[PROJECT]

[NAME]

LEAN SIX SIGMA BLACK BELT

DMAIC

DEFINE

MEASURE

ANALYSE

IMPROVE

CONTROL

Identify Customer Problem

Translate to Practical Problem

Translate to statistical problem

Identify Statistical solution

Translate to practical solution

1. What is the Project?2. Define measurement system3. Validate measure-ment system

10. Validate measurement system (X)

12. Implement process controls

4. Actual process performance

5. Define statistical success

6. Identify causes of defects

7. Determine vital causes

8. Define optimal settings

9. Define tolerance limits

11. Determine new process capability

X & Y

• Y is the outcome of the process.

• X are factors that influence Y

• Usually the project starts with an Y that is not very specific. This is good for discussion but not for a Lean Six Sigma Project.

• Therefore the so called “external” Y needs to be translated to an “internal” Y that is specific, concrete and measurable (slice the project)

• Use the Voice of the Customer (VOC) to capture the requirements

EXAMPLE

• Passengers are not satisfied with public transport.

• External Y = Public transport is not good

• You try to make this a bit more specific:

• Possible technique: Pareto, research

• The biggest problem is:

• Internal Y = the bus is often not on time

Not on time

No place t

o sit

Unfrien

dly sta

ff

Too noisy

Dirty

Too ex

pensiv

e

Not safe

too hot

too cold

unfrien

dly pass

enge

rs0

200

400

600

800

1000

1200

1400

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

CountCumulative %

STEP 1: PROJECT CHARTER (WHAT IS THE PROJECT?)

DEFINE

Business Case Process start and finish

[Short description of the process. How should it work (which is the customer requirement) and why is this process so important?]

[Clearly define where the process you want to improve starts and where it ends.]

Problem Statement In Scope and Out of Scope

[Where does the process deviate from the customer requirement, and why is this a problem?]

[Hint: don’t make it too big!]

Goal Statement Expected Benefits

[Short description of the improvement target.Examples:Shorten throughput time with xx hourReduce number of errors with xx%]

Don’t do it if there is not enough benefit!

SIPOC DIAGRAMSuppliers

• Raw materials• Sources• Manufacturers• Suppliers

Inputs

• Manpower• Resources• Equipment

Process Outputs

• Product• Timely

delivery• Increased

quality

Customers

• Young people• Students• Service

holders

Requirements

• Customer Satisfaction

• Expected quality

• Reduced Backlog

Look for new

customer

segment

Find custom

er needs

Identify critical needs

Develop prototy

pe

Test prototype & go

to product

ion

STEP 1: PROCESS DESCRIPTION OF THE PROCESS TO IMPROVE

DEFINE

Champion: [Name sponsor]

Process owner: [Name]

Sr. employees: [Members project]

Financial Analyst: [Name]

Master Black Belt: [Name]

Black Belt: [Name]

STEP 2: PROJECT TEAMDEFINE

STEP 2: MEASURABLE CCR + SPECIFICATIONS

Unit: [Output of the Process.

Example: a cookie from a cookie factory]

Chance: [Number of possible defects

Example: 2 (see below)]

Defect: [What leads to an unhappy customer?

Example: a broken cookie, a cookie without a peanut]

Unit of Measure: [Unit in which the output is measured

Example: broken yes/no

peanut: yes/no]

MEASURE

STEP 2: MEASUREMENT SYSTEMMEASURE

Definition of the data

How is the measurement unit determined?

How is the data collected?

Automated or manual?Who collects the data?

Frequency and collection dates?

How will the data be used?

Test hypothesisRoot-cause analysis

How will the data be presented?

Pareto, Histogram,Control Chart, Probability

plot,Box plot, …

STEP 3: VALIDATION OF THE MEASUREMENT SYSTEM

• [Gage R&R or Kappa test]

• or

• [The data is currently being used as source for management info and both the Process Owner and the Campion recognize this as representative for the process.]

