Risk Evaluation of the Destination Port Logistics based on Self-Organizing Map Computing

Abstract

We consider a destination port logistics service provider (DPLSP), which wants to improve its service quality by reducing risk of delivery time delay. This paper diagnoses potential risk factors that estimate the performances of the DPLSP who provides services only after the arrival of freight, with the intention of reducing supply chain risk and improve supply chain performance through creative computing approach. Self-organizing feature map (SOFM) computing is a type of artificial neural network based on an unsupervised learning algorithm. We propose the approach of SOFM computing for the purpose of clustering risk data of DPLSPs from a less subjective perspective and then rank the cluster results into different levels based on the total risk value of each cluster. Numerical studies to test the effectiveness of this model would be carried out using air import logistics lead-time reports from a large DPLSP. The results illustrate that the proposed approach could successfully cluster and rank the risk data according to their values.