Technical sustainability of cloud-based blockchain integrated with machine learning for supply chain management

Simon Wong, John Kun Woon Yeung, Yui yip Lau, Joseph So

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Knowing the challenges of keeping and manipulating more and more immutable transaction records in a blockchain network of various supply chain parties and the opportunities of leveraging sophisticated analyses on the big data generated from these records, design of a robust blockchain architecture based on a cloud infrastructure is proposed. This paper presents this technical design with consideration of the technical sustainability in terms of scalability and big data processing and analytics. A case study was used to illustrate how the technical sustainability is achieved by applying the proposed technical design to the real-time detection of the maritime risk management. This case also illustrates how machine learning mechanism helps to reduce maritime risk by guiding a cargo ship to adjust to the planned or safe route from a detour to a danger zone. This paper also discusses the implications for further research direction.

Original languageEnglish
Article number8270
JournalSustainability (Switzerland)
Volume13
Issue number15
DOIs
Publication statusPublished - 23 Jul 2021

Keywords

  • Blockchain
  • Cloud infrastructure
  • Data analytics
  • Machine learning
  • Supply chain
  • Technical sustainability

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