On the use of schedule risk analysis for project management

The purpose of this paper is to give an overview on the existing literature and recent developments on the research on Schedule Risk Analysis (SRA) in Project Management (PM) to measure the sensitivity of activities and resources in the project network. SRA is a technique that relies on Monte-Carlo simulation runs to analyze the impact of changes in activity durations and costs on the overall project time and cost objectives. First, the paper gives an overview of the most commonly known sensitivity metrics from literature that are widely used by PM software tools to measure the time and cost sensitivity of activities as well as sensitivity for project resources. Second, the relevance of these metrics in an integrated project control setting is discussed based on some recent research studies. Finally, a short discussion on the challenges for future research is given. All sections in this paper are based on research studies done in the past for which references will be given throughout the manuscript

An overview of recent research results and future research avenues using simulation studies in project management

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented

Dynamic scheduling: integrating schedule risk analysis with earned value management

The topic of this paper is dynamic project scheduling to illustrate that project scheduling is a dynamic process that involves a continuous stream of changes and is a never ending process to support decisions that need to be made along the life of the project. The focus of this paper lies on three crucial dimensions of dynamic scheduling which can be briefly outlined along the following lines: (i) Baseline scheduling to construct a timetable that provides a start and end date for each project activity, taking activity relations, resource constraints and other project characteristics into account, and aiming to reach a certain scheduling objective, (ii) risk analysis to analyze the strengths and weaknesses of your project schedule in order to obtain information about the schedule sensitivity and the possible changes that undoubtedly occur during project progress and (iii) project control to measure the (time and cost) performance of a project during its progress and use the information obtained during the scheduling and risk analysis steps to monitor and update the project and to take corrective actions in case of problems. The focus of the current paper is on the importance and crucial role of the baseline scheduling component for the two other components, and the integration of the schedule risk and project control component in order to support a better corrective action decision making when the project is in trouble

Welcome to OR&S! Where students, academics and professionals come together

In this manuscript, an overview is given of the activities done at the Operations Research and Scheduling (OR&S) research group of the faculty of Economics and Business Administration of Ghent University. Unlike the book published by [1] that gives a summary of all academic and professional activities done in the field of Project Management in collaboration with the OR&S group, the focus of the current manuscript lies on academic publications and the integration of these published results in teaching activities. An overview is given of the publications from the very beginning till today, and some of the topics that have led to publications are discussed in somewhat more detail. Moreover, it is shown how the research results have been used in the classroom to actively involve students in our research activities

On the use of empirical or artifical project data

This paper gives a brief overview of the artificial and empirical project data generated and collected by the researchers from the Operations Research and Scheduling (OR&S) group from Ghent University in Belgium. Artificial data are generated by project network generators under a strict design to control both the network structure and the resource constraints, while the empirical project data are collected over a time horizon of multiple years, using a standardized collection and classification method. All data are publicly available on the OR&S website www.projectmanagement.ugent. be/research/data) and can be used anywhere for academic purposes. More detailed information on the network and resource parameters used to generate the artificial data and the classification process for the collection of empirical data is available in a paper published in the Journal of Modern Project Management (Vanhoucke et al., 2016)

Project regularity : development and evaluation of a new project characteristic

The ability to accurately characterize projects is essential to good project management. Therefore, a novel project characteristic is developed that reflects the value accrue within a project. This characteristic, called project regularity, is expressed in terms of the newly introduced regular/irregular-indicator RI. The widely accepted management system of earned value management (EVM) forms the basis for evaluation of the new characteristic. More concretely, the influence of project regularity on EVM forecasting accuracy is assessed, and is shown to be significant for both time and cost forecasting. Moreover, this effect appears to be stronger than that of the widely used characteristic of project seriality expressed by the serial/parallel-indicator SP. Therefore, project regularity could also be useful as an input parameter for project network generators. Furthermore, the introduction of project regularity can provide project managers with a more accurate indication of the time and cost forecasting accuracy that is to be expected for a certain project and, correspondingly, of how a project should be built up in order to obtain more reliable forecasts during project control

A statistical method for estimating activity uncertainty parameters to improve project forecasting

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)

Classroom expriments on project management communication

This manuscript gives a brief overview of three sets of experiments in the classroom with students following a Project Management (PM) course module using a blended learning approach. The impact of communication on the student performance using business games as well as the advantages of the use of integrative case studies and their impact on the learning experience of these students are tested. The performance of students is measured by their quantitative output on the business game or case exercise, while their learning experience is measured by the student evaluations. The experiments have been carried out on a sample of students with a different background, ranging from university students with or without a strong quantitative background but no practical experience, to MBA students at business schools and PM professionals participating in a PM training. The results have been presented at an international workshop on computer supported education in Lisbon (Portugal) in 2015 and details have been published in Vanhoucke and Wauters (2015)

A new approach to minimize the makespan of various resource-constrained project scheduling problems

Conferência realizada de 30 de março a 2 de abril de 2014This abstract presents a new solution approach to solve the resource-constrained project scheduling problem in the presence of multiple modes with mode identity constraints and two types of logical constraints. Apart from the traditional AND constraints with minimal time-lags, these precedences are extended to OR constraints. These logical constraints extend the set of relations between pairs of activities and make the RCPSP definition somewhat different from the traditional RCPSP research topics in literature. It is known that the RCPSP with AND constraints, and hence its extension to OR constraints, is NP-hard