Making Assumptions

Not long after data gathering has started, you may realize certain information is unavailable or perhaps unreliable. Complete, accurate, and up-to-date data for all the information needed is rarely obtainable, especially when modeling a new system about which very little is known. For system elements about which little is known, assumptions must be made. There is nothing wrong with assumptions as long as they can be agreed upon, and it is recognized that they are only assumptions. Any design effort must utilize assumptions where complete or accurate information is lacking.

Many assumptions are only temporary until correct information can be obtained or it is determined that more accurate information is necessary. Often, sensitivity analysis, in which a range of values is tested for potential impact, can give an indication of just how accurate the data really needs to be. A decision can then be made to firm up the assumptions or to leave them alone. If, for example, the degree of variation in a particular activity time has little or no impact on system performance, then a constant activity time may be used. Otherwise, it may be important to define the exact distribution for the activity time.

Another approach in dealing with assumptions is to run three different scenarios showing a "best-case" using the most optimistic value, a "worst-case" using the most pessimistic value, and a "most-likely-case" using a best-estimate value. This will help determine the amount of risk you want to take in assuming a particular value.