using six sigma tools in a lean environment - aimcal product/process search. introduction ... dorian...

Problem-solving Techniques for Use in a Lean EnvironmentUsing Six Sigma Tools in a Lean Environment

© ABBA Roller, LLC, 2012—Presented at AIMCAL 2012 Technical Conference

Agenda

11 Introduction

77 Validation

2.12.1 Multi-Variable Analysis

55 Variable Search

22 Clue Generation

66 Full Factorial Analysis

2.22.2 Concentration Chart

2.32.3 Component Search

2.42.4 Paired Comparison

2.52.5 Product/Process Search

IntroductionThe Lean “Wall”


Your Lean Journey is well underway



5S Is Complete

Start Sort Set in Order Shine Standardize Sustain



All Waste Sources Identified



Current and Future State Value Stream Maps CompleteVSMCurrentState.igx

State Street Assembly

12000 L6400 RTray = 20 pieces2 Shifts

200 T

STAMPING

C/T = 1 secs.C/O = 1 hoursUptime = 85%27600 secs. avail.EPE = 2 weeks

1

5 days

1 seconds

7.61 days

39 seconds

1.85 days

46 seconds

2.66 days

62 seconds

2 days

40 seconds

4.5 days lead time = 23.6 days

VA/T = 188 secondsTraveled = 38.3 yards

8.33 yards 20 yards 1.67 yards 6.67 yards 1.67 yardsTravel

S. WELD #1

C/T = 39 secs.C/O = 10 minutesUptime = 100%2 Shifts27600 secs. avail.

1

S. WELD #2

C/T = 46 secs.C/O = 10 minutesUptime = 80%2 Shifts27600 secs. avail.

1

ASSEMBLY #1

C/T = 62 secs.C/O = 0 secs.Uptime = 100%2 Shifts27600 secs. avail.

1

ASSEMBLY #2

C/T = 40 secs.C/O = 0 secs.Uptime = 100%2 Shifts27600 secs. avail.

1Staging

SHIPPING

4600 L2400 R

1100 L600 R

1600 L850 R

1200 L640 R

2700 L1440 R

MRP

Production Control

Michigan Steel Co.

500 ft coils

6 weekForecast

WeeklyFax

90/60/30 dayForecasts

DailyOrder

Daily ShipSchedule

5 daysCoils

Tues. + Thurs.

1xDaily

Weekly Schedule

This diagram is provided with permission from the Lean Enterprise Institute, Inc. publication "Learning to See". Customer Demand:18400 pieces per Month(Takt Time 60 seconds)

VSMFutureState.igx

State Street Assembly

Tray = 20 pieces2 Shif ts

STAMPING

EPE = 1 Shif tC/O = 10 mins.

1.5 day s

1 seconds

1.5 day s

165 seconds

2 day s Production Lead Time = 5 day s

Processing Time = 166 seconds

WELD + ASSY.

Takt = 60 secs.C/T = 55 secs.C/O = 0 secs.Uptime = 100%2 Shif ts

3

Staging

SHIPPING

Production Control

Michigan Steel Co.

6 weekForecast

WeeklyFax

90/60/30 dayForecasts

DailyOrder

Bin

Coil

20

20

20

20

Coi

l

Changeov er

Weld

changeover

Welder uptime

Total work <= 165 sec

1.5 day s

Coils

(at the press)1.5 day s

L

R

2 day s

20

Batch

1x Daily

Daily(Milk Run)

This diagram is prov ided with permission f rom the Lean Enterprise Institute, Inc. publication "Learning to See".Customer Demand:

18400 pieces per Month(Takt Time 60 seconds)

Pacemaker

Finished Goods StrategyTAKT

Pitch

Flow & FIFO

Interval

Pull



This Is Where the Lean Methodology Leaves You Hanging



• Rigorous structure requires higher skill sets

• Typically implemented by Engineering

• Too much guessing—rarely consult Production

For Problem Solving…Deploy Six Sigma Toolset



Most Significant Improvements Realized With Production Involvement• Toolset must be easy to understand, deploy and use• Statistical significance must be maintained


Introduction

Dorian Shainin (September 26, 1914 – January 7, 2000) was an influential American quality consultant, aeronautics engineer, author, and college professor most notable for his contributions in the fields of industrial problem solving, product reliability, and quality engineering, particularly the creation and development of the “Red X” concept.

