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Slide 1Operations/Quality © Van Mieghem
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
Slide 2Operations/Quality © Van Mieghem
What is Quality?
Here is how specific brands ranked in J.D. Power and Associates' annual initial quality survey.
The study is based on responses from more than 52,000 people who bought or leased new 2003 cars and trucks.
The survey is done in the first 90 days of ownership. The figures represent the number of problems per 100 vehicles.
Source: J.D. Power 2003 Initial Quality Study
Lexus 76
Cadillac 103
Infiniti 110
Acura 111
Buick 112
Mercury 113
Porsche 117
BMW 118
Toyota 121
Jaguar 122
Honda 128
Volvo 128
Chevrolet 130
Audi 132
Mercedes-Benz
132
INDUSTRY AVERAGE
133
Oldsmobile 134
Chrysler 136
Ford 136
Dodge 137
Lincoln 139
Nissan 139
Pontiac 142
Hyundai 143
Volkswagen 143
GMC 144
Suzuki 144
Jeep 146
Subaru 146
Mazda 148
Mitsubishi 148
Saturn 158
Saab 160
Mini 166
Kia 168
Land Rover 190
Hummer 225
76
103
110
111
112
113
117
118
121
122
128
128
130
132
132
133
134
136
136
137
139
139
142
143
143
144
144
146
146
148
148
158
160
166
168
190
225
0 50 100 150 200 250
Lexus
Cadillac
Infiniti
Acura
Buick
Mercury
Porsche
BMW
Toyota
Jaguar
Honda
Volvo
Chevrolet
Audi
Mercedes-Benz
INDUSTRY AVERAGE
Oldsmobile
Chrysler
Ford
Dodge
Lincoln
Nissan
Pontiac
Hyundai
Volkswagen
GMC
Suzuki
Jeep
Subaru
Mazda
Mitsubishi
Saturn
Saab
Mini
Kia
Land Rover
Hummer
Slide 3Operations/Quality © Van Mieghem
8 Dimensions of Quality
Performance Features Serviceability Aesthetics Perceived Quality Reliability Conformance Durability
Q of design
Q of process conformance to design = process capability
Slide 4Operations/Quality © Van Mieghem
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
Slide 5Operations/Quality © Van Mieghem
www.quality.nist.gov 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
Quality in Practice:2. Malcolm Baldrige National Quality Award
Slide 6Operations/Quality © Van Mieghem
2002 Motorola Commercial, Government & Industrial Solutions
Sector (Manufacturing)SSM Health Care (Health care)Branch-Smith Printing Division (Small business)
2001Clarke 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)
19973M Dental Products Division (mfg)Merrill Lynch Credit Corporation (service)Solectron Corporation (mfg)Xerox Business Services (service)
1996ADAC Laboratories (mfg)Custom Research Inc. (small business)Dana Commercial Credit Corp. (service)Trident Precision Manufacturing, Inc. (small business)
Malcolm Baldridge Award Winners
Malcolm Baldrige National Quality Award Recipients' Stock Outperforms S&P 500
For the past seven years, the Commerce Department's National Institute of Standards and Technology has compared winners of the Malcolm Baldrige National Quality Award to the Standard & Poor's 500.
The Baldrige group consistently has outperformed the S&P 500; this year the Baldrige group beat the S&P 500 by 4.4 to 1.
Slide 7Operations/Quality © Van Mieghem
Quality in Practice:3. ISO 9000 and 4. ?
Series of standards agreed upon by the International Organization for Standardization (ISO): (http://www.iso.ch/iso/en/iso9000-14000/iso9000/iso9000index.html)
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
Slide 8Operations/Quality © Van Mieghem
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
Design
Production
ImproveProduct
Quality LeverBenefits of Building Q in Early
Low VisibilityReward
High VisibilityReward
Time
Slide 9Operations/Quality © Van Mieghem
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
Slide 10Operations/Quality © Van Mieghem
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.
Slide 11Operations/Quality © Van Mieghem
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?
