how_do_we_forecast_our_plan_with_kanban
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| how_do_we_forecast_our_plan_with_kanban [2017/04/19 02:50] – created hpsamios | how_do_we_forecast_our_plan_with_kanban [2017/04/20 19:16] – [Notes] hpsamios | ||
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| Kanban is an empirical practice, and so generates data in the normal course of work that will allow you to prepare forecasts. | Kanban is an empirical practice, and so generates data in the normal course of work that will allow you to prepare forecasts. | ||
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| For Kanban we could use cycle time to understand expectations and compare this to upcoming known list of work. Simplistically, | For Kanban we could use cycle time to understand expectations and compare this to upcoming known list of work. Simplistically, | ||
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| - 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. | ||
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