1. Process Quality & Improvement Module
Quality & the Voice of the Customer
What is Quality?
Quality Programs in practice
Voice of the Customer
Process Capability and Improvement
Process Capability
Checking for Improvement: Quality Wireless
Control Charts & Voice of the Process
Statistical Process Control (SPC)
Quality Wireless (B)
Why 6-Sigma?
Flyrock Tires
J.A. Van Mieghem/Operations/Quality

2. 8 Dimensions of Quality Performance Features Serviceability Aesthetics
Perceived Quality
Reliability
Conformance
Durability
Q of design
Q of process conformance
to design = process capability
J.A. Van Mieghem/Operations/Quality

3. Quality in Practice: 1. Elements of TQM
Management by fact
Cross-functional (process) approach
Culture and leadership
Customer focus
Employee focus
High performance focus
Continuous improvement
Benchmarking
External alliances - the value chain
Source: Eitan Zemel
J.A. Van Mieghem/Operations/Quality

4. Quality in Practice: 2. Malcolm Baldrige National Quality Award
1 Leadership 110
2 Strategic Planning 80
Strategy Development Process
3 Customer and Market Focus 80
4 Information and Analysis 80
5 Human Resource Development and Management 100
6 Process Management 100
Product and Service Processes
Support Processes
Supplier and Partnering Processes
7 Business Results 450
TOTAL POINTS 1000
J.A. Van Mieghem/Operations/Quality

5. Malcolm Baldridge Award Winners
2002 
Motorola Commercial, Government & Industrial Solutions Sector (Manufacturing)
SSM Health Care (Health care)
Branch-Smith Printing Division (Small business)
2001
Clarke American Checks, Inc., San Antonio, Texas (manufacturing);
Pal's Sudden Service, Kingsport, Tenn. (small business);
Chugach School District, Anchorage, Alaska (education);
Pearl River School District, Pearl River, N.Y. (education);
University of Wisconsin-Stout, Menomonie, Wis. (education).
2000
Dana Corporation-Spicer Driveshaft Division, Toledo, Ohio (manufacturing)
KARLEE Company, Inc., Garland, Texas (manufacturing)
Operations Management International, Inc., Greenwood Village, Colo. (service)
Los Alamos National Bank, Los Alamos, N.M. (small business).
1999
STMicroelectronics, Inc. - Region Americas (mfg)
BI (service)
The Ritz-Carlton Hotel Company, L.L.C. (service)
Sunny Fresh Foods (small business)
1998
Boeing Airlift and Tanker Programs (mfg)
Solar Turbines Incorporated (mfg)
Texas Nameplate Company, Inc. (small business)
1997
3M Dental Products Division (mfg)
Merrill Lynch Credit Corporation (service)
Solectron Corporation (mfg)
Xerox Business Services (service)
1996
ADAC Laboratories (mfg)
Custom Research Inc. (small business)
Dana Commercial Credit Corp. (service)
Trident Precision Manufacturing, Inc. (small business)
J.A. Van Mieghem/Operations/Quality

6. Quality in Practice: 3. ISO 9000 and 4. ?
Series of standards agreed upon by the International Organization for Standardization (ISO): (
Adopted in 1987
More than 100 countries
A prerequisite for global competition?
ISO 9000: “document what you do and then do as you documented.”
Most companies providing service strive for ISO9002, while mfg companies that do design go for 9001
The familiar three standard (below) have now been integrated into ISO9001:2000.
Design
Procurement
Production
Final test
Installation
Servicing
ISO 9003
ISO 9002
ISO 9001
J.A. Van Mieghem/Operations/Quality

7. Benefits of Building Q in Early
Costs of Quality
Cost of Conformance
Cost of Appraisal
Cost of Prevention
Cost of Non-Conformance
Cost of Internal Failure
Cost of External Failure
100:1
10:1
1:1
Product
Design
Process
Production
Improve
Quality Lever
Benefits of Building Q in Early
Low Visibility
Reward
High Visibility
Time
J.A. Van Mieghem/Operations/Quality

