what is cutting edge in lean six sigma?
TRANSCRIPT
Air Academy
Associates
Copyright2004
What is Cutting Edge in Lean Six Sigma?
Mark J. Kiemele
Air Academy Associates
December 2, 2004
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Lean Six Sigma Evolution
Interchangeable
Parts
Eli Whitney
Interchangeable
Parts
Eli Whitney
Assembly Line
Assembly Line
Model
Variety
Model
VarietyJidoka
S. Toyoda
Jidoka
S. Toyoda
Mass / Batch
Alfred P. Sloan
Mass / Batch
Alfred P. Sloan
Waste
Elimination
Waste
Elimination
System
Synchronization
System
Synchronization
Time & Motion
Division of Labor
F. Taylor
Time & Motion
Division of Labor
F. Taylor
Mass Production
Henry Ford
Mass Production
Henry Ford
Just – In – Time
K. Toyoda
Just – In – Time
K. Toyoda
Supermarket
Systems
Supermarket
Systems
Total Quality
E. Deming,
et al
Total Quality
E. Deming,
et al
SQC
ShewhartWestern Electric
SQC
ShewhartWestern Electric
DOE
Taguchi et al
DOE
Taguchi et al
Toyota
Production
System
T. Ohno
Toyota
Production
System
T. Ohno
Six Sigma
Motorola
Six Sigma
Motorola
Employee
Partnership
Drucker
Employee
Partnership
Drucker
Standard
Costing
Standard
Costing
LeanSix Sigma
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Lean Six Sigma Principles
• Specify value in the eyes of the customer
• Identify the value stream and eliminate waste / variation
• Make value flow smoothly at the pull of the customer
• Involve, align and empower employees
• Continuously improve knowledge in pursuit of perfection
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Lean Six Sigma: Making the Transitions Quickly and Smoothly
Lean Six SigmaTools
Data Information
Customer Satisfaction
and Improved
Profitability
ProcessImprovement
Questions:
Critical Thinking
Knowledge
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Questions Leaders & Managers Need to Ask
1. Which value stream are you supporting and who is the recipient of the value, i.e., who is the customer? Who is the value stream owner and who are the players or team members? How well does the team work together?
2. Within the value stream, which process or processes have the highest priority for improvement? Show me the data that led to this conclusion.
For the process or processes targeted for improvement,
3. How is the process performed? How does the value flow? What activity is value added and what is non-value added?
4. What are the process performance measures, i.e., how will we gauge if a process is improving? Why did we choose those? How accurate and precise is the measurement system? Show me the data.
5. What are the customer-driven requirements or specifications for all of the performance measures? Are the process performance measures in control and how capable is the process? Show me the data. What are the improvement goals for the value stream or process performance measures?
StrategyD
efin
eM
easu
re
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Questions Leaders & Managers Need to Ask (cont.)
6. What kinds of waste and cost of poor quality exist in the value stream or process and what is the financial and/or customer impact? Show me the data.
7. What are all the sources of variability in the value stream or process and which of those do we control? How do we control them and what is our method of documenting and maintaining this control? Show me the data.
8. Are any sources of waste or variability supplier-dependent? If so, what are they, who are the suppliers, and how are we working together to eliminate waste and variability? Show me the data.
9. What are the key input variables that affect the average and standard deviation of the measures of performance? How do you know this? Show me the data.
10. What are the relationships between the measures of performance and the key input variables? Do any of the key input variables interact? How do you know for sure? Show me the data.
An
alyz
e
Strategy
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Questions Leaders & Managers Need to Ask (cont.)
11. What settings or values for the key input variables will optimize the measures of performance? How do you know this? Show me the data.
12. For the optimal settings of the key input variables, what kind of variability still exists in the performance measures? How do you know? Show me the data.
13. Have we implemented a process flow and control system to sustain the gains and continuously improve the process? Show me the data.
14. How much improvement has the value stream or process shown in the past six months? How much time and/or money have our efforts saved the company? Show me the data.
Co
ntr
ol
Strategy
Imp
rove
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• A recently developed technique based on combinatorics
• Used to test myriad combinations of many factors (typically qualitative)
where the factors could have many levels
• Uses a minimum number of runs or combinations to do this
• Software (e.g., ProTest) is needed to select the minimal subset of all
possible combinations to be tested so that all n-way combinations are tested.
