1

Six Sigma: Point and Counterpoint

By

Hans J. BajariaMultiface, Inc.

6721 Merriman RoadGarden City, Michigan 48135 1956

USAPhone: 734-421-6330

Fax: 734-421-1142email: hbajaria@aol.com

Web site: www.multiface.com

Presented atADCATS Conference

BYUProvo, Utah

June 15, 2000

2

Muchtalkedaboutside

of the coin

Otherside

of the coin

1Interpretation from learning viewpoint

Interpretation from cultural viewpoint

For Against

3

PointShort-term PPM = 0.001Long-term PPM = 3.4 (with 1.5 sigma shift)

CounterpointThere is nothing outside 3 sigma.

2

4

0 1 2 3 4 5 6 -1-2-3-4-5-6

There is nothing outside

RealityApproximation

2

5

ZE

HN

DE

UT

SC

HE

MA

RK

f(x)0.40.30.20.10

10 Deutsche Mark bill is a form of evidence that there isnothing outside of 2

6

No. value ModelPPM

RealityPPM

1 +1 or -1 158,500158,5002 +2 or -2 23,000 23,0003 +3 or -3 1,350 04 +4 or -4 31.5 05 +4.5 or -4.5 3.4 0

2

7

Supporting evidence:

• Deming• Shewhart• Neave• Gauss• Wheeler

2

8

What is the big deal?You cannot reflect achievement in terms of sigma level.

Better option:Draw before and after picture.

2

9

Point1.5 sigma shift is assumed to compute 3.4 PPM of a six sigma process.

CounterpointAmount of shift and type of shift are a matter of discovery and not a matter of assumption. It is important to focus on target problem as well as variation problem.

3

10

Supporting evidence:•Target of a stamping process jumps around when a coil is changed. This jump is directly affects downstream quality. Partially used coils are returned to the supplier.

3

11

StampingPress

Coil 1

Coil 2

Coil 3

Off-target:With every change in coil output target changes.

3

12

What is the big deal?The problem-solving process is desensitized if we allow for target shifts.

Better option:Label target shift as a problem and solve it.

3

13

PointSix sigma performance is said to be achieved when process variation is half that of specification range (Cp = 2) and target shift is 1.5 sigma.

CounterpointTaguchi: Performance is measured by uniformity around target with no reference to specification.

4

14

Supporting evidence:

•Loss = K[average -

4

15

-6 -3 3 6

-6 -3 3 6

Loss = 3.25K

41.5 shiftLoss = 3.25K

Specification range

16

What is the big deal?Operational excellence efforts should be measured independent of specification range.

4

Better option:Measure target performance first, variation performance then.

17

PointPPM calculations associated with six sigma apply to attribute data as well as variable data.

CounterpointPPM calculations do not apply to attribute data.

5

18

Strategy 1 Strategy 2

Variable data

Reduce variation Move target

5

Before

After

19

Deal withexisting system

Deal witha new system

Strategy 1 Strategy 2

Attribute data

Reduce variation Move target

5

20

Supporting evidence:

• For variable data, target shift and variation are independent. Therefore, it is easier to choose a problem-solving tactic.

• For attribute data, target shift and variation are dependent. Therefore, it is difficult to choose a problem-solving tactic.

5

21

Example of how people play games with attribute data

5

22

90% board

10% board

Good

Bad

100,000 ppm

Electronic Board Manufacturer 5

Process

23

ppm 200

rdjoints/boa 2,000x boards 1,000

rdjoints/boa 4x boards 100

5

24

5

2,000,000 joints

400 joints

Good

Bad

200 ppm

Electronic Board Manufacturer

Process

25

What is the big deal?A strategic error could be made if distinction between variable data and attribute data is ignored.

Better optionChoose between improving existing system and developing a new system.

5

26

PointMain emphasis is on reducing variation.

CounterpointMain emphasis should be on reducing variability, not on reducing variation.Variability has three components: instability, variation, and off-target. Once we surpass the 3 sigma quality, instability and off-target problem conditions become major contributors to variability.

6

27

-20 +20-8 +8

Specification range

Six sigma quality range

Current performance

Problem condition:Instability (Operational disturbance)

6

28

-20 +20

Problem condition:Excessive Variation (Lack of complete understanding)

-15 +15

Current performance

Specification range

Six sigma quality range

6

29

-20 +200

Problem condition:Off-target ( inability to perform task even at system’s best)

-18 -2

Current performance

Specification range

Six sigma quality range

6

30

GLUE BUILDUPON THE ROLL

Instability:Glue build up

Variation:• Temperature• Speed• Glue viscosity• Paper Moisture• Pressure

Top paper

Bottom paper

6

31

Supporting evidence:

•Shewhart: It is almost impossible to reduce variation in the presence of instability.

