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A Case Study of How Maersk Adopts Cloud-Based Blockchain Integrated with Machine Learning for Sustainable Practices

Author

Listed:
  • Simon Wong

    (Division of Science, Engineering and Health Studies, College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hong Kong, China)

  • John Kun-Woon Yeung

    (Data Science Academy, Hong Kong, China)

  • Yui-Yip Lau

    (Division of Business and Hospitality Management, School of Professional Education and Executive Development, The Hong Kong Polytechnic University, Hong Kong, China)

  • Tomoya Kawasaki

    (Department of Systems Innovation, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

Abstract

In the last six years, there has been a rise in research interest with regard to the applications of blockchain technology in supply chains and how these applications bring benefits to supply chain management. In a broader sense, an essential research focus that has been discussed in the literature is the way in which this emerging blockchain technology in supply chains brings sustainable benefits to a community. The rationale for incorporating cloud technology into a blockchain and integrating the blockchain with machine learning for supply chain applications is to maintain technical sustainability. While previous studies suggested and reported sustainable practices of applying blockchain technology in supply chains, the means with which these practices are brought about by the cloud-based blockchain integrated with machine learning (CBML) have not been thoroughly explored in the literature. The case study presented in this paper aims to fill this gap by exploring technically, environmentally, economically, and socially sustainable practices through the use cases of CBML for supply chain management by the international leading container shipping company Maersk. The use cases by Maersk presented in published documents were collected from the Internet and then analyzed. This document analysis was performed in two ways. The first way was a technical review of the blockchain technology used by Maersk with a consideration of technical sustainability to ensure scalability and big data analytics. The other way was to analyze the applications of the CBML by Maersk to indicate how environmental sustainability, economic sustainability, and social sustainability can be achieved. On the other hand, this paper also highlights the negative technical, environmental, economic, and social sustainability impacts caused by Maersk and discusses implications for future research directions.

Suggested Citation

  • Simon Wong & John Kun-Woon Yeung & Yui-Yip Lau & Tomoya Kawasaki, 2023. "A Case Study of How Maersk Adopts Cloud-Based Blockchain Integrated with Machine Learning for Sustainable Practices," Sustainability, MDPI, vol. 15(9), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7305-:d:1134639
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    References listed on IDEAS

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    1. Vincent Charles & Ali Emrouznejad & Tatiana Gherman, 2023. "A critical analysis of the integration of blockchain and artificial intelligence for supply chain," Annals of Operations Research, Springer, vol. 327(1), pages 7-47, August.
    2. Vineet Paliwal & Shalini Chandra & Suneel Sharma, 2020. "Blockchain Technology for Sustainable Supply Chain Management: A Systematic Literature Review and a Classification Framework," Sustainability, MDPI, vol. 12(18), pages 1-39, September.
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    Cited by:

    1. Muen Uddin & Shitharth Selvarajan & Muath Obaidat & Shams Ul Arfeen & Alaa O. Khadidos & Adil O. Khadidos & Maha Abdelhaq, 2023. "From Hype to Reality: Unveiling the Promises, Challenges and Opportunities of Blockchain in Supply Chain Systems," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
    2. Son Nguyen & Aengus Leman & Zhe Xiao & Xiuju Fu & Xiaocai Zhang & Xiaoyang Wei & Wanbing Zhang & Ning Li & Wei Zhang & Zheng Qin, 2023. "Blockchain-Powered Incentive System for JIT Arrival Operations and Decarbonization in Maritime Shipping," Sustainability, MDPI, vol. 15(22), pages 1-22, November.

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