What is Cutting Edge in Lean Six Sigma?

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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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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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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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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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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








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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


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


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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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

Documents

What is Cutting Edge in Lean Six Sigma?