Introduction to Six SigmaILC Fall Conference

1 November, 2019

How do you problem solve today?

2

3

Agenda

4

Define

Project charter

Develop project plan

Process mapping

Measure

Gather baseline data

Descriptive statistics

Visualize data by charting

Measurement System Analysis

Analyze

Cause and effect analysis

5 Why’s

Improve

Brainstorming Techniques

Corrective actions

Control

Standard work instructions

Visual management

Poke-Yoke

Engineering Controls

SPC

Change management

Six Sigma

5

1σ2σ3σ4σ5σ6σ 1σ 2σ 3σ 4σ 5σ 6σ

LSL USL

Average

Six Sigma

6

187,800,000 pieces mail/day

Defect Rate(/million)

6σ 3.4 99.99966% 6395σ 230 99.97700% 431944σ 6,210 99.38000% 1,164,3603σ 66,800 93.33000% 12,526,2602σ 308,000 69.10000% 58,030,200

Sigma Level Yield Mail Errors (/day)

Standard Pork

7

Draw a pig

Tape the pig to the wall when you are done

You have 40 seconds – start now!

Standard Pork Activity

•Do the pigs look like each other? •Which pig is the right pig?

8

Define

9

Define – Project CharterBusiness ImpactWhy should the business do this?

How does this tie into company goals/ initiatives?

Opportunity/ Problem StatementWhat is occurring, what “pain” are people experiencing? What is the magnitude of the problem? When did the problem start?Where is the problem occurring?Why do we think we can generate value?

Key MetricsWhat are the improvement objectives and targets?

How do you measure success?Y = f(x)

Project ScopeScope in - what are the boundaries of the project?

Scope out - what areas will not be addressed?

Project Plan

When do you plan to hit key milestones?

Team Selection

Who is leading, co-leading and member of your team? Time commitment for each member

10

Define – Project Charter

Have you answered all the questions in the charter? • Is your charter objective? Fact

driven? • Clear expectations of scope? • What resources do you require? • How do you measure success?• Is your project “do-able”?• Should you really be doing this

project?

Leadership does not support your project• Scope to large• Pre-determined solution • Proper resources not assigned• No clear metric/ success

measurement

11

Define – Project CharterBusiness Impact

Uniform pigs are needed to ensure that they can easily fit into their pig crates in order to be taken to the Iowa State Fair.

Opportunity/ Problem Statement

Everyone’s pig looks different from each other, and we don’t know which pig is the correct pig. The problem started when everyone finished drawing their pigs at the ILC Fall Conference.

We can generate value by uniform pig pictures!

Key MetricsY = f(x)Pig Uniformity = f (height, overall shape)

Height Spec = 4.25” +/- 0.25”

Project ScopeScope in – Pig height and overall shape

Scope out – color of pig, paper type, writing material, thickness of lines

Project Plan

Complete by end of workshop on Tuesday, October 29th per project agenda

Team Selection

Leader: Ann BuckCo-leader: Theresa KoziolMembers: Workshop attendees

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Project Agenda

13

Time Description Leader Notes9:00 AM Define Buck Project Plan, Project Charter, Process Mapping9:15 AM9:30 AM9:45 AM

10:00 AM10:15 AM10:30 AM10:45 AM11:00 AM11:15 AM11:30 AM11:45 AM12:00 PM

ImproveBuck

Prioritization, Corrective Actions

ControlBuck

Standard Work Instructions, Visual Management, Change Management

Baseline Data, Descriptive Statistics, Visualize Data, Measurement System Analysis

MeasureBuck

AnalyzeBuck

Cause and Effect Analysis, 5 Why's, FMEA

Define – Process Mapping

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Process• Equipment• People• Materials• Measurements• Processes