• HTT is not a DOE technique, although the terminology is similar
• A run or row in an HTT matrix is, like DOE, a combination of different factor
levels which, after being tested, will result in a successful or failed run
• HTT has its origins in the pharmaceutical business where in drug discovery
many chemical compounds are combined together (combinatorial chemistry)
at many different strengths to try to produce a reaction.
• Other industries are now using HTT, e.g., software testing, materials
discovery, IT (see IT example on next page)
High Throughput Testing (HTT)
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HTT Example
• An IT function in a company wanted to test all 2-way combinations of a variety of computer configuration-related options or levels to see if they would function properly together.
• Here are the factors with each of their options:
Motherboards (5) : Gateway, ASUS, Micronics, Dell, Compaq
RAM (3) : 128 MB, 256 MB, 512 MB
BIOS (3) : Dell, Award, Generic
CD (3) : Generic, Teac, Sony
Monitor (5) : Viewsonic, Sony, KDS, NEC, Generic
Printer (3) : HP, Lexmark, Cannon
Voltage (2) : 220, 110
Resolution (2) : 800x600, 1024x768
• How many total combinations are there?
• What is the minimum number of these combinations we will have to test (and which ones are they) in order to determine if every 2-way combination (e.g., Dell Bios with Teac CD) will indeed work properly together?
• To answer this question, we used Pro-Test software. The answer is 25 runs and those 25 combinations are shown on the next page.
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High Throughput Testing (HTT) (for all two-way combinations)
5 Levels 3 Levels 3 Levels 3 Levels 5 Levels 3 Levels 2 Levels 2 Levels
Motherboard RAM BIOS CD Monitor Printer Voltage Resolution
Case 1 ASUS 256 MB Dell Generic Viewsonic Lexmark 110 V 800 x 600
Case 2 Compaq 512 MB Dell Teac Sony HP 220 V 1024 x 768
Case 3 Gateway 128 MB Generic Sony KDS Cannon 220 V 800 x 600
Case 4 Dell 128 MB Award Teac NEC Cannon 110 V 1024 x 768
Case 5 Micronics 256 MB Generic Teac Generic Lexmark 220 V 1024 x 768
Case 6 Gateway 256 MB Award Sony Sony HP 110 V 1024 x 768
Case 7 Micronics 512 MB Award Generic Viewsonic Cannon 220 V 1024 x 768
Case 8 ASUS 512 MB Generic Teac KDS HP 220 V 1024 x 768
Case 9 Compaq 128 MB Award Generic Generic HP 110 V 800 x 600
Case 10 Micronics 512 MB Generic Teac Sony Lexmark 110 V 800 x 600
Case 11 Dell 256 MB Award Generic KDS Lexmark 110 V 1024 x 768
Case 12 Gateway 512 MB Dell Sony Generic Lexmark 110 V 1024 x 768
Case 13 Compaq 256 MB Generic Sony Viewsonic Cannon 220 V 1024 x 768
Case 14 ASUS 128 MB Dell Sony NEC Cannon 220 V 800 x 600
Case 15 Micronics 128 MB Dell Sony KDS Lexmark 220 V 800 x 600
Case 16 Gateway 128 MB Generic Teac Viewsonic HP 110 V 800 x 600
Case 17 Dell 128 MB Dell Sony Sony Cannon 110 V 1024 x 768
Case 18 ASUS 256 MB Award Sony Generic Cannon 220 V 1024 x 768
Case 19 Compaq 512 MB Dell Sony NEC Lexmark 110 V 800 x 600
Case 20 Gateway 256 MB Generic Generic NEC Cannon 220 V 800 x 600
Case 21 Micronics 512 MB Generic Teac NEC HP 220 V 800 x 600
Case 22 ASUS 256 MB Generic Generic Sony HP 110 V 800 x 600
Case 23 Dell 512 MB Generic Sony Viewsonic HP 220 V 1024 x 768
Case 24 Compaq 256 MB Dell Generic KDS Cannon 220 V 1024 x 768
Case 25 Dell 128 MB Generic Sony Generic HP 110 V 800 x 600
Full Factorial = 8100 runs HTT = 25 runs
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Document Approval Process Example
• Your company has recently initiated a digital approval of time sensitive documents. The system should allow the user to process an Engineering Change Request (ECR), Request for Quote (RFQ), and Engineering Commitment (Commit). The system should handle electronic formats as well as allow the tracking of paper formats. The departments which require approval are the Program Management office, the Electrical Engineering department, the Mechanical Engineering department, and the Software Engineering department. Final approval is done through either the controller or the leadership committee.
