Models layered with rules and conditions communicate a sophisticated view of the world. Once enhanced with Monte Carlo software simulation capabilities, these spreadsheet models can extract revealing details about the systems being modeled. All statistical analysis models, regardless of sophistication, have limitations. Do we know what they are? Consider: does the model effectively capture all the relevant risks? When were the assumptions last reviewed for validity? Under what situations do the assumptions fail, producing illogical outcomes?
Sensitivity analysis as performed by @RISK gives a view of a model’s dynamics. With it we can extract some idea of expectations and relationships from the model. More information is accessible using advanced simulation tools such as Stress Analysis. Stress analysis on the model is not a de facto catch all for every possible situation; it highlights the possibilities of imaginable situations. Based on knowledge and assumptions of past data, stress scenarios demonstrate a view of negative consequences and possible opportunities. We know all too well that past data has some degree of obsolescence.
While the model communicates a range of possibilities with associated likelihoods, what the model doesn’t tell us is what to do with the information. If we get certain answers, how do we translate it to some action or decision? Decision evaluation criteria need to be established with courses of action – what do we do if the answer is this, vs. that?
Make informed decisions based on the model intelligence, don’t let the model make the decisions.
Palisade Training Team