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.

The increasing exposure of global supply chains to severe disruptions such as the ones related to the COVID-19 pandemic, clearly demonstrated the need for novel data-driven risk management paradigms that monetise data from internal and external stakeholders to support supply chain security, resilience, and sustainability. We first motivate the challenges that supply chains are facing under the new realities. We then provide a critical taxonomy of the relevant literature and identify gaps which include: (i) the impact of security on supply chain operations; (ii) cost effective resiliency strategies and practices; and (iii) the social and labour dimensions of sustainability. We then propose a new generalised framework that encompasses all the identified challenges, gaps in literature and in practice, and opportunities in supply chain management research. The proposed framework is validated through a real-world case study of the organic food supply chain. This validation further highlights the need for data-driven digital technologies that enable data collection and management, secure storage and effective data processing towards data monetisation for supply chain security, cost-competitive resilience, and sustainability across end-to-end operations.

Artificial intelligence and data analytics capabilities have enabled the introduction of automation, such as robotics and Automated Guided Vehicles (AGVs), across different sectors of the production spectrum which successively has profound implications for operational efficiency and productivity. However, the environmental sustainability implications of such innovations have not been yet extensively addressed in the extant literature. This study evaluates the use of AGVs in container terminals by investigating the environmental sustainability gains that arise from the adoption of artificial intelligence and automation for shoreside operations at freight ports. Through a comprehensive literature review, we reveal this research gap across the use of artificial intelligence and decision support systems, as well as optimisation models. A real-world container terminal is used, as a case study in a simulation environment, on Europe’s fastest-growing container port (Piraeus), to quantify the environmental benefits related to routing scenarios via different types of AGVs. Our study contributes to the cross-section of operations management and artificial intelligence literature by articulating design principles to inform effective digital technology interventions at non-automated port terminals, both at operational and management levels.