1. Managing Quality

2. Chapter Objectives Be able to:
Discuss the various definitions and dimensions of quality and why quality is important to operations and supply chains.
Describe the different costs of quality, including internal and external failure, appraisal, and prevention costs.
Know what TQM is, along with its seven core principles.
Calculate process capability ratios and indices and set up control charts for monitoring continuous variables and attributes.
Describe the key issues associated with acceptance sampling, as well as the use of OC curves.
Distinguish between Taguchi’s quality loss function and the traditional view of quality.

3. Managing Quality Quality defined Total cost of quality
Total quality management (TQM)
Statistical quality control
Managing quality across the supply chain.

4. Definitions of Quality
ASQ:
The characteristics of a product or service that bear on its ability to satisfy stated or implied needs
Fitness for use (value perspective)
Free from defects (conformance perspective)
How would you evaluate the quality of the following?
Software package
Hand-held vacuum cleaner
No-frills air flight

5. Quality as a Competitive Advantage
Strategic Quality
Quality as a Competitive Advantage

6. Dimensions of Quality Performance Features Reliability Durability
Conformance
Aesthetics
Serviceability
Perceived Quality
Which dimensions do you think are directly affected by Operations and Supply Chain activities?

7. Quality Dimension Examples
New Car
Tax Preparation
Performance
Tow capability; gas mileage
Cost and time to prepare taxes
Features
Accessories; extended warranty
Advance on refund check; E- filing
Reliability
Miles between required service
Not applicable
Durability
Expected useful life of the engine, transmission, body
Conformance
Number of defects in the car
Number of mistakes on the tax return
Aesthetics
Styling, interior appearance
Neatness of the return
Serviceability
Qualified mechanics in the area? Maintenance time and cost?
Will the tax preparation firm talk with the IRS in case of an audit?
Perceived Quality
How do prices for used vehicles hold up?
What is the reputation of the firm?

8. Defensive Quality Quality analyzed in economic terms
Total Cost of Quality:
Failure Costs
Appraisal Costs
Prevention Costs

9. Total Cost of Quality — Traditional View
Note: Horizontal axis is reversed from that used in the text to reflect that the goal is zero defects.

10. The total costs of quality fall as defect levels decrease
Zero Defects View
The total costs of quality fall as defect levels decrease
Note: Horizontal axis is reversed from that used in the text to reflect that the goal is zero defects.

11. Total Quality Management (TQM)
Managing the entire organization so that it excels in all dimensions important to the customer  Product development  Marketing  Operations  Supply chain  Support services

12. TQM Principles Customer focus Leadership involvement
Continuous improvement
Employee empowerment
Quality assurance (including SQC or SPC)
Strategic partnerships
Strategic quality plan
SQC = Statistical Quality Control
SPC = Statistical Process Control

13. TQM Principles Expanded
Customer focus
Each employee has a customer whether internal or external to the company
Leadership involvement
Must be ‘top’ down, throughout the company
If not, major cause of TQM failures
Continuous improvement
Supports other core principles

14. Continuous Improvement (CI) versus “Leaps” Forward
Performance
Time

15. TQM Principles Expanded
Employee empowerment
Key to success
Lack of empowerment major cause of TQM/SPC failures
Quality assurance
Quality Function Deployment (QFD) discussed in Chapter 6
Statistical quality control (SQC), also called statistical process control (SPC)
Acceptance sampling (OC curve)
SQC = Statistical Quality Control
SPC = Statistical Process Control

16. TQM to Quality Assurance “Did we do it right?”
Switching Focus . . .
TQM to Quality Assurance
“Did we do it right?”

17. We Noted That Organizations Must ...
Understand which quality dimensions are important
Develop products and services that will meet users’ quality needs
Put in place business processes capable of meeting these needs
Verify that business processes are meeting the specifications

18. Six Sigma Methodology
Core value is having less than 3.4 defects per million opportunities (DPMO). Key elements are:
Understanding and managing customer requirements
Aligning key business processes to achieve those requirements
Using rigorous data analysis to understand and ultimately minimize variation in those processes
Driving rapid and sustainable improvement to business processes.
The core value defect level is based on allowing the mean of a process to drift to within about 4.5 standard deviations of either specification limit. A true six sigma variation around a mean centered within specifications would correspond to a defect level of 2 parts per billion.

