Jeroen Colin

A multivariate approach for top-down project control using earned value management

Project monitoring and the related decision to proceed to corrective action are crucial components of an integrated project management and control decision support system (DSS). Earned value management/earned schedule (EVM/ES) is a project control methodology that is typically applied for top-down project schedule control. However, traditional models do not correctly account for the multivariate nature of the EVM/ES measurement system. We therefore propose a multivariate model for EVM/ES, which implements a principal component analysis (PCA) on a simulated schedule control reference. During project progress, the real EVM/ES observations can then be projected onto these principal components. This allows for two new multivariate schedule control metrics (T2 and SPE) to be calculated, which can be dynamically monitored on project control charts. Using a computational experiment, we show that these multivariate schedule control metrics lead to performance improvements and practical advantages in comparison with traditional univariate EVM/ES models. Two multivariate project schedule control metrics are presented using well-known EVM metrics.Principal component analysis is used to build a correlation reference and to test the multivariate metrics.A large simulation study compares the novel control approach with the current best practice.Improvements are obtained in comparison with the traditional project control approach.

A comparison of the performance of various project control methods using earned value management systems

Two new project schedule control procedures using EVM are proposed: EVM-FPB and EVM-SNB.The effort spent by a project manager provides a functional distinction for the discussed project control approaches.The placement of control points in the project is inspired by critical chain/buffer management.Monte Carlo simulations are used to calculate tolerance limits for the control approaches and quantify their performance.The EVM-SNB approach is found to outperform the other project control approaches. Recent literature on project management has emphasised the effort which is spent by the management team during the project control process. Based on this effort, a functional distinction can be made between a top down and a bottom up project control approach. A top down control approach refers to the use of a project control system that generates project based performance metrics to give a general overview of the project performance. Actions are triggered based on these general performance metrics, which need further investigation to detect problems at the activity level. A bottom up project control system refers to a system in which detailed activity information needs to be available constantly during the project control process, which requires more effort. In this research, we propose two new project control approaches, which combines elements of both top down and bottom up control. To this end, we integrate the earned value management/earned schedule (EVM/ES) method with multiple control points inspired by critical chain/buffer management (CC/BM). We show how the EVM/ES control approach is complementary with the concept of buffers and how they can improve the project control process when cleverly combined. These combined top down approaches overcome some of the drawbacks of traditional EVM/ES mentioned in the literature, while minimally increasing the effort spent by the project manager. A large computational experiment is set up to test the approach against other control procedures within a broad range of simulated dynamic project progress situations.

Empirical perspective on activity durations for project-management simulation studies

AbstractSimulation has played an important role in project-management studies of the last decades, but in order for them to produce practical results, a realistic distribution model for activity durations is indispensable. The construction industry often has needed historical records of project executions, to serve as inputs to the distribution models, but a clearly outlined calibration procedure is not always readily available, nor are their results readily interpretable. This study seeks to illustrate how data from the construction industry can be used to derive realistic input distributions. Therefore, the Parkinson simulation model with a lognormal core is applied to a large empirical dataset from the literature and the results are described. From a discussion of these results, an empirical classification of project executions is presented. Three possible uses are presented for the calibration procedure and the classification in project management simulation studies. These were validated using a case ...