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Monte Carlo or bust - the next chapter

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This event was a follow up to the event held on 8th September 2009 and dealt with the specific estimating problems encountered during the concept phase of the project and how the use of Monte-Carlo modelling can reduce the cost uncertainties.

In most projects 75-80% of the costs are committed to during or prior to Design, when the functional requirements have not yet been finalised. By modelling that estimating process we have opportunities to engineer-in value to the project, but that requires an understanding of the estimating model.

Traditionally the estimating model is based upon political considerations (around known assumptions) or comparison of physical characteristics of the project where there are known characteristics and developing sizing and complexity weightings. In this approach assumptions tend to become solutions and models of the estimate (when they become untenable) are replaced with other models which are as flawed (guess is replaced with further guesses).

The approach that Nira suggested was that we should replace our traditional model with one that develops the model to fit the client and that guesses are OK as long as they are replaced by guesses which have an evolving logic (Logical Argument Based Model, LABM)
LABM provides estimates but those estimates are linked to specific assumptions and justifications, these are translated into cost estimates which are capable of being reflected upon and challenged to develop new estimates. Each iteration of this cycle provides more subjective assumptions and justifications.

Nira then showed how using this data in a mathematical model we could test various assumptions, such as spend to save strategies. By designing a decision making tool (based on Monte-Carlo simulations) to establish a breakeven point with inherent uncertainty influences which demonstrate the probability of achieving a Through Life Cost saving using that particular assumption, taking into account a reducing value of cost (NPV). He demonstrated how this could be applied especially in maintenance cost simulation.

In conclusion if we capture the assumptions and determine their logic the use of the suite of tools Nira demonstrated can be used to better adapt the model to the clients needs.

A number of questions followed the presentation covering areas such as:-

  • The use of optimism bias scoring
  • What type of questions help develop a model and design
  • How does the model reflect real-world (Nira gave some examples from work he is presently involved in)
  • That the strength of these models was in providing a basis for informed debate



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