Responsible for the development of over 20 statistical engineering techniques that have become the core of the “Shainin System” for quality and reliability improvement.

Shainin Methodology

Without Demming, the U.S. would not have a quality policy;without Juran, it would not have a quality direction;

without Shainin, it would not have solved quality problems.


Introduction

• Define and Measure Green Y– What is being measured?– Can it be measured reliably?

• Generate Clues– Develop list of candidate variables (factors)– Red X—Primary factor– Pink X—Secondary factor

• Perform Full Factorial DOE– Quantify the importance of the candidate variables and all

interactions

Overview of Shainin Methodology


Introduction

• Validation Run– Can the problem be turn on and off?

• Optimization Run– What are the optimum levels of the variables

Overview of Shainin Methodology


Clue Generation

• Understand type of variation– Positional—variation within-unit– Cyclical—unit-to-unit variation– Temporal—time-to-time variation

• Define measurement scale– Variable data—2 to 4 samples– Attribute data (pass/fail)—20 to 50 samples– Likert scale (1=worst, 10=best)—5 to 10 samples

Overview


Clue Generation

• Conduct investigation– Multi-Variable Analysis—Type of variation unknown– Concentration Chart—Positional variation– Component Search—Cyclical variation– Paired Comparison—Cyclical variation– Product/Process Search—Temporal variation

Overview

Clue GenerationMulti-Variable Analysis

11

22

33

44

55

66

88

On each wafer, five dice are measured

Sampling is performed over three consecutive weeks

Wafers are selected from three locations in the batch

Each batch takes two hours to process (4 batches/shift)

Three wafers are sampled from each batch exiting the deposition process

Two deposition chambers are used

Samples are taken from two consecutive batches per on first and second shifts

77 Samples are taken on Monday thru Wednesday

Green Y:Wafer Thickness Variation

Temporal Variation Cyclical Variation Positional Variation

Weeks 3

Days 3

Shifts per Day 2

Batches per Shift 4

Wafers per Batch 3

Deposition Chambers 2

Locations per Batch 3

Positions per Wafer 5

Total Samples = 3 x 3 x 2 x 4 x 3 x 2 x 3 = 1,296

Total Readings = 1,296 x 5 = 6,480

Problem: Thickness in wafers exiting the semiconductor deposition process.



Positional VariationPositional Variation Cyclical VariationCyclical Variation

Temporal VariationTemporal Variation



Concentration Charts

• Used with Positional Variations• Map location of defects

– Use part drawing– Generate grid on part

• Plot all defect types and counts on map• Focus investigation on high concentration areas

Overview


Component Search

• Used with Cyclical Variations• Units must be capable of assembly and disassembly

without changing Green Y• Use Paired Comparisons if assembly/disassembly

changes Green Y

Overview


Component Search

• Methodology—Develop baseline– Collect very good units—Best of Best (BOB)– Collect very bad units—Worst of Worst (WOW)– Disassemble and reassemble two times measuring Green Y

after each reassembly– Conduct Significance Test

Are the differences between BOBs and WOWs statistically significant?

– Calculate decision limitsDefine the expected range of values for BOBs and WOWs.

Methodology


Component Search

• Methodology—Conduct experiments– Rank-order list of candidate factors (A, B, C, …)– Swap one factor at a time between BOB and WOW– Measure Green Y after each swap– Any factor-swap that causes Green Y to fall outside decision

limits is important– Perform capping run with important factors at WOW level and all

others at BOB level

Methodology


Component Search

• Factors D and G are important• Capping Run of DWGWRB showed reversal

Example

BOB WOWInitial -40 0Disass'y 1 -35 -5Disass'y 2 -37 -7Factor A -40 -5Factor B -35 0Factor C -35 -5Factor D -20 -5Factor E -40 0Factor F -40 -5Factor G -20 -5Factor H -35 0Capping Run -1 -40


