Project complexity has been extensively explored in the literature because of its contribution towards the failure of major projects in terms of cost and time overruns. Focusing on the interface of Project Complexity and Interdependency Modelling of Project Risks, we propose a new process that aids capturing interdependency between project complexity, complexity induced risks and project objectives. The proposed modelling approach is grounded in the theoretical framework of Expected Utility Theory and Bayesian Belief Networks. We consider the decision problem of identifying critical risks and selecting optimal risk mitigation strategies at the commencement stage of a project, taking into account the utility function of the decision maker with regard to the importance of project objectives and holistic interaction between project complexity and risk. The proposed process is supported by empirical research that was conducted in the construction industry in order to explore the current practices of managing project complexity and the associated risks. The experts interviewed acknowledged the contribution of the proposed process to the understanding of complex dynamics between project complexity attributes and risks. Application of the proposed process is illustrated through a simulation study.

Preprint submitted to International Journal of Production Economics Highlights • A SCRM process integrating all stages of the risk management process is proposed and operationalised. • The operationalisation scheme adapts and integrates various techniques from diversified research areas. • 'Probability-conditional expected utility' matrix is introduced to assess interdependent risks. • 'Weighted net evaluation of risk mitigation' is proposed to capture the decision maker's risk appetite. • Propositions are introduced to elucidate the significance of modelling interdependent risks. Highlights (for review)

In this paper, we introduce an integrated supply chain risk management process that is grounded in the theoretical framework of Bayesian Belief Networks capturing interdependency between risks and risk mitigation strategies, and integrating all stages of the risk management process. The proposed process is unique in four different ways: instead of mapping the supply network, it makes use of Failure Modes and Effects Analysis to model the risk network which is feasible for modelling global supply chains; it is driven by new dependency based risk measures that can effectively capture the network wide impact of risks for prioritisation; it utilises the concept of Shapley value from the field of cooperative game theory to determine a fair allocation of resources to the critical risks identified; and the process helps in prioritising potential risk mitigation strategies (both preventive and reactive) subject to budget and resource constraints. We demonstrate its application through a simulation study

Ekici, Şule Önsel (Dogus Author) -- Conference full title: 2015 International Conference on Industrial Engineering and Systems Management (IESM) : October 21-23, 2015, Seville, Spain.Supply chains have become complex and vulnerable and therefore, researchers are developing effective techniques in order to capture the complex structure of the supply network and interdependency between supply chain risks. Researchers have recently started using Bayesian Belief Networks for modelling supply chain risks. However, these models are still focused on limited domains of supply chain risk management like supplier selection, supplier performance evaluation and ranking. We have developed a comprehensive risk management process using Bayesian networks that captures all three stages of risk management including risk identification, risk assessment and risk evaluation. Our proposed new risk measures and evaluation scheme of different combinations of control strategies are considered as an important contribution to the literature. We have modelled supply network as a Bayesian Belief Network incorporating the supply network configuration, probabilistic interdependency between risks, resulting losses, risk mitigation control strategies and associated costs. An illustrative example is presented and three different models are solved corresponding to different risk attitudes of the decision maker. Based on our results, it is not always viable to implement control strategy at the most important risk factor because of the consideration of mitigation cost, relative loss and probabilistic interdependency between connected risk factors

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