Most Construction Risk analysis tools available today stress a stochastic approach to modeling risk and offer an alternate deterministic approach that is discretized to Best-case, Worst-case, and Likely. The scant deterministic approach can hardly model real projects risks. The vague stochastic approach, by itself, does not begin to model real projects either.
The lack of proper tools has derailed construction risk analysis. It is not logical to assume that productivity profile risk is similar to weather risk. While there may be some isolated success stories, and while there are usually other contributing factors, the overwhelming majority of large and complex projects risk analysis fail to identify, model, and analyze risks. The result is almost always delayed projects that cost a lot more than originally contemplated.
Estimating, design, procurement, permits, weather, and latent conditions are among the major factors that are blamed for delays and cost overruns. Can anyone seriously contemplate modeling estimating risks based on random probabilistic model and expect useful results? Modeling weather risks as best-case, worst-case, and likely scenarios would be equally as irrational.
Most productivity-related labor risk is quantitative by its very nature. Organizations typically maintain a good history for the various crew production rates. Such data deserves better modeling than a purely stochastic approach, or a fatally over-simplified deterministic one.
Proper modeling dictates a hybridized approach that enhances the quantitative component while allowing a mixed calculation mode with dials and switches for the risk analyst to manage the process.
I am hoping to prioritize writing a full article detailing the enhanced hybrid quantitative model I proposed.