8. Components of Quality Voice of the customer Voice of the process
Customer Needs
Quality of Design
Voice of the process
Quality of Conformance
Process Capability
Process Control and Improvement
J.A. Van Mieghem/Operations/Quality

9. Voice of the Customer: Linking Customer Needs to Business Processes
Business Process Customer Need Internal Metric
Overall Quality
Product (30%)
Sales (30%)
Installation (10%)
Repair (15%)
Billing (15%)
Reliability (40 %) % Repair Call
Easy to Use (20%) % Calls for Help
Features/Functions (40%) Function Performance Test
Knowledge (30%) Supervisor Observations
Response (25%) % Proposals Mad on Time
Follow-Up (10%) % Follow-Up Made
Delivery Interval (30%) Average Order Interval
Does Not Break (25%) % Repair Reports
Installed When Promised % Installed on Due Date
No Repeat Trouble (30%) % Repeat Reports
Fixed Fast (25%) Average Speed of Repair
Kept Informed (10%) % Customers Informed
Accuracy, No Surprise (45%) % Billing Inquiries
Response on First Call (35%) % Respolved First Call
Easy to Understand (10%) % Billing Inquiries
Source: Kordupleski et al., CMR ‘93.
J.A. Van Mieghem/Operations/Quality

10. Voice of the Customer: Quality Function Deployment
What do customers want?
Are all preferences equally important?
Will delivering perceived needs deliver a competitive advantage?
How can we change the product?
How do engineering characteristics influence customer perceived quality?
How does one engineering attribute affect another?
What are the appropriate targets for the engineering characteristics?
J.A. Van Mieghem/Operations/Quality

11. House of Quality Customer Requirements Importance to Cust.
Easy to close
Stays open on a hill
Easy to open
Doesn’t leak in rain
No road noise
Importance weighting
Engineering Characteristics
Energy needed
to close door
Check force on level ground
to open door
Water resistance 10 6 9 2 3 7 5 X
Correlation:
Strong positive
Positive
Negative
Strong negative *
Competitive evaluation
X = Ours
A = Comp. A
B = Comp. B
(5 is best) AB
X AB
XAB
A X B
X A B
Relationships:
Strong = 9
Medium = 3
Small = 1
Target values
Reduce energy
level to 7.5 ft/lb
Reduce force
to 9 lb.
to 7.5 ft/lb.
current level
Maintain
Technical evaluation 4 1 A BA
BXA
Door seal
resistance
Accoust. Trans.
Window
House of Quality
Source: Hauser and Clausing 1988
J.A. Van Mieghem/Operations/Quality

12. Linked Houses From Customer To Manufacturing
Engineering
Characteristics
Parts
Key Process
Production
House of
Quality
Deployment
Process
Planning I II
III IV
Customer Attributes
J.A. Van Mieghem/Operations/Quality

13. Startup and Pre-production costs
Benefits of QFD
Startup and Pre-production costs
at Toyota Auto Body
Japanese auto maker with QFD made fewer changes than US company without QFD
Design Changes US
Before QFD
Japan
After QFD
(39% of preQFD costs)
90% of total Japanese changes complete
Job # 1 t
months
months
1 - 3
months
Job # 1
1 - 3
months
time
Source: Hauser and Clausing 1988
J.A. Van Mieghem/Operations/Quality

14. More New Product Development Tools
Value analysis / Value engineering
Design for manufacturability
Robust design
J.A. Van Mieghem/Operations/Quality

15. Value Analysis/Value Engineering
Achieve equivalent or better performance at a lower cost while maintaining all functional requirements defined by the customer
Does the item have any design features that are not necessary?
Can two or more parts be combined into one?
How can we cut down the weight?
Are there nonstandard parts that can be eliminated?
J.A. Van Mieghem/Operations/Quality

