Empirical evaluation of existing and novel approaches for project forecasting and control

For a project manager, it is essential to be able to produce reliable forecasts of the eventual duration and cost of the project in progress, and furthermore, to obtain meaningful metrics that describe the current project performance and can trigger well-informed corrective actions. Therefore, six studies have been performed on this subject that constitute the main body of the dissertation. A brief overview of every study is now provided. In a first study, a real-life project database is created, outranking the existing empirical databases from project management literature in both size and diversity. To ensure the quality of the added project data, a database construction and evaluation framework based on the so-called project cards is developed. These project cards incorporate the concepts of dynamic scheduling and introduce two novel evaluation measures for the authenticity of project data. Furthermore, an overview of the constructed database leads to statements on the difference between planned and actual project performance. In a second study, the accuracy of the most commonly used earned value management (EVM) time and cost forecasting methods is evaluated on the constructed real-life database. The desired real forecasting outcomes based on the actual project progress data are also supported by a Monte Carlo simulation study. It is demonstrated that highly accurate time and cost forecasts can be obtained by applying the EVM methodology. Furthermore, the best performing forecasting methods for the projects in the considered database are identified, also taking into account timeliness and the influence of the project network structure. In a third study, we evaluate the accuracy and timeliness of three promising EVM-based deterministic techniques and their mutual combinations on the real-life project database. More specifically, two techniques respectively integrate rework and activity sensitivity in EVM time forecasting as extensions, while a third innovatively calculates schedule performance from time-based metrics. The results indicate that all three of the considered state-of-the-art techniques are valuable. In a fourth study, 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 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. In a fifth study, the reference class forecasting (RCF) technique is compared with the most common traditional project forecasting methods, such as those based on Monte Carlo simulation and EVM. RCF bypasses human judgment by basing forecasts on the actual outcomes of past projects similar to that being forecasted, whereas project managers traditionally produce cost and time forecasts by predicting the future course of specific events. The conducted evaluation is entirely based on real-life project data and shows that RCF indeed performs best, for both cost and time forecasting, and therefore supports the practical relevance of the technique. In a sixth study, EVM is integrated with the exponential smoothing forecasting approach. This results in an extension of the known EVM cost and time forecasting formulas. A clear correspondence between the established approaches and the newly introduced method - called the XSM - is identified, which could facilitate future implementation. Additionally, the RCF technique can be incorporated into the XSM. Results from a series of real-life projects show that, for both time and cost forecasting, the XSM exhibits a considerable overall performance improvement with respect to the most accurate project forecasting methods identified by previous research, especially when incorporating the RCF concept.

Publication year:2016

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