A k-means clustering for supply chain risk management with embedded network connectivity

Abstract

In recent years, increased attention has been shown to the supply chain risk management due to the occurrences of several high profile disruptions which resulted in significant social, economic and political impact globally. However, there are not direct and easy ways of understanding the risk of an entire supply chain. In this paper, a network connectivity embedded k-means clustering approach has been proposed to determine at-risk clusters of nodes that share similar risk profiles and linkages with the focal company. It uses a multiple dimensional feature vector to represent the risks that nodes are facing, their geographical locations, supply chain attributes and network connectivity attributes. The clustering approach is able to reduce the complexity of a large supply chain network to facilitate in-depth targeted analysis and simulations. The effectiveness of the proposed approach has been illustrated by experiments that successfully identify the risk clusters and critical risk zones. Reference to this paper should be made as follows: Yin, X.F., Fu, X., Ponnambalam, L. and Goh, R.S.M. (2016) 'A k-means clustering for supply chain risk management with embedded network connectivity', Int. is the Director of the Computing Science Department at the A*STAR Institute of High Performance Computing (IHPC). At IHPC, he leads a team of more than 50 scientists in performing world-leading scientific research, developing technologies to commercialisation, and engaging and collaborating with industry. The research focus areas include high performance computing (HPC), distributed computing, big data analytics, intuitive interaction technologies, and complex systems. His expertise is in discrete event simulation, parallel and distributed computing, and performance optimisation and tuning of applications on large-scale computing platforms. He received his PhD in Electrical and Computer Engineering from the National University of Singapore. This paper is a revised and expanded version of a paper entitled 'A network connectivity embedded clustering approach for supply chain risk assessment' presented at