Title: Deep learning for blockchain in medical supply chain risk management

Authors: Fei Jiang; Chen-Xian Jiang; Jian Xin Li

Addresses: College of ASEAN Studies/Academy of China-ASEAN International and Regional Studies, Guangxi Minzu University, Nanning, Guangxi, China; School of Modern Circulation, Guangxi International Business Vocational College, Nanning, Guangxi, China ' College of ASEAN Studies/Academy of China-ASEAN International and Regional Studies, Guangxi Minzu University, Nanning, Guangxi, China; School of Modern Circulation, Guangxi International Business Vocational College, Nanning, Guangxi, China ' School of Electronic Information, Dongguan Polytechnic, Dongguan, Guangdong Province, China

Abstract: The COVID-19 pandemic's severe shortage of essential medical supplies created significant risks in the medical supply chain operation. Supply chain managers have started focusing on decision-making based on multiple data sources to correctly foresee uncertainty and develop a proactive and predictable intelligent risk management system. These features make using blockchain and Deep Learning (DL) methods in Supply Chain Risk Management (SCRM) feasible but are still in the initial stages. This work provides a comprehensive and detailed literature analysis and emphasises that many blockchains and DL methods are used in various stages of medical SCRM. At the same time, by deploying blockchain and DL and combining them with necessary questionnaires, an effective SCRM model can be used to detect major supply chain risks. By outlining the unresolved challenges that must be solved before the large-scale deployment of DL and blockchain applications. Ultimately, research points out the key direction of future research in this field and promotes global supply chain cooperation.

Keywords: blockchain; deep learning; medical supply chain; risk management; IoT.

DOI: 10.1504/IJGUC.2023.131014

International Journal of Grid and Utility Computing, 2023 Vol.14 No.2/3, pp.250 - 263

Received: 24 Jun 2022
Received in revised form: 29 Aug 2022
Accepted: 27 Oct 2022
Published online: 18 May 2023 *

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