An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Statistical Project Control. <s> This slide presentation reviews the use of a Statistical Project Control Tool (SPCT) for managing engineering projects. A literature review pointed to a definition of project success, (i.e., A project is successful when the cost, schedule, technical performance, and quality satisfy the customer.) The literature review also pointed to project success factors, and traditional project control tools, and performance measures that are detailed in the report. The essential problem is that with resources becoming more limited, and an increasing number or projects, project failure is increasing, there is a limitation of existing methods and systematic methods are required. The objective of the work is to provide a new statistical project control tool for project managers. Graphs using the SPCT method plotting results of 3 successful projects and 3 failed projects are reviewed, with success and failure being defined by the owner. <s> BIB001 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Statistical Project Control. <s> Cost and schedule measures are the most important support activities for the success of a project; it provides the basis for process improvement and project management. This paper reports practical experiences on using EVM (Earned Value Management) and SPC(Statistical Process Control) in cost/ schedule measure. The analysis of experience data indicates the distributions of CPI(Cost Performance Index) and SPI(Schedule Performance Index) index are generally following the normal distribution. And consequently, it is reasonable and effective to employ SPC in EVM. <s> BIB002 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Statistical Project Control. <s> The earned value management (EVM) method is considered as a powerful tool that supports the management of project scope, time, and cost. However, conventional EVM may cause some project managers to ignore the information that it can provide about the performance variations of their projects as well as their causes and effects. This paper attempts to refine and improve the performance of traditional EVM by the introduction of statistical control chart techniques. Individual control charts are used as tools to monitor project performance data so that adverse changes can be detected in a timely manner. This allows analysis of in-progress project cost and schedule performance variations and highlights possible needs for corrective action. A comparative analysis between traditional EVM and the new approach is undertaken to identify the effectiveness of the new approach. <s> BIB003 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Statistical Project Control. <s> It is well-known that well managed and controlled projects are more likely to be delivered on time and within budget. The construction of a (resource-feasible) baseline schedule and the follow-up during execution are primary contributors to the success or failure of a project. Earned value management systems have been set up to deal with the complex task of controlling and adjusting the baseline project schedule during execution. Although earned value systems have been proven to provide reliable estimates for the follow-up of cost performance, they often fail to predict the total duration of the project. In this chapter, results of a large simulation study to evaluate the forecast accuracy of earned value based predictive metrics are presented. No detailed mathematical calculations are presented in the chapter, but instead an overview from a project life cycle point-of-view is presented. Details can be found at the end of the chapter (key terms and their definitions) or in the references cited throughout this chapter. <s> BIB004 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Statistical Project Control. <s> Project control has been a research topic since decades that attracts both academics and practitioners. Project control systems indicate the direction of change in preliminary planning variables compared with actual performance. In case their current project performance deviates from the planned performance, a warning is indicated by the system in order to take corrective actions. <s> BIB005
The project control approach of this paper is set up to indicate the direction of change in preliminary planning variables, set by the baseline schedule, compared with actual performance during project progress. In case the project performance of projects in progress deviates from the planned performance, a warning is indicated by the system in order to take corrective actions. In the literature, various systems have been developed to measure deviations between planned and actual performance in terms of time and cost to trigger actions when thresholds are exceeded. Although the use of threshold values has been explained in the study of Section 3.4 to trigger the corrective action process, nothing has been said about the probability that real project problems occur once these threshold values are exceeded. Indeed, little research is done on the use and setting of these threshold values and their accuracy to timely detect real project problems. Therefore, it is believed that future research should point to this direction. The vast amount of data available during project progress should allow the project manager to use statistical techniques in order to improve the discriminative power between in-control and out-of-control project progress situations. The use of these socalled Statistical Project Control (SPC) systems should ideally lead to an improved ability to trigger actions when variation in a project's progress exceeds certain predefined thresholds. The use of statistical project control is not new in the literature and has been investigated by Lipke and Vaughn  , Bauch and Chung BIB001 , Wang et al. BIB002 , Leu and Lin BIB003 , and National Research Council  . These papers mainly focus on the use of statistical project control as an alternative for the Statistical Process Control used in manufacturing processes. Despite the fact that both approaches have the same abbreviation SPC, the statistical project control approach should be fundamentally different from the statistical process control BIB005 . Therefore, it is believed that the future research on SPC should go much further than the models and techniques presented in these papers. SPC should be a new approach to control projects based on the analysis of data that is generated before the start of the project (static) as well as during project progress (dynamic) BIB004 . This data analysis should allow the user to set automatic thresholds using multivariate statistics for EVM/ES systems and SRA systems in order to replace the often subjective action thresholds set by project managers based on wild guesses and experience. Fundamental research is therefore crucial to validate the novel statistical techniques to investigate their merits and pitfalls and to allow the development of project control decision support systems based on a sound methodology. Research on this relatively new project control topic is available in Colin and Vanhoucke BIB005  .