In Part 2 of this series I finished by asking what should be done with historical data, now that we have decided that storing it is probably a good idea. I won’t keep you waiting any longer.
Auditing and calibration of the model at both the micro and macro level. It’s as important as any other element of risk or statistical analysis, or indeed the model building itself. At the distribution level historical data helps to both parameterise the distributions and in fact select them in the first place. As a minimum a few data points will help you to understand possible central tendencies and variability for your risks, and also generate a list of feasible distributions to choose from. With a reasonable number of observations @RISK for Excel can be used to fit distributions to the data taking care of both distribution selection and parameterisation simultaneously. Only five data points are technically needed, but a reasonable fit will require either more than that or other holistic information to achieve validity.
At the macro level total project cost estimates are often ignored from the portfolio perspective. Commonly high percentiles are reported from such models to use in a ‘contingency’ calculation, such as the P90 or P95. Whilst a high percentile, the P90 (say) should still be exceeded 10% of the time! If your projects never go over this percentile then either there are some major mitigating factors not included in the model or the volatility is being consistently overstated. Likewise, the P10 for total cost (these ‘good’ percentiles are rarely if ever reported or considered in project cost estimation work) should be bettered in roughly 10% of projects. If this is not the case then the upside risk has been overstated. This may be due to misconceptions about the positive skewing present in most cost/delay risks or mistakes made in the parameterisation of the risks where the estimate (“most likely” etc.) is actually the “best case” or close to it, rather than a central tendency of the process over time. There could also be other possibilities.
No matter how you look at it, the collection and intelligent use of historical data is integral to effective and useful risk analysis and management, and critical to achieving valid Monte Carlo simulation results. If you aren’t currently recording everything you can get your hands on start right now!
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