Paired Comparisons

• Used with Cyclical Variations when Component Search not valid

• Uses multiple (6 or 8) pairs of BOBs and WOWs• Develop rank ordered list of candidate factors• Uses combination formula for significance

Overview

TotalEnd-Counts

Confidence Level

6 90%7 95%10 99%13 99.9%


Paired Comparison

• Measure all factors for all units• Designate each reading as a BOB or WOW• Arrange in rank order from smallest to largest• Draw a line when BOB readings change to WOW

readings (top end-count)• Draw a line when WOW readings change to BOB

readings (bottom end-count)• Add top and bottom end counts

Procedure

ExamplePaired Comparison

Your own footer Your Logo

Rank Ordered DataRaw Data

Total End Count = 7

There is a 95% confidence thatthe BOBs are really better thanthe WOWs

Analysis

B = BOB, W = WOW

0.015 W0.018 W0.014 W0.022 W0.019 W0.018 B0.016 B0.023 B0.017 W0.019 W0.011 W0.007 W0.024 B0.023 B0.021 B0.017 B

0.007 W0.011 W0.014 W0.015 W0.016 B0.017 W0.017 B0.018 W0.018 B0.019 W0.019 W0.021 B0.022 W0.023 B0.023 B0.024 B

TotalEnd-Counts

Confidence Level

6 90%7 95%10 99%13 99.9%

}

}

4

3


Product/Process Search

• Used with Temporal Variations• Separates important process parameters that may shift,

drift or fluctuate with time• Uses same concepts as Paired Comparisons• Compares the process parameters associated with 6 or

8 BOB and WOW product pairs

Overview



• Record parameters for all samples– Parameters are measured directly, not by settings– BOBs and WOWs cannot be determined until after processing

• Select 6 or 8 BOB/WOW pairs• Run a Paired Comparison to identify important factors

Overview


Variable Search

• Objectives– Isolate important variables– Tighten tolerances of important variables– Loosen tolerances of unimportant variables

Overview

Technique focuses on variables associated with known significant factors.


Variable Search

• Methodology– Ball Park

Are correct variables and levels identified?– Separation of Factors

Separate important and unimportant variables– Capping Run

Validate the important variables as significant– Factorial analysis

Quantify the magnitude and desired levels of important variables

Overview


Variable Search

• Perform Full Factorial DOE on important variables– Further isolate those that

contribute most to the problem– Provide guidance variables

needing tighter tolerances

• Conduct Validation Run– Demonstrate ability to turn

problem on/off– Develop confidence solution is

permanent

Next Steps


Summary

• Deploy Six Sigma toolset when engaging in Lean Continuous Improvement

• Reduce number of required experiments by utilizing Shainin Methodology

• Involve Production personnel…not just Engineers


A Final Note

Citations• Dorian Shainin and Peter Shainin, Better Than Taguchi Orthogonal

Arrays (John Wiley & Sons, Ltd., 1988)• Richard D. Shainin, How Lean is Your Six Sigma Program?• Bhote and Bhote, World Class Quality (ANACOM, 2000)• Donald J. Wheeler, Understanding Industrial Experimentation (SPC

Press, Inc. 1988)• Tapping, Luyster and Shuker, Value Stream Management

(Productivity Press, 2002)


A Final Note

Contact InformationPeter KochABBA [email protected]

Backup Slides

Your Logo

Problem-solving Techniques for Use in a Lean EnvironmentUsing Six Sigma Tools in a Lean Environment


Agenda

Your Logo

11

22

33

44

55

66

77

Six Sigma Tools in a Lean Environment

Validation

Shainin Methodology

Variable Search

Design of Experiments Techniques

Clue Generation

Full Factorial Analysis

The DMAIC ProcessSix Sigma Tools in a Lean Environment

11Create current state Value Stream Map.Collect data for operations (TAKT, Manning, Setup , etc.)Identify Critical Elements (Bottlenecks, Inventory levels, etc.)

