اندازه گیری بهره وری کنترل پروژه با استفاده از داده های پروژه های خیالی و تجربی

Dynamic scheduling refers to the integration of three important phases in the life cycle of a project: baseline scheduling, schedule risk analysis and project control. In this paper, the efficiency of controlling a project is measured and evaluated using a Monte-Carlo simulation study on fictitious and empirical project data. In the study, the construction of a project baseline schedule acts as a point-of-reference for the schedule risk analysis and project control phases. The sensitivity information obtained by the schedule risk analyses (SRA) and the earned value management (EVM) information obtained during project control serve as early warning control parameters that trigger corrective actions to bring projects back on track in case of problems. The focus in this paper lies on the time performance of a project, and not on the prediction and controlling of the project costs. The contribution of this paper is twofold. First, this paper summarizes the main conclusions of various experiments performed in a large simulation study on the efficiency of project control techniques and the ability to trigger corrective actions in case of project problems. The main purpose of these simulation experiments is to understand why EVM and/or SRA work so well in some projects and fail so miserably in others. This study has been awarded by the International Project Management Association in 2008 on the IPMA world congress in Rome (Italy). Secondly, the paper compares the results obtained on fictitious project data with additional tests performed on a set of real-life data from 8 Belgian companies from various sectors

Project scheduling began as a research track within the mathematical field of Operations Research in order to mathematically determine start and finish times of project activities subject to precedence and resource constraints while optimizing a certain project objective (such as lead-time minimization, cash-flow optimization, etc.). The initial research done in the late 1950s mainly focused on network based techniques such as CPM (Critical Path Method) and PERT (Programme Evaluation and Review Technique) which are still widely recognized as important project management tools and techniques. From this moment on, a substantial amount of research has been carried out covering various areas of project scheduling (e.g. time scheduling, resource scheduling, cost scheduling). Today the project scheduling research continues to grow in the variety of its theoretical models, in its magnitude and in its applications. While the research has expanded over the last decennia, leading to project scheduling models with deterministic and stochastic characteristics, single- and multi-mode execution activities, single and multiple objectives, and a wide variety of resource assumptions, the practitioners and software tools mainly stick to the often basic project scheduling principles. This can probably be explained by the limited capability of a project schedule to cope with the uncertainty that characterizes the real life execution of the project. Indeed, the benefits of a resource-constrained project schedule have been questioned by many practitioners, and the effort someone puts into the development of a project schedule is often not in line with the benefits. Moreover, “a project schedule will change anyway due to circumstances” is often a widely used excuse to skip this important step in the project life cycle. Nevertheless, project scheduling plays a crucial role in the life cycle of a project and can play a key role in the failure or success of a project. In order to appreciate the importance of a project schedule, it should be generally accepted that the usability of a project schedule is rather limited and only acts as a point of reference in the project life cycle. Consequently, a project schedule should especially be considered as nothing more than a predictive model that can be used for resource efficiency calculations, time and cost risk analysis, project control and performance measurement, and so on. In this paper, the project baseline schedule is assumed to be given and plays a central reference point in the analysis of a project schedule's risk and during the project performance measurement and control phase. This triangular relation between the project schedule, the risk analysis and the project control phase is called dynamic scheduling throughout this paper. The purpose and contribution of this paper is twofold. First, it aims at reviewing previous research efforts on project management control using schedule risk analysis and earned value management. Although various papers have described these two techniques, little effort has been done to put these techniques in a dynamic project control environment. To that purpose, a review on research results will be given in order to filter out practical rules-of-thumb interesting for project managers. Secondly, it lies in the intention of this paper to illustrate that both fictitious and empirical data are used to validate the obtained results. It should be noted that this paper takes a strict time focus on the project and hence little or no attention is paid to the dynamic nature of cost performance measurements and predictions. The following sections of this paper are structured as follows: Section 2 gives an overview of the three dimensions of dynamic project scheduling and refers to important research sources in literature. In a first subsection, an overview is given of the main measures used in a schedule risk analysis (SRA) study and references to recent studies are highlighted. A second subsection deals with the project control dimension which makes use of earned value management (EVM) parameters to measure a project's time performance. The recent and renewed attention on EVM for measuring and controlling the duration of a project is briefly summarized in this section. In a third subsection, the use of the SRA and EVM information as early warning control parameters to trigger corrective actions are explained in detail. In a final subsection, three research hypotheses are formulated that will be tested in the computational experiment of this paper. Section 3 describes the input parameters of the computational experiment using Monte-Carlo simulations and gives an overview of the main conclusions that can be drawn on the efficiency of controlling projects and their corrective actions. These results are obtained on both fictitious project data as well as on an empirical data set containing project information of 8 Belgian companies. Section 4 draws general conclusions and highlights paths for future research.

Project management in general and project control and performance measurement in particular have a long history in the academic research literature. The earned value management methodology, originally developed for DoD projects, has received academic attention since its development and has received, with the development of some recent extensions such as Earned Schedule (Lipke, 2003), an increasing attention the last seven years. Schedule risk analysis using Monte-Carlo simulations has been used widely in academic as well as practical environments and can be used to refine the black-and-white view on the critical path by measuring the sensitivity of project activities and their potential impact on the project objective. This paper gives the construction of a baseline schedule for a project a central place for the integration of a schedule risk analysis with an earned value management control approach in order to efficiently control projects and to accurately trigger corrective actions in case the project performance drops below a predefined threshold. This paper gives an integrative review of different studies on the efficiency of project management control. Moreover, results from an IPMA award winning simulation study on fictitious project data are compared with empirical data obtained from 8 Belgian companies. The simulation experiments have clearly shown that information obtained during the scheduling step (baseline plan) as well as sensitivity information obtained by SRA and project performance evaluation obtained through EVM should allow the project manager to improve the project control process and the corrective action decision making process. The main conclusions of the computational experiments can be summarized as follows. First, it has been shown that the topological structure of a project network is a main driver for the variability and efficiency of the project control phase. This topological information can be easily and automatically calculated by any software tool, and based on this information, the software tool can automatically propose suggestions to the user. An example is given in Vanhoucke and Shtub (2011) where the project management tool ProTrack is extended by an assistant that generates tips and hints to users based on topological project information. Secondly, it has been shown that the efficiency of project control can be significantly improved by combining a top–down and a bottom–up project control approach. The efficiency is measured as a combination of effort a project manager has to put in the control process and the return the project manager gets when taking corrective actions. Clearly, the results show that both a top–down and bottom–up approach allow the project manager to set action threshold, and hence to reduce effort, while improving the positive return of his/her actions. Finally, thanks to the observation that the results for simulation experiments on a large and controlled fictitious data set are confirmed by results on a small set of empirical data, we believe that this increases the credibility of this research study. Given the importance to understand and improve the corrective action decision making process in a dynamic project control environment, future research lines should be set up. It lies in our intentions to focus on two funded research lines. First, a deeper knowledge on the impact of variability on the project objective is necessary to further enhance our understanding on activity sensitivity information. Secondly, further research should extend the current body of knowledge on project performance measurements with statistical techniques borrowed from the process engineering industry and based on principles taken from Statistical Process Control (SPC). The increasing computer power and automated measurement techniques have led to the development of statistical control techniques able to cope with a large amount of variables within a reasonable time, possibly leading to an increase in the overall project management control efficiency.