[bumby]

My experience is that most project managers take a non-probabilistic approach.

Say you have your usual list of breakdown tasks and assign a time/budget estimate for each in terms of “low”, “most likely”, and “high”. The intuitive answer is to sum up the “most likely” for your total estimate. However, this ignores the probability that a delay in one task affects others.

Instead, if you take into account the covariance relationship between tasks (using historic or simulated data) you often find that “most likely” summation has a quite low probability of being met. For the org that applied this, there was a less than 20% chance we’d meet or best that intuitive estimate. No wonder we were chronically over budget and over schedule!

[myth2018]

I've been reading the "Software Estimation: Demystifying the Black Art" from Steve McConnell.

He introduces a distinction that, at least for me, has been instrumental: estimations and plans are different things.

Estimations are honest, based on past performance data and probabilistic on their very natures.

Plans are, on the other hand, built with a target date in mind, taking into account the estimate previously made, desired delivery dates from customers and everything we are so used to.

By planing fulfillment of tasks closer to the estimates, you decrease the risk of the plan failing. You can build a shorter schedule and assume that staff will work overtime, assume more optimistic estimates and so on, but, then, the risk of failure will be higher. Such risk will, of course, never be zero though.

It's a simple distinction, but it has important implications. We don't feel anymore the pressure of making pessimistic, therefore dishonest estimates just out of fear of being pressed to cut the schedule. And also gave us a better argumentative tool to negotiate schedules with our clients.

I think it's also useful for making all the probabilities a bit clearer to project managers. It's like "OK, I know that you need me to commit with a delivery date, but I'm also going to make clear to you that there are some risks involved and I wanna make everybody aware of them"

[bumby]

That’s an important distinction. The way we handled it was by letting managers define their acceptable level of risk and then use the model to define the estimates in that context.

For example, if they were ok with a 60% chance of making or beating a cost estimate, the forecast could be much more aggressive than, say, a management expectation of 90% chance of being on budget

[myth2018]

Thanks for sharing this. I think I'll experiment presenting the situation to a customer using such model as soon as I have a opportunity. Sounds good.

[bumby]

This might be helpful:

https://www.nasa.gov/pdf/741989main_Analytic%20Method%20for%...

It’s a straightforward enough primer that it can be done in Excel, including simulating the data if necessary.

Even if this type of model is too simple for actual estimation, it’s a useful (and sobering) tool to help managers understand why their intuitive estimates can so often be incorrect.