When Will It Be Done?
When Will It Be Done? (WWIBD-Single)
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When Will It Be Done?

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Completed on 2018-07-29

About the Book

If you have ever been asked “When Will It Be Done?” (WWIBD) then you have been asked to make a forecast. My guess is that when you were asked that question, you struggled mightily to answer it. You struggled because there are so many possible things that could go wrong (and go right) when trying to forecast the completion date an item. I am even going to guess that whatever prediction you made ended up being wrong. The fact that you were wrong was not because you were incompetent or because you did not try hard enough. You were wrong because we think about forecasts incorrectly. This book clear up that confusion and will give you all the tools you need in order to make accurate forecasts. Your customers demand predictability from you and you demand predictability from your teams. Isn’t it time you started to deliver on your promises?

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

About the Author

Daniel Vacanti
Daniel Vacanti

Daniel Vacanti has spent most of the last 20 years focusing on Lean and Agile practices.  In 2007, he helped to develop the Kanban Method for knowledge work.  He managed the world’s first project implementation of Kanban that year, and has been conducting Kanban training, coaching, and consulting ever since.  In 2011 he founded Corporate Kanban, Inc., which provides world-class Lean training and consulting to clients all over the globe—including several Fortune 100 companies.  In 2013 he founded ActionableAgileTM which provides industry leading predictive analytics tools and services to any flow-based process. Daniel holds a Masters in Business Administration and regularly teaches a class on lean principles for software management at the University of California Berkeley.

Bundles that include this book

When Will It Be Done?
Actionable Agile Metrics for Predictability
2 Books
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About the Contributors

Prateek Singh
Prateek Singh

Principal - Ultimate Software

Todd Conley
Todd Conley

CTO - ActionableAgile

Table of Contents

    • Preface
      • Why Write This Book
      • Assumptions
      • Who Should Read This Book
      • Conventions Used
      • How to Read
      • ActionableAgile.com
  • Section I: Forecasting
    • Chapter 1: The Basic Principles of Forecasting
      • Some Basic Principles of Forecasting
      • The answer to WWIBD is a forecast!
      • Conclusion
      • Key Learnings and Takeaways
  • Section II: Forecasts for Single Items
    • Chapter 2: How To Make A Forecast For A Single Item
      • Cycle Time
      • Cycle Time Scatterplots
      • Percentiles Are Forecasts!
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 3: How To Improve Forecasts For Single Items - What To Do
      • The Most Important Chart That You Have Never Heard Of (And How to Use It)
      • Reforecast When You Get More Information
      • Flow Efficiency
      • Internal and External Variability
      • Another Way To See the Effects of Improved Forecasts
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 4: How To Improve Forecasts For Single Items – What Not To Do
      • Never Communicate a Forecast in Terms of an Average
      • The Most Likely Outcome Is Not Very Likely
      • Your Data Is Not Normal
      • Do Not Waste Time Estimating and Planning
      • Do Not Ignore Pull Policies
      • Conclusion
      • Key Learnings and Takeaways
  • Section III: Forecasts for Multiple Items
    • Chapter 5: How To Make A Forecast For Multiple Items
      • A Quick Thought Experiment
      • Probabilistic Thinking Redux
      • Forecasts for Multiple Items
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 6: How To Improve Forecasts For Multiple Items – What To Do
      • Consistent Throughput
      • Reforecast Based on New Information
      • Consider Different Selection Techniques for Inputs
      • Pay Attention to Your Model’s Assumptions
      • Conclusion
      • Key Learning and Takeaways
    • Chapter 7: How to Improve Forecasts for Multiple Items – What Not to Do
      • Do Not Use Averages
      • Do Not Use Little’s Law for Forecasting
      • Do Not Estimate
      • Forget Curve Fitting
      • Conclusion
      • Key Learnings and Takeaways
  • Section IV: How To Know If You Can Trust Your Forecasts
    • Chapter 8: Process Stability As Defined by Little’s Law
      • A Little’s Law Refresher
      • We Need a Little Help
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 9: How to Visualize System Stability
      • What makes a CFD a CFD?
      • Constructing a CFD
      • Work In Progress
      • Approximate Average Cycle Time
      • Average Throughput
      • Conclusion
      • Key Learnings and Takeaways
    • Chapter 10 - Improving System Stability
      • Matching Arrivals to Departures
      • One Way or Another, Finish All Work That is Started
      • Do Not Let Items Age Unnecessarily
      • Key Learnings and Takeaways
  • Section V: Putting It All Together
    • Chapter 11: How to Get Started
      • A Recipe for Getting Started
      • Some Other Things to Consider When Getting Started
      • Conclusion
      • Key Learnings And Takeaways
    • Chapter 12: Putting It All Together
      • Standups for Predictability
      • Retrospectives for Predictability
      • How to Do Release Planning
      • Some Other Ideas
      • Segmenting WIP
      • Other Forecasting Techniques
      • But What If I Don’t Care About Predictability?
      • Key Learnings and Takeaways
  • Section VI: Case Studies
    • Chapter 13: Case Study - Ultimate Software
      • Ultimate Software
      • Starting With Scrum
      • Problems With Scrum Adoption
      • Progression to Kanban
      • Results with Kanban
      • Organization Wide Impact
      • Probabilistic Release Planning (Monte Carlo)
      • Release Tracking
      • Daily Product Review
      • Feature Visualization
      • Next Steps
      • Moving Beyond Development
      • Key Learnings And Takeaways
    • Chapter 14: Case Study - Linear Projections vs Monte Carlo Simulation
      • The Setup
      • The Analysis
      • Conclusion
      • Key Learnings And Takeaways
    • Chapter 15: Case Study - Siemens HS
      • Introduction
      • History
      • Actionable Metrics
      • How Metrics Changed Everything
      • Conclusion
      • Key Learnings and Takeaways
    • Acknowledgments
    • About the Author
  • References

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