wants
Design processes that facilitates doing the job right the first time
Keep track of results
Extend these concepts to suppliers

24. Elements of TQM Continual improvement Competitive benchmarking
Employee empowerment
Team approach
Decisions based on facts
Knowledge of tools
Supplier quality
Champion
Quality at the source
Suppliers

25. Continuous Improvement
Philosophy that seeks to make never-ending improvements to the process of converting inputs into outputs.
Kaizen: Japanese word for continuous improvement.

26. Quality at the Source
The philosophy of making each worker responsible for the quality of his or her work.

27. Six Sigma Statistically Conceptually
Having no more than 3.4 defects per million
Conceptually
Program designed to reduce defects
Requires the use of certain tools and techniques
Six sigma: A business process for improving quality, reducing costs, and increasing customer satisfaction.

28. 3 Sigma and 6 Sigma Quality
Process mean
Lower specification
Upper specification
1350 ppm
1.7 ppm
+/- 3 Sigma
+/- 6 Sigma

29. Six Sigma Programs Six Sigma programs Employed in Improve quality
Save time
Cut costs
Employed in
Design
Production
Service
Inventory management
Delivery

30. Six Sigma Management Providing strong leadership
Defining performance metrics
Selecting projects likely to succeed
Selecting and training appropriate people

31. Six Sigma Technical Improving process performance Reducing variation
Utilizing statistical models
Designing a structured improvement strategy

32. Six Sigma Team Top management Program champions Master “black belts”
“Green belts”

33. Six Sigma Process
Define
Measure
Analyze
Improve
Control
DMAIC

34. Obstacles to Implementing TQM
Lack of:
Company-wide definition of quality
Strategic plan for change
Customer focus
Real employee empowerment
Strong motivation
Time to devote to quality initiatives
Leadership

35. Obstacles to Implementing TQM
Poor inter-organizational communication
View of quality as a “quick fix”
Emphasis on short-term financial results
Internal political and “turf” wars

36. Basic Steps in Problem Solving
Define the problem and establish an improvement goal
Define measures and collect data
Analyze the problem
Generate potential solutions
Choose a solution
Implement the solution
Monitor the solution to see if it accomplishes the goal

37. The PDSA Cycle
Plan Do
Study
Act

38. Process Improvement
Process Improvement: A systematic approach to improving a process
Process mapping
Analyze the process
Redesign the process

39. The Process Improvement Cycle
Implement the
Improved process
Select a
process
Study/document
Seek ways to
Improve it
Design an
Evaluate
Document

40. Basic Quality Tools Flowcharts Check sheets Histograms Pareto Charts
Scatter diagrams
Control charts
Cause-and-effect diagrams
Run charts

41. Check Sheet Monday Billing Errors A/R Errors Wrong Account
Wrong Amount
A/R Errors
Monday

42. 80% of the problems may be attributed to 20% of the
Pareto Analysis
80% of the problems may be attributed to 20% of the
causes.
Smeared
print
Number of defects
Off center
Missing
label
Loose
Other

43. Control Chart
970
980
990
1000
1010
1020 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UCL
LCL

44. Cause-and-Effect Diagram

45. Run Chart
Time (Hours)
Diameter

46. Tracking Improvements
UCL
LCL
Process not centered
and not stable
Process centered
and stable
Additional improvements
made to the process

47. Methods for Generating Ideas
Brainstorming
Quality circles
Interviewing
Benchmarking

48. Benchmarking Process Identify a critical process that needs improving
Identify an organization that excels in this process
Contact that organization
Analyze the data
Improve the critical process

49. Lean Production
“A systematic approach to identifying and eliminating waste(non-value-added activities) through continuous improvement by flowing the product at the pull of the customer in pursuit of perfection.”

50. Basic Elements of Lean

51. Waste in Operations

52. Waste in Operations

53. Stevenson 10
Quality Control

54. Learning Objectives
List and briefly explain the elements of the control process.
Explain how control charts are used to monitor a process, and the concepts that underlie their use.
Use and interpret control charts.
Use run tests to check for nonrandomness in process output.
Assess process capability.

55. Phases of Quality Assurance
Inspection and
corrective
action during
production
Inspection of lots
before/after
production
Quality built
into the
process
Acceptance
sampling
Process
control
Continuous
improvement
The least
progressive
The most
progressive

56. Inspection How Much/How Often Where/When Centralized vs. On-site
Inputs
Transformation
Outputs
Acceptance
sampling
Process
control
Acceptance
sampling

57. Inspection Costs Cost Optimal Amount of Inspection Total Cost
Cost of inspection
Cost of passing
defectives

58. Where to Inspect in the Process
Raw materials and purchased parts
Finished products
Before a costly operation
Before an irreversible process
Before a covering process (e.g., painting/final assembly)

59. Examples of Inspection Points

60. Statistical Control
Statistical Process Control: Statistical evaluation of the output of a process during production
Quality of Conformance: A product or service conforms to specifications

61. Control Chart Control Chart
Purpose: to monitor process output to see if it is random
A time ordered plot representative sample statistics obtained from an on going process (e.g. sample means)
Upper and lower control limits define the range of acceptable variation

62. 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

63. Statistical Process Control
The essence of statistical process control is to assure that the output of a process is random so that future output will be random.

