Forecasting Accuracy of In-Progress Activity Duration and Cost Estimates

Abstract

When a project activity has already started, tracking information such as percentage complete and current activity duration and cost can be easily retrieved. This information can be used to update the project schedule to anticipate the eventual project duration and cost more precisely. But hardly any studies analyzed how more accurate or reliable activity tracking information can be compared with the initial (planned) estimates, let alone which mathematical forecasting expressions are the most accurate. This paper quantified forecasting accuracy by extracting over 3,000 activities with partial tracking information (i.e., those which have already started but are not yet complete) from a real project data set. Two expressions for forecasting the activity duration and cost were tested by comparing their performance with initial (planned) and final (actual) values. The contributions to the body of knowledge are fourfold. First, it was shown that activity tracking information considerably outperforms planned estimates. Second, using two expressions can significantly minimize the deviations of time and cost estimates. Third, remaining activities’ duration and cost estimates can be closely modeled with log-normal distributions as a function of the activities’ percentage complete. Fourth, variability decreases linearly as activities approach their end. These findings allow project managers to better anticipate and model the duration and cost variability of ongoing activities and to improve the forecasting accuracy of the project duration and cost estimates.