16. Robust Quality: Taguchi’s View of Cost of Variability
Non-conformance to design cost
$$$
Lower
Tolerance
Design
Spec
Upper
Actual value
Lower
Tolerance
Design
Spec
Upper
Traditional View
Taguchi’s View
J.A. Van Mieghem/Operations/Quality

17. Quality & the Voice of the Customer: Key Learning Objectives
Elements of TQM / Baldridge / ISO 9000
Costs of Quality
Components of Quality
Voice of the Customer
Linking business processes to customer needs
Product Design Methodologies:
Convert customer needs to product and process specifications: QFD
Value Engineering
J.A. Van Mieghem/Operations/Quality

18. Process Quality & Improvement Module
Operations Management:
Process Quality & Improvement Module
Quality & the Voice of the Customer
What is Quality?
Quality Programs in practice
Voice of the Customer
Process Capability and Improvement
Process Capability
Checking for Improvement: Quality Wireless
Control Charts & Voice of the Process
Statistical Process Control (SPC)
Quality Wireless (B)
Why 6-Sigma?
Flyrock Tires
J.A. Van Mieghem/Operations/Quality

19. Process Capability Percent defective Sigma-capability
Proportion of output that does not meet customer specifications
Sigma-capability
Number of standard deviations from the mean of the process output to the closest specification limit.
J.A. Van Mieghem/Operations/Quality

20. Quality Wireless (A): Capability
Within Specs
Out of Specs
J.A. Van Mieghem/Operations/Quality

21. Quality Wireless (A): Capability
Proportion of days within specification in = 491/731 = 0.672
The call center had a mean hold time of with a standard deviation of With a specification of 110 seconds or less,
σ-capability of call center = (110 – 99.67)/24.24
= 0.426
The call center is a sigma process.
Expected fraction of days within specifications from a sigma process = NORMSDIST(0.426) = 0.665
J.A. Van Mieghem/Operations/Quality

22. What is Process Improvement?
After
Before
Critical customer requirement
“Defects”=
Service is unacceptable to customers
Product/Service
Output Measure
J.A. Van Mieghem/Operations/Quality

23. Continuous Improvement: PDCA Cycle (Deming Wheel)
Institutionalize the change or abandon or do it again.
Plan a change aimed
at improvement.
4. Act
1. Plan
3. Check
2. Do
Study the results;
did it work?
Execute the change.
J.A. Van Mieghem/Operations/Quality

24. Quality Wireless (A): Checking for Improvement
Performance in April 2005: Mean = 79.50, Standard deviation = 16.86
What is the probability of observing such a sample if performance has not improved relative to ?
Mean hold in = 99.67
Standard deviation = 24.24
Given that April 2005 had 30 days, we need to consider distribution of samples of size 30. The standard deviation of sample means = 24.24/√30 = 4.43
Probability of observing a sample of size 30 with mean or less = NORMDIST(79.50, 99.67, 4.43, 1) = 2.64E-06
J.A. Van Mieghem/Operations/Quality

25. Process Quality & Improvement Module
Operations Management:
Process Quality & Improvement Module
Quality & the Voice of the Customer
What is Quality?
Quality Programs in practice
Voice of the Customer
Process Capability and Improvement
Process Capability
Checking for Improvement: Quality Wireless
Control Charts & Voice of the Process
Statistical Process Control (SPC)
Quality Wireless (B)
Why 6-Sigma?
Flyrock Tires
J.A. Van Mieghem/Operations/Quality

26. Has Process Performance Changed? Quality Wireless (B)
Average hold time from September 1-10 =86.6 seconds
Ray yells at supervisors
Performance improves from September to an average hold of 74.4 seconds
What do you think of Ray’s management style?
J.A. Van Mieghem/Operations/Quality

27. Performance Measurement Implications:. Inventory Manager. and
Performance Measurement Implications: Inventory Manager and Weight watchers
J F M A M J J A S O N
WIP
Award Given
Manager repents and kicks...
J F M A M J J A S O N D J F
J F M A M J J A S O N D J F M A M J
.. and concludes that kick ... mgt works !?
month
Weight watchers
rule: measure only
weekly…
J.A. Van Mieghem/Operations/Quality

