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. History: how did we get here…
Deming and Juran outlined the principles of Quality Management.
Tai-ichi Ohno applies them in Toyota Motors Corp.
Japan has its National Quality Award (1951).
U.S. and European firms begin to implement Quality Management programs (1980’s).
U.S. establishes the Malcolm Baldridge National Quality Award (1987).
Today, quality is an imperative for any business.

7. (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

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

9. 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

10. Six Sigma Quality

11. Six Sigma Roadmap (DMAIC)
Next Project
Define
Customers, Value, Problem Statement
Scope, Timeline, Team
Primary/Secondary & OpEx Metrics
Current Value Stream Map
Voice Of Customer (QFD)
Validate Project $
Celebrate Project $
Measure
Assess specification / Demand
Measurement Capability (Gage R&R)
Correct the measurement system
Process map, Spaghetti, Time obs.
Measure OVs & IVs / Queues
Control
Document process (WIs, Std Work)
Mistake proof, TT sheet, CI List
Analyze change in metrics
Value Stream Review
Prepare final report
Validate Project $
Validate Project $
Improve
Optimize KPOVs & test the KPIVs
Redesign process, set pacemaker
5S, Cell design, MRS
Visual controls
Value Stream Plan
Analyze (and fix the obvious)
Root Cause (Pareto, C&E, brainstorm)
Find all KPOVs & KPIVs
FMEA, DOE, critical Xs, VA/NVA
Graphical Analysis, ANOVA
Future Value Stream Map
Validate Project $

12. Six Sigma Organization

13. Quality Improvement
Continuous Improvement
Quality
Traditional
Time

14. 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?

15. Tools used for continuous improvement
1. Process flowchart

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

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

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

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

20. Tools used for continuous improvement
6. Histogram
Frequency

21. 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

22. Summary of Tools Process flow chart Run diagram Control charts
Fishbone
Check sheet
Histogram
Pareto analysis

23. Case: shortening telephone waiting time…
A bank is employing a call answering service
The main goal in terms of quality is “zero waiting time”
- customers get a bad impression
- company vision to be friendly and easy access
The question is how to analyze the situation and improve quality

24. How can we reduce waiting time?
The current process
Operator
Receiving
Party
Customer A
Customer B
How can we reduce waiting time?

25. 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

26. 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

27. 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

28. Ideas for improvement Taking lunches on three different shifts
Ask all employees to leave messages when leaving desks
Compiling a directory where next to personnel’s name appears her/his title

29. Results of implementing the recommendations
Before…
…After A B C D E F
Frequency
Percentage
100% 0%
49%
71.2%
100
200
300
87.1%
100% B C A D E F
Frequency
Percentage 0%
100
200
300
Improvement

30. 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)

31. Statistical Process Control (SPC)
Every output measure has a target value and a level of “acceptable” variation (upper and lower tolerance limits)
SPC uses samples from output measures to estimate the
mean and the variation (standard deviation)
Example
We want beer bottles to be filled with 12 FL OZ ± 0.05 FL OZ
Question:
How do we define the output measures?

32. In order to measure variation we need…
The average (mean) of the observations:
The standard deviation of the observations:

33. Average & Variation example
Number of pepperoni’s per pizza: 25, 25, 26, 25, 23, 24, 25, 27
Average:
Standard Deviation:
Number of pepperoni’s per pizza: 25, 22, 28, 30, 27, 20, 25, 23
Average:
Standard Deviation:
Which pizza would you rather have?

34. When is a product good enough?
Incremental
Cost of
Variability
High
Zero
Lower
Tolerance
Target
Spec
Upper
Traditional View
a.k.a
Upper/Lower Design Limits
(UDL, LDL)
Upper/Lower Spec Limits
(USL, LSL)
Upper/Lower Tolerance Limits
(UTL, LTL)
The “Goalpost” Mentality

35. But are all ‘good’ products equal?
Incremental
Cost of
Variability
High
Zero
Lower
Spec
Target
Upper
Taguchi’s View
“Quality Loss Function”
(QLF)
LESS VARIABILITY implies BETTER PERFORMANCE !

