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S. Chopra/Operations/Quality 1
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
S. Chopra/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
S. Chopra/Operations/Quality 3
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
S. Chopra/Operations/Quality 4
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
Malcolm Baldridge National Quality Award
S. Chopra/Operations/Quality 5
Malcolm Baldridge Award Winners Ames Rubber Corporation (1993) Armstrong World Industries Building Products
Operations (1995) AT&T Consumer Communications Services (1994) AT&T Network Systems Group (1992) AT&T Universal Card Services (1992) Cadillac Motor Car Company (1990) Chugach School District (2001) Clarke American Checks (2001) Corning Telecommunications Products Division (1995) Dana Corporation (2000) Eastman Chemical Company (1993) Federal Express Corporation (1990) Globe Metallurgical Inc. (1988) Granite Rock Company (1992) GTE Directories Corporation (1994) IBM Rochester (1990)
Karlee Company, Inc. (2000) Los Alamos National Bank (2000) Marlow Industries (1991) Milliken & Company (1989) Motorola Inc. (1988) Operations Management International (2000) Pal’s Sudden Service (2001) Pearl River School District (2001) The Ritz-Carlton Hotel Company (1992) Solectron Corporation (1991) Texas Instruments Incorporated - Defense Systems &
Electronics Group (1992) University of Wisconsin-Stout (2001) Wainwright Industries, Inc. (1994) Wallace Co., Inc. (1990) Westinghouse Electric Corporation - Commerical Nuclear
Fuel Division (1988) Xerox Corporation - Business Products & Systems (1989) Zytec Corporation (1991)
Last Updated: May 28, 2002
S. Chopra/Operations/Quality 6
ISO 9000 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.”
Source: Adapted from Chase & Aquilano
Design Procurement Production Final test Installation Servicing
ISO 9003ISO 9002
ISO 9001
S. Chopra/Operations/Quality 7
S. Chopra/Operations/Quality 8
S. Chopra/Operations/Quality 9
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
ProductDesign Process
DesignProduction
ImproveProduct
Quality LeverBenefits of Building Q in Early
Low VisibilityReward
High VisibilityReward
Time
S. Chopra/Operations/Quality 10
Components of Quality
Voice of the customer
– Customer Needs
– Quality of Design
Voice of the process
– Quality of Conformance
– Process Capability
Process Control and Improvement
S. Chopra/Operations/Quality 11
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 InquiriesSource: Kordupleski et al., CMR ‘93.
S. Chopra/Operations/Quality 12
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?
House of Quality
Source: Hauser and Clausing 1988
Customer Requirements
Importance to Cust.
Easy to close
Stays open on a hill
Easy to open
Doesn’t leak in rain
No road noiseImportance weighting
Engineering Characteristics
Ener
gy n
eede
d to
clo
se d
oor
Che
ck fo
rce
on
leve
l gro
und
Ener
gy n
eede
d to
ope
n do
or
Wat
er re
sist
ance
10 6 6 9 2 3
7
5
3
3
2
X
X
X
X
X
Correlation:Strong positivePositiveNegativeStrong negative
X*
Competitive evaluationX = OursA = Comp. AB = Comp. B(5 is best)
1 2 3 4 5
X AB
X AB
XAB
A X B
X A B
Relationships:Strong = 9
Medium = 3
Small = 1Target values
Red
uce
ener
gy
leve
l to
7.5
ft/lb
Red
uce
forc
eto
9 lb
.
Red
uce
ener
gy to
7.5
ft/lb
.
Mai
ntai
ncu
rren
t lev
el
Technical evaluation(5 is best)
54321
B
A
X
BAX B
AX
BX
A
BXABA
X
Doo
r sea
l re
sist
ance
Acc
oust
. Tra
ns.
