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Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to identify critical risks and selecting optimal risk mitigation strategies at the commencement stage of a project. Probabilities were given within the referenced paper (uniform priors were given to root nodes). The vertices are:
Lack of experience with the involved team (YES, NO);
Use of innovative technology (YES, NO);
Lack of experience with technology (YES, NO);
Strict quality requirements (YES, NO);
Multiple contracts (YES, NO);
Multiple stakeholders and variety of perspectives (YES, NO);
Political instability (YES, NO);
Susceptibility to natural disasters (YES, NO);
Contactor's lack of experience (YES, NO);
Suppliers' default (YES, NO);
Delays in design and regulatory approvals (YES, NO);
Contract related problems (YES, NO);
Economic issues in country (YES, NO);
Major design changes (YES, NO);
Delays in obtaining raw material (YES, NO);
Non-availability of local resources (YES, NO);
Unexpected events (YES, NO);
Increase in raw material price (YES, NO);
Changes in project specifications (YES, NO);
Conflicts with project stakeholders (YES, NO);
Decrease in productivity (YES, NO);
Delays/interruptions (YES, NO);
Decrease in quality of work (YES, NO);
Low market share/reputational issues (YES, NO);
Time overruns (YES, NO);
Cost overruns (YES, NO);
Qazi, A., Quigley, J., Dickson, A., & Kirytopoulos, K. (2016). Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects. International Journal of Project Management, 34(7), 1183-1198.