19. Six Sigma Methodology Two basic Six Sigma processes are:
DMAIC (Define-Measure-Analyze-Improve-Control) — an updated version of the PDCA process promoted by Deming.
DMADV (Define-Measure-Analyze-Design-Verify)
DMAIC is an updated version of the PDSA (Plan-Do-Study-Act) process developed by Walter Shewhart and later promoted by W. Edwards Deming as the PDCA (Plan-Do-Check-Act) process for improvement

20. The PDCA Cycle Do
Plan
Check
Act

21. Common Improvement Tools
Cause and effect diagrams (aka “Fishbone” or Ishikawa diagrams)
Check sheets
Pareto analysis
Run charts and scatter plots
Bar graphs
Histograms

22. Flight delays at Midway
A Services Example
Flight delays at Midway
Cause and Effect Diagrams
Check Sheets
Pareto Analysis

23. Problem: Delayed Flights
No one is sure why, but plenty of opinions
“Management by Fact”
CI Tools we will use:
Fishbone diagram
Check sheets
Pareto analysis

24. Cause and Effect Diagram
ASKS: What are the possible causes?
Root cause analysis — open and narrow phases

25. Generic C&E Diagram

26. Midway C&E diagram

27. Check Sheets Event: Day 1 Day 2 Day 3 Late arrival II I Gate occupied
Too few agents
Accepting late passengers
III
(root cause analysis -- closed phase)

28. Pareto Analysis (sorted histogram)
Late passengers
100
Late arrivals
Late baggage to aircraft 85 70
Weather 65
Other (160)

29. Percent of each out of 480 total incidents ...
Late passengers 21%
Late arrivals 18%
Late baggage to aircraft 15%
Weather 14%
Other 33%

30. Run Charts and Scatter Plots
Measure
Run
Time
Variable Y
Scatter
Variable X

31. Histograms
Frequency
Measurements

32. Process Capability
Answers the Question: Can the process provide acceptable quality consistently?

33. Process Capability Ratio (Cp)
Upper Tolerance Limit – Lower Tolerance Limit 6σ
Where σ is the estimated
standard deviation
for the individual observations
Upper and lower tolerance limits are also called the upper and lower specification limits (USL and LSL).

34. Process Capability ratio of 1 (99.7% within tolerance range)
Shown Graphically:
Process Capability ratio of 1
(99.7% within tolerance range)

35. “Six Sigma Quality”
When a process operates with 6σ variation centered between the tolerance limits, only 2 parts out of a billion will be unacceptable.

36. Process Capability Index (Cpk)
Used when the process is not precisely centered between the tolerance limits.

37. Discovering “problems”
Inspect every item
Expensive to do
Testing can be destructive, should be simply unnecessary
Statistical techniques Statistical process control (SPC) Acceptance Sampling

38. Statistical Process Control
“Representative” samples are measured
good, but not perfect, picture of process
Sampling by Variable (continuous values — length, weight, area, volume, etc.)
Sampling by Attribute (good, bad, # defects/unit, %)
Variable: a continuous range of possible values (length, width, weight, temperature, pressure…..)
Attribute: Limited range of discrete values (defects per unit, yield per lot, failures per sample group, errors per order…)

39. Example: Fabric Dyeing
Rolls of fabric go through dyeing process
Target temperature of 140 degrees Too low ? Too high ?
Temperature must be “monitored” and action taken when something is “unusual”
Is temperature a “variable” or an “attribute”?

40. Step 1: Sampling the Process
Observation
Sample 1 2 3 4 5
136
137
144
141
138
143
140
139
135 6
142 7 8 9 10
Things
should be
working
OK when
we do
this . . .
Table 4.5 on page 100 in the text. Note: There is an error in Table 4.6 for observation 5 on day 9, should be 139.
Emphasize that when data is collected to determine possible control limits for the process, the process should be executed as consistently as possible to eliminate all but the natural variations (i.e., no intentional adjustments to improve results!)
Discuss importance of doing everything as consistently as possible during the collection of data to establish control limits so that only natural causes of variation are present.