Inputs = X’s Outputs = Y’s

• Y1• Y2• Y3• Y…• Yn

Process Map

15

Draw Pig• Paper• Pen• Tape Measure • Stop Watch

• Pig Shape• Pig Dimensions

Inputs = X’s Outputs = Y’s

Measure

16

Measure – Gather Baseline Data

Continuous Data

Variable measured on a scale that can be infinitely divided

• Temperature• Weight• Length• Cost • Time

Discrete Data

Counts

• Number of defects• Machine Center• Operators

17

Measure – Descriptive Statistics

Central Tendency

• Property that data tends to group around a “center” point

1. Mean 2. Median 3. Mode

Variability

• Property that the process does not produce the same results every time

1. Range2. Standard Deviation 3. Variance

18

Measure – Descriptive Statistics

Central Tendency

• Mean• Average of a set of data

• Mode• Most frequently observed value in a group

• Median• If you were to arrange your data in ascending or descending order, the data point in the

center

19

Measure – Descriptive Statistics

Variability

• Range• Difference between the max and min value of your data set

• Standard Deviation

• Variance • Square of standard deviation

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How spread out the data is from the mean

Average degree that each data

point differs from the mean

Measure – Displaying Statistical Data

Time Series Charts

• Individual data values plotted in sequential order

Aggregate Data Charts

• Data values grouped together by their frequency of occurrence

21

Measure – Displaying Statistical Data

Time Series Charts

22

130117104917865523926131

2200

2000

1800

1600

1400

1200

1000

Index

KOV

Time Series Plot of KOV

9181716151413121111

0.048

0.036

0.024

0.012

0.000

Observation

Indi

vidu

al V

alue

_X=0.0278

UCL=0.04827

LCL=0.00733

9181716151413121111

0.03

0.02

0.01

0.00

Observation

Mov

ing

Rang

e

__MR=0.00770

UCL=0.02515

LCL=0

1

1

1

1

I-MR Chart of KOV

23

Measure – Displaying Statistical Data

Aggregate Data Charts

60-38-560-38-460-38-3

4000

3500

3000

2500

2000

1500

1000

KIV_1

KOV

Boxplot of KOV

40000.0

5000.0

0100.0

5100.0

0200.0

5200.0

0300.0

0001 0051 0002 0052 0003 0053 000

2522 301.4 3461517 325.8 741

Mean StDev N

K

ytisneD

VO

12_VIK

020

H lamroN

VOK fo margotsi

60-38-560-38-460-38-3

2100

2000

1900

1800

1700

1600

1500

1400

1300

KIV_1

KOV

Interval Plot of KOV95% CI for the Mean

Individual standard deviations were used to calculate the intervals.

4000350030002500200015001000KOV

Dotplot of KOV

Each symbol represents up to 3 observations.

0.07500.06250.05000.03750.02500.01250.0000

LSL 0Target *USL 0.054Sample Mean 0.0309401Sample N 666StDev(Overall) 0.0127486StDev(Within) 0.0120218

Process Data

Pp 0.71PPL 0.81PPU 0.60Ppk 0.60Cpm *

Cp 0.75CPL 0.86CPU 0.64Cpk 0.64

Potential (Within) Capability

Overall Capability

PPM < LSL 0.00 7613.26 5031.41PPM > USL 33033.03 35239.24 27544.55PPM Total 33033.03 42852.50 32575.95

Observed Expected Overall Expected WithinPerformance

LSL USLOverallWithin

Process Capability Report for Gaugeband_1

Measure – Process Capability

24

𝐶𝐶𝐶𝐶 = 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 𝑉𝑉𝑜𝑜𝑜𝑜𝑜𝑉𝑉 𝑉𝑉𝑐𝑐𝑐𝑐𝑜𝑜𝑉𝑉𝑐𝑐𝑉𝑉𝑐𝑐𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 𝑉𝑉𝑜𝑜 𝑜𝑜𝑜𝑉𝑉 𝑝𝑝𝑐𝑐𝑉𝑉𝑉𝑉𝑉𝑉𝑐𝑐𝑐𝑐

= 𝑈𝑈𝑈𝑈𝑈𝑈 −𝑈𝑈𝑈𝑈𝑈𝑈6𝜎𝜎

= 𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑐𝑐𝑇𝑇𝑇𝑇𝑉𝑉𝑉𝑉𝑁𝑁𝑇𝑇𝑜𝑜𝑐𝑐𝑐𝑐𝑇𝑇𝑇𝑇 𝑝𝑝𝑐𝑐𝑉𝑉𝑉𝑉𝑉𝑉𝑐𝑐𝑐𝑐 𝑣𝑣𝑇𝑇𝑐𝑐𝑉𝑉𝑇𝑇𝑜𝑜𝑉𝑉𝑉𝑉𝑇𝑇

