> 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.
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.