Slide 12Operations/Quality © Van Mieghem
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 noise
Importance weighting
Engineering Characteristics
Ene
rgy
need
ed
to c
lose
doo
r
Che
ck f
orce
on
leve
l gro
und
Ene
rgy
need
ed
to o
pen
door
Wat
er r
esis
tanc
e
10 6 6 9 2 3
7
5
6
3
2
X
X
X
X
X
Correlation:
Strong positive
Positive
NegativeStrong negative
X*
Competitive evaluation
X = 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/l
b
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)
5
4321
B
A
X
BA
X B
A
X
B
X
A
BXABA
X
Doo
r se
al
resi
stan
ce
Acc
oust
. Tra
ns.
Win
dow
Mai
ntai
ncu
rren
t lev
el
Mai
ntai
ncu
rren
t lev
el
X
Slide 13Operations/Quality © Van Mieghem
Linked Houses From Customer To Manufacturing
EngineeringCharacteristics
PartsCharacteristics
Key ProcessCharacteristics
ProductionCharacteristics
House ofQuality
PartsDeployment
ProcessPlanning
ProductionPlanning
I II III IV
Eng
inee
ring
Cha
ract
eris
tics
Par
tsC
hara
cter
isti
cs
Key
Pro
cess
Cha
ract
eris
tics
Cus
tom
er A
ttri
bute
s
Slide 14Operations/Quality © Van Mieghem
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
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
Slide 15Operations/Quality © Van Mieghem
More New Product Development Tools
Value analysis / Value engineering
Design for manufacturability
Robust design
Slide 16Operations/Quality © Van Mieghem
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?
Slide 17Operations/Quality © Van Mieghem
Robust Quality: Taguchi’s View of Cost of Variability
Traditional View Taguchi’s View
Non-conformance to design cost
$$$
0
LowerTolerance
DesignSpec
UpperTolerance
Actual value Lower
ToleranceDesignSpec
UpperTolerance
Slide 18Operations/Quality © Van Mieghem
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
Slide 19Operations/Quality © Van Mieghem
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
Slide 20Operations/Quality © Van Mieghem
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.
Slide 21Operations/Quality © Van Mieghem
Quality Wireless (A): Capability
Distribution 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
Nu
mb
er o
f D
ays
Out of SpecsWithin Specs
Slide 22Operations/Quality © Van Mieghem
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
Slide 23Operations/Quality © Van Mieghem
What is Process Improvement?
Product/ServiceOutput Measure
“Defects”=Service is
unacceptable to customers
Critical customer requirementBeforeAfter
Slide 24Operations/Quality © Van Mieghem
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.
Slide 25Operations/Quality © Van Mieghem
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
Quality Wireless (A): Checking for Improvement
Slide 26Operations/Quality © Van Mieghem
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
Slide 27Operations/Quality © Van Mieghem
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?
Has Process Performance Changed? Quality Wireless (B)
Slide 28Operations/Quality © Van Mieghem
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
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
Weight watchersrule: measure only weekly…
Slide 29Operations/Quality © Van Mieghem
Statistical Process Control (SPC):Sources of Variability and Conceptual Framework
Every process has variation that comes from two sources:
1. Inherent (common cause)
2. 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.
Slide 30Operations/Quality © Van Mieghem
Statistical Process Control: Control Charts
m - 3Lower Control Limit
hypothesized (sampled)
process output
mean m
m + 3Upper Control Limit
F(z)99.74
%
Signal that a special cause has occurred
t
Control Improvement
Slide 31Operations/Quality © Van Mieghem
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
Slide 32Operations/Quality © Van Mieghem
Calibration versus Quality:Sharp shooters
Slide 33Operations/Quality © Van Mieghem
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
Slide 34Operations/Quality © Van Mieghem
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?
Slide 35Operations/Quality © Van Mieghem
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.
Slide 36Operations/Quality © Van Mieghem
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. output typically within the control limits m 3
– 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.
Slide 37Operations/Quality © Van Mieghem
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
Slide 38Operations/Quality © Van Mieghem
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
Slide 39Operations/Quality © Van Mieghem
Why 6-Sigma?
2 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:– 99.9997% (“5 nines”) – Close to flaw-free for most businesses, with just 3.4 failures per
million opportunities (e.g. products, services or transactions).
