Research on supply network resilience considering the ripple effect with collaboration

Abstract

Local disruptions can be propagated from one firm to another in a supply network (SN) and eventually influence the whole SN. Therefore, numerous studies on SN resilience considering the ripple effect have been reported recently. However, previous studies paid less attention to this phenomenon from a network structure perspective: if a firm is facing the risk of failure, then its partners may help it to mitigate the risk of failure by collaboration during the process of disruption propagation. Specifically, how SN structures (e.g. characterised by different scaling exponents) and other parameters (e.g. redundancy) influence the effectiveness of collaboration on improving SN resilience considering the ripple effect is not clear. Accordingly, we propose a ripple effect with collaboration (REC) model to consider the aforementioned phenomenon. We also present three new SN resilience metrics to evaluate SN resilience. Then, using both generated (by a novel SN generating model) and real-life SNs, we simulate the SN resilience considering REC under random and targeted disruptions. Our results demonstrate that the effectiveness of collaboration can be affected by SN structures and other parameters, and collaboration can even negatively affect SN resilience in some cases. We also summarise managerial implications and give future research directions.

KEYWORDS:

• Supply network
• supply network resilience
• ripple effect
• collaboration
• supply network disruption

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is partially supported by the Natural Science Foundation of Southwest University of Science and Technology (grant number 20zx7117).

Notes on contributors

Xiao-qiu Shi

Xiao-qiu Shi received the Ph.D. degree from Sichuan University, Chengdu, China, in 2019. He is currently an assistant professor with Southwest University of Science and Technology, Mianyang, China. His research interests include supply networks, complex networks and machine learning.

Xue-jiao Yuan

Xue-jiao Yuan received the M.S. degree from Southwest University of Science and Technology, Mianyang, China, in 2015. She currently works for Southwest University of Science and Technology. Her research interests include supply networks and computer sciences.

Ding-shan Deng

Ding-shan Deng received the B.S. degree from Sichuan University, Chengdu, China, in 2014. He is currently pursuing the Ph.D. degree with Sichuan University, Chengdu, China. His research interests include supply networks, complex networks and evolutionary algorithms.