• Formulate the testing plan to cover all two way combinations.
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Document Approval Process Example (cont.)
• Suppose we have one constraint and that is: Program Management cannot do an Engineering Commitment type document.
• Generate the test cases to cover all two way combinations.
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Accounts Receivable Example
Accounts
Receivable
Process
A: Follow-Up (Letter vs. Phone)
B: Method (In-House vs. Outsourcing)
C: Frequency (Individual vs. Batching)
y = time between date on invoice and when invoice is paid
-1 +1
-1 +1
-1 +1
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Accounts Receivable Example (cont.)
This design matrix is called a 23 = 8 run full factorial design for 3 factors, each evaluated at 2 levels, with 6 replications.
Factor A B CRow # Follow-Up Method Frequency Y1 Y2 Y3 Y4 Y5 Y6
1 -1 -1 -1 49 46 56 59 47 442 -1 -1 1 79 84 86 78 86 913 -1 1 -1 51 55 64 53 63 614 -1 1 1 93 96 81 79 80 885 1 -1 -1 47 46 44 51 40 496 1 -1 1 59 61 69 62 54 667 1 1 -1 46 52 49 55 59 428 1 1 1 62 61 64 68 69 60
Data Collection Matrix
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Accounts Receivable Example (cont.)
Run A B C AB AC BC ABC
1 -1 -1 -1 +1 +1 +1 -1
2 -1 -1 +1 +1 -1 -1 +1
3 -1 +1 -1 -1 +1 -1 +1
4 -1 +1 +1 -1 -1 +1 -1
5 +1 -1 -1 -1 -1 +1 +1
6 +1 -1 +1 -1 +1 -1 -1
7 +1 +1 -1 +1 -1 -1 -1
8 +1 +1 +1 +1 +1 +1 +1
Complete Orthogonal or Balanced Matrix
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Accounts Receivable Example (cont.)
The following is the regression output for the y-hat model.
Y-hat ModelFactor Name Coeff P(2 Tail) Tol Acti
ve
Const 62.583 0.0000A Follow-Up -6.95833 0.0000 1 XB Method 2.04167 0.0131 1 XC Frequency 11.417 0.0000 1 X
AB -0.41667 0.5989 1 X
AC -4.12500 0.0000 1 X
BC -0.95833 0.2298 1 X
ABC 0.41667 0.5989 1 X
Rsq 0.8907
Adj Rsq 0.8715
Std Error 5.4444
F 46.5464
Sig F 0.0000
Source SS df MS
Regression 9658.0 7 1379.7Error 1185.7 40 29.6Total 10843.7 47
Significant
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Advertising Example
Advertising
Process
(What) A: Location (Mountains vs. Beach)
(Who) B: Group (Spouse vs. Family)
(When) C: Season (Summer vs. Winter)
y (Rating)
Data Collection Matrix Will Be Set Up Like This
Location Group Season
Run A B C
1 Mountains Spouse Summer
2 Mountains Spouse Winter
3 Mountains Family Summer
4 Mountains Family Winter
5 Beach Spouse Summer
6 Beach Spouse Winter
7 Beach Family Summer
8 Beach Family Winter
-1 +1
-1 +1
-1 +1
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Advertising Example (cont.)
Factor A B C
Row # Y1 Y2 Y3 Y bar
1 -1 -1 -1 8 4 1 4.0690
2 -1 -1 1 6 1 3 4.3793
3 -1 1 -1 7 2 4 3.6207
4 -1 1 1 5 6 6 5.6207
5 1 -1 -1 4 3 8 4.2759
6 1 -1 1 1 8 2 5.0690
7 1 1 -1 2 5 5 3.1379
8 1 1 1 3 7 7 5.4828
Location Group Season
2.2350
2.0601
2.1114
2.0426
2.4770
2.2980
1.7055
2.4874
s
Note: 1 = 1st Choice8 = Last Choice
smaller ratings are better
Completed Data Collection Matrix
...
...
...
...
...
...
...
...
...
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Advertising Example (cont.)
