A comparison of activity ranking methods for taking corrective actions during project control

Abstract

Monitoring and controlling projects in progress is key to support corrective actions in case of delays and to deliver these projects timely to the client. Various project control methodologies have been proposed in literature to include activity variability in the project schedule and measure the performance of projects in progress. Much of these studies rely on a schedule risk analysis to rank activities according to their time sensitivity and expected impact on the total project duration. This paper compares two classes of activity ranking methods to improve the corrective action process of projects under uncertainty. Each method ranks activities based on certain criteria and places the highest ranked activity in a so-called action set that is then used to take certain corrective actions. The first method is the analytical based ranking method which relies on exact or approximate analytical calculations to provide a ranking of activities. This analytical ranking method will be compared with a second simulation-based ranking method that relies on Monte Carlo simulations to measure the sensitivity of each activities. Results on a set of artificial projects show that the analytical ranking method and one specific simulation-based ranking outperform all other methods, not only for predicting the contribution of actions on the expected project duration and its variability, but also in the efficiency of the project manager’s control.