6

32

No. value Model

Primaryproblem

RealityPrimaryproblem

1 +1 or -1 VariationVariation

2 +2 or -2 Variation Variation

3 +3 or -3 Variation V, I, O

4 +4 or -4 Variation V, I, O5 +4.5 or -4.5Variation I, O

6

I = InstabilityO = Off-targetV = Variation

33

What is the big deal?If variation is the only assumed component of variability, we could be actually working on the wrong problem condition.

6

Better optionDecompose variability in three parts. Choose the part to be resolved based on quality principles.

34

PointMore attention on reducing variation. Less attention on developing robustness.

CounterpointRobustness can altogether eliminate the need for reducing variation.

7

35

We need engineering foundation to develop robustness.

Part A

Part B

TotalGap

Processes that make PART A and PART Bare incapable of controlling TOTAL GAPwithin specified limits.

Finding solutionto this problemwithout buyingnew machineswould mean“tolerance robust”design.

7

36

Part A

Part B

TotalGap

Use of spring as a forgiving mechanism made assembly insensitive to PART A and PART B tolerances.

Tolerance Robust

spring

7

37

What is the big deal?Variation reduction is emphasized at the cost of other options.

Better optionConsider robustness as the first option. Consider variation reduction as a second option.

7

38

CounterpointReliability tools, multivariate tools, and observational studies receive lighter discussions.

8PointSix sigma is a broader collection of methods.

39

Supporting evidence:Reliability engineering• Supplier of clutch systems have a problem

with leaky cylinders in the field.• The cylinders meet all manufacturing tests

and all design tests before shipment.• The solution to this problem will require

reliability engineering expertise.

8

40

Supporting evidence:Multivariate toolsCharacteristic A of a part is good. Characteristic B of a part is also good. But the part itself is nonfunctional. The solution to this problem will require expertise in multivariate analysis.

8

41

Supporting evidence:Observational studiesPart A is good, Part B is good, but Parts A and B cannot be assembled. This problem can be investigated with observational studies.

8

42

What is the big deal?Six sigma collection of methods is only a subset. Therefore, it can only address subset of problems.

8

Better optionCollection of methods must include a larger class to address problems from a bigger set.

43

CounterpointHow do you perform DOE to solve transactional problem?We deal with possible actions in transactional problems.We deal with investigative variables in design or manufacturing problems.

PointSix sigma can tackle transactional problems.

9

44

Supporting evidence:WARD’s Auto World reports……“One of the Big 3 is taking up to 120 days, and in some cases even longer, to pay for work delivered.”

What statistical methods will you use to find the root cause?

9

45

What is the big deal?Transactional Problems are solved by statistical thinking not by statistical methods.

Better optionLarge number of situations require that we counter the problem rather than determine the root-cause. Methods must be inclusive of statistical thinking to deal with possible actions.

9

46

PointSix sigma is a breakthrough strategy to solve a broader class of problems.

CounterpointIndustry evils which have been gobbling lots of money are not even touched.

10

47

Supporting evidence:Key characteristics:• Industry spends millions to control key

characteristics.• Some key characteristics are at six

sigma level, and yet performance is at less than three sigma level.

10

48

Supporting evidence:

Reliability:• Industry spends millions on testing.• Industry does long cycle durability

tests.• What is most needed are short

period reliability tests.

10

49

Supporting evidence:SPC:•Industry continues to spend money using SPC for monitoring.

•Diagnostic SPC is where money can be better spent.

10

50

Supporting evidence:Process capability:•Industry sinks lots of money in absurdity.

•Cpk is an absurd rule.•Cp -Cpk and Cpis a

better set of rules.

10

51

What is the big deal?Six sigma strategy has not created a breakthrough in the class of industry money gobblers.

10

Better optionA strategy must include a larger class of problems including industry evils.

52

PointSix-sigma training includes training in statistical methods. However, statistics is taught as a science of confirmation.

CounterpointPeople needing statistical help need direction not necessarily confirmation.

11

53

11Supporting evidence:

Example 1

Space shuttle success is largely attributed to use of statistics as a indication.

54

Example 2• There is in an

indication that you will die if you jump from fifth floor?

• Would you like confirmation?

• Would you volunteer yourself as a sample?

11

55

What is the big deal?Statistics as a science of confirmation requires bigger sample sizes. Statistics as a science of indication requires much smaller sample sizes. Investigation feasibility and costs becomes an issue.

