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FAST Forecasting

FAST Forecasting is not actually part of the FAST framework, just as there is no concrete estimation method included the Scrum framework. It is here as a suggestion/option for forecasting in a FAST Agile environment.

We believe it to be the quickest and most lightweight forecasting tool/process and thus in keeping with the rest of FAST.

Here are the steps to FAST forecasting
  • FAST Product Director will start a forecasting phase during the FAST meeting
  • Only large items are estimated in FAST e.g. features
  • The Product Director will describe the feature (or what they feel is remaining in the feature if it is already under way)
  • The Product Director can take and answer a handful of questions from the tribe if more clarity is needed
  • Developers are very careful not to anchor their size estimate to the room or their neighbours (i.e. they have been trained in FAST forecasting)
  • Using the FAST Forecasting app on their mobile devices, each developer enters their estimate (rounded to an agreed amount e.g. in units of 50 or 20 man days)
  • The FAST Forecasting tool merely averages the estimates and Voilà - you have sized the remaining work in your backlog in a matter of minutes
Of course, you can do this all manually using pieces of paper and a ballot system...


Ron Quartel came up with this idea when looking for a light weight forecasting method. He was first inspired by a method taught by Ash Maurya in his Lean Startup two day class and then again when reading Thinking, Fast and Slow by Daniel Kahnema. FAST Forecasting also has much in common with the spirit of Blink Estimation.




Thinking, Fast and Slow by Daniel Kahnema

Excerpt from Chapter 7 - "A Machine for Jumping to Conclusions" on a method to decorrelate error.

... imagine that a large number of observers are shown glass jars containing pennies and are challenged to estimate the number of pennies in each jar. As James Surowiecki explained in his best-selling The Wisdom of Crowds, this is the kind of task in which individuals do very poorly, but pools of individual judgements do remarkably well.

Some individuals greatly overestimate the true number, others underestimate it, but when many judgments are averaged, the average tends to be quite accurate. The mechanism is straightforward: all individuals look at the same jar, and all their judgments have a common basis.

On the other hand, the errors that individuals make are independent of the errors made by others, and (in the absence of a systematic bias) they tend to average to zero. However... error reduction works well only when the observations are independent and their errors uncorrelated. If the observers share a bias, the aggregation of judgments will not reduce it.

Allowing the observers to influence each other effectively reduces the size of the sample, and with it the precision of the group estimate. To derive the most useful information from multiple sources of evidence, you should always try to make these sources independent of each other.

This rule is part of good police procedure. When there are multiple witnesses to an event, they are not allowed to discuss it before giving their testimony. The goal is not only to prevent collusion by hostile witnesses, it is also to prevent unbiased witnesses from influencing each other. Witnesses who exchange their experiences will tend to make similar errors in their testimony, reducing the total value of the information they provide.