Note: this is the only AMP class I plan to run in Germany for the second half of 2023, so please join us if you can!

“When will it be done?” “How many items will I get in the next release?” “When will all of the items in the backlog be completed?” Those are some of the first questions that your customers will ask you once you start work for them. This hands-on workshop will provide you with the tools you need to answer those questions predictably. In this tutorial, attendees will learn what metrics are necessary for accurate forecasting, how to visualize those metrics in appropriate analytics, how to use those analytics make reliable forecasts and understand risk, and, finally, how to make meaningful interventions for overall process improvement.

Everyone who attends this class will get a free attempt at ProKanban.org's Professional Applied Metrics (PAM) certification. For more information about this certification, please see https://prokanban.org/professional-applied-metrics

Learning Outcomes

• An understanding of what Lean-Agile metrics are necessary for predictability
• An understanding of some basic principles of forecasting--especially in complex environments
• 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 to know how many items will be completed by a 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

This Tutorial 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
• 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 frameworks or methodologies and is interested in how to improve the overall predictability and efficiency of their current practices

Topic Areas

• Forecasting Principles: an introduction to the role that uncertainty plays when making forecasts and some techniques to use to mitigate its impact
• 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

What's Included

• 2-days training with Daniel Vacanti and Peter Götz
• Exam token to take the Professional Applied Metrics (PAM) certification by ProKanban.org.
• Digital copy of course materials
• Digital copies of two books by Daniel Vacanti

Venue Address

      Drygalski-Allee 25, 81477 München
      https://www.google.com/maps/place/Drygalski-Allee+25,+81477+M%C3%BCnchen,+Germany/@48.0947974,11.5054917,17z/data=!3m1!4b1!4m6!3m5!1s0x479dd92270f0477f:0x61a5d6cf7b4ebfc5!8m2!3d48.0947974!4d11.5054917!16s%2Fg%2F11b8v4d4wp?coh=164777&entry=tt