Abstract
Just like any physical system, projects have entropy that must be managed by spending energy. The entropy is the project’s tendency to move to a state of disorder (schedule delays, cost overruns), and the energy process is an inherent part of any project management methodology. In order to manage the inherent uncertainty of these projects, accurate estimates (for durations, costs, resources, …) are crucial to make informed decisions. Without these estimates, managers have to fall back to their own intuition and experience, which are undoubtedly crucial for making decisions, but are are often subject to biases and hard to quantify. This paper builds further on two published calibration methods that aim to extract data from real projects and calibrate them to better estimate the parameters for the probability distributions of activity durations. Both methods rely on the lognormal distribution model to estimate uncertainty in activity durations and perform a sequence of statistical hypothesis tests that take the possible presence of two human biases into account. Based on these two existing methods, a new so-called statistical partitioning heuristic is presented that integrates the best elements of the two methods to further improve the accuracy of estimating the distribution of activity duration uncertainty. A computational experiment has been carried out on an empirical database of 83 empirical projects. The experiment shows that the new statistical partitioning method performs at least as good as, and often better than, the two existing calibration methods. The improvement will allow a better quantification of the activity duration uncertainty, which will eventually lead to a better prediction of the project schedule and more realistic expectations about the project outcomes. Consequently, the project manager will be able to better cope with the inherent uncertainty (entropy) of projects with a minimum managerial effort (energy).
Highlights
Project Management is the discipline to manage, monitor and control the uncertainty inherent to projects
This paper starts with the observation that the relation between managerial effort and the ability to reduce the project uncertainty lies at the heart of many research studies, this relation is Entropy 2019, 21, 952; doi:10.3390/e21100952
Just like any physical system, projects have entropy that must be managed by spending energy, and this process of energy is called project management
Summary
Project Management is the discipline to manage, monitor and control the uncertainty inherent to projects. In some research papers that rely on the concept of entropy as a way to express that projects have the natural tendency to move to a state of disorder, authors have referred to the relation between entropy (uncertainty) and energy (effort) They have proposed different entropy measures to enable the project manager to better predict the project uncertainty and eventually reduce it by taking better actions. A new so-called calibration method is proposed that should help project managers to better quantify the project uncertainty by providing better estimates for the activity durations Such calibration procedures are relatively new in the literature, since they rely on a combination of statistical data analysis and the correction for human biases.
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