Theorizing the role of Artificial Intelligence in Supply Chain processes: unveiling managerial perspectives and strategic applications

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Aim of the Special Issue

This special issue contributes novelty to the literature by offering a comprehensive examination of the role of ArtificiaI Intelligence (AI) in Supply Chain Management (SCM) from a managerial and strategic viewpoint. While previous studies have acknowledged AI’s potential within supply chains, our special issue bridges the gap by focusing on empirical evidence and managerial insights. By emphasizing real-world implementation and strategic considerations, we provide a fresh perspective on how AI transforms SCM processes and decision-making. Additionally, our call for innovative theoretical frameworks aims to capture the unique characteristics of AI in SCM, distinguishing it from traditional automation and advancing theoretical advancements in the field. 

By focusing on empirical research, we aim to clarify the nuanced role of AI in SCM processes, decision-making paradigms, and strategic implementations. Through rigorous investigation, we seek to uncover how supply chain managers navigate AI adoption complexities, employ AI strategies for competitive advantage, and develop theoretical frameworks that accurately capture the multifaceted relationship between AI and SCM. This special issue intends to advance the academic discourse by providing insights into AI’s impact on SCM and enhancing understanding of its strategic applications.

The relevance of this special issue is underscored by the increasing importance of AI in addressing challenges within supply chain management. With the evolution of AI from a theoretical concept to a practical necessity, there is a growing need for empirical research to elucidate its impact on SCM processes and strategies. The fragmented understanding of AI’s application across different SCM functions highlights the timeliness of our special issue, which seeks to consolidate empirical evidence and provide insights into AI’s strategic applications from a managerial perspective. By addressing this gap, we contribute to the ongoing discourse on AI’s transformative potential in SCM and its implications for organizational strategies and operations.

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List of Topic Areas

• The AI's strategic function in designing and refining the supply network, analyzing how AI-enabled analytics influence the decision-making processes across organizational dyads and networks. 
• The support of AI throughout the product lifecycle, from inception to decline, assessing its role in managing portfolio complexities and synchronization within the supply chain. 
• AI's predictive prowess in forecasting consumer demand, crafting trade promotions, and optimizing pricing strategies, considering the interplay between supply chain actors. 
• AI's utility in identifying revenue opportunities and managing supply constraints, with the objective of achieving optimal resource allocation through the lens of multiple supply chain stakeholders.
• AI's contributions to inventory positioning and supply flow planning, evaluating how AI-driven decisions at one node impact customer satisfaction and profitability across the supply chain. 
• The role of AI in supply chain risk management, focusing on how it aids in forecasting and responding to disruptions, both within and between organizations, to formulate robust business continuity strategies. 
• The strategic employment of digital supply networks in strengthening B2B communication, engagement, and retention, with a keen focus on how technology is utilized across multiple communication channels to foster a cohesive customer relationship. 
• The amplification of customer service capabilities via technology in a B2B context, enabling service representatives to leverage comprehensive supply chain insights for superior decision-making and issue resolution. 
• AI's efficacy in forecasting purchasing costs, accurately discerning requirements, and evaluating supplier and regional factors, contributing to proactive end-to-end cost management strategies. 
• The role of AI in defining category strategies, aiding in supplier selection, and leading value-driven procurement initiatives. 
• The transformation of sourcing processes through AI, which includes enhancing the efficiency of supplier self-registration, competitive bidding, and automated negotiations. 
• AI's contribution to the digitization of contract management, aiming to achieve cost reductions and meet performance benchmarks.
• The capacity of AI to structure effective collaborations, promoting mutually beneficial relationships between buyers and suppliers that drive procurement success. 
• The application of AI in enhancing the efficiency and effectiveness of production processes, from the optimization of individual operations to the improvement of overall manufacturing performance. 
• The strategic utilization of AI for the judicious allocation and utilization of scarce resources, aiming to optimize both the production outputs and the operational workflows. 
• How AI supports the enhancement of coordination and visibility across all components of the manufacturing network, from machines and personnel to overarching processes. 
• The role of AI in establishing a connected operational ecosystem that provides real-time insights into operational performance, facilitating rapid decision-making and continuous improvement. 
• AI's role in orchestrating the complex processes of order confirmation, aggregation, and fulfillment across a network of warehouses, stores, and suppliers, integrating systems like distributed order management, advanced warehouse management, and transportation management systems. 
• The design and impact of AI in developing an adaptable network of fulfillment points that can swiftly adjust capacity to accommodate changing demand levels. 
• AI's capabilities in determining the most efficient shipping modes and routes, considering factors such as logistics requirements, hub locations, cost efficiencies, and shipping schedules. 
• The optimization of the synergy between the human workforce, technological tools, and physical assets through AI, aiming to improve order precision, inventory management, and the pace of the fulfillment cycle. 
• The role of AI in automating the entire transportation journey, from the initial departure to the final mile delivery, emphasizing enhancements in efficiency, cost optimization, and reduction in delivery lead times. 
• The fundamental ways AI supports sustainability and the principles of the circular economy within the interconnected fabric of supply chain networks. 
• The catalytic role of AI in eco-design and in fostering cohesive collaboration among diverse stakeholders across the entire product and service lifecycle. 
• The capability of AI to architect supply chain platforms that elevate resource efficiency and amplify the value generated across the network. 
• AI's influence on the enactment of corporate sustainability strategies within supply chain operations, striving to optimize the consumption of energy and resources and minimize emissions at both micro and macro network levels.

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available here.

Author guidelines must be strictly followed. Please see here.

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”. 

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key Deadlines

Closing date for abstract submission: 30/07/2024
Email for abstract submissions: [email protected]
Opening date for manuscripts submissions: 31/05/2024 
Closing date for manuscripts submission: 31/12/2024

Guest Editors

Antonella Moretto, Politecnico di Milano, Italy, [email protected]
Michela Guida, Politecnico di Milano, Italy, [email protected]
Maria Elana Latino, Università del Salento, Italy, [email protected]
Maria Jesús Saénz, Massachusetts Institute of Technology, USA, [email protected]
Ilya Jackson, Massachusetts Institute of Technology, USA, [email protected]