Single Item Forecasts
Our ability to estimate how long a single item is going to take to get done is largely overestimated(because we are bad at estimating 
). One of the best ways to get a handle around the uncertainty and getting a better idea of how long something might take to get done, is to actually start measuring how long they currently take. This is where the metric of Cycle Time comes in. The most common reason to lower cycle time is to increase throughput. If each item gets done faster, more items will get done over a period of time - sounds obvious. But another great reason for lowering cycle time is to lower variability. Going back to the commute example, if we timed our commute everyday, we would be able to get an idea of what the distribution of our commute times is. Similarly, if we timed our stories and recorded how long it takes for them to complete, we can start making some sense of the variability. The graph below is the cycle time scatter plot for a team. It shows the days it took for stories that closed in the first quarter of 2015. Along the vertical axis is the number of days a story took and the horizontal axis is the time line. Each dot here represents at least one story.
). One of the best ways to get a handle around the uncertainty and getting a better idea of how long something might take to get done, is to actually start measuring how long they currently take. This is where the metric of Cycle Time comes in. The most common reason to lower cycle time is to increase throughput. If each item gets done faster, more items will get done over a period of time - sounds obvious. But another great reason for lowering cycle time is to lower variability. Going back to the commute example, if we timed our commute everyday, we would be able to get an idea of what the distribution of our commute times is. Similarly, if we timed our stories and recorded how long it takes for them to complete, we can start making some sense of the variability. The graph below is the cycle time scatter plot for a team. It shows the days it took for stories that closed in the first quarter of 2015. Along the vertical axis is the number of days a story took and the horizontal axis is the time line. Each dot here represents at least one story.
The horizontal lines running through the graph show what percentage of these stories took 12 days or less, 9 days or less and so on. Based on the above graph we can say that any story this team starts has an 85% chance of getting done in 12 days or less. Half the stories(50%) get done in 6 days or less. Now, when this team is handed a new story to work on, they can use this data to communicate to the story's customer how many days it will take with what level of confidence. This is a much better way of forecasting and "estimating" the amount of time a single item would take than taking averages or estimating points.
Next : Forecasting Multiple Items
No comments:
Post a Comment