Slide 40Operations/Quality © Van Mieghem
Magnitude of Difference Between Sigma Levels
# sigma’s Area Spelling Time Distance
1 Floor space of Soldier Field
170 misspelled words per page in a book
31.75 years per century
Here to the moon
2 Floor space of large supermarket
25 misspelled words per page in a book
0.45 years per century
1.5 times around the world
3 Floor space of small hardware store
1.5 misspelled words per page in a book
3.5 months per century
London to New York
4 Your living room 1 misspelled word per 30 pages
2.5 days per century
Basel to Zurich
5 The button of your telephone
1 misspelled word in a set of encyclopedias
30 minutes per century
Leverone to Norris
6 diamond 1 misspelled word in a library
6 seconds per century
Four steps from your chair
Slide 41Operations/Quality © Van Mieghem
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)
Probability that process/product meets specs3 -sigma 4 - sigma 5 - sigma 6 - sigma
# of steps/parts1 93.3% 99.4% 99.98% 99.9997%
10 50.1% 94.0% 99.8% 99.997%50 3.2% 73.2% 98.8% 99.98%
100 0.1% 53.6% 97.7% 99.97%144 0.00% 40.8% 96.7% 99.95%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%
0.001%
0.01%
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
* These numbers allow a mean shift of 1.5 .
Slide 42Operations/Quality © Van Mieghem
Relationship Between Sigma Capability, Proportion Defects, and Cpk
LSL m USL
z z
z Sigma Process Probability of meeting specs Defects per million Cpk
z 2 F (z ) - 1 2 - 2 F (z ) z / 31 0.682689480482 317310.519518 0.332 0.954499875928 45500.124072 0.673 0.997300065554 2699.934446 1.004 0.999936627931 63.372069 1.335 0.999999425790 0.574210 1.676 0.999999998020 0.001980 2.007 0.999999999997 0.000003 2.33
Cm m
pk = minLSL
3 ,
USL -
3
* These are “raw” numbers; excluding mean shifts.
Slide 43Operations/Quality © Van Mieghem
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:1. Define the problem to determine what needs to be improved
2. Measure the current state against the desired state
3. Analyze the root causes of the business gap
4. Improve by team brainstorming, selecting and implementing the best solutions
5. Control the long-term sustainability of the improvement by establishing monitoring mechanisms, accountabilities and work tools
Slide 44Operations/Quality © Van Mieghem
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:1. Align executives to the right objectives and targets using a balanced scorecard
2. Mobilize improvement teams using DMAIC
3. Accelerate results by action learning and integrating all teams so the cumulative impact on the organization is “accelerated.”
4. Govern the process through visible executive sponsorship, review, and sharing best practices with other parts of the organization
Slide 45Operations/Quality © Van Mieghem
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?
Slide 46Operations/Quality © Van Mieghem
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 =
LSLmmUSL
,min
Slide 47Operations/Quality © Van Mieghem
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 =
Slide 48Operations/Quality © Van Mieghem
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) =
UCL
396.2
400
403.8
X
Sample Mean
time
385 390 395 400 405 410 415
= 4
LSL USL
LCL
26.110
4
X
01
23
-3-2
-1
Slide 49Operations/Quality © Van Mieghem
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?
Slide 50Operations/Quality © Van Mieghem
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) =
UCL
396.2
400
403.8
X
time
385 390 395 400 405 410 415
= 4
LSL USL
LCL
26.110
4
X
= 4
403
01
23
-3-2
-1
403
Sample Mean
Slide 51Operations/Quality © Van Mieghem
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?
Slide 52Operations/Quality © Van Mieghem
Quality Performance at Flyrock: Quality graphs for improved 6 process
A: Meeting Customer Specs
Sigma capability = Prob(Meeting specs) =
B: Keeping Process in Control
Prob(sample mean within control band) = Prob(investigate) =
UCL
396.2
400
403.8
X
Sample Mean
time
385 390 395 400 405 410 415
LSL USL
01
23
-3-2
-1 53.010
67.1
X
401.6
LCL398.4
Slide 53Operations/Quality © Van Mieghem
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?
Slide 54Operations/Quality © Van Mieghem
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) =
UCL
400
X
Sample Mean
time
385 390 395 400 405 410 415
LSL USL
01
23
-3-2
-1 53.010
67.1
X
401.6
LCL398.4
403
01
23
-3-2
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Slide 55Operations/Quality © Van Mieghem
Key Learning Objectives: Six Sigma
Specification limits: Voice of the customer– Use output (population) distribution with mean m, stdev – 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
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