Bayesian-based Project Monitoring: Framework Development and Model Testing
Keywords:Bayesian Networks, Risk Register, Risk Factors, Project Monitoring
AbstractDuring project implementation, risk becomes an integral part of project monitoring. Therefore. a tool that could dynamically include elements of risk in project progress monitoring is needed. This objective of this study is to develop a general framework that addresses such a concern. The developed framework consists of three interrelated major building blocks, namely: Risk Register (RR), Bayesian Network (BN), and Project Time Networks (PTN) for dynamic project monitoring. RR is used to list and to categorize identified project risks. PTN is utilized for modeling the relationship between project activities. BN is used to reflect the interdependence among risk factors and to bridge RR and PTN. A residential development project is chosen as a working example and the result shows that the proposed framework has been successfully applied. The specific model of the development project is also successfully developed and is used to monitor the project progress. It is shown in this study that the proposed BN-based model provides superior performance in terms of forecast accuracy compared to the extant models.
Anbari, F, Earned Value Method and Extension, Project Manage Journal, 34(4), 2003, pp. 12-23.
Arizaga, J.F., A Methodology for Project Risk Analysis Using Bayesian Belief Networks Within A Monte Carlo Simulation Environment, University of Maryland, USA, 2007.
Ayuningtyas, R., and Hartono, B., Project Monitoring using Bayesian Networks: Framework and Model, Paper presented at the 5th AUN/SEED-net Regional Conference on Manufacturing Engineering & the 1st Human Factors and Ergonomics Society of the Philippines Conference, De LaSalle University, Manila, Philippines, 2013
Gardoni, P., Reinschmidt, K.F., and Kumar, R., A Probabilistic Framework for Bayesian Adaptive Forecasting of Project Progress, Zachry Department of Civil Engineering, Texas A&M University, USA, 2007.
Jacob, D., Forecasting Project Schedule Completion with Earned Value Metrics, The Measurable News 3, 2003, pp. 7-9.
Lipke, W., Schedule is Different, The Measurable News 3, 2003, pp. 31-34.
PMI, Guide to Project Management Body of Knowledge 2000 ed. Pennsylvania, Project Management Istitute, USA, 2000.
Standish Group, Chaos Report, The Standish Group International Inc., Boston, 1995.
Vandervoorde, S., amd Vanhoucke, M., A Comparison of Different Project Duration Forecasting Methods Using Earned Value Metrics, International Journal of Project Management, 24, 2006, pp. 289-302.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).