Y-hat Model
Factor Name Coeff P(2 Tail) Tol Act
ive
ConstA
BCAB
ACBC
ABC
Rsq
Adj Rsq
Std ErrorF
Sig
SourceRegression
ErrorTotal
4.45690 0.0000Location 0.03448 0.8107 1.000
Group 0.00862 0.9523 1.000XSeason 0.68103 0.0000 1.000
X
0.18966 0.1886 1.000
0.10345 0.4727 1.0000.40517 1.000 X
0.01724 0.9047 1.000
0.1274
0.10012.1904
4.6712
0.0001
SS df MS156.9 7 22.41074.7 224 4.8
1231.6 231
The following is the regression analysis output.
Significant
0.0053
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Advertising Example (cont.)
Example of an Interaction Plot
Summer
Winter
FamilySpouse
Interaction Plot of Group vs. Season Constants: Location = -1 (Mountain)
0
1
2
3
4
5
6
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Group
Re
sp
on
se
Va
lue
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Suppose that, in the auto industry, we would like to investigate the following automobile
attributes (i.e., factors), along with accompanying levels of those attributes:
A: Brand of Auto: -1 = foreign +1 = domestic
B: Auto Color: -1 = light 0 = bright +1 = dark
C: Body Style: -1 = 2-door 0 = 4-door
+1 = sliding door/hatchback
D: Drive Mechanism: -1 = rear wheel 0 = front wheel +1 = 4-wheel
E: Engine Size: -1 = 4-cylinder 0 = 6-cylinder +1 = 8-cylinder
F: Interior Size: -1 2 people 0 = 3-5 people +1 6 people
G: Gas Mileage: -1 20 mpg 0 = 20-30 mpg +1 30
mpg
H: Price: -1 $20K 0 = $20-$40K +1 $40K
In addition, suppose the respondents chosen to provide their preferences to product
profiles are taken based on the following demographic:
J: Age: -1 25 years old +1 35 years old
K: Income: -1 $30K +1 $40K
L: Education: -1 < BS +1 BS
“Market Research” Example
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123456789
101112131415161718
Run* A B C D E F G H ---------+++++++++
-0+-0+-0+-0+-0+-0+
L - + - + - + - + K - - + + - - + + J - - - - + + + +
y1 y2 y3 y4 y5 y6 y7 y8
---000+++---000+++
-0+-0+0+-+-00+-+-0
-0+0+--0++-0+-00+-
-0+0+-+-00+--0++-0
-0++-00+-0+-+-0-0+
-0++-0+-0-0+0+-0+-
Segmentation of the population or
Respondent Profiles
Question: Choose the best design for evaluating this scenario
Answer: L18 design with attributes A - H in the inner array and factors J, K, and L in the outer array, resembling an L18 robust design, as shown below:
* 18 different product profiles
y s
“Market Research” Example (cont.)
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Product Type
(1 or 2)
Sales VolumeProcess
• Two companies merge.
• They want to know how best to align their combined sales force with regard to their products and customers.
• They conduct a pilot study based on the following IPO:
Sales Person Background
(Company 1 or 2)
Customer Type
(1 or 2)
Sales Example
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Sales Data Collection Template
Run Product Type Sales Person Customer Type(X1) Background (X2) (X3)
1 Product 1 Company 1 Customer 1
2 Product 1 Company 1 Customer 2
3 Product 1 Company 2 Customer 1
4 Product 1 Company 2 Customer 2
5 Product 2 Company 1 Customer 1
6 Product 2 Company 1 Customer 2
7 Product 2 Company 2 Customer 1
8 Product 2 Company 2 Customer 2
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Sales Data
Factor A B C
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14 Y15 Y16 Y bar S
1 1 1 1
2 1 1 2
3 1 2 1
4 1 2 2
5 2 1 1
6 2 1 2
7 2 2 1
8 2 2 2
Product Type
Sales Backgrd
Customer Type
Row #
25.1776
44.6442
42.1345
27.0798
5.2692
24.2180
19.2393
24.2611 27.365728.2788
19.6705 6.0673 11.62386.0673 9.7824
25.7681
12.9308
4.9598
5.5407
5.9054
6.7315
6.2546
27.726827.7589 25.4121 37.2007 26.1666 21.8319 36.734220.4490 26.5073 35.2291 27.5054 4.8886
