عنوان مقاله [English]
Usually, projects are implemented in dynamic and complex environments due to their inherent uncertainties and risks. The purpose of risk management is to improve project performance via systematic risk assessment and response.
Companies have limited resources for managing all project risks; therefore, they need to prioritize the important ones. In particular, resources should be allocated to managing risks with higher priorities. In classical approaches,
probability and impact are two commonly used criteria in project risk assessment; however, these criteria do not sufficiently address all its aspects. Moreover, there may be interrelations and dependencies among the various criteria.
In order to overcome these drawbacks, we proposed a practical framework for evaluating risk in projects. The proposed framework has three main steps. First, we identify project risks and determine those of importance to be evaluated by multiple attribute decision-making (MADM) techniques. Then, we use a fuzzy analytic network process (fuzzy-ANP) for calculating criteria weights.
The model is capable of considering dependencies among the different criteria. Also, the model calculates consistency indices for the fuzzy pair-wise comparison matrices. Finally, the outputs of fuzzy-ANP calculations are used in a fuzzy-based technique for order preference by similarity to ideal solution (fuzzy-TOPSIS) for ranking risks based on their importance.
A case study of an Iranian power plant project is presented to demonstrate the applicability and performance of the proposed model. By different mechanisms, more than 100 risks were identified and categorized according to their sources. Next, we determine 10 important risks as alternatives for the fuzzy-ANP and fuzzy-TOPSIS procedures. We conclude that inadequate staff skill is the most important risk in such projects. Among other risks, difficulties in project financing are very important.
In order to verify the obtained results and justify the proposed method, we calculated weights of the criteria (and sub-criteria) and ranked the risks using 6 different methods. We use the extent fuzzy-AHP and fuzzy
prioritization approach for calculating the weights of criteria (and sub-criteria). According to obtained results, significant differences are observed in the weights of sub-criteria when dependencies are considered. In
addition, there are no significant differences between rankings of risks for different methods. The results show that the proposed method is a suitable approach when performance ratings and weights are vague and imprecise.