MEASURE

Conclusion: We have a reliable Measurement System

STEP 4: CURRENT PERFORMANCE

• [Control Chart, example I-Chart (Control charts, individuals)]

• Probability plot (Graph)

Observation

Indiv

idual V

alu

e

332925211713951

0,18

0,17

0,16

0,15

0,14

0,13

0,12

0,11

0,10

_X=0,13555

UCL=0,16408

LCL=0,10703

1

1

I Chart of Proces X

ANALYZE

Conclusions: The data is representative for the process. The data is normally distributed. (or not)

Proces X

Perc

ent

0,180,170,160,150,140,130,120,110,100,09

99

95

90

80

70

605040

30

20

10

5

1

Mean 0,1356StDev 0,01326N 35AD 0,936P-Value 0,016

Probability Plot of Proces XNormal - 95% CI

STEP 4: CURRENT CAPABILITY OF THE PROCESS

• Process capability (Quality tools, capability analysis, normal)

We have a target that currenty is not met. Most data should be between LSL and USL.Conclusion: We have a problem.

ANALYZE

0,1620,1440,1260,1080,0900,072

LSL USL

LSL 0,06Target *USL 0,1Sample Mean 0,135554Sample N 35StDev(Within) 0,0133565StDev(Overall) 0,0133565

Process Data

Cp 0,50CPL 1,89CPU -0,89Cpk -0,89

Pp 0,50PPL 1,89PPU -0,89Ppk -0,89Cpm *

Overall Capability

Potential (Within) Capability

PPM < LSL 0,00PPM > USL 1000000,00PPM Total 1000000,00

Observed PerformancePPM < LSL 0,01PPM > USL 996115,04PPM Total 996115,05

Exp. Within PerformancePPM < LSL 0,01PPM > USL 996115,04PPM Total 996115,05

Exp. Overall Performance

WithinOverall

Process Capability of Proces X

Note: if the data is not normally distributed, only these parts are relevant.

STEP 5: STATISTIC SUCCESS

• Compare Process capability of department 1 (to be improved)

• With Process capability of department 2 (is performing better)

0,1620,1440,1260,1080,0900,072

LSL USL

LSL 0,06

Target *USL 0,1

Sample Mean 0,135554Sample N 35

StDev(Within) 0,0132909

StDev(Overall) 0,0133565

Process Data

Cp 0,50

CPL 1,89CPU -0,89

Cpk -0,89

Pp 0,50

PPL 1,89

PPU -0,89Ppk -0,89

Cpm *

Overall Capability

Potential (Within) Capability

PPM < LSL 0,00

PPM > USL 1000000,00PPM Total 1000000,00

Observed Performance

PPM < LSL 0,01

PPM > USL 996264,09PPM Total 996264,09

Exp. Within Performance

PPM < LSL 0,01

PPM > USL 996115,04PPM Total 996115,05

Exp. Overall Performance

WithinOverall

Process Capability of Amsterdam

ANALYZE

0,140,120,100,080,060,04

LSL USL

LSL 0,06

Target *USL 0,1

Sample Mean 0,0899916Sample N 35

StDev(Within) 0,0216307

StDev(Overall) 0,0216307

Process Data

Cp 0,31

CPL 0,46CPU 0,15

Cpk 0,15

Pp 0,31

PPL 0,46

PPU 0,15Ppk 0,15

Cpm *

Overall Capability

Potential (Within) Capability

PPM < LSL 28571,43

PPM > USL 257142,86PPM Total 285714,29

Observed Performance

PPM < LSL 82792,73

PPM > USL 321791,34PPM Total 404584,07

Exp. Within Performance

PPM < LSL 82792,73

PPM > USL 321791,34PPM Total 404584,07

Exp. Overall Performance

WithinOverall

Process Capability of Leeuwarden

Based on this analysis we hope to conclude that department is a suitable benchmark

STEP 6: INVENTORY OF CAUSES

We have measured two (or more) different teams/species/types/whatever, and we have measured a specific thing like: size, throughput time, number of failures, etc.

Now we want to know which factors could explain the difference between these teams/types/etc.

Example: Why is team A making far less mistakes than team B?

First make a long list of all causes (we call this a list of X’s). Use brainstorming and/or data-analysis

Then shorten the list until max 8 major causes remain.

Use techniques like: common sense, knowledge, experience and hypothesis testing.

ANALYZE

STEP 6: HYPOTHESIS TESTING, X=SOME-CAUSE

ANALYZE

Hypothesis: For Some-cause there is no difference between X en Y.

