Decision Support for Project Management by using Diagnostic Inference and Explaining-Away in Bayesian Networks

2020-05-29
Yet, Barbaros
Pişirir, Erhan
Risk and uncertainty are natural elements of projects. A project manager has to manage these elements effectively to improve the chance of project success. Yet, well-known biases and heuristics about decision-making under uncertainty limit a project manager's ability to deal with these elements, especially for complex projects, and decision support tools can be helpful for this task. This paper focuses on building a decision support model for reasoning with uncertainty in project control. We propose a Bayesian Network (BN) model that uses the notions of diagnostic reasoning and 'explaining-away' to infer the effort requirements, progress and risks of project tasks. The proposed model aids the project manager in reasoning with uncertainty and risk factors when monitoring project progress. We use a project example to demonstrate the model structure, its use, benefits and limitation compared to conventional project control tools.
Citation Formats
B. Yet and E. Pişirir, “Decision Support for Project Management by using Diagnostic Inference and Explaining-Away in Bayesian Networks,” Zaragoza, İspanya, 2020, p. 131, Accessed: 00, 2021. [Online]. Available: https://icdsst2020.files.wordpress.com/2020/05/icdsst-2020-proceedings.pdf.