Applying Metrics for Predictability

• An understanding of what Lean-Agile metrics are necessary for predictability
• An ability to make accurate forecasts for single items including how to forecast when an individual story, epic, or feature will be done
• An ability to make accurate forecasts for multiple items including how to know when all items remaining in a backlog will be done or an ability to project how many items will be completed by given release date and the risks associated with each
• An understanding of how to use flow metrics and analytics to achieve a stable process including an understanding of why a stable process is necessary for accurate forecasting

Who Should Attend

• The Applying Metrics for Predictability course is for anyone who has been asked to answer “When Will It Be Done?” or otherwise had to give an estimate for a User Story, Epic, Feature, Project, and/or Release. This includes executives, managers, or team members who want better understanding and transparency into the health and performance of their process. Anyone who currently uses Agile or Lean Methodologies and is interested in how to improve the overall predictability and efficiency of their current practices will benefit from Applying Metrics for Predictability.

• Flow Metrics: a deep dive into WIP, Cycle Time, and Throughput—including why you need to track them, how they are related through Little’s Law, and why they are important for forecasting
• Flow Analytics: an introduction to Cumulative Flow Diagrams (CFDs), Scatterplots, Histograms, and other supporting charts—including a detailed discussion of what they are, how to generate them correctly, and how to utilize them for better predictability
• Forecasting Using Monte Carlo Simulation: how to use flow metrics to answer “When will it be done?”—including an introduction to Monte Carlo Simulation and statistical sampling methods for forecasting (no advanced mathematics required!)
• Quantifying Risk and Risk Management: how an understanding of risk is crucial to developing accurate forecasts—including how to quantify risk in the different analytics’ charts and how changing forecast inputs can help to develop an overall risk profile
• How to Get Started: how to immediately apply these techniques to your current project or process to achieve the results you are looking for—including what data to collect, how to mine your data, and how much data you need to begin