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Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?

Accepted version
Peer-reviewed

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Type

Article

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Abstract

Digitalisation is expected to transform end-to-end supply chain operations by leveraging the technical capabilities of advanced technology applications. Notwithstanding the operations-wise merits associated with the implementation of digital technologies, individually, their combined effect has been overlooked owing to limited real-world evidence. In this regard, this research explores the joint implementation of Artificial Intelligence (AI) and Blockchain Technology (BCT) in supply chains for extending operations performance boundaries and fostering sustainable development and data monetisation. Specifically, this study empirically studied the tuna fish supply chain in Thailand to identify respective end-to-end operations, observe material and data-handling processes, and envision the implementation of AI and BCT. Therefore, we first mapped the business processes and the system-level interactions to understand the governing material, data, and information flows that could be facilitated through the combined implementation of AI and BCT in the respective supply chain. The mapping results illustrate the central role of AI and BCT in digital supply chains' management, while the associated sustainability and data monetisation impact depends on the parameters and objectives set by the involved system stakeholders. Afterwards, we proposed a unified framework that captures the key data elements that need to be digitally handled in AI and BCT enabled food supply chains for driving value delivery. Overall, the empirically-driven modelling approach is anticipated to support academics and practitioners' decision-making in studying and introducing digital interventions toward sustainability and data monetisation.

Description

Keywords

Supply chain digitalisation, Artificial Intelligence, Blockchain Technology, Data monetisation, Sustainability, Fish supply networks

Journal Title

Ann Oper Res

Conference Name

Journal ISSN

0254-5330
1572-9338

Volume Title

Publisher

Springer Science and Business Media LLC
Sponsorship
British Council in India (UKUTP201100194)
Engineering and Physical Sciences Research Council (EP/K02888X/1)
EPSRC (via Aston University) (56532 (EP/S036091/1))
EPSRC (via Aston University) (EP/S036091/1)