28. Statistical Process Control (SPC):
Statistical Process Control (SPC): Sources of Variability and Conceptual Framework
Every process has variation that comes from two sources:
Inherent (common cause)
External (assignable cause)
Objective: Identify inherent variability and eliminate external variability.
First get the process “in control” by eliminating external variability. (A process is “in control” if it has only inherent variability.)
To improve the system, attack common causes (methods, people, material, machines). This is the role of management.
J.A. Van Mieghem/Operations/Quality

29. Statistical Process Control: Control Charts
m - 3s
Lower Control Limit
hypothesized (sampled) process output mean m
m + 3s
Upper Control Limit
F(z)
99.74%
Signal that a special cause has occurred t
Control Improvement
J.A. Van Mieghem/Operations/Quality

30. Various Patterns in Control Charts
Pattern Description Possible Causes
Normal Random Variation
Lack of Stability Assignable (or special) causes (e.g. tool,
material, operator, overcontrol
Cumulative trend Tool Wear
Cyclical Different work shifts, voltage
fluctuations, seasonal effects
J.A. Van Mieghem/Operations/Quality

31. Calibration versus Quality: Sharp shooters
J.A. Van Mieghem/Operations/Quality

32. SPC – Quality Wireless (B)
After the improvements, daily hold time has an average of and a standard deviation of
Since we are considering samples of size 10 (10 days), we need to consider the distribution of sample means. Sample means have an average of and a standard deviation of 16.86/√10 = 5.33.
Probability of observing 86.6 or higher even if process is in control = 1-NORMDIST(86.6, 79.50, 5.33, 1) =
J.A. Van Mieghem/Operations/Quality

33. SPC – Quality Wireless (B)
Probability of observing 74.4 or lower even if process is in control = NORMDIST(74.4, 79.50, 5.33, 1) =
What we need is a hypothesis test each time we observe a sample – Does the sample belong to the in-control population or not?
J.A. Van Mieghem/Operations/Quality

34. SPC – Setting Control Limits
Upper Control Limit = UCL = Mean + 3σXbar
Lower Control Limit = LCL = Mean - 3σXbar
In the case of Quality Wireless
UCL = ×5.33 = 95.49
LCL = ×5.33 = 63.51
The process was in control when samples with means of 86.6 and 74.4 were observed.
J.A. Van Mieghem/Operations/Quality

35. Control Charts & Voice of the Process: Key Learning Objectives
The role of variability in evaluating performance
A process that is
in control has only inherent (from common cause) variation
There is a known distribution for the (sampled) output with mean m and std. dev. s  output typically within the control limits m  3s
out of control has variation from an assignable cause
Observations outside the control limits are so unlikely if process is in control that it is likely that process is out of control
Pareto analysis to identify key causes of error
SPC framework for process control and improvement
SPC Tools:
Viewing quality data as a run chart to infer performance over time.
Constructing control charts .
Identifying whether a process is in or out of control.
Then link this to improvement:
Constructing a Pareto diagram to prioritize areas for improvement.
J.A. Van Mieghem/Operations/Quality

36. Process Quality & Improvement Module
Operations Management:
Process Quality & Improvement Module
Quality & the Voice of the Customer
What is Quality?
Quality Programs in practice
Voice of the Customer
Process Capability and Improvement
Process Capability
Checking for Improvement: Quality Wireless
Control Charts & Voice of the Process
Statistical Process Control (SPC)
Quality Wireless (B)
6-Sigma: What and Why?
Flyrock Tires
J.A. Van Mieghem/Operations/Quality

37. 99.9% Suppliers At least 20,000 wrong prescriptions per year
More than 15,000 newborns dropped by doctors or nurses
No electricity, water or heat for 8.6 hours each year
No telephone service or TV transmission for nearly 10 minutes each week
Two short (or long) landings at O’Hare each week
J.A. Van Mieghem/Operations/Quality

