Forecasts

Purpose

Forecasts are used to assess potential future business performance. They are predictions as to the outcomes of a project. They provide project managers and their management insight on how well the project is expected to perform in terms of cost, schedule, or overall deliverable performance. They also identify the range of possible outcomes for planning purposes.

Application

Because of their varied applications, forecasts may take on a variety of forms. They may provide a graphic perspective on the likelihood of completion within a given time frame. They may also include information from a statistical sampling or Monte Carlo analysis, providing information on the number of samples that met a certain time or cost projection, or they may simply be statements of intended or expected outcomes, highlighting the elements that may have the greatest direct impact on the accuracy of the forecast.

Content

The content sources for forecasts are dependent on the type of forecast in question. For statistical analyses and projections, Monte Carlo analyses and other forecasting tools may be used. Tools like Computer Associates’ Risk-Plus or Palisades’ @Risk develop the curves based on the available project and statistical information on a task-by-task or work package-by-work package basis. Monte Carlo tools process the individual task information and create a series of samples to determine the relative likelihood of particular outcomes. Although only one outcome is possible, Monte Carlo generates hundreds or thousands of samples to provide a curve illustrating the time or cost targets with the highest probability of occurrence.

For less statistically driven analyses, projections are often provided by individuals performing the work or by those familiar with the nature of the work and who have assessed progress to date. In addition to time and cost projections, these analyses may also incorporate some assessment of project performance and deliverable performance, particularly because it may vary from the original assessments and projections. This information is frequently provided by individuals who understand the nature of the work and the progress to date. Anyone providing performance information should be intimately familiar with customer expectations, the contract (or statement of work or memorandum of understanding), the business case, and the nature of the work completed to date. The performance aspects of a forecast are largely speculative, unless product or project performance is directly tied to measurable criteria, which are linked directly to intermediate deliverables.

Approaches

Forecasts may be narrative and/or graphic in nature and may take a short- or longterm perspective. Because they are predictive by nature, the level of perceived accuracy should be noted to differentiate between wild guesses and information rooted in statistical depth. If the forecasts are provided as a component of a larger reporting requirement, they may include a schedule for the next reassessment. If the forecast is the second or later in a series, it may be accompanied by a previous forecast, coupled with an explanation for any dramatic shifts from previous projections.

Considerations

A forecast is a glance into the “crystal ball” of project performance. Some forecasts are couched in such a way that they sound like absolutes. They are not. They are studied analyses of what may happen, versus what will happen. The data sources, the methodologies, the statistical validity, and the type of analysis will all influence the look, feel, and accuracy of a forecast. As a practical matter, some forecasters choose to present information precisely as it is generated by the tools. Because many tools apply detailed statistics, a forecast completion date (with a specific percentage level of certainty) may be generated down to the day, hour, minute, and second. Budget forecasts from such tools often are presented to the penny. Although such apparent accuracy is impressive, it does not convey the nature of a forecast as a prediction. It creates an impression of certainty where no certainty exists. Such false precision should be considered carefully before the information is incorporated into a forecast.