Better optionWhile developing solutions use statistics for indication. While proving solutions use statistics for confirmation.

11

56

PointSix sigma training is a training in statistical philosophy, methods, and strategies.

CounterpointEngineering is the real customer of statistics. In six sigma training, no effort is made to mix statistically-based strategies with engineering-based strategies.

12

57

Supporting evidence:•Engineering considerations require that we pick one of the tactics at the start of the investigation.

•Statistical considerations reveal what might be the best choice of tactics.

•Four engineering tactics: control, optimize, modify, and recreate.

12

58

Supporting evidence:•Engineering considerations require that we start where the problem is obvious.

•Forgiving principle: Start downstream, not upstream.

12

59

Supporting evidence:•Engineering considerations require that we check the investigation turf before randomization.•Statistics can help us design an investigation.•Turf check: It reveal whether we are in the right ball park.

12

60

Supporting evidence:•Observational studies are more friendly and comforting to engineering field of investigations.

•Design of experiments are more friendly to development and teaching environments.

•Both make sense but observational studies cost less.

12

61

What is the big deal?Engineering strategies play a primary role in the ultimate success or failure of problem-solving exercise. Statistical methods simply make engineering strategies more efficient.

12

Better optionMix engineering and statistical strategies for a speedy breakthrough.

62

PointSix sigma is a vertical system.

CounterpointIf we are not careful, six sigma will become horizontal.

13

63

Define the problem (use SPC)

Solve the problem

Implement a solution

Vertical system

Horizontal system

Use SPC Use SPC Use SPC

Use SPCUse SPCUse SPC

13

64

C-S-I-N

Chart

Solve

Implement

NextPotential or Actual

Problem

Example of a vertical system13

65

Supporting evidence:•ISO 9000 had a potential of being a vertical system.•ISO 9000 is practiced as a horizontal system.

13

66

Supporting evidence:•SPC had a potential of being a vertical system.•SPC is practiced as a horizontal system.

13

67

What is the big deal?Quality culture only thinks horizontally.

Better optionQuality culture should think vertically and then horizontally.

13

68

PointDocumented case studies using six sigma methods are presented as the strongest evidence of six sigma success.

CounterpointCase studies in public domain available on various web sites are sketchy. There is no mention of specific engineering or statistical strategies used in solving the problems. These studies make problem-solving look like a one-shot proposition.

14

69

Supporting evidence:Latzko: By relying on the six sigma criteria, management is lulled into the idea that something is being done about quality, whereas any resulting improvement is accidental.

We all remember Hawthorne effect!

14

70

What is the big deal?Are we making true improvement with six sigma methods or just getting skilled at telling stories?

14

Better optionDraw before and after pictures of the metrics that got changed. List specific philosophy, strategy, tactic, and methods that were deployed.

71

Oblivious points:1. We missed the other side of the coin.2. There is nothing outside 3 sigma.3. Amount of shift and type of shift are

not a matter assumption.4. Performance cannot be measured

effectively by using specification as a reference.

5. PPM calculations do not apply to attribute data.

6. Main emphasis should not be on reducing variation alone.

Summary

72

Managerial points:7. Reducing variation and

robustness compete.8. Six sigma methods are a subset,

not the whole set.9. Statistical thinking is more

important for transactional problems than statistical methods.

10. Industry evils remain untouched.

Summary

73

Engineering points11. Statistical methods are

taught as a science of confirmation.

12. Absence of engineering strategies is noticeable.

Summary

74

Cultural points13. Quality culture is likely to

treat six sigma as a horizontal system.

14. Web site sketchy studies cannot be taken at face values.

Summary

75

Ideas applicable to your conference

76

If critical characteristics have Cpks of 1.33, the performance Cpk is not guaranteed to be 1.

Criticalcharacteristics

Performance

Cpk 1.33 Cpk 1Deterministic

Probabilistic

77

Deterministictolerance equation

Performance+ Max-Min Materialconditions =

Deterministictolerance equation

Performance+ Statistical Materialconditions =

Probabilistictolerance equation

Performance+Statistically truncatedmaterial conditions =

Rigid material, geometrically perfect shape

Rigid material, geometrically perfect shape

Flexible material, imperfect shape

Deterministictolerance equation

Performance+ Statistical Materialconditions =

Actual material, actual shape

Advances in tolerance stack up methodology

78

Target first,variation then.

1 2

3

79

Variation first,target then.

1

3

2

80

Target rule:

Cp - Cpk 0.33

Variation rule:

Cp 1.33