Process Capability Report

Cp < 1 is not capable

Cp= 1 is technically capable

Cp goal = 1.33 or 1.67

Measure – Process Capability

Defect Rate(/million)

6σ 3.4 99.99966% 2 6395σ 230 99.97700% 1.67 431944σ 6,210 99.38000% 1.33 1,164,3603σ 66,800 93.33000% 1 12,526,2602σ 308,000 69.10000% 0.33 58,030,200

Sigma Level Yield Mail Errors (/day)Cpk

25

SWI for Measuring Pig

1.Locate your pig and remove from wall2.Obtain measuring device3.Measure height of pig4.Record pig height

26

What can we evaluate?

1.Central Tendency a) Meanb) Medianc) Mode

2.Variability a) Rangeb) Standard Deviation c) Variance

3. Time Seriesa) I-MR

4.Aggregate a) Box Plotb) Histogramc) Interval Plot

5.Process Capability

27

Process Capability

Defect Rate(/million)

6σ 3.4 99.99966% 25σ 230 99.97700% 1.674σ 6,210 99.38000% 1.333σ 66,800 93.33000% 12σ 308,000 69.10000% 0.33

Sigma Level Yield Cpk

28

Measure – Measurement System Analysis

𝜎𝜎 𝑀𝑀𝑉𝑉𝑇𝑇𝑐𝑐𝑐𝑐𝑐𝑐𝑉𝑉𝑐𝑐𝑉𝑉𝑇𝑇𝑜𝑜𝑈𝑈𝑆𝑆𝑐𝑐𝑜𝑜𝑉𝑉𝑐𝑐

2 = 𝜎𝜎𝑅𝑅𝑉𝑉𝑝𝑝𝑉𝑉𝑇𝑇𝑜𝑜𝑇𝑇𝑅𝑅𝑉𝑉𝑇𝑇𝑉𝑉𝑜𝑜𝑆𝑆2 + 𝜎𝜎 𝑅𝑅𝑉𝑉𝑝𝑝𝑐𝑐𝑉𝑉𝑅𝑅𝑐𝑐𝑉𝑉𝑉𝑉𝑅𝑅𝑉𝑉𝑇𝑇𝑉𝑉𝑜𝑜𝑆𝑆2

29

Variation due to Gage

• Same part• Same instrument• Same operator• Same set-up• Same environ.

Variation due to Operators

• Different operators

• Same instrument• Same part

Measure – Setting up MSA

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1. Decide on basic design - two MSA Standards for Gage R&R

2. Plan for TestChoose study parts that represent the populationChoose operators that would normally perform the testDo not have the operator complete all the replicates in succession

3. Test – Observe the operators

MSA Standard # Parts # Repeats # OperatorsShort Form 5 2 2Long Form 10 3 3

Measure – MSA Form

31

RunOrder Parts Operators Measurement1 2 Operator 12 4 Operator 13 1 Operator 14 5 Operator 15 3 Operator 16 4 Operator 27 3 Operator 28 1 Operator 29 2 Operator 2

10 5 Operator 211 1 Operator 112 4 Operator 113 3 Operator 114 5 Operator 115 2 Operator 116 1 Operator 217 2 Operator 218 4 Operator 219 5 Operator 220 3 Operator 2

Measure – Measurement System Analysis

32

Gage name: Date of study:

Reported by: Tolerance: Misc:

