Risk propagation based dynamic transportation route finding mechanism
Purpose - The purpose of this paper is to propose a novel risk assessment approach that considers the inter-relationship between supply chain risks and the structure of network at the same time. To reduce the impact of the supply chain risk and enhance the flexibility of transportation route finding during the product delivery, the authors propose a way to model the risk propagation and how to integrate it with the supply chain network using Bayesian Belief Network (BBN). The key risk indicators (KRI) of each vertex and edge of the supply chain network which are measured or computed by the proposed approach can be utilized to develop the optimal transportation route in the execution phase. Design/methodOlogy/approach - BBN is utilized to illustrate the relations among supply chain risks which may take place in a certain vertex. To apply the BBN to the supply chain network, the authors develop the framework to integrate BBN and the supply chain network by using the general functions that describe the characteristics of the risk factors and inter-relationships between vertices. Findings - By using the proposed risk assessment and dynamic route-finding approach, it is possible to reduce the unexpected cost from the supply chain risk and overcome the limitations of previous risk management strategies which focus on developing counter plans and assume the independency of supply chain risks. Practical implications - The proposed approach describes how to develop KRI-BBN to model the risk propagation and to integrate the KRI-BBN and supply chain network. The KRIs directly measured or computed by KRI-BBN in real time can be utilized to alternate supply chain execution plans such as inventory management, demand management and product flow management. Transportation problem considering risk is developed to show how to apply the proposed approach and numerical experiments are conducted to prove the cost effectiveness. Originality/value - The contribution of this paper lies in the way of developing KRI-BBN to assess the supply chain risk and modelling of the risk propagation by integrating KRI-BBN with supply chain network. With the proposed risk assessment approach, it is able to alternate the transportation route to minimize the unexpected cost and transportation cost simultaneously.
risk propagation transportation supply chain network bayesian belief network risk management supply chain management dynamic control