Crude oil supply chain is extremely complex and vulnerable to various risks. Clear understanding of these risks can help supply chain managers to make effective decisions. This paper identifies main risks related to crude oil supply chain and implements decision making trial and evaluation laboratory (DEMATEL) method to determine the interdependency between risks and then applies analytic network process (ANP) method to evaluate the importance of each risk and to determine the best response strategy. The results show that the most important risk area is the regulatory and environmental risks and that the transference and cooperation policy is rated as the best response strategy. Contributions of this paper are fourfold: (1) it provides a comprehensive framework of risks that need to be considered in crude oil supply chain risk management (SCRM) context; (2) it shows how DEMATEL can be applied to analyze the interdependency between risks; (3) it illustrates how various risks and risk management strategies can been assessed through the ANP approach to aid managers in their decision making processes; and (4) most important contribution is customization of crude oil SCRM in Iran.

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Downstream demand inference (DDI) emerged in the supply chain theory, allowing an upstream actor to infer the demand occurring at his formal downstream actor without need of information sharing. Literature showed that simultaneously minimizing the average inventory level and the bullwhip effect isn't possible. In this paper, the authors show that demand inference is not only possible between direct supply chain links, but also at any downstream level. The authors propose a bi-objective approach to reduce both performance indicators by adopting the genetic algorithm. Simulation results show that bullwhip effect can be reduced highly if specific configurations are selected from the Pareto frontier. Numerical results show that demand's time-series structure, lead-times, holding and shortage costs, don't affect the behaviour of the bullwhip effect indicator. Moreover, the sensitivity analysis show that the optimization approach is robust when faced to varied initializations. Finally, the authors conclude the paper with managerial implications in multi-level supply chains.

Supply chains consist of several actors from supplier, manufacturer, distributer, wholesaler and retailers connected to each other by financial, material and informational flows. Optimal performance of supply chains requires set of actions that coordinate the members’ decisions [1], [2]. In many cases, members are trying to optimize their own objectives which can lead to asymmetric information by keeping some strategic information private. Although, this information asymmetry is a challenge affecting the coordination of supply chain, but it is achievable if proper set of coordinating mechanism executed. This paper presents a comprehensive literature review of supply chain coordination under asymmetric information and tries to analyze the trend in the context and address the evolution and gaps in existing literature.