Part-to-PartReprodRepeatGage R&R

80

40

0

Perc

ent

% Contribution% Study Var

10 9 8 7 6 5 4 3 2 110 9 8 7 6 5 4 3 2 110 9 8 7 6 5 4 3 2 1

0.030

0.015

0.000

Parts

Sam

ple

Rang

e

_R=0.00813

UCL=0.02094

LCL=0

Operator 1 Operator 2 Operator 3

10 9 8 7 6 5 4 3 2 110 9 8 7 6 5 4 3 2 110 9 8 7 6 5 4 3 2 1

0.05

0.03

0.01

Parts

Sam

ple

Mea

n

__X=0.02836UCL=0.03668

LCL=0.02003

Operator 1 Operator 2 Operator 3

10987654321

0.050

0.025

0.000

Parts

Operator 3Operator 2Operator 1

0.050

0.025

0.000

Operators

10987654321

0.05

0.03

0.01

Parts

Aver

age

Operator 1Operator 2Operator 3

Operators

Components of Variation

R Chart by Operators

Xbar Chart by Operators

Measurement by Parts

Measurement by Operators

Parts * Operators Interaction

Gage R&R (ANOVA) Report for Measurement• How do we evaluate if our measurement system is capable?

• Measurement System capability index: 1. % Contribution (% Gage R&R)

Compares variation of each group to overall variation

2. % Study Variation Compares measurement system variation to total variation

3. Discrimination Index (# of Distinct Categories) # of divisions that the measurement system can accurately measure across product variation

Results

33

Gage name: Date of study:

Reported by: Tolerance: Misc:

Part-to-PartReprodRepeatGage R&R

100

50

0

Perc

ent

% Contribution% Study Var

5432154321

0.10

0.05

0.00

Parts

Sam

ple

Rang

e

_R=0.038

UCL=0.1242

LCL=0

Operator 1 Operator 2

5432154321

4.8

4.4

4.0

Parts

Sam

ple

Mea

n

__X=4.377UCL=4.448LCL=4.306

Operator 1 Operator 2

54321

4.8

4.4

4.0

Parts

Operator 2Operator 1

4.8

4.4

4.0

Operators

54321

4.8

4.4

4.0

Parts

Aver

age

Operator 1Operator 2

Operators

Components of Variation

R Chart by Operators

Xbar Chart by Operators

Measurement by Parts

Measurement by Operators

Parts * Operators Interaction

Gage R&R (ANOVA) Report for Measurement

Measure – MSA Tips

1. Go back to your notes of your observations during the test

2. Can you standardize any difference you saw?

3. Retrain

4. Re-do Gage R&R

34

Analyze

35

Analyze – Cause and Effect Analysis

36

Problem

Fishbone Diagram

Assessment of Potential Causes

• Start with the highest ranked potential causes

• Define how you can determine if they are actual causes?• Is there data you can review or collect?• Talk to people involved in the process?• Make observations?

• Once you’ve determined actual or potential causes, then proceed to the 5 Why’s…

37

Analyze – 5 Why’s

Cause 1 Cause 2 Cause 3

Why 1?Why 2?Why 3? Why 4?Why 5?

38

Ask “why” 5x’s to get to root cause!

Five Whys Jefferson Memorial Example

Improve

39

Improve – Corrective Actions

40

Standard Pork

Take a piece of paper and fold it into thirds –both portrait and landscape direction. Unfold it again/lay flat.

41

Standard Pork

42

Draw a pig

Tape the pig to the wall when you are done

You have 40 seconds – when we go to the next slide!

43

1

2 3

4

5

6

7

8

9

10

11

12

13

43

Standard Pork

•Pigs any better in this round? •Where the instructions clear? •Did the instructions set you up for success?

44

Round II: Measure your

pigs height

45

What can we evaluate?

1.Central Tendency a) Meanb) Medianc) Mode

2.Variability a) Rangeb) Standard Deviation c) Variance

3. Time Seriesa) I-MR

4.Aggregate a) Box Plotb) Histogramc) Interval Plot

5.Process Capability

46

Control

47

Control

Engineering Controls

Poke-Yoke

Visual Controls

Standard Work

48

Control – Standard Work Instructions

49

Standard Pork – Standard Work

50

Draw a pig

Tape the pig to the wall when you are done

You have 40 seconds – when we go to the next slide!

