projectmanagement {bnRep} | R Documentation |
projectmanagement Bayesian Network
Description
Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects.
Format
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:
- C1
Lack of experience with the involved team (YES, NO);
- C2
Use of innovative technology (YES, NO);
- C3
Lack of experience with technology (YES, NO);
- C4
Strict quality requirements (YES, NO);
- C5
Multiple contracts (YES, NO);
- C6
Multiple stakeholders and variety of perspectives (YES, NO);
- C7
Political instability (YES, NO);
- C8
Susceptibility to natural disasters (YES, NO);
- R1
Contactor's lack of experience (YES, NO);
- R2
Suppliers' default (YES, NO);
- R3
Delays in design and regulatory approvals (YES, NO);
- R4
Contract related problems (YES, NO);
- R5
Economic issues in country (YES, NO);
- R6
Major design changes (YES, NO);
- R7
Delays in obtaining raw material (YES, NO);
- R8
Non-availability of local resources (YES, NO);
- R9
Unexpected events (YES, NO);
- R10
Increase in raw material price (YES, NO);
- R11
Changes in project specifications (YES, NO);
- R12
Conflicts with project stakeholders (YES, NO);
- R13
Decrease in productivity (YES, NO);
- R14
Delays/interruptions (YES, NO);
- O1
Decrease in quality of work (YES, NO);
- O2
Low market share/reputational issues (YES, NO);
- O3
Time overruns (YES, NO);
- O4
Cost overruns (YES, NO);
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
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.