36. Capability Index (Cpk)
It shows how well the performance measure fits the design specification based on a given tolerance level
A process is ks capable if

37. Capability Index (Cpk)
Another way of writing this is to calculate the capability index:
Cpk < 1 means process is not capable at the ks level
Cpk >= 1 means process is capable at the ks level

38. Accuracy and Consistency
We say that a process is accurate if its mean is close to
the target T.
We say that a process is consistent if its standard deviation
is low.

39. Example 1: Capability Index (Cpk)
UTL
LTL X
X = 10 and σ = 0.5
LTL = 9
UTL = 11

40. Example 2: Capability Index (Cpk)
X = and σ = 0.5
LTL = 9
UTL = 11
LTL X
UTL

41. Example 3: Capability Index (Cpk)
X = 10 and σ = 2
LTL = 9
UTL = 11
LTL X
UTL

42. Example
Consider the capability of a process that puts pressurized grease in an aerosol can. The design specs call for an average of 60 pounds per square inch (psi) of pressure in each can with an upper tolerance limit of 65psi and a lower tolerance limit of 55psi. A sample is taken from production and it is found that the cans average 61psi with a standard deviation of 2psi.
Is the process capable at the 3s level?
What is the probability of producing a defect?

43. Solution
LTL = 55 UTL = s = 2
No, the process is not capable at the 3s level.

44. Solution P(defect) = P(X<55) + P(X>65)
=P(Z<(55-61)/2) + 1 – P(Z<(65-61)/2)
=P(Z<-3) + 1 – P(Z<2)
=G(-3)+1-G(2)
= – (from standard normal table) =
2.4% of the cans are defective.

45. Example (contd) Suppose another process has a sample mean of 60.5 and
a standard deviation of 3.
Which process is more accurate? This one.
Which process is more consistent? The other one.

46. Control Charts
Upper Control Limit
Central Line
Lower Control Limit
Control charts tell you when a process measure is exhibiting abnormal behavior.

47. Two Types of Control Charts
X/R Chart
This is a plot of averages and ranges over time (used for performance measures that are variables)
p Chart
This is a plot of proportions over time (used for performance measures that are yes/no attributes)

48. Statistical Process Control with p Charts
When should we use p charts?
When decisions are simple “yes” or “no” by inspection
When the sample sizes are large enough (>50)
Sample (day)
Items
Defective
Percentage 1
200 10
0.050 2 8
0.040 3 9
0.045 4 13
0.065 5 15
0.075 6 25
0.125 7 16
0.080

49. Statistical Process Control with p Charts
Let’s assume that we take t samples of size n …

50. Statistical Process Control with p Charts

51. Statistical Process Control with p Charts
UCL = 0.117
p = 0.066
LCL = 0.015

52. Statistical Process Control with X/R Charts
When should we use X/R charts?
It is not possible to label “good” or “bad”
If we have relatively smaller sample sizes (<20)

53. Statistical Process Control with X/R Charts
Take t samples of size n (sample size should be 5 or more)
X is the mean for each sample
R is the range between the highest and the lowest for each sample

54. Statistical Process Control with X/R Charts
X is the average of the averages.
R is the average of the ranges

55. Statistical Process Control with X/R Charts
define the upper and lower control limits…
Read A2, D3, D4 from
Table TN 8.7

56. Example: SPC for bottle filling…
Sample
Observation (xi)
Average
Range (R) 1
11.90
11.92
12.09
11.91
12.01 2
12.03
11.97
12.07 3
12.02
11.93 4
11.96
12.06
12.00
11.98 5
11.95
12.10 6
11.99
11.94 7
12.04 8 9 10
12.05

57. Example: SPC for bottle filling…
Calculate the average and the range for each sample…
Sample
Observation (xi)
Average
Range (R) 1
11.90
11.92
12.09
11.91
12.01
11.97
0.19 2
12.03
12.07
12.00
0.15 3
12.02
11.93
11.99 4
11.96
12.06
11.98 5
11.95
12.10 6
11.94
0.12 7
12.04 8
0.13 9 10
12.05
0.17

58. Then…
is the average of the averages
is the average of the ranges

59. Finally…
Calculate the upper and lower control limits

60. The X Chart
UCL = 12.10
X = 12.00
LCL = 11.90

61. The R Chart
LCL = 0.00
R = 0.15
UCL = 0.32

62. The X/R Chart
LCL
UCL X
What can you conclude?
LCL R
UCL