Keith Melchiors’ article, “Estimating Risk-Based Cost & Contingency” in Pharmaceutical Manufacturing magazine, volume 11, issue 7, is an excellent analytical tool to quantify capital project cost and contingencies. Monte Carlo simulation has been around for a long time, but I don’t think I have ever seen it applied so cleanly and precisely thanks, in part, to the use of spreadsheet technology.The next logical extension of the project ‘go/no-go’ decision is to apply similar analytical tools to the determination of the financial attractiveness -- breakeven point and net present value (NPV) -- of the project. Estimating the cost is only half of the equation. Estimating the benefit of the investment is the other half. One could adapt Melchiors’ Monte Carlo tool almost directly to the problem of estimating benefit with a statistically calculated level of certainty.However, in the real world, human emotions many times trump analytical tools. The project developer’s hand waving and promises of how great it’s gonna be two, three, four years down the road can foster an unsubstantiated euphoria that leads to a bad decision. Likewise, management’s reluctance to prosecute a project because of some preconceived notions -- such as previous track record of the developer, out of the manager’s comfort zone, not-invented-here syndrome, detraction from other pet projects, etc. -- can kill a perfectly attractive project.The challenge then is to obtain a bias-free consensus on the benefit in the investment that would be realized if the project is implemented. The decision maker, the ‘go/no-go’ person, is human. He/She has opinions and, in fact, was promoted into his/her management position to exert some sage wisdom into decisions. Unless the manager is a mathematician or statistician, a pure, quantitative proposition typically won’t fly. The decision manager needs to emotionally buy into the project and take some ownership for its success.Monte Carlo simulation and random numbers can be too far out of the decision maker’s comfort zone; and, in today’s fast pace world, he/she may not want to take the time to get up to speed on what he/she might consider statistical snake oil. This could make him/her more prone to say ‘no.’ A project has a better chance of success if the manager not only approves it but buys into it. Therefore, as Machiavellian as it may appear, a project developer must convince the ‘go/no-go’ manager within the manager’s aegis that the project should be a ‘go.’In a similar spirit of recognizing uncertainty and statistically quantifying it, I have developed a spreadsheet for assessing benefit that follows much of Melchiors' Monte Carlo methodology, but uses personal inputs from the project developer and the go/no-go manager rather than a random number generator to evaluate different scenarios.