# Differences

This shows you the differences between two versions of the page.

 how_do_we_forecast_our_plan_with_kanban [2017/04/19 02:50]Hans Samios how_do_we_forecast_our_plan_with_kanban [2017/04/20 19:16] (current)Hans Samios [Notes] 2017/04/20 19:16 Hans Samios [Notes] 2017/04/19 02:50 Hans Samios 2017/04/19 02:50 Hans Samios created 2017/04/20 19:16 Hans Samios [Notes] 2017/04/19 02:50 Hans Samios 2017/04/19 02:50 Hans Samios created Line 15: Line 15: - At our best rate of completion, then the remaining 45 items (assuming normal rules of the game where features are sized at < 1 quarter of work)  will be complete in 900 (45 x 20) days. Based on work I’ve done in the past there less than is a 10% chance of this happening but we can produce “better” view of this probability by doing a simple Monte Carlo analysis. - At our best rate of completion, then the remaining 45 items (assuming normal rules of the game where features are sized at < 1 quarter of work)  will be complete in 900 (45 x 20) days. Based on work I’ve done in the past there less than is a 10% chance of this happening but we can produce “better” view of this probability by doing a simple Monte Carlo analysis. - At our average rate of completion (25 days), we will complete the work in 1125 (45 x 25) days. This has a 50% chance of happening. We are reasonably comfortable about this estimate as we are using an average which, pretty much by definition is a 50/50 proposition. - At our average rate of completion (25 days), we will complete the work in 1125 (45 x 25) days. This has a 50% chance of happening. We are reasonably comfortable about this estimate as we are using an average which, pretty much by definition is a 50/50 proposition. - - If we want a more predictable view of the date, we would could loo at the highest cycle time, in this case 30 days. Time to complete remaining work is 1350 (45 x 30) days. We can be reasonably confident this will happen so call it a 85% chance. Again Monte Carlo modeling would improve this. + - If we want a more predictable view of the date, we would could look at the highest cycle time, in this case 30 days. Time to complete remaining work is 1350 (45 x 30) days. We can be reasonably confident this will happen so call it a 85% chance. Again Monte Carlo modeling would improve this. You might be wondering whether 5 “observations” is enough to give us good data. Basically the idea is that if you have 5 observations like this, then the probability that the next cycle time is beyond the range we already have is 25% (ie chance that we already have all the cycle times that we will actually produce is 75% - see [[how_can_we_forecast_when_we_do_not_have_a_lot_of_data|How Can We Forecast When We Do Not Have a Lot of Data?]] for thinking process.) In other words, you don’t need a lot of data here. You might be wondering whether 5 “observations” is enough to give us good data. Basically the idea is that if you have 5 observations like this, then the probability that the next cycle time is beyond the range we already have is 25% (ie chance that we already have all the cycle times that we will actually produce is 75% - see [[how_can_we_forecast_when_we_do_not_have_a_lot_of_data|How Can We Forecast When We Do Not Have a Lot of Data?]] for thinking process.) In other words, you don’t need a lot of data here.
• /home/hpsamios/hanssamios.com/dokuwiki/data/pages/how_do_we_forecast_our_plan_with_kanban.txt