Collaborative risk management in sensitive logistics nodes

Purpose – The integration of business processes across multiple companies can provide economic benefits, thus ensuring the sustainability of the involved companies. However these advantages are accompanied by many potential risks. For instance, emerging disruptions within a supply chain can spread over several partners resulting in economic losses. The purpose of this paper is to focus on the development of a collaborative cross-supply chain framework that reduces the extent of loss after the occurrence of unforeseen damaging events in sensitive logistics nodes. Design/methodology/approach – To handle the impacts of unforeseen damaging events in sensitive logistics nodes, the focus of our approach lies on the implementation of a collaboratively applicable concept for sensitive logistics nodes that supports a rapid collaborative decision-making among the partners. Therefore, different damaging events as well as the logistics and transport processes in freight villages are determined and implemented in an agent-based simulation model. Findings – Multi-agent simulation can be used for a scenario-based collaborative risk management in sensitive logistics nodes. By executing simulation experiments the impacts of selected damaging events can be analysed and used for the development of scenario specific strategies for the partners in sensitive logistics nodes with the objective to maintain the flow of goods after disturbances. Practical implications – The paper proposes a multi-agent simulation, which can be used collaboratively in a cross-supply chain team to support decision-making processes in sensitive logistics nodes after a disruption has occurred. To get an effective and efficient tool, involved companies are included into the development and implementation processes. Originality/value – The value of this paper lies in the approach of a multi-agent based simulation system that supports a collaborative risk management across different companies and different supply chains in sensitive logistics nodes.