Standard Work for Pig Drawing

0. Pick up your pen (1 sec)

1. Draw a letter M at the top left intersection. Bottom center of M touches the intersection. (3 sec)

2. Draw the letter W at the bottom left intersection. Top center of W touches the intersection. (3 sec)

3. Draw the letter W at the bottom right intersection. Top center of W touches the intersection. (3 sec)

4. Draw an arc from the letter M to the top right intersection. (2 sec)

5. Draw another arc from the top right intersection to the bottom right W. (4 sec)

6. Draw an arc between the two bottom W’s. (2 sec)

7. Draw the letter O in the center left box. (3 sec)

8. Draw an arc from the letter M to the tangent of the circle. (2 sec)

9. Draw an arc from the left W to the tangent of the circle. (2 sec)

10. Draw an arc for the eye. Half way between M and circle. (2 sec)

11. Draw an arc for the mouth. Half way between W and circle. Must be a happy pig!! (2 sec)

12. Draw the cursive letter e near the top of arc on the right. (3 sec)

13. And finally draw two dots in the middle of the circle for the pigs nose. (3 sec)

14. Put your pen down and hang up your pig. (5 sec)

51

Total Time Required = 40 sec.

Round III: Measure your

pigs height

52

What can we evaluate?

1.Central Tendency a) Meanb) Medianc) Mode

2.Variability a) Rangeb) Standard Deviation c) Variance

3. Time Seriesa) I-MR

4.Aggregate a) Box Plotb) Histogramc) Interval Plot

5.Process Capability

53

Standard work for pig drawing

54

0. Pick up your pen (1 sec)

1. Draw a letter M at the top left intersection. Bottom center of M touches the intersection. (3 sec)

2. Draw the letter W at the bottom left intersection. Top center of W touches the intersection. (3 sec)

3. Draw the letter W at the bottom right intersection. Top center of W touches the intersection. (3 sec)

4. Draw an arc from the letter M to the top right intersection. (2 sec)

5. Draw another arc from the top right intersection to the bottom right W. (4 sec)

6. Draw an arc between the two bottom W’s. (2 sec)

7. Draw the letter O in the center left box. (3 sec)

8. Draw an arc from the letter M to the tangent of the circle. (2 sec)

9. Draw an arc from the left W to the tangent of the circle. (2 sec)

10. Draw an arc for the eye. Half way between M and circle. (2 sec)

11. Draw an arc for the mouth. Half way between W and circle. Must be a happy pig!! (2 sec)

12. Draw the cursive letter e near the top of arc on the right. (3 sec)

13. And finally draw two dots in the middle of the circle for the pigs nose. (3 sec)

14. Put your pen down and hang up your pig. (5 sec) Total Time Required = 40 sec.

Control – Visual Management

55

Control – Poke-yoke

56

Control – Engineering Controls

57

Control – Statistical Process Control (SPC)

58

9181716151413121111

0.048

0.036

0.024

0.012

0.000

Observation

Indi

vidu

al V

alue

_X=0.0278

UCL=0.04827

LCL=0.00733

9181716151413121111

0.03

0.02

0.01

0.00

Observation

Mov

ing

Rang

e

__MR=0.00770

UCL=0.02515

LCL=0

1

1

1

1

I-MR Chart of KOV

Control - Change Management

1. If you don’t include everything in the numerator, you will have no change

2. The stronger your numerator, the easier it will be to overcome resistance

3. The stronger your resistance, the harder it will be to change

59

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 =𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝐶𝐶 × 𝐹𝐹𝑉𝑉𝐹𝐹𝑉𝑉𝐹𝐹 𝑆𝑆𝐹𝐹𝐶𝐶𝐶𝐶𝑉𝑉 × 𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹𝐶𝐶𝐶𝐶𝐹𝐹 𝑆𝑆𝐹𝐹𝐶𝐶𝐹𝐹𝐶𝐶 × 𝑃𝑃𝐹𝐹𝐶𝐶𝑉𝑉𝑉𝑉𝐶𝐶𝐹𝐹𝐶𝐶 𝐹𝐹𝑉𝑉 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶

𝑅𝑅𝐶𝐶𝑉𝑉𝑉𝑉𝑉𝑉𝐹𝐹𝐶𝐶𝐶𝐶𝑅𝑅𝐶𝐶

Control – Change Management

60

61

Questions?