In recent years, a variety of novel approaches for fulfilling the important management task of accurately forecasting project duration have been proposed, with many of them based on the earned value management (EVM) methodology. However, these state-of-the-art approaches have often not been adequately tested on a large database, nor has their validity been empirically proven. Therefore, we evaluate the accuracy and timeliness of three promising deterministic techniques and their mutual combinations on a 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 and is appropriately called earned duration management or EDM(t). The results indicate that all three of the considered techniques are relevant. More concretely, the two EVM extensions exhibit accuracy-enhancing power for different applications, while EDM(t) performs very similar to the best EVM methods and shows potential to improve them.
This paper presents an overview of the existing literature on project control and earned value management (EVM), aiming at fulfilling three ambitions. First, the journal selection procedure allows to discern between high-quality journals and more popular business magazines. Second, the collected papers on project control and EVM, published in the selected journals, are classified based on a framework consisting of six distinct classes. Third, the classification framework indicates current trends and potential areas for future research, which can be summarized as follows: (i) increased attention to the stochastic nature of projects, (ii) enhanced validation of the proposed methodology using a large historical dataset or a simulation experiment, (iii) expansion of integrated control models, focusing on time and cost as well as other factors such as quality and sustainability, and (iv) development and validation of corrective action procedures.
In this paper, the authors focus on the stability of earned value management (EVM) forecasting methods. The contribution is threefold. First of all, a new criterion to measure stability that does not suffer from the disadvantages of the historically employed concept is proposed. Second, the stability of time and cost forecasting methods is compared and contrasted by means of a computational experiment on a topologically diverse data set. Throughout these experiments, the forecasting accuracy is reported as well, facilitating a trade-off between accuracy and stability. Finally, it is shown show that the novel stability metric can be used in practical environments using two real-life projects. The conclusions of this empirical validation are found to be largely in line with the computational results.
This Handbook was the first APM Body of Knowledge Approved title for the Association for Project Management. Over the course of five editions, Gower Handbook of Project Management has become the definitive desk reference for project management practitioners. The Handbook gives an introduction to, and overview of, the essential knowledge required for managing projects.
In this paper, 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 and on the earned value management (EVM) forecasting accuracy. Moreover, the database is publicly available and can thus become the basis for many future studies related to project management, of which a few are suggested in this paper. To further support these studies, the database will continuously be extended utilizing the project cards. Furthermore, the project cards can also serve didactical purposes.
Martens, Annelies; Vanhoucke, Mario (Elsevier, 2018)
The goal of project control is monitoring the project progress during project execution to detect potential problems and taking corrective actions when necessary. Tolerance limits are a tool to assess whether the project progress is acceptable or not, and generate warnings signals that act as triggers for corrective action to the project manager. In this paper, three distinct types of tolerance limits that have been proposed in literature are validated on a large and diverse set of real-life projects mainly situated in the construction sector. Moreover, a novel approach to construct tolerance limits that integrate the project risk information into the monitoring process is introduced. The results of the empirical experiment have shown that integrating project-specific information into the construction of the tolerance limits results in a higher efficiency of the monitoring process. More specifically, while including cost information increases the efficiency only marginally, incorporating the available resource information substantially improves the efficiency of the monitoring process. Furthermore, when projects are not restricted by scarce resources, the efficiency can be enhanced by integrating the available project risk information.
Batselier, Jordy; Vanhoucke, Mario (Springer, 2017)
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.
In this paper, we provide a Nearest Neighbour based extension for project control forecasting with Earned Value Management. The k-Nearest Neighbour method is employed as a predictor and to reduce the size of a training set containing more similar observations. An Artificial Intelligence (AI) method then makes use of the reduced training set to predict the real duration of a project. Additionally, we report on the forecasting stability of the various AI methods and their hybrid Nearest Neighbour counterparts. A large computer experiment is set up to assess the forecasting accuracy and stability of the existing and newly proposed methods. The experiments indicate that the Nearest Neighbour technique yields the best stability results and is able to improve the AI methods when the training set is similar or not equal to the test set. Sensitivity checks vary the amount of historical data and number of neighbours, leading to the conclusion that having more historical data, from which the a relevant subset can be selected by means of the proposed Nearest Neighbour technique, is preferential.
Monitoring the performance of projects in progress and controlling their expected outcome by taking corrective actions is a crucial task for any project manager. Project control systems are in use to quantify the project performance at a certain moment in time, and allow the project manager to predict the expected outcome if no action is taken. Consequently, these systems serve as mechanism that provide warning signals that tell the project manager when it is time to take corrective actions to bring the expected project outcome back on track. In order to trust these generated warning signals, the project manager has to set limits on the provide performance metrics that serve as thresholds for these actions.
This paper gives an overview of different approaches discussed in the literature to control projects using such actions thresholds. First and foremost, the paper discusses three classes of actions thresholds,ranging from very easy-to-use rules-of-thumb to more advanced statistical project control methodologies. Each of these tools have been the subject to research studies, each of which aim at showing their power to predict project problems during its progress. In addition, the paper will emphasize the fundamental different between statistical project control using tolerance limits and statistical process control for projects. Finally, three different quality metrics to evaluate the performance of such control methods are presented and discussed.
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