Cycle Time Analytics - Fast #NoEstimate Forecasting & Decision Making
Speaker
Description
If you are struggling to forecast project delivery dates and cost, or you want to eliminate the story estimation process because you feel it is waste, or you need to build the business case for hiring more staff, then this session is relevant to you. All estimates have uncertainty, and understanding how multiple uncertain factors compound is the first step to improving project and team predictability. A major benefit of Lean is the low weight capture of cycle time metrics. This session looks at how to use historical cycle time data to answer questions of forecasting and staff skill balancing. This session compares the benefits of using cycle time for analysis over current planning techniques such as velocity, burn-down charts, and cumulative flow diagrams. This session takes you on a journey of what to do after capturing cycle time data or what to do if you have no history to rely upon. Key session takeaways include - - Why story point estimation and forecasts fail to deliver. - Why cycle time data follows a known distribution pattern and how to leverage this. - How much data is needed for reliable forecasting, and what to do if you have less (or none). - Forecasting project date and cost from cycle time data. - Finding the right balance of staff skills (for example the number of developers and QA staff).
Speaker
Description
If you are struggling to forecast project delivery dates and cost, or you want to eliminate the story estimation process because you feel it is waste, or you need to build the business case for hiring more staff, then this session is relevant to you. All estimates have uncertainty, and understanding how multiple uncertain factors compound is the first step to improving project and team predictability. A major benefit of Lean is the low weight capture of cycle time metrics. This session looks at how to use historical cycle time data to answer questions of forecasting and staff skill balancing. This session compares the benefits of using cycle time for analysis over current planning techniques such as velocity, burn-down charts, and cumulative flow diagrams. This session takes you on a journey of what to do after capturing cycle time data or what to do if you have no history to rely upon. Key session takeaways include - - Why story point estimation and forecasts fail to deliver. - Why cycle time data follows a known distribution pattern and how to leverage this. - How much data is needed for reliable forecasting, and what to do if you have less (or none). - Forecasting project date and cost from cycle time data. - Finding the right balance of staff skills (for example the number of developers and QA staff).