55.7260 47.6398 53.067144.2883 44.405645.440342.5929 44.0490 49.8674 51.4262 45.0203 46.1667 42.5750 44.2589 42.0020 46.4481 4.0265
64.8133 59.7940 57.4953 49.988253.7867 62.919458.602559.2039 63.1648 60.9481 55.8881 63.0693 69.2662 62.3641 59.8280 68.1362 60.5792
43.7002 47.9114 42.108039.3640 36.950845.5104 40.6765 35.6169 41.1578 48.1319 35.2099 35.9524 44.8527 52.4872 41.483731.9741
28.6466 19.6648 13.410913.7568 25.506819.3635 23.7301 18.6467 18.3673 28.9306 25.5717 26.7314 26.3305 35.5960 23.530625.1564
34.4622 24.9315 29.7725 23.031327.2452 19.834525.2329 27.3195 35.4069 25.9173 25.4296 19.5491 19.9485 38.7334 18.0904 25.531513.6000
16.3209 13.3042 12.6003 16.605318.3477 13.1322 9.118123.2100 12.9625 8.6016 5.147920.6429 15.1887 13.3389 11.5990 14.005213.9631 4.4963
4.699213.7310 0.7392 10.4474 10.9791 5.4045 6.4816 1.3712 9.6965 19.6465
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A Regression Model for Sales
Y-hat Model
Factor Name Coeff P(2 Tail) Tol Act
ive
Const 31.108 0.0000A Product Type -12.896 0.0000 1 XB Sales Background 0.35436 0.4595 1 XC Customer Type -0.29684 0.5354 1 X
AB -6.67300 0.0000 1 X
BC -5.53276 0.0000 1 X
ABC 3.97682 0.0000 1 X
Rsq 0.9032
Adj Rsq 0.8984
Std Error 5.4032 99% Prediction IntervalF 188.1411
Sig F 0.0000
Source SS df MS
Regression 32956.3 6 5492.7Error 3532.6 121 29.2Total 36488.9 127
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Product 1
Sales Background
For Customer 1
Product 2
1 2
Sales
Product 1
Sales Background
For Customer 2
Product 2
1 2
Sales
Graphical Analysis of Sales Data(Interaction Plots)
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Advertising Example
Building a Prediction Model
TV Advertising ($)
Magazine Advertising ($)
Web Advertising ($)
Giveaway
1 = No Giveaway2 = Giveaway
# New Users / Visitors
Advertising
Process
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Data for The Past Year (By Week)
Week TV ($) MAG ($) WEB ($) GiveAway # New Users1 11 6 6 No 554172 10 6 17 No 351103 13 6 14 No 453244 19 6 9 No 614215 6 16 3 No 289536 7 16 6 No 368237 6 16 13 No 241478 5 16 13 No 229289 8 19 15 No 2542310 17 19 5 No 5854311 6 19 17 No 1681112 8 19 4 No 3888313 11 5 12 No 4115014 20 5 5 No 7400115 16 5 3 No 6860116 14 5 17 No 4979717 18 17 16 No 4651918 10 17 10 No 4112719 10 17 12 No 4476620 7 17 7 No 3190921 15 16 7 Yes 4066822 20 16 8 Yes 6920423 10 16 11 Yes 4221924 14 16 10 Yes 4842625 11 8 9 Yes 2109026 14 8 9 Yes 33830
Week TV ($) MAG ($) WEB ($) GiveAway # New Users27 13 8 6 Yes 1210828 20 8 6 Yes 3689329 9 19 6 Yes 2652730 5 19 4 Yes 1553831 14 19 11 Yes 7112632 11 19 5 Yes 2799033 10 20 17 Yes 8762734 10 20 4 Yes 2703035 16 20 9 Yes 6607536 20 20 12 Yes 8934737 12 17 4 Yes 3489038 19 17 16 Yes 9600939 16 17 4 Yes 3642440 7 17 6 Yes 2486841 11 6 5 Yes 847642 5 6 11 Yes 1379443 9 6 11 Yes 2575544 17 6 9 Yes 3764845 6 17 17 Yes 6248746 14 17 7 Yes 4819247 9 17 4 Yes 2661948 6 17 12 Yes 4759549 14 22 3 Yes 5991350 20 20 20 Yes 15432251 20 20 20 Yes 16732252 8 7 8 Yes 3455
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Modeling an Advertising Process
Factor Name Coeff P(2 Tail) Tol Act
ive
Const 42743.6 0.0000A TV ($) 19331.1 0.0000 0.855 XB MAG ($) 10344.3 0.0000 0.548 XC WEB ($) 9933.2 0.0000 0.950 XD GiveAway 189.717 0.8044 0.864 X
BD 11444.3 0.0000 0.777 X
CD 17534.4 0.0000 0.965 X
BB 7210.7 0.0306 0.514 X
Rsq 0.9525
Adj Rsq 0.9444
Std Error 4867.8461 99% Prediction IntervalF 117.4625
Sig F 0.0000
Source SS df MS
Regression 1.95E+10 7 2.78E+09Error 9.72E+08 41 2.37E+07Total 2.05E+10 48
Regression Model
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Advertising Example (cont.)
