While supply chain risk management offers a rich toolset for dealing with risk at the dyadic level, less attention has been given to the effectiveness of risk management in complex supply networks. We bridge this gap by building an agent based model to explore the relationship between topological characteristics of complex supply networks and their ability to recover through inventory mitigation and contingent rerouting. We simulate upstream supply networks, where each agent represents a supplier. Suppliers' connectivity patterns are generated through random and preferential attachment models. Each supplier manages its inventory using an anchor-andadjust ordering policy. We then randomly disrupt suppliers and observe how different topologies recover when risk management strategies are applied. Our results show that topology has a moderating effect on the effectiveness of risk management strategies. Scale-free supply networks generate lower costs, have higher fillrates, and need less inventory to recover when exposed to random disruptions than random networks. Random networks need significantly more inventory distributed across the network to achieve the same fill rates as scalefree networks. Inventory mitigation improves fill-rate more than contingent rerouting regardless of network topology. Contingent rerouting is not effective for scale-free networks due to the low number of alternative suppliers, particularly for short-lasting disruptions. We also find that applying inventory mitigation to the most disrupted suppliers is only effective when the network is exposed to frequent disruptions; and not cost effective otherwise. Our work contributes to the emerging field of research on the relationship between complex supply network topology and resilience.

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Simulations are increasingly used in training and education because of their success and their advantages as a learning method. However, it has also been observed that the dynamic complexity of simulations creates learning difficulties, and that performance tends to plateau quickly at a level well below benchmark performance. To overcome this difficulty, a gradual-increase-in-complexity approach is proposed, which suggests developing simpler versions of a simulation game that can be used as part of the training. Accordingly, the authors developed a series of inventory-management simulations and conducted an experiment. The results indicate an improvement in the success of the inventory-management simulation as a training tool. 2009 Wiley Periodicals, Inc. Complexity 15: 31-42, 2010

The stock management studies in the system dynamics literature implicitly or explicitly assume that measurement delays are negligible. This assumption may be true in some cases but it will not hold in all cases. As a matter of fact, either long or short, measurement delays are always present in dynamic feedback control systems. In this paper, two stock management problems, which incorporate signifi cant delays in measuring the stock level, are considered: the fi rst problem does not have a supply line delay, while the second problem does. For the second problem, we further assume that the level of the supply line cannot be directly measured. The standard anchor-and-adjust heuristic creates unstable oscillations in both cases due to the delay involved in accessing the value of the stock. On the other hand, the improved decision-making heuristic introduced in this paper creates a stable and fast response in the dynamic behavior of the stock because it accounts for the fact that the information is delayed.