Define and Measure

22Create Future State VSM with key strategies(Push vs. Pull, Pacemaker, Finished Goods Strategy, etc.)

Analyze

33Generate CluesFormal Design of ExperimentsValidation and Optimization

Improve and Control




• Principles– Balance—all levels of all variables are tested– Replication—identify signal to noise ratio– Randomization—eliminate biases

• Goals– Separate and quantify main effects (factors) and

higher order interactions

Characteristics of DOEs



• Requires 2n Experiments– 2 variables = 4 experiments– 4 variables = 16 experiments– 10 variables = 1,024 experiments

Characteristics of DOEs

Eliminating As Many Variables As Possible Increases Efficiency and Reduces Cost

© ABBA Roller, LLC, 2012—Presented at AIMCAL 2012 Technical Conference Your Logo

Comparison of TechniquesDesign of Experiments Techniques

Full Factorial: 2n experimentsFractional Factorial: 2n-p experimentsHalf Factorial (p=1), Quarter (p=2)

Classical Methods

Fractional FactorialOrthogonal, Not BalancedAssumes interactions are negligable

Taguchi Method

Identify Candidate FactorsUse Full FactorialValidation to Confirm Uniqueness

Shainin Method

ContrastContrastBalanceBalance

Alias: Same contrast for different factors

Orthogonal: Equal number +’s and –’s in a contrast


Shainin Methodology

• Define and Measure Green Y– What is being measured?– Can it be measured reliably?

• Generate Clues– Develop list of candidate variables (factors)– Red X—Primary factor– Pink X—Secondary factor

Overview


Shainin Methodology

• Perform Full Factorial DOE– Quantify the importance of the candidate variables

and all interactions• Validation Run

– Can the problem be turn on and off?• Optimization Run

– What are the optimum levels of the variables

Overview

Weigh two groups of

3 balls

If equal: Select remaining 9 balls.

If unequal: Select heavier 9 balls



Weigh two groups of27 balls

Divide balls into three groups (27-27-26)

Factor Elimination ExampleShainin Methodology


Weigh two groups of

1 ball

Weigh two groups of

9 balls



If equal: Remaining ball is Red X

If unequal: Heavier ball is Red X

• Problem Statement– 80 steel balls.– All weigh the same

except one which is heavier.

• Equipment– Balance scale with

center mark.– Two pans (equal

weight).• Find the Red X ball in no

more than four tries on the scale.


Shainin Methodology

• Define the Green Y– Specific, Achievable, Realistic and Relevant

• Verify the measurement system– Accuracy 5X better than tolerance of Green Y– Within-instrument variation– Instrument-to-instrument variation– Operator-to-operator variation

Define and Measure Green Y


Shainin Methodology

• Quantify and measure Green Y– Quantify range of defects

• Investigate problem history– Out-of-box failures– Constant rate failures– Wear-out failures

Define and Measure Green Y

These Rules Are Used in All Investigations


Clue Generation

• Families of Variations– Positional—variation within-unit– Cyclical—unit-to-unit variation– Temporal—time-to-time variation

• Determine number of samples and frequency– Capture 80% of variation level

Overview


Clue Generation

• Conduct investigation– Multi-Variable Analysis—Family of variation unknown– Concentration Chart—Positional variation– Component Search—Cyclical variation– Paired Comparison—Cyclical variation– Product/Process Search—Temporal variation

• Standard Green Y Rules apply

Overview


11

22

33

44

55

66

88

On each wafer, five dice are measured

Sampling is performed over three consecutive weeks

Wafers are selected from three locations in the batch

Each batch takes two hours to process (4 batches/shift)

Three wafers are sampled from each batch exiting the deposition process

Two deposition chambers are used

Samples are taken from two consecutive batches per on first and second shifts

77 Samples are taken on Monday thru Wednesday

Green Y:Wafer Thickness Variation

Temporal Variation Cyclical Variation Positional Variation

Weeks 3

Days 3

Shifts per Day 2

Batches per Shift 4

Wafers per Batch 3

Deposition Chambers 2

Locations per Batch 3

Positions per Wafer 5

Total Samples = 3 x 3 x 2 x 4 x 3 x 2 x 3 = 1,296

Total Readings = 1,296 x 5 = 6,480

Problem: Thickness in wafers exiting the semiconductor deposition process.