38. Why 6-Sigma? 2 sigma: 4 sigma: 6 sigma:
69.1% of products and/or services meet customer requirements with 308,538 defects per million opportunities.
4 sigma:
99.4% of products and/or services meet customer requirements ... but there are still 6,210 defects per million opportunities.
6 sigma:
% (“5 nines”) – Close to flaw-free for most businesses, with just 3.4 failures per million opportunities (e.g. products, services or transactions).
J.A. Van Mieghem/Operations/Quality

39. Six Sigma: Core methodology
Six Sigma was introduced by Motorola in 1986 as a new method for standardizing the way defects are counted (in response to increasing complaints from the field sales force about warranty claims)
The problem-solving framework and work-breakdown structure can be easily remembered using the acronym DMAIC:
Define the problem to determine what needs to be improved
Measure the current state against the desired state
Analyze the root causes of the business gap
Improve by team brainstorming, selecting and implementing the best solutions
Control the long-term sustainability of the improvement by establishing monitoring mechanisms, accountabilities and work tools
J.A. Van Mieghem/Operations/Quality

40. Six Sigma: From original defects control to overall business improvement methodology
AlliedSignal (now Honeywell) and GE successfully applied and popularized Motorola’s Six Sigma methodology as part of leadership development, going far beyond counting defects.
Now, six sigma is an overall high-performance system that executes business strategy using four steps:
Align executives to the right objectives and targets using a balanced scorecard
Mobilize improvement teams using DMAIC
Accelerate results by action learning and integrating all teams so the cumulative impact on the organization is “accelerated.”
Govern the process through visible executive sponsorship, review, and sharing best practices with other parts of the organization
J.A. Van Mieghem/Operations/Quality

41. Magnitude of Difference Between Sigma Levels
J.A. Van Mieghem/Operations/Quality

42. Probability that process/product
Why 6-Sigma?
Impact of # of parts/stages in a process
Impact of mean shift
P(output outside specs) vs. P(detection of mean shift)
0.001%
0.01%
0.1%
1.0%
10.0%
100.0% 1 10
100
1000
10000
100000
# steps/components
Probability that process/product
meets specs
3 -sigma
4 - sigma
5 - sigma
6 - sigma
* These numbers allow a mean shift of 1.5 s.
J.A. Van Mieghem/Operations/Quality

43. Relationship Between Sigma Capability, Proportion Defects, and Cpk
LSL m
USL
z s
* These are “raw” numbers; excluding mean shifts.
J.A. Van Mieghem/Operations/Quality

44. Quality Performance at Flyrock: 1. How well do we meet customer specs?
At the extruder, the rubber for the AX-527 tires had thickness specifications of 400  10 ‘thou’ (.001’’). Susan and her staff had analyzed many samples of output from the extruder and determined that if the extruder settings were accurate, the output produced by the extruder had a thickness that was normally distributed with a mean of 400 thou and a standard deviation of 4 thou.
If the setting is accurate, what proportion of the rubber extruded will be within specifications?
Notes:
J.A. Van Mieghem/Operations/Quality

45. = How well is process capable of meeting customer specifications?
Quality Performance at Flyrock: 1. How well do we meet customer specs? Definition of Process Capability
Link: Voice of the Customer with Voice of the Process
Process Capability
= How well is process capable of meeting customer specifications?
Equivalent Measures of Process Capability:
1. Proportion of output flow units meeting customer specs
Example: at Flyrock:
2. Sigma-capability = the number of std. deviations to the closest specification limit
Example: the Sigma capability of Flyrock’s extrusion process =
J.A. Van Mieghem/Operations/Quality

46. Quality Performance at Flyrock: 2. Statistical Process Control
Susan has asked operators to take a sample of 10 sheets of rubber each hour from the extruder and measure the thickness of each sheet. Based on the average thickness of this sample, operators will decide whether the extrusion process is in control or not.
Given that Susan plans 3-sigma control limits, what upper and lower control limits should she specify to the operators?
UCL =
LCL =
Notes:
J.A. Van Mieghem/Operations/Quality