Win
dow
Mai
ntai
ncu
rren
t lev
el
Mai
ntai
ncu
rren
t lev
el
X- + - -+ +
S. Chopra/Operations/Quality 14
Linked Houses From Customer To Manufacturing
EngineeringCharacteristics
PartsCharacteristics
Key ProcessCharacteristics
ProductionCharacteristics
House ofQuality
PartsDeployment
ProcessPlanning
ProductionPlanning
I II III IV
Engi
neer
ing
Cha
ract
eris
tics
Parts
Cha
ract
eris
tics
Key
Pro
cess
Cha
ract
eris
tics
Cus
tom
er A
ttrib
utes
S. Chopra/Operations/Quality 15
Benefits of QFD
Startup and Preproduction costs at Toyota Auto Body
Japanese automaker with QFD made fewer changes than US company without QFD
time20 - 24months
90% of total Japanese changes complete
Job # 1
Japan
US
Design Changes
14 - 17months
1 - 3months
1 - 3months
Before QFD
After QFD(39% of preQFD costs)
tJob # 1
Source: Hauser and Clausing 1988
S. Chopra/Operations/Quality 16
More New Product Development Tools
Value analysis / Value engineering
Design for manufacturability
Robust design
S. Chopra/Operations/Quality 17
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?
S. Chopra/Operations/Quality 18
Robust Quality: Taguchi’s View of Cost of Variability
Traditional View Taguchi’s View
Non-conformance to design cost
$$$
0Lower
ToleranceDesignSpec
UpperTolerance
Actual value Lower
ToleranceDesignSpec
UpperTolerance
S. Chopra/Operations/Quality 19
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
S. Chopra/Operations/Quality 20
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
S. Chopra/Operations/Quality 21
Process Capability
Percent defective– 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.
S. Chopra/Operations/Quality 22
Quality Wireless (A): CapabilityDistribution of Average Daily Hold Time for 2003-04
0
2
4
6
8
10
12
14
16
18
20
39 43 47 51 55 59 63 67 71 75 79 83 87 91 95 99 103
107
111
115
119
123
127
131
135
139
143
147
151
155
159
163
Average Daily Hold Time
Num
ber o
f Day
s
Out of SpecsWithin Specs
S. Chopra/Operations/Quality 23
Quality Wireless (A): Capability Proportion of days within specification in 2003-04 =
491/731 = 0.672 The call center had a mean hold time of 99.67 with a
standard deviation of 24.24. 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 0.426-sigma process. Expected fraction of days within specifications from a 0.426-sigma process = NORMSDIST(0.426) = 0.665
S. Chopra/Operations/Quality 24
What is Process Improvement?
S. Chopra/Operations/Quality 25
Continuous Improvement:PDCA Cycle (Deming Wheel)
Institutionalize the change or abandon or do it again.
Execute the change.Study the results; did it work?
1. Plan
2. Do3. Check
4. Act
Plan a change aimed at improvement.
S. Chopra/Operations/Quality 26
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 2003-04?– Mean hold in 2003-04 = 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 79.50 or less = NORMDIST(79.50, 99.67, 4.43, 1) = 2.64E-06
S. Chopra/Operations/Quality 27
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
S. Chopra/Operations/Quality 28
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 11-20 to an average hold of 74.4 seconds
What do you think of Ray’s management style?
S. Chopra/Operations/Quality 29
Performance of Inventory Manager
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
WIP
J F M A M J J A S O N D J F M A M J
WIP.. and concludes that kick ... mgt works !?
month
month
month
S. Chopra/Operations/Quality 30
Statistical Process Control: Source of Variability
Inherent (common cause)
External (assignable cause)
Objective: Identify inherent variability and eliminate 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.
S. Chopra/Operations/Quality 31
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
S. Chopra/Operations/Quality 32
SPC – Quality Wireless (B)
After the improvements, daily hold time has an average of 79.50 and a standard deviation of 16.86.
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 79.50 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) = 0.0915
S. Chopra/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) = 0.1693
What we need is a hypothesis test each time we observe a sample – Does the sample belong to the in-control population or not?