Conclusion: Some-cause is a relevant X. (or not)

Show that there is or is not a statistically significant difference in distribution and average between X and Y.Thus you need a similar row of data of X and Y.

Verify that data (I-chart, probability plot).

Check which test needs to be done:• For spread • and for average.

Note: these tests depend on; is Y discrete or continuous? Is X discrete or continuous? And do we have 2 or more groups of X?

STEP 6: SUMMARY OF CONCLUSIONS

Max 8 important X’s1. PQR2. STU3. VWZ4. ..5. ..6. ..7. ..8. ..

ANALYZE

Result: data: Short list of possible causes, max 8Process map: most critical process steps (focus)

STEP 7: DETERMINE THE VITAL FEW ROOT CAUSES

IMPROVE

From 8 important to (max) 3 root causes!!

using Lean techniques and hypothesis testing

HYPOTHESIS TESTING DECISION TREE

Internal Y

Discrete data for Y

Continuous data for X

Logistic Regression

Discrete data for X

Chi Square-

test

Continuous data for Y

Continuous data for X Regression

Discrete data for X

Mean problem

1 group of data for X

2 groups of data for

XMore

groups of data for X

Variance problem

2 groups of data for

XMore

groups of data for X

Normal:1-Way Anova

Not-normal:Kruskal-Wallis test

Normal:2-Sample t-test

Not-normal:Mann-Whitney test

Normal:F-test

Not-normal:Levene’s test

Normal:Bartlett’s test

Not-normal:Levene’s test

Normal:1-Sample t-test

Not-normal:1-Sample Wilcoxon test

STEP 7: SUMMARY OF CONCLUSIONSIMPROVE

3 root causes (X’s)

1. Root cause 12. Root cause 23. Root cause 3

STEP 8: DESIGN IMPROVEMENTS PER ROOT CAUSE

Determine per root cause the optimal setting!

IMPROVE

Root Cause Optimal solution

STEP 9: DEVELOP PRACTICAL SOLUTIONS PER ROOT CAUSE

IMPROVE

Root cause

Practical solution

Test method (step 11)

Effect costs conclusion

Pre requisites

STEP 10: EVERYONE IN THE PROCESS KNOWS THE NEW WAY OF WORKING, AND IS CAPABLE OF DOING IT

CONTROL

Prove that everyone understands the new method using the new work instructions or the Standard Operating Procedures (SOP).We prove that now and in the future we can rely on our measurement system by performing an analysis on the main X's.

 

LTL corrected

Target UTL corrected

Unit of measurement

Gage R&R in %

Procedure/Sop nr

remarks

X1              

X2              

X3              

X4              

STEP 11: CALCULATE NEW “PROCESS CAPABILITY”

CONTROL

Conclusion: success on short term has been proven!

0,150,140,130,120,110,10

LSL USL

LSL 0,1Target *USL 0,14Sample Mean 0,131175Sample N 35StDev(Within) 0,00841327StDev(Overall) 0,00841327

Process Data

Cp 0,79CPL 1,24CPU 0,35Cpk 0,35

Pp 0,79PPL 1,24PPU 0,35Ppk 0,35Cpm *

Overall Capability

Potential (Within) Capability

PPM < LSL 0,00PPM > USL 200000,00PPM Total 200000,00

Observed PerformancePPM < LSL 105,51PPM > USL 147103,66PPM Total 147209,17

Exp. Within PerformancePPM < LSL 105,51PPM > USL 147103,66PPM Total 147209,17

Exp. Overall Performance

WithinOverall

Process Capability of Amsterdam

Observation

Indiv

idual V

alu

e

332925211713951

0,16

0,15

0,14

0,13

0,12

0,11

_X=0,13118

UCL=0,15435

LCL=0,10800

I Chart of Amsterdam

Amsterdam

Perc

ent

0,160,150,140,130,120,110,10

99

95

90

80

70

605040

30

20

10

5

1

Mean 0,1312StDev 0,008352N 35AD 0,285P-Value 0,607

Probability Plot of AmsterdamNormal - 95% CI

STEP 12: IMPLEMENTATION PROCESS CONTROL

CONTROL

Conclusion: success on longer term is guaranteed!

Handover project + sign off+ thanks team

Root cause Solution Assurance