47. Quality Performance at Flyrock: Quality graphs for current process
A: Meeting Customer Specs
Sigma capability =
Prob(Meeting specs) =
B: Keeping Process in Control
Prob(sample mean within control band) =
Prob(investigate) =
Sample Mean X
UCL
403.8
LSL
USL 1 2 3 -3 -2 -1
s = 4
400
385
390
395
400
405
410
415
LCL
396.2
time
J.A. Van Mieghem/Operations/Quality

48. Quality Performance at Flyrock: 3. Impact and Detection of Mean Shift
If a bearing is worn out, the extruder produces a mean thickness of 403 thou when the setting is 400 thou. Under this condition, what proportion of produced sheets will be defective?
Assuming the earlier control limits, what is the probability that a sample taken from the extruder with the worn bearings will be out of control?
On average, how many hours are likely to go by before the worn bearing is detected?
J.A. Van Mieghem/Operations/Quality

49. Quality Performance at Flyrock: current process … but worn bearing (mean shift)
A: Meeting Customer Specs
Prob(Meeting specs) =
B: Keeping Process in Control
Prob(sample mean within control band) =
Prob(investigate) = 1 2 3 -3 -2 -1
Sample Mean X
UCL
403.8
403
LSL
USL
s = 4
s = 4
400
385
390
395
400
403
405
410
415
LCL
396.2
time
J.A. Van Mieghem/Operations/Quality

50. Quality Performance at Flyrock: 4. Improving Process Capability
What if extrusion is to become a 6-Sigma process?
Target mean =
Target standard deviation =
Process improvement has resulted in the extrusion process having a mean of 400 thou and a standard deviation of 1.67 thou. What should the new control limits be?
UCL =
LCL =
What is the proportion of defectives produced?
Notes:
J.A. Van Mieghem/Operations/Quality

51. Quality Performance at Flyrock: Quality graphs for improved 6s process
A: Meeting Customer Specs
Sigma capability =
Prob(Meeting specs) =
B: Keeping Process in Control
Prob(sample mean within control band) =
Prob(investigate) =
Sample Mean X
403.8
LSL
USL 1 2 3 -3 -2 -1
UCL
401.6
400
LCL
398.4
385
390
395
400
405
410
415
396.2
time
J.A. Van Mieghem/Operations/Quality

52. Quality Performance at Flyrock: 5. Benefits of a 6Sigma process
Return to the case of the worn bearing where extrusion produces a mean thickness of 403 thou when the setting is 400 thou. Under this condition, what proportion of produced sheets will be defective (for the 6-sigma extrusion process)?
Assuming the new control limits, what is the probability that a sample taken from the extruder with the worn bearings will be out of control?
On average, how many hours are likely to go by before the worn bearing is detected?
Notes:
J.A. Van Mieghem/Operations/Quality

53. Flyrock: Improved 6sigma process … but with worn bearing (mean shift)
A: Meeting Customer Specs
Prob(Meeting specs) =
B: Keeping Process in Control
Prob(sample mean within control band) =
Prob(investigate) =
Sample Mean X -3 -2 -1
403
LSL
USL 1 2
UCL
401.6 3 -3 -2 -1
400 1 2
LCL
398.4 3
385
390
395
400
403
405
410
415
time
J.A. Van Mieghem/Operations/Quality

54. Key Learning Objectives: Six Sigma
Specification limits: Voice of the customer
Use output (population) distribution with mean m, stdev s
This is where we determine sigma-capability
Control limits used to verify if process is in control (internal), i.e., is maintaining capability: Voice of the process
Use sample mean distribution with mean m, stdev
Six Sigma and Process capability are measures of the quality delivered (external): links VoP with VoC
Improving capability may require variability reduction and/or mean shift
Typically, customer specs are fixed and cannot be relaxed
Reducing number of stages/parts improves capability
J.A. Van Mieghem/Operations/Quality