oop 2012 - predictability & meansurement with kanban

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OOP 2012 Munich January 2012 Predictability & Measurement with Kanban David J. Anderson David J. Anderson & Associates Twitter: agilemanager Email: [email protected]

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Metrics, measurement, probabilistic forecasting and setting expectations to enable predictable delivery with Kanban. Track session from OOP 2012 Munich

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Page 1: OOP 2012 - Predictability & Meansurement with Kanban

OOP 2012

MunichJanuary 2012

Predictability & Measurementwith Kanban

David J. AndersonDavid J. Anderson & Associates

Twitter: agilemanager Email: [email protected]

Page 2: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Book PublishedApril 2010

A 72,000 wordintro to the topic

Available fromdjaa.com

Page 3: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012German

published January, 2011

Translation byArne Roock & Henning Wolf

of IT-Agile

Page 4: OOP 2012 - Predictability & Meansurement with Kanban

http://www.limitedwipsociety.org

Yahoo! Groups: kanbandev

Yahoo! Groups: kanbanops

http://leankanbanuniversity.com

LinkedIn Groups: Software Kanban

Page 5: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Delivering predictability with Kanban

requires some different techniques

for different types of work such as

software maintenance and support

or

major project work

Page 6: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Service-oriented work

Page 7: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Create a regular delivery cadence

Develop a strong config management capability

Develop capability to deploy effectively

Build code with high quality

Page 8: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Lead Time Distribution

0

0.5

1

1.5

2

2.5

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

106

Days

# C

Rs

MARCH

Lead Time Distribution

0

0.5

1

1.5

2

2.5

3

3.5

Days

CR

s &

Bu

gs

APRIL

OutliersMajority of CRs range 30 -> 55

Understand capability by studying the natural philosophy of the work

Page 9: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

SLA expectation of51 days with 98% on-time

Lead Time Distribution

0

0.5

1

1.5

2

2.5

3

3.5

Days

CR

s &

Bu

gs

SLA expectation of44 days with 85% on-time

Observe Flow with a spectral analysis histogram of lead time

Mean of 31 days

Page 10: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

44 or 51 days will not be good enough for some feature requests, so offer a package of classes of

service

Page 11: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Package of Classes with SLAs

As soon as possible Full transparency

100% on-time providing 24 days advance notice

Up to 51 days 98% on-time guarantee

Up to 51 days 50% on-time

Page 12: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Lead time

Features Delivered

Standard Class Items

Fixed Date Items

Expedite Item

Page 13: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Allocate capacity across classes of service in order to deliver against anticipated demand

5 4 43 2 2= 20 total

Allocation

10 = 50%

...

+1 = +5%

4 = 20%

6 = 30%

InputQueue

DevReady In Prog DoneDoneIn Prog

DevelopmentAnalysis BuildReady Test

ReleaseReady

Page 14: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Major Project Work

Page 15: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Requires all the same underlying data as used in service oriented

work

plus

Page 16: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Major Project with two-tiered kanban board

Page 17: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

Observe Flow with a Cumulative Flow Diagram

Device Management Ike II Cumulative Flow

020406080

100120140160180200220240

Time

Fe

atu

res

Inventory Started Designed Coded Complete

WIP

Avg. Lead Time

Avg. Throughput

Page 18: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

Little’s Law

ThroughputLead Time

WIP=

Page 19: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

Cumulative Flow andPredictive Modeling with S-Curve

Device Management Ike II Cumulative Flow

020406080

100120140160180200220240

Time

Fe

atu

res

Inventory Started Designed Coded Complete

Typical S-curve

Page 20: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

Simulating S-Curve with a Z

Device Management Ike II Cumulative Flow

020406080

100120140160180200220240

Time

Fe

atu

res

Inventory Started Designed Coded Complete

20%

60%

20%

Slope in middle3.5x - 5x slope

at ends 5x

Page 21: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

Track actual throughput against projection

Device Management Ike II Cumulative Flow

020406080

100120140160180200220240

Time

Fe

atu

res

Inventory Started Designed Coded Complete

Track delta between planned and actual

each day

Page 22: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Unplanned Work Report

Dark Matter

Scope Creep

Page 23: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

Planning a large project

Device Management Ike II Cumulative Flow

020406080

100120140160180200220240

Time

Fe

atu

res

Inventory Started Designed Coded Complete

Slope in middle3.5x - 5x slope

at ends 5x

Required throughput (velocity)

2006 2008

During the middle 60% of the project schedule we need Throughput (velocity) to average 220

features per month

Page 24: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

Little’s Law

ThroughputLead Time

WIP=

Target to achieve plan

From observed capability

Treat as Fixed variable

Determines staffing level

Page 25: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

Changing the WIP limit without maintaining the staffing level ratio represents a change to the way of

working. It is a change to the system design. And will produce a change in the observed ‘common

cause’ capability of the system

Page 26: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

Plan based on currently observed capability and current working

practices. Do not assume process improvements.

If changing WIP to reduce undesirable effects (e.g.

multitasking), get new sample data (perform a spike) to observe the

new capability

Page 27: OOP 2012 - Predictability & Meansurement with Kanban

Kanban

2012

ÞWIP = 22, round up to 25.5 teams, 5 per team

Þ If current working practice is 1 unit WIP per person then 5 people are needed to per team

Little’s Law

55 / week0.4 week

WIP=

Target to achieve plan

From observed capability

Determines staffing level

Page 28: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

Conclusions

Page 29: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

For Service-oriented work, create predictability with

a regular delivery cadencea strong config management capability

capability to deploy effectivelycode with high quality

For major projects

understand peak throughput (velocity)model the s-curve on work complete

treat the avg. lead time as the fixed variableuse Little’s Law to calculate WIP limits

and staffing levels

Page 31: OOP 2012 - Predictability & Meansurement with Kanban

Advanced

Kanban

About…David Anderson is a thought leader in managing effective software teams. He leads a consulting, training and publishing business dedicated to developing, promoting and implementing sustainable evolutionary approaches for management of knowledge workers.

He has 30 years experience in the high technology industry starting with computer games in the early 1980’s. He has led software teams delivering superior productivity and quality using innovative agile methods at large companies such as Sprint and Motorola.

David is the author of two books, Agile Management for Software Engineering – Applying the Theory of Constraints for Business Results, and Kanban – Successful Evolutionary Change for your Technology Business.

David is a founder of the Lean Kanban University, a business dedicated to assuring quality of training in Lean and Kanban throughout the world.

http://leankanbanuniversity.comEmail: [email protected] Twitter: agilemanager