3 ms·
> For example, the historical throughput of your team can be used to run a simulation of future throughput. Some outcomes will be more likely than others and yo
by hrktb 6y ago
> For example, the historical throughput of your team can be used to run a simulation of future throughput. Some outcomes will be more likely than others and you can use these numbers to come up with statements like: “41 items or more can be completed in 30 days with a certainty of 85%”.
As usual, there are two base assumptions to this:
- your work environment didn’t and won’t have any significant change for a long time (tools, requirements, team composition etc.)
- your team output is somewhat stable, with no month with 300 tickets and others with only 50 tickets.
In my experience these two assumptions are surprisingly rare to justify in any decently growing company.
In particular as company grows, tools change, organization and processes change, people change. On top of that there is often seasonality.
We can still throw around forecasts, but IMO in these conditions it has no more legitimacy than random guesses.
- dirklectisch 6y agoAuthor of the article here. Significant changes will indeed make your forecast not come true. But mostly people are not asking to predict for situations with significant changes. Incremental changes will just be incorporated in the forecasts over time. The forecast continually adjusts to new information becoming available. It's interesting to know that after about ten work items completed the number become pretty stable. So it's easy to reset or adjust the forecast in case of a big change in the team. I was personally surprised to learn how stable the output of a team is if its composition doesn't change. Tools have some impact on productivity but so big that you have to throw away your predictions. No one is complaining anyway if you over deliver a little.
- hrktb 6y ago> people are not asking to predict for situations with significant changes That’s for coming up changes. Your predictions will also be of poor quality after a significant change as you lose the link with historical data. In the end there might be only a small window between two changes where they are worth anything. To be clear I am not saying you shouldn’t make prediction anyway, just that the effort to come with “a system” is not worth it in a lot of situation. For context, tech industry has one of the highest turnover, a team losing/gaining a member is not some rare event. A new boss coming in to change process or teams isn’t either. [0] https://business.linkedin.com/talent-solutions/blog/trends-and-research/2018/the-3-industries-with-the-highest-turnover-rates https://business.linkedin.com/talent-solutions/blog/trends-a...