Actionable Agile Metrics Volume II
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Actionable Agile Metrics Volume II

Advanced Topics in Predictability

About the Book

The only reason to collect data is to take action. We take action because of our relentless pursuit of improvement.  That is what professionalism is all about.  Taking action, however, requires a considered and deliberate analysis of the data at hand.  We don't take action for action's sake but rather we take action based on how our data speaks to us.  Unfortunately, our data speaks to us through the garbled language of variation.  Few even recognize the language of variation.  Far fewer even understand it. This book will act as a translator.

Once variation is controlled, the next step is turn our process data into meaningful forecasts about what can or cannot be done. But how do we forecast when we have complicated work-breakdown hierarchies, spiraling WIP, and excessive dependencies? This book will help with that too.

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    • Leadership
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About the Editors

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 the 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 ActionableAgileTM which provides industry-leading predictive analytics tools and services organizations that utilize Lean-Agile practices. In 2014 he published his 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” class with and in 2018 he published his second book, “When Will It Be Done?”. Most recently, Daniel co-founded whose aim is to create a safe, diverse, inclusive community to learn about Kanban.

Table of Contents

  • Preface
    • Why Write This Book
    • Assumptions
    • Who Should Read This Book
    • Conventions Used
    • How to Read
  • Section I: Variation and Predictability
  • Chapter 1 – Wilt the Stilt
  • Chapter 2 – Variation Defined
    • The Second Principle for Understanding Data
    • Two Types of Variation
    • Exceptional Variation
    • The Two Mistakes of Data Analysis
    • Flow Metrics and Variation
    • Conclusion
  • Chapter 3 – Variation and Predictability
    • A Definition of Predictable Process
    • Sequential Analysis for Predictability
    • What About Little’s Law?
    • Conclusion
  • Chapter 4 – Process Behaviour Charts
    • Table Data vs. Visualized Data
    • The Running Record
    • Rules for Presenting Data
    • The First Principle of Understanding Data
    • The X Chart
    • The Moving Range Chart
    • The XmR Chart
    • Conclusion
  • Chapter 5 - How Much Data?
    • How Much Data?
    • PBCs with less than 20 data points
    • When to Update Limits
    • Conclusion
  • Chapter 6 – Detecting Signals on PBCs
    • The Trend is Not Your Friend
    • Detection Rules for XmR Charts
    • Zero Bounded Data
    • Sign, Sign, Everywhere a Sign
    • Did Wilt Improve?
    • An Additional Rule?
    • An Example Using Cycle Time
    • Conclusion
  • Chapter 7 – XmR Charts and the Four Basic Metrics of Flow
    • XmR Charts and Cycle Time
    • XmR Charts and Throughput
    • XmR Charts and WIP
    • XmR Charts and WIP Age
    • Conclusion
  • Chapter 8 – Myths and Other Considerations
    • What If I Know I Have Bad Data?
    • PBCs Using Medians
    • The Wrong Ways to Use PBCs
    • The Only Data Considerations You Need
    • Conclusion
  • Chapter 9 - VoP, VoC, and Predictability
    • Predicable Process vs. Arbitrary Targets
    • VoC, VoP, and Scrum
    • Three Types of Action
    • Conclusion
  • Section II: Advanced Monte Carlo Simulation and Predictability
  • Chapter 10 - Monte Carlo Simulation Revisited
    • A Quick Thought Experiment
    • Probabilistic Thinking
    • Forecasts for Multiple Items
    • Conclusion
    • Key Learnings and Takeaways
  • Chapter 11 - Different Sampling Methods
    • Is Our Default Sampling Algorithm Incorrect?
    • All Models Are Wrong, But Some Are Random
    • What Happened To Our Team?
    • Conclusion
  • Chapter 12 - What Percentile Do I Choose?
    • Here Comes The Rain Again
    • Continuous Forecasting
    • Conclusion
  • Chapter 13 - Scaling Monte Carlo
    • Single Team With Work Item Hierarchy
    • Single Team Working On Multiple Items
    • Multiple Teams With Dependencies
    • Just When You Thought It Was Safe…
    • Conclusion
  • Chapter 14 - A Parting Thought
  • Appendices
  • Appendix A - The Basic Metrics of Flow
    • Work In Progress
    • Cycle Time
    • Work Item Age
    • Throughput
    • Conclusion
  • Appendix B - How To Construct an XmR Chart
    • The XmR Chart for Cycle Time
    • Conclusion
  • Endnotes
  • Bibliography
  • Acknowledgements
  • About The Author

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