Only slightly less risky than Russian roulette, the game called RFP can be only slightly less deadly. When a government agency puts out an RFP–request for proposal–and the bids start coming in, its decision makers are then faced with the challenge of selecting the most advantageous bid. On the face of it, this may seem like a no-brainer–look at the costs, the projected results, and pick the most convincing numbers.
But here’s the catch. Each company submitting a proposal is likely to take a different approach to uncertainty, to risk assessment. When it comes to uncertainty, time and money are the two biggies, and making a seamless intersection between the two is a classic problem in operations management. The bidders use different numbers for uncertain values, and so their projections are not easily compared.
To bring competing bids into alignment, government agencies typically develop their own internal bids against which to measure. But until they can put probability into play, they are still stuck with decision making under uncertainty. The results, as the news often reminds us, can be disastrous.
Monte Carlo simulation to the rescue. At least one consulting company is now helping agencies to evaluate proposals using Monte Carlo software that specifies the range of uncertainty with probability distributions that bring time and money into the same plane and produce optimized projections. This makes the choice of proposal–and evaluation of that decision–less risky. Now an agency can compare apples and apples.