41. Step 2: Calculate the Mean and Range for Each SampleX R 1
139.2 8 2
140 5 3
139.4 9 4 6 7
141.4
139
140.2 10
139.6
X = 139.8° R = 5.3°

42. Step 3A: Use These Values to Set Up X and R charts
Upper control limit for X chart:
UCLX = X + A2 × R = 142.9
Lower control limit for X chart:
LCLX = X – A2 × R = 136.7
Since there are five observations (n) in each sample, the value for A2 from Table 4.5 in the text is 0.58

43. Step 3B: Use These Values to Set Up X and R charts (cont’d)
Upper control limit for R chart:
UCLR = D4 × R = 11.2
Lower control limit for R chart:
LCLR = D3 × R = 0
D3 and D4 values taken from Table 4.5 on page 94 of the text where the sample size n = 5.

44. Use the Charts to Plot the Following Data . . .
Sample X R 11
141.2 8 12
142 9 13
144 14
140 5 15
139.6 4 16
140.8
Out of Control Sample

45. What is the process capability ratio for our dyeing example?
What conclusions can you draw?
σ = 2.41 from sample data

46. What would need to be for us to have “” quality ?
12σ = UTL – LTL = 148 – 132
σ = 16/12 = 1.33

47. Sampling by Attribute p = (8 late)/(50 deliveries) = 0.16
Gonzo Pizza is interested in tracking the proportion (%) of late deliveries
Like before, you take several samples of say, 50 observations each when things are “typical”
For each sample, you calculate the proportion of late deliveries and call this value p. For example:
Example 4.9 in text on pages
Discuss difference between “typical” (natural causes presents) and abnormal (assignable causes present in addition to natural causes).
p = (8 late)/(50 deliveries) = 0.16

48. Gonzo Pizza (cont’d)
For all samples, calculate the average p:
p = 0.10

49. Gonzo Pizza (cont’d)
Calculate standard deviation for the p-chart as follows:
Where n = size of each sample = 50

50. Gonzo Pizza (cont’d)
And the control limits are: UCLp = p + z × Sp = LCLp = p – z × Sp = – 0.026, or zero Here z is 3, but can be chosen as other values to increase the sensitivity of the chart to changes in the process.

51. Gonzo Pizza
Although text says to go ahead with control charts, consider that it is probably too early to develop them since the process is not yet in control (i.e., late deliveries are too high a percentage at present). A more practical approach would be:
First, fix the more obvious problem(s)
Then take new samples
Then put in place control charts

52. Acceptance Sampling Some definitions Acceptable quality level (AQL)
Maximum defect level for 100% customer acceptance
Lot tolerance percent defective (LTPD)
Highest defect level customer will tolerate
Consumer’s risk, 
Probability of accepting a bad lot
Producer’s risk, 
Probability of rejecting a good lot
Operating characteristics (OC) curve
Probability of accepting a lot given the actual fraction defective in the entire lot and the sampling plan being used.

53. Putting the terms together
OC Curve
Figure 4.10, page 106 in the text.

54. The Big Picture
So how do TQM, continuous improvement, and all these statistical techniques “fit” together?

55. 3 Lines of Defense PREVENT defects from occurring
TQM and continuous improvement
DISCOVER problems early
Process control charts
CATCH DEFECTS before used or shipped
inspection / acceptance sampling

56. Traditional View of the Cost of Variability$
Low
Spec
High
Target
Cost of
Bad Quality

57. Taguchi’s Quality Loss Function
An alternative perspective on the
cost of quality

58. Consider Big Bob’s Axles ...
Axles have slightly larger or smaller diameter than target value (
Wheels have slightly
larger or smaller holes
than target value
What are the possible outcomes?

59. Taguchi’s view of the cost of variability$
Low
Spec
High
Target
Cost of
Bad Quality
What are the managerial implications?
(HINT: think continuous improvement)

60. TQM Principles Expanded
Strategic partnerships
Value of good suppliers and distributors i.e., GIGO (garbage in, garbage out)
Quality consistent throughout supply chain
Strategic quality plan
ISO 9000 family of quality standards,
American Society for Quality,

61. Managing Quality Case Study
Dittenhoefer’s Fine China