Evaluating Benefits: Getting Statistical (Part 1)
William lives and works as an IT project manager in South Florida, and is a member of the South Florida chapter of PMI. He created StatisticalPERT.com.
“The new order-entry system will increase revenue by $1M in the first month of operation through a new add-on sales capability.”
Suppose that is the first, summarized benefit listed in in a benefits analysis report. The proposed investment is a new web-based, order-entry system for clients wanting to purchase products from your organization. As you ponder this benefit, what is your first thought?
You ought to be suspicious.
And it has nothing specifically to do with the nicely rounded $1M figure. In fact, the report’s details might demonstrate reasonable assumptions and sound, quantitative methods used to calculate the projected $1M increase in revenue. Why, then, should you be suspicious?
Because most quantitative benefits are not modeled using a binomial distribution, where there are only two possible outcomes; rather, most quantitative benefits are continuous and probabilistic in nature, having many possible outcomes--and each potential outcome has its own, corresponding probability of occurrence.
Benefits Uncertainty
Examine that benefit summary once more: “The new order-entry system will increase revenue by $1M.” It describes with certainty that if the organization proceeds with the proposed investment of a new order-entry system, it will generate $1M in new revenue in the first month of operation.
This is like an example …
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