An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> If Time Is <s> Reliable forecasting is instrumental in successful project management. In order to ensure the successful completion of a project, the project manager constantly monitors actual performance and updates the current predictions of project duration and cost at completion. This study introduces a new probabilistic forecasting method for schedule performance control and risk management of on-going projects. The Bayesian betaS-curve method (BBM) is based on Bayesian inference and the beta distribution. The BBM provides confidence bounds on predictions, which can be used to determine the range of potential outcomes and the probability of success. Furthermore, it can be applied from the outset of a project by integrating prior performance information (i.e., the original estimate of project duration) with observations of new actual performance. A comparative study reveals that the BBM provides, early in the project, much more accurate forecasts than the earned value method or the earned schedule method and as accurate forecasts as the critical path method without analyzing activity-level technical data. <s> BIB001 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> If Time Is <s> The earned value method (EVM) is recognized as a viable method for evaluating and forecasting project cost performance. However, its application to schedule performance forecasting has been limited due to poor accuracy in predicting project durations. Recently, several EVM-based schedule forecasting methods were introduced. However, these are still deterministic and have large prediction errors early in the project due to small sample size. In this paper, a new forecasting method is developed based on Kalman filter and the earned schedule method. The Kalman filter forecasting method (KFFM) provides probabilistic predictions of project duration at completion and can be used from the beginning of a project without significant loss of accuracy. KFFM has been programmed in an add-in for Microsoft Excel and it can be implemented on all kinds of projects monitored by EVM or any other S-curve approach. Applications on two real projects are presented here to demonstrate the advantages of KFFM in extracting additional information from data about the status, trend, and future project schedule performance and associated risks. <s> BIB002 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> If Time Is <s> Meant to complement rather than compete with the existing books on the subject, this book deals with the project performance and control phases of the project life cycle to present a detailed investigation of the project’s time performance measurement methods and risk analysis techniques in order to evaluate existing and newly developed methods in terms of their abilities to improve the corrective actions decision-making process during project tracking. As readers apply what is learned from the book, EVM practices will become even more effective in project management and cost engineering. Individual chapters look at simulation studies in forecast accuracy, schedule adherence, time sensitivity, activity sensitivity and using top-down or bottom-up project tracking. The book also offers an actual real-life case study, a short tutorial on the EVM use in ProTrack, and conclusions on the relative effectiveness for each technique presented. <s> BIB003
Money, Accuracy Pays. The "if time is money, accuracy pays"  statement highlights the relevance and importance of accuracy in the simulation studies presented in this paper. Measuring and improving the accuracy of predictive methods to forecast the final duration of a project in progress using EVM/ES systems is crucial for project control to monitor the project time objectives, and since time is money, also the cost objectives. Recent research efforts in project duration forecasting have focused on improving the accuracy of forecasts by combining the existing EVM/ES forecasting techniques or even by borrowing principles from the traditional forecasting literature and adapting them to a project control setting. Although researchers have recommended combined forecasts for over half a century, their use in a project control environment is relatively new, and it is therefore believed that future research efforts should focus on forecasting improvement techniques. The use of composite forecasting methods or the extensions to, for example, Kalman filter BIB002 or Bayesian inference BIB001 are excellent examples of future research avenues for project duration forecasting. However, the quality of forecasting metrics does not only depend on the average accuracy measured by the sum of absolute or relative errors over all review periods, but it also depends on the stability of the forecasts over the periods. Indeed, when project managers use the periodic forecasts to monitor and control the performance of projects in progress, it is very important to have a reliable value for each period such that actions can be taken based on the well-considered view on the forecasts over the last few periods. Stability is an important aspect in this control process since it avoids overreactions based on a single value for the forecast but instead puts focus on a series of forecasts having similar (stability) and reliable (accuracy) values. Various methods for assessing the stability of forecasts have been discussed in the literature, and an overview would fall outside the scope of this paper. It is however believed that these efforts can and will be used in a project control setting in future research efforts. Research studies to determine which of the two aspects of forecasting quality, accuracy, or stability is most important should be relevant for both academics and professionals. Future research should focus on further improvements of forecasting accuracy and stability and the possible trade-offs between these two quality dimensions. Stability studies in project management are not new. Studies on cost forecasting using EVM have been done by Christensen and Heise  , and Christensen and Payne  , among others. Time forecasting stability studies using ES are relatively new and are done by Henderson and Zwikael  . A third possible extension and future research direction in a project control setting is the relevance and importance of the baseline schedule. Since all EVM/ES performance metrics and forecasts are measured relative to the baseline schedule, the quality of the baseline schedule could be an important driver for forecasting accuracy/stability. The connection between the baseline schedule and the EVM/ES methodology can be analysed by a relatively new concept, known as the so-called schedule adherence and could potentially play an important role in this future research direction. The concept of schedule adherence is originally proposed by Lipke  as a simple extension of the earned schedule method resulting in the so-called p-factor. The p-factor is defined as the portion of earned value accrued in congruence with the baseline schedule, that is, the tasks which ought to be either completed or in progress. The rationale behind this new measure is that performing work not according to the baseline schedule often indicates activity impediments or is likely a cause of rework. The basic assumption behind this new approach lies in the idea that whenever impediments occur (activities that are performed relatively less efficiently compared to the project progress), resources are shifted from these constrained activities to other activities where they could gain earned value. However, this results in a project execution which deviates from the original baseline schedule. Consequently, this might involve a certain degree of risk, since the latter activities are performed without the necessary inputs and might result into a certain portion of rework. Up to today, the concept has not passed the test of logic yet, and future research will indicate whether it has merit in a control setting. To the best of our knowledge, the concept has only been preliminary analysed and investigated in a simulation study published in Vanhoucke BIB003  .