64. Statistical Process Control
The Control Process
Define
Measure
Compare
Evaluate
Correct
Monitor results

65. Statistical Process Control
Variations and Control
Random variation: Natural variations in the output of a process, created by countless minor factors
Assignable variation: A variation whose source can be identified

66. Normal Distribution Mean     95.44% 99.74%
Standard deviation

67. Control Limits Sampling distribution Process distribution Mean
Lower control limit
Upper control limit

68. SPC Errors Type I error Type II error
Concluding a process is not in control when it actually is.
Type II error
Concluding a process is in control when it is not.

69. Type I and Type II Errors

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

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

72. Control Charts for Variables
Variables generate data that are measured.
Mean control charts
Used to monitor the central tendency of a process.
X bar charts
Range control charts
Used to monitor the process dispersion
R charts

73. Mean Control Chart for Variables

74. Mean Control Chart for Variables

75. Range Control Chart for Variables
Monitors Process Dispersion D3 and D4 are found in the Table for 3-σ Control Limits

76. Table for 3-σ Control Limits

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

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

79. Control Chart for Variables/Example
A quality inspector took five samples, each with four observations (n = 4), of the length of time for glue to dry. The analyst computed the mean of each sample and then computed the grand mean. All values are in minutes. Use this information to obtain three-sigma (i.e., z = 3) control limits for means of future times. It is known from previous experience that the standard deviation of the process is .02 minute.

80. Control Chart for Variables/Example

81. Control Chart for Variables/Example
A quality inspector took five samples, each with four observations (n = 4), of the length of time for glue to dry. The analyst computed the mean of each sample and then computed the grand mean. All values are in minutes. Use this information to obtain three-sigma (i.e., z = 3) control limits for means of future times.

82. Control Chart for Variables/Example
Standard Deviation is not given

83. Control Chart for Variables/Example
Plot the sample means in to control chart.

84. Control Chart for Variables/Example
For previous example, construct a Range chart From the Table for 3-σ Control Limits, for n= 4 (observations per sample), D4 = 2.28 and D3=0 UCL = 2.28(0.046) = LCL = 0(0.046) = 0 Note that each sample’s range falls within these Control Limits

85. Control Chart for Attributes
p-Chart - Control chart used to monitor the proportion of defectives in a process
c-Chart - Control chart used to monitor the number of defects per unit
Note for both p-Charts and c-Charts, if LCL is negative, then set LCL to zero.
Attributes generate data that are counted.

86. Use of p-Charts When observations can be placed into two categories.
Good or bad
Pass or fail
Operate or don’t operate

87. p-Charts

88. p-Chart Example
An inspector counted the number of defective monthly billing statements of a company telephone in each of 20 samples. Using the following information, construct a control chart that will describe percent of the chance variation in the process when the process is in control. Each sample contained 100 statements.

89. p-Chart Example

90. p-Chart Example

91. p-Chart Example
Plot the sample proportions in the Control Chart

92. Use of c-Charts
Use only when the number of occurrences per unit of measure can be counted; non-occurrences cannot be counted.
Scratches, chips, dents, or errors per item
Cracks or faults per unit of distance
Breaks or Tears per unit of area
Bacteria or pollutants per unit of volume
Calls, complaints, failures per unit of time

93. c-Charts

94. c-Chart Example
Rolls of coiled wire are monitored using a c-chart. Eighteen rolls have been examined, and the number of defects per roll has been recorded in the following table. Is the process in control? Plot the values on a control chart using three standard deviation control limits

95. c-Chart Example

96. Use of Control Charts
At what point in the process to use control charts
What size samples to take
What type of control chart to use
Variables
Attributes

97. Run Tests Run test – a test for randomness
Any sort of pattern in the data would suggest a non-random process
All points are within the control limits - the process may not be random

98. Nonrandom Patterns in Control charts
Trend
Cycles
Bias
Mean shift
Too much dispersion

99. Counting Runs Counting Above/Below Median Runs (7 runs)
Counting Up/Down Runs (8 runs)
U U D U D U D U U D
B A A B A B B B A A B

100. NonRandom Variation
Managers should have response plans to investigate cause
May be false alarm (Type I error)
May be assignable variation

101. Determine whether a process is in control?
Transform the data into As and Bs, and Us and Ds.
Count the number of Runs in each case
N is number of observations

102. Determine whether a process is in control
Expected number of Runs
Standard Deviation of Runs
Z of Runs

103. Determine whether a process is in control

104. Example
Twenty sample means have been taken from a process. The means are shown in the following table. Use median and up/down run tests with z = 2 to determine if assignable causes of variation are present. Assume the median is 11.0

105. Example
Although the median test does not reveal any pattern, because its ztest value is within the range ± 2, the up/down test does; its value exceeds +2. Consequently, nonrandom variations are probably present in the data and, hence, the process is not in control

106. Process Capability Tolerances or specifications Process variability
Range of acceptable values established by engineering design or customer requirements
Process variability
Natural variability in a process
Process capability
Process variability relative to specification
For a process to be capable, its capability index (ideally) should be 1.33 or higher

107. Improving Process Capability
Simplify
Standardize
Mistake-proof
Upgrade equipment
Automate

108. Traditional cost function
Taguchi Loss Function
Cost
Target
Lower spec
Upper spec
Traditional cost function
Taguchi cost function

109. QUIZ NEXT SESSION QUALITY MANAGEMENT AND CONTROL
(STEVENSON CHAPTERS 9 & 10)