S. Chopra/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 = 79.50 + 3×5.33 = 95.49– LCL = 79.50 - 3×5.33 = 63.51
The process was in control when samples with means of 86.6 and 74.4 were observed.
S. Chopra/Operations/Quality 35
Control Charts & Voice of the Process:Key Learning Objectives
The role of variability in evaluating performance A process
– in control has only inherent (from common cause) variation– out of control has variation from an assignable cause
SPC framework for process control and improvement
S. Chopra/Operations/Quality 36
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
S. Chopra/Operations/Quality 37
Why 6-Sigma? 2 sigma: 69.146% of products and/or services meet customer requirements
with 308,538 defects per million opportunities.
4 sigma: 99.379% of products and/or services meet customer requirements ...
but there are still 6,210 defects per million opportunities.
6 sigma: 99.99966% – As close to flaw-free as a business can get, with just
3.4 failures per million opportunities (e.g. products, services or transactions).
S. Chopra/Operations/Quality 38
Why 6-Sigma?
Impact of # of parts/stages in a process
Probability that process/product meets specs3 -sigma 4 - sigma 5 - sigma 6 - sigma
# of steps/parts1 93.3% 99.4% 100.0% 100.0%
10 50.1% 94.0% 99.8% 100.0%50 3.2% 73.2% 98.8% 100.0%
100 0.1% 53.6% 97.7% 100.0%144 0.00% 40.8% 96.7% 100.0%369 10.0% 91.8% 99.9%740 1.0% 84.2% 99.7%
1044 0.1% 78.4% 99.6%1590 0.00% 69.1% 99.5%
19581 1.0% 93.6%42559 0.00% 86.5%
100000 71.2%1000000 3.3%
0.0%
0.0%
0.1%
1.0%
10.0%
100.0%
1 10 100 1000 10000 100000 1000000
# steps/components
Probability that process/productmeets specs
3 -sigma
4 - sigma
5 - sigma
6 - sigma
S. Chopra/Operations/Quality 39
Why 6-Sigma? Robustness to Mean Shifts
100 130 160
LSL USL
= 10
100 143 160
LSL USL
= 10
LSL USL
= 5
100 130 160
LSL USL
= 5
100 143 160
99.9 % 99.9 %
S. Chopra/Operations/Quality 40
Why 6-Sigma? 6-Sigma Quality at Flyrock
At the extruder, the rubber for the AX-527 tires had thickness specifications of 400 10. 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 and a standard deviation of 4.
If the setting is accurate, what proportion of the rubber extruded will be within specifications?
S. Chopra/Operations/Quality
Process Capability: Sigma Capability Sigma capability is the number of standard deviations
from the mean to the closest specification limit. Sigma capability of extrusion process =
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?
S. Chopra/Operations/Quality 42
Impact of Mean Shift
If a bearing is worn out, the extruder produces a mean thickness of 403 when the setting is 400. Under this condition, what proportion of defective sheet will the extruder produce? Assuming the control limits in (2), 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.
S. Chopra/Operations/Quality 43
Why 6-Sigma? Rapid Detection
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 and a standard deviation of 1.667. What should the new control limits be? What is the proportion of defectives produced?
S. Chopra/Operations/Quality 44
Improving Process Capability
Return to the case of the worn bearing in (3) where extrusion produces a mean thickness of 403 when the setting is 400. Under this condition, what proportion of defective sheets will the extruder produce (for the 6-sigma process)? Assuming the control limits in (5), 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.
S. Chopra/Operations/Quality 45
Key Learning Objectives: SPC Specification limits: Voice of the customer Process capability is a measure of the quality
delivered (external): links VoP with VoC Improving capability may require variability reduction
and/or mean shift Control limits used to verify if process is in control
(internal), i.e., is maintaining capability: Voice of the process
Higher process capability reduces defectives and speeds up detection of assignable cause