Positional VariationPositional Variation Cyclical VariationCyclical Variation

Temporal VariationTemporal Variation



Concentration Charts

• Used with Positional Variations• Map location of defects

– Use part drawing– Generate grid on part

• Plot all defect types and counts on map• Focus investigation on high concentration areas

Overview


Component Search

• Used with Cyclical Variations• Units must be capable of assembly and

disassembly without changing Green Y• Use Paired Comparisons if

assembly/disassembly changes Green Y

Overview


Component Search

• Methodology– Collect very good units—Best of Best (BOB)– Collect very bad units—Worst of Worst (WOW)– Disassemble and reassemble two times measuring

Green Y after each reassembly– Conduct Significance Test—are the differences

between BOBs and WOWs statistically significant – Calculate decision limits– Rank-order list of candidate factors

Methodology


Component Search

• Methodology (cont.)– Swap one factor at a time between BOB and WOW– Measure Green Y after each swap– Any factor-swap that causes Green Y to fall outside

decision limits is important– Perform capping run with important factors at WOW

level and all others at BOB level– Perform DOE to separate magnitude and direction of

important factors

Methodology


Component Search

• Nomenclature– Assign letter to each candidate factor (e.g., A, B, C)– Subscript of letter indicates BOB or WOW level

• AB—the BOB level of factor A• CW—the WOW level of factor C

– The letter RB and RW refer to the balance of the factors at their BOB and WOW levels, respectively

Methodology


Component Search

• Significance Test– D = difference between median of BOB values and

median of WOW values– d = Average differences in the BOB and WOW

readings{(HighB-LowB) + (HighW-LowW)} / 2

– Calculate D/d ratio• >1.25—BOBs and WOWs are statistically significant.• <1.25—Keep looking…may be a process issue

Methodology


Component Search

• Decision Limits (DL)– DLB = DB ± 2.776d/1.81– DLW = DW ± 2.776d/1.81

Methodology


Component Search

• Factors D and G are important• Capping Run of DWGWRB showed reversal

Example

BOB WOWInitial -40 0Disass'y 1 -35 -5Disass'y 2 -37 -7Factor A -40 -5Factor B -35 0Factor C -35 -5Factor D -20 -5Factor E -40 0Factor F -40 -5Factor G -20 -5Factor H -35 0Capping Run -1 -40


Paired Comparisons

• Used with Cyclical Variations• Used when Component Search not valid• Uses multiple (6 or 8) pairs of BOBs and WOWs• Develop rank ordered list of candidate factors• Uses combination formula for significance

Overview

TotalEnd-Counts

Confidence Level

6 90%7 95%10 99%13 99.9%


Paired Comparison

• Measure all factors for all units• Designate each reading as a BOB or WOW• Arrange in rank order from smallest to largest• Draw a line when BOB readings change to

WOW readings (top end-count)• Draw a line when WOW readings change to

BOB readings (bottom end-count)• Add top and bottom end counts

Procedure

ExamplePaired Comparison

Your own footer Your Logo

Rank Ordered DataRaw Data

Total End Count = 7

There is a 95% confidence thatthe BOBs are really better thanthe WOWs

Analysis

B = BOB, W = WOW

0.015 W0.018 W0.014 W0.022 W0.019 W0.018 B0.016 B0.023 B0.017 W0.019 W0.011 W0.007 W0.024 B0.023 B0.021 B0.017 B

0.007 W0.011 W0.014 W0.015 W0.016 B0.017 W0.017 B0.018 W0.018 B0.019 W0.019 W0.021 B0.022 W0.023 B0.023 B0.024 B

TotalEnd-Counts

Confidence Level

6 90%7 95%10 99%13 99.9%

}

}

4

3



• Used with Temporal Variations• Separates important process parameters that

may shift, drift or fluctuate with time• Uses same concepts as Paired Comparisons• Compares the process parameters associated

with 6 or 8 BOB and WOW product pairs

Overview



• Record parameters for all samples– Parameters are measured directly, not by settings– BOBs and WOWs cannot be determined until after

processing• Select 6 or 8 BOB/WOW pairs• Run a Paired Comparison to identify important

factors

Overview


Variable Search

• Objectives– Isolate important variables– Tighten tolerances of important variables– Loosen tolerances of unimportant variables

Overview

Technique focuses on variables associated with known significant factors.