Factor Name Low High Exper
A TV ($) 5 20 12.5B MAG ($) 5 22 13.5C WEB ($) 3 17 10D GiveAway 1 2 1.5
Prediction
Y-hat 42743.574Std Error 4867.8461
99% Prediction Interval
Lower Bound 28140.036Upper Bound 57347.113
Knowledge Gained:
Prior to regression analysis our level of knowledge was:
More $$ spent = More new users
Now we know that:
# New Users =
42743 + ($TV) x 19331 + ($MAG) x 10344 + ($WEB) x 9933 + (GiveAway) x 189 + ($MAG x GiveAway) x 11444 + ($WEB x GiveAway) x 17534 + ($MAG x $MAG) x 7210
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Modeling The Drivers of Turnover
Process of
Deciding to
Stay / Leave
Human Resources
External Market Factors(Local Labor Market Conditions)
Local Unemployment Rate
Local Employment Alternatives
Turnover Rate
Company’s Market Share
Organizational Characteristics and Practices
Supervisor Stability
Lateral / Upward Mobility
Layoff Climate
Employee Attributes
Time Since Last Promotion
Education Level
Job Stability History
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Version of Excel (N)
Optimization of a
Computer Software
Genetic Algorithm
Implementation*
F Value at Start # of Function EvaluationsRequired to Reach the Solution
Correctness of Answer
CPU Type (N)Type of Design (N)
Type of OS (N)
F Value at Step Down
CC at Start
CC at Step Down
Size of the Gene MultiplierStep Down Ratio
Time Out Condition
Number of SignificantFactors and Interactions in
the Regression Equation (N)
Proportion of Solutions foundwithin the Time Constraint
* Author of the Algorithm said that the only way to choose the critical values for the input parameters was by trial and error. The software developer thought otherwise and used a balanced design matrix to find the critical values.
Software Development Example
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Length
Thickness
Width
# of Layers
Cost
# of Holes
PWB
Pricing
Model
Building a Pricing Model
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Process (Discrete Event) Simulation
• Permits the study and design of complex systems
• Allows for modeling/visualizing a process flow
• Tracks entities (customers, products, complaints, etc.) through a system
• Offers the ability to integrate variability and other process dynamics into the flow
• Studies/models event queuing, determining where bottlenecks will occur
• Used to gain knowledge about the relationship between components in a system
• Facilitates experimentation by allowing changes to be made quickly and gaining immediate feedback as to the result of the change, without having to physically change anything
• Used to study alternative system configurations and develop prototype processes
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Simulating a Call Center
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Process Simulation Advantages
• Simulation can be done before actual processes exist and/or without changing the actual process
• Allows us to ask “What If” often and get the results quickly
• Calculate performance metrics
• Optimize process parameters
• Compare different process maps
• Identify bottlenecks or problem areas (Risk management)
• Perform cost/benefit tradeoff analyses
• Communicate effectively
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Lean Six Sigma: Making the Transitions Quickly and Smoothly
Lean Six SigmaTools
Data Information
Customer Satisfaction
and Improved
Profitability
ProcessImprovement
Questions:
Critical Thinking
Knowledge
Leadership and Implementation Accountability
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Contact Information:
Air Academy Associates, LLC1650 Telstar Drive, Ste 110
Colorado Springs, CO 80920
719-531-0777Facsimile: 719-531-0778
Email: [email protected]: www.airacad.com
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Rev2
Questions?
Dr. Mark J. Kiemele