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Project Management

Lies, damned lies and forecasts!

My colleague Miriam Gilbert has blogged recently about Forecasting and its potential pitfalls.  Her first post “Don’t believe your forecasts” talks about  why forecasts fail and the research evidence:

For the “scientists”:

  • simple statistical forecast models are better than complex ones as they don’t try to find non-existent patterns in the past to predict the future,
  • statistical regularity does not equal predictability

For the “experts”:

  • human judgement is no better than simple statistical models at predicting the future,
  • experts do not predict more accurately than a moderately well-informed man/woman in the street!

For all:

  • People are often extremely surprised by the extent of their forecast mistakes

Her second post “Forecasts lie – get over it!” identifies nine things you can do to make the process of forecasting more valuable.

Both of these reminded me of some material I put together on Project Estimating, where I outlined five methods for estimating costs and timescales when developing project plans.  Here are some slides I use on a workshop on Project Estimating.

 

The other thought that Miriam’s posts triggered was that there are plenty of simulation tools and techniques available to help managers with their forecasts and to reduce the risks of implementing bad solutions.  You can download my article “Simulation: a world of what if?” which briefly introduces System Dynamics, Discrete Event Simulation and Agent-based Modelling. 

 

Often, you have to make assumptions that a solution (e.g. a revised process) will be implementable and will indeed improve performance. In these cost-conscious times, it’s even more important to improve your chances of success. That’s why more organisations are turning to simulation, to “road test” their options and improve their chances of successful implementation. 

 

Clearly, in the real world, things are complex and often lots of factors interact to create an outcome. Simulation is not the same as a forecast, but it helps us to understand complexity and to be more confident in the impact of any proposed changes.

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