Variable Search

• Methodology– Ball Park

Are correct variables and levels identified?– Separation of Factors

Separate important and unimportant variables– Capping Run

Validate the important variables as significant– Factorial analysis

Quantify the magnitude and desired levels of important variables

Overview


Variable Search

• Create rank ordered list of variables (A, B, C, etc.) in descending order

• Determine the Best (B) and Marginal (M) levels for each variable

• Determine sample size based on variable type• Run two initial experiments (RB and RM) to

determine Green Y separation

Ball Parking—Initial Experiment

Standard Green Y Rules Apply


Variable Search

• Evaluate results– Large difference between Green Y for RB and RM

indicates Red X is contained in the variable set.– Modest difference between Green Y for RB and RM

indicates Red X may not be contained in the variable set.

– Little to no difference: keep looking for correct variables.

Ball Parking—Evaluation of Experiment

If a DOE were run instead, the significance of the variables would not be known until multiple experiments are completed


Variable Search

• Repeat initial experiment two more times• Determine statistical significance

– All RB Green Y’s better than all RM Green Y’s with no overlap

– D/d > 1.25

Ball Parking—Replication of Experiment


Variable Search

• Run a pair of tests (AMRB and ABRM)• Calculate decision limits• Evaluate results

– If Green Y for AB and AM stay within their respective decision limits—A is unimportant

– If Green Y for AB and AM lies within opposite decision limits—A is Red X (important)

– If Green Y for AB and AM lies outside its decision limits—A is a Pink X (important)

Separation of Factors with Capping Run


Variable Search

• Repeat experiments varying other factors• Perform Capping Run with important variables



Variable Search

• Run a pair of tests (AMRB and ABRM)• Calculate decision limits• Repeat experiments varying other factors• Perform a Capping Run

– If complete reversal is not achieved, keep looking


RememberVariable Search is NOT looking for important factors. It is looking for the LEVELS of the factors identified during Clue Generation

that generate BOBs and WOWs


Variable Search

• If more than one important variable is identified, perform Full Factorial analysis– Balanced—All factors at all levels are tested– Replication—2 to 4 tests per level– Randomized—Eliminate biases

• Shift over time• Trend over time• Cyclical pattern over time

Factorial Analysis

Variable SearchFactorial Analysis


11

22

33

44

55

66

88

Select factors: A, B, C, D

Determine if “noise” is a factor

Construct factorial matrix showing all combinations

Run experiments for each combination (matrix cell)

Select two levels for each factor, (+) and (-)

Randomize sequence of testing

Repeat steps 4 and 5 as needed

77 Calculate averages of readings in each cell

Variable SearchFactorial Analysis


99 1010Calculate contribution of each factor Construct Analysis of Variance table and graphs


Variable Search

• Demonstrate ability to turn problem on and off

• Develop sufficient confidence solution is permanent

• Predict how much better one process is than the other with 90% confidence

Validation Run


A Final Note

Citations• Dorian Shainin and Peter Shainin, Better Than Taguchi Orthogonal

Arrays (John Wiley & Sons, Ltd., 1988)• Richard D. Shainin, How Lean is Your Six Sigma Program?• Bhote and Bhote, World Class Quality (ANACOM, 2000)• Donald J. Wheeler, Understanding Industrial Experimentation (SPC

Press, Inc. 1988)• Tapping, Luyster and Shuker, Value Stream Management

(Productivity Press, 2002)


A Final Note

Contact InformationPeter KochABBA [email protected]

using six sigma tools in a lean environment - aimcal product/process search. introduction ... dorian...

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