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Monthly Archives: January 2013

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22 01, 2013

Product Forecasting and the Planning Fallacy

By |2017-05-23T15:42:02-08:00January 22nd, 2013|Blog|0 Comments

Experience shows that what happens is always the thing against
which one has not made provision in advance

— John Maynard Keynes

An old cliché in forecasting is that when it comes to the single-valued forecast, the only thing you can say with certainty is that it’s wrong. More than just plain wrong numbers, a reliance on single-valued forecasts suffers from these problems:

  • Communicates over-confidence about the forecast and your knowledge of the future
  • Discourages a team from seriously considering events that may increase or decrease project value
  • Fosters complacency about the need to develop options and contingency plans

I have a nagging suspicion that none of what I’ve just written is a surprise to you, even if you’re one of those single-valued forecasters. It’s ironic that many people avoid the topic of uncertainty because they are…uncertain about what to do about it. The popular literature on decision-making has only raised awareness of how challenged we all are at estimating uncertainty and risk, and it’s easy to rebuff Monte Carlo simulation as a lot of statistical mumbo-jumbo. We in turn are not surprised when we speak with R&D teams about forecasting and hear a variant of “we don’t estimate uncertainty because we just don’t know enough.”

21 01, 2013

Learning From Our Forecasting Foibles

By |2017-05-23T15:42:02-08:00January 21st, 2013|Blog|0 Comments

The prevalent tendency to underweight, or ignore, distributional information is perhaps the major source of error of intuitive prediction…The analyst should therefore make every effort to frame the forecasting problem so as to facilitate utilizing all the distributional information that is available to the expert.

—Daniel Kahneman and Amos Tversky

Most people involved in risky enterprises forecast costs or benefits as a single estimate in each time period, rather than a range that reflects the unknowable nature of the future. That’s why we can readily compile the following examples. Below you’ll find a few instances of deterministic (expressed as a point or line estimate rather than a range) forecasts that missed the mark. In some cases we have the benefit of hindsight over many years of forecasts, or across many independent experts, so that we can easily see just how difficult forecasting can be.

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