Actionable Agile Metrics For Predictability: Tenth Anniversary Edition
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Actionable Agile Metrics For Predictability: Tenth Anniversary Edition

About the Book

“When will it be done?”

That is probably the first question your customers ask you once you start working on something for them.  Think about how many times you have been asked that question.  How many times have you ever actually been right?

We can debate all we want whether this is a fair question to ask given the tremendous amount of uncertainty in knowledge work, but the truth of the matter is that our customers are going to inquire about completion time whether we like it or not.  Which means we need to come up with an accurate way to answer them.  The problem is that the forecasting tools that we currently utilize have made us ill-equipped to provide accurate answers to reasonable customer questions. 

Until now.

(You can also find out more at  https://www.actionableagile.com)

About the Author

Daniel Vacanti
Daniel S. Vacanti

Daniel Vacanti is a 25-plus year software industry veteran who has spent most of his career focusing on Lean and Agile practices. In 2007, he helped to develop Kanban as a strategy for knowledge work and managed the world’s first project implementation using Kanban that year. He has been conducting Lean-Agile training, coaching, and consulting ever since. In 2013 he founded ActionableAgile® which provides industry-leading predictive analytics tools and services to organizations that utilize Lean-Agile practices. In 2014 he published his first book, “Actionable Agile Metrics for Predictability”, which is the definitive guide to flow-based metrics and analytics. In 2017, he helped to develop the “Professional Scrum with Kanban” (PSK) class with Scrum.org and in 2018 he published his second book, “When Will It Be Done?”. 2020 saw Daniel co-found ProKanban.org whose aim is to create a safe, diverse, and inclusive community to learn about Kanban. Most recently, Daniel published his third book, “Actionable Agile Metrics for Predictability Volume II”.

Bundles that include this book

Lean Agile Predictability Bundle
3 Books
$64.98
Bought separately
$45.00
Bundle Price

Table of Contents

    • Foreword
    • Preface
      • 10th Anniversary Edition
      • All Editions
      • Why Write this Book?
      • Who Should Read this Book
      • Conventions Used
      • ActionableAgile.com
    PART I: Foundations of Predictability
    • Chapter 1 - The Most Important Part of Predictability
      • Background
      • The Moral of the Story
      • All Assumptions Are Equal. But Some Assumptions Are More Equal Than Others.
      • Predictability
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 2 - Setting Up For Predictability
      • Modeling Flow
      • Defining Arrivals and Departures
      • Why Start and Finish Matter
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 3 - The Basic Metrics of Flow
      • Work In Progress
      • Cycle Time
      • Throughput
      • Work Item Age
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 3a - Flow Metrics Data and Calculation
      • What Data To Collect
      • Flow Metrics Calculation
      • Randomness
      • The Flaw of Averages
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 4 - Introduction to Cycle Time Scatterplots
      • What is a Cycle Time Scatterplot?
      • Percentile Lines
      • Your Data is Not Normal
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 5 - Service Level Expectations
      • Calculating an SLE
      • SLEs for Different Work Item Types
      • Percentiles as Intervention Triggers
      • Right-Sizing
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 6 - The Work Item Aging Chart
      • What Is a Work Item Aging Chart?
      • Additional Data
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 7 - Leveraging the Aging Chart for Predictability
      • The Daily Meeting
      • Percentiles As Intervention Triggers Redux
      • Actions to Take
      • Conclusion
      • Key Learnings and Takeaways
    PART II: More Flow Principles for Predictability
    • Chapter 8 - Introduction to CFDs
      • What makes a CFD a CFD?
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 8a - Constructing a CFD
      • A Simple Example
      • What About Knowledge Work?
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 9 - Flow Metrics and CFDs
      • Work In Progress
      • Approximate Average Cycle Time
      • Average Throughput
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 10 - Interpreting CFDs
      • Mismatched Arrivals and Departures
      • Flat Lines
      • Stair Steps
      • Bulging Bands
      • Disappearing Bands
      • The S-Curve
      • A Boring CFD
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 11 - Conservation of Flow Part I
      • Arrivals and Departures Revisited
      • Arrivals and Departures on a CFD
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 12 - Conservation of Flow Part II
      • Just-in-time Prioritization
      • Just-in-time Commitment
      • Exceptions to Conservation of Flow
      • Conditioning Flow and Predictability
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 13 - Flow Debt
      • Approximate Average Greater Than Actual Average
      • Approximate Average is Less Than Actual Average
      • Approximate Average Roughly Equal to Actual Average
      • How Different is Different?
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 14 - Pull Policies
      • Class of Service
      • The Impact of Class of Service on Predictability
      • Slack
      • Conclusion
      • Key Learnings and Takeaways
    PART III: Getting Started With Predictability
    • Chapter 15 - Getting Started
      • Defining Your Process
      • Capturing Data
      • How Much Data?
      • Create an Aging Chart
      • Create a Scatterplot
      • Some Pitfalls to Consider
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 16 - Next Steps
      • Forecasting
      • Little’s Law (Again)
      • Other Methods to be Wary About
      • Continue Learning
    Appendices
    • Appendix A - Introduction to Little’s Law
      • We Need a Little Help
      • Little’s Law from a Different Perspective
      • It is all about the Assumptions
      • Assumptions as Process Policies
      • Segmenting WIP
      • Kanban Systems
      • Size Does Not Matter
      • Forecasting
      • Conclusion
      • Key Learnings and Takeaways
    • Appendix B - Interpreting Cycle Time Scatterplots
      • The Triangle
      • Clusters of Dots
      • Gaps
      • Internal and External Variability
      • Conclusion
      • Key Learnings and Takeaways
    • Appendix C - Cycle Time Histograms
      • What is a Histogram?
      • Constructing a Histogram
      • Conclusion
      • Key Learnings and Takeaways
    • Endnotes
      • Chapter 1
      • Chapter 2
      • Chapter 3
      • Chapter 3a
      • Chapter 4
      • Chapter 5
      • Chapter 6
      • Chapter 7
      • Chapter 8
      • Chapter 8a
      • Chapter 10
      • Chapter 11
      • Chapter 12
      • Chapter 13
      • Chapter 14
      • Chapter 15
    • Bibliography
    • Acknowledgements for the 10th Anniversary Edition
    • About The Author

Other books by this author

Actionable Agile Metrics for Predictability
Daniel S. Vacanti
When Will It Be Done?
Daniel Vacanti
Actionable Agile Metrics Volume II
Daniel S. Vacanti
Umsetzbare Agile Metriken Band II (Deutsche Ausgabe)
Daniel S. Vacanti and TranslateAI
Flow Metrics for Scrum Teams
Will Seele and Daniel Vacanti
Quando Estará Pronto?
Daniel Vacanti and Rodrigo Pinto
The Kanban Pocket Guide
Daniel Vacanti, Prateek Singh, and Colleen Johnson
Actionable Agile Metrics for Predictability - Spanish
Daniel Vacanti and Ulises Gonzalez

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