When Will It Be Done?

Preface

1. Why Write This Book
2. Assumptions
3. Who Should Read This Book
4. Conventions Used
5. How to Read
6. ActionableAgile.com
7. Section I: Forecasting

Chapter 1: The Basic Principles of Forecasting

1. Some Basic Principles of Forecasting
2. The answer to WWIBD is a forecast!
3. Conclusion
4. Key Learnings and Takeaways
5. Section II: Forecasts for Single Items

Chapter 2: How To Make A Forecast For A Single Item

1. Cycle Time
2. Cycle Time Scatterplots
3. Percentiles Are Forecasts!
4. Conclusion
5. Key Learnings and Takeaways

Chapter 3: How To Improve Forecasts For Single Items - What To Do

1. The Most Important Chart That You Have Never Heard Of (And How to Use It)
2. Reforecast When You Get More Information
3. Flow Efficiency
4. Internal and External Variability
5. Another Way To See the Effects of Improved Forecasts
6. Conclusion
7. Key Learnings and Takeaways

Chapter 4: How To Improve Forecasts For Single Items – What Not To Do

1. Never Communicate a Forecast in Terms of an Average
2. The Flaw of Averages
3. The Most Likely Outcome Is Not Very Likely
4. Your Data Is Not Normal
5. Do Not Waste Time Estimating and Planning
6. Do Not Ignore Pull Policies
7. How an Expedite Request Sunk the Titanic
8. Conclusion
9. Key Learnings and Takeaways
10. Section III: Forecasts for Multiple Items

Chapter 5: How To Make A Forecast For Multiple Items

1. A Quick Thought Experiment
2. Probabilistic Thinking Redux
3. Forecasts for Multiple Items
4. Conclusion
5. Key Learnings and Takeaways

Chapter 6: How To Improve Forecasts For Multiple Items – What To Do

1. Consistent Throughput
2. Reforecast Based on New Information
3. Consider Different Selection Techniques for Inputs
4. Pay Attention to Your Model’s Assumptions
5. Conclusion
6. Key Learning and Takeaways

Chapter 7: How to Improve Forecasts for Multiple Items – What Not to Do

1. Do Not Use Averages
2. Do Not Use Little’s Law for Forecasting
3. Do Not Estimate
4. Forget Curve Fitting
5. Conclusion
6. Key Learnings and Takeaways
7. Section IV: How To Know If You Can Trust Your Forecasts

Chapter 8: Process Stability As Defined by Little’s Law

1. A Little’s Law Refresher
2. We Need a Little Help
3. Conclusion
4. Key Learnings and Takeaways

Chapter 9: How to Visualize System Stability

1. What makes a CFD a CFD?
2. Constructing a CFD
3. Work In Progress
4. Approximate Average Cycle Time
5. Average Throughput
6. Conclusion
7. Key Learnings and Takeaways

Chapter 10 - Improving System Stability

1. Matching Arrivals to Departures
2. One Way or Another, Finish All Work That is Started
3. Do Not Let Items Age Unnecessarily
4. Key Learnings and Takeaways
5. Section V: Putting It All Together

Chapter 11: How to Get Started

1. A Recipe for Getting Started
2. Some Other Things to Consider When Getting Started
3. Conclusion
4. Key Learnings And Takeaways

Chapter 12: Putting It All Together

1. Standups for Predictability
2. Retrospectives for Predictability
3. How to Do Release Planning
4. Some Other Ideas
5. Segmenting WIP
6. Other Forecasting Techniques
7. But What If I Don’t Care About Predictability?
8. Key Learnings and Takeaways
9. Section VI: Case Studies

Chapter 13: Case Study - Ultimate Software

1. Ultimate Software
2. Starting With Scrum
3. Problems With Scrum Adoption
4. Progression to Kanban
5. Results with Kanban
6. Organization Wide Impact
7. Probabilistic Release Planning (Monte Carlo)
8. Release Tracking
9. Daily Product Review
10. Feature Visualization
11. Next Steps
12. Moving Beyond Development
13. Key Learnings And Takeaways

Chapter 14: Case Study - Linear Projections vs Monte Carlo Simulation

1. The Setup
2. The Analysis
3. Conclusion
4. Key Learnings And Takeaways

Chapter 15: Case Study - Siemens HS

1. Introduction
2. History
3. Actionable Metrics
4. How Metrics Changed Everything
5. Conclusion
6. Key Learnings and Takeaways

Acknowledgments

About the Author

1. References