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RED BEAD EXPERIMENT Maarten Hoppen Donderdag 5 november 2015

Acnl2015 maarten hoppe-red bead experiment

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Page 1: Acnl2015 maarten hoppe-red bead experiment

RED  BEAD  EXPERIMENT  Maarten  Hoppen  Donderdag  5  november  2015

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W EdwardsDeming

David P.Joyce

Many thanks to!

Joakim SundénTwitter: @joakimsunden

[email protected]

Joakim

Sundén

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White Bead Company

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The White Bead Co. IIP

ISO9000

WE LISTEN TO OUR PEOPLE

Only at their annual appraisal

CMMI 5

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WE WANT YOU!

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We Are Recruiting!3 willing workers. Must be willing to put in best efforts. Continuation of job is based on performance. No experience in making beads necessary. Educational requirements-nil.

2 inspectors. Must be able to distinguish red from white. Must be able to count to 20. No experience necessary.

1 chief inspector. Same qualifications as inspector. Must be able to speak in a loud voice.

1 recorder. Precise, follow orders, writing capability, human judgement

ABOVE AVERAGE WORKERS ONLY NEED APPLY

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FINISHED FILES ARE THE RESULT OF YEARS

OF SCIENTIFIC STUDY COMBINED WITH

THE EXPERIENCE OF MANY YEARS.

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SHOUT OUT YOUR COUNT

3 - 2 - 1 - SHOUT

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FINISHED FILES ARE THE RESULT OF YEARS

OF SCIENTIFIC STUDY COMBINED WITH

THE EXPERIENCE OF MANY YEARS.

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Lessons learned • Did incentives work?

• Did the motivational posters help?

• Did have a CMMI standard process work?

• What could managers do to improve things for the workers?

• What are the red beads in your company?

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Lessons learned • It’s the system not the workers • It’s management thinking that designed the system • Arbitrary numerical targets were completely ineffective • Rewarding or punishing the workers had no effect • Rigid and precise procedures are not sufficient to produce

quality • Keeping the ‘best’ workers did not work

Management tampering creates more problems than it solves • Posters and slogans are at best useless and can be insulting and

create resentment • The biggest source of variation was in the system

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Theory of Variation

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The First Principle of the Theory of Variation

We Should Expect Things toVary,

They Always Do

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My performancePe

rfor

man

ce

Time

My performance✓

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My performancePe

rfor

man

ce

Time

My performancePe

rfor

man

ce

Time

My performancePe

rfor

man

ce

Time

My performancePe

rfor

man

ce

Time

My performancePe

rfor

man

ce

Time

My performancePe

rfor

man

ce

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

My performance

Perf

orm

ance

Time

Statistical Process Control Charts

Perf

orm

ance

Time

Upper Control Limit

Lower Control Limit

Average

Statistical Process Control Charts

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The Second Principle of the Theory of Variation

Understanding Variation Will Tell us

What to Expect

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Signal or Noise?

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Velocity

ControlLimits

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Signal or Noise?

Velocity

Reward!

No more Mr Nice Guy!

Managerrepents...

Tough managementworks!

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Signal or Noise?

Velocity

Reward!

No more Mr Nice Guy!

Managerrepents...

Tough managementworks!

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Signal or Noise?

Velocity

Reward!

No more Mr Nice Guy!

Managerrepents...

Tough managementworks!

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Signal or Noise?

Velocity

Reward!

No more Mr Nice Guy!

Managerrepents...

Tough managementworks!

Signal or Noise?

Reward!

Manager Repents

No more Mr Nice guy!

Tough Management works!

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Signal or Noise?Signal or Noise?

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Velocity

ControlLimits

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Signal or Noise?

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Velocity

ControlLimits

What to expect

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Velocity

Average

What to expect

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What to expect

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Velocity

Average

Expectations...

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Velocity

Expectations

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Setting a TargetPe

rfor

man

ce

Time

Upper Control Limit

Lower Control Limit

Average

Setting a targetSetting a TargetPe

rfor

man

ce

Time

Upper Control Limit

Lower Control Limit

Average

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“I recently asked a colleague [CIO] whether he would prefer to deliver a project somewhat late and overbudget, but rich with business benefits, or one that is on time and underbudget but of scant value to the business.

He thought it was a tough call, and then went for the on- time scenario. Delivering on time and within budget is part of his IT department’s performance metrics. Chasing after the elusive business value, over which he thought he had little control anyway, is not.”

Cutter Sr. Consultant Helen Pukszta

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If you give a manager a numerical target, he’ll

make it, even if he has to destroy the company in

the process.

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The Third Principle of the Theory of Variation

Work on the Causes of Variation,

Which are Always Found in the System

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Majority of Performance is Down to the System

Majority of Performance is Down to the System

A bad system will defeat a good person

every time.

System

Individual

95%

5%

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System Conditions Work Design Policies Measures Structure Roles Procedures Information Job skills Knowledge

Business Cases FundingPoor requirements

Inspection Compliance

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The Fourth Principle of the Theory of Variation

Understanding Variation Tells you When

Something has Happened

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Special Cause vs Common Cause

Special Cause vs Common Cause

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Velocity

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Did we improve? Did we improve?

0

10

20

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Changeintroduced

after sprint 8

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SPC for comparison SPC for comparison

Team A

Team B

Team C