Fabrizio Dallari

Purpose: Grocery retailers in the fast-moving consumer goods (FMCG) sector are reconfiguring their logistics networks to address rising operational complexity and technological disruption. In this context, warehouse automation is moving beyond operational support to play a strategic role, enabling firms to redesign processes and adapt logistics systems to dynamic market and technological conditions. Despite the growing relevance of automation, empirical understanding of how automated technologies are selected, combined, and adapted in grocery DCs remains limited. This study addresses this gap by investigating how grocery retailers implement warehouse automation to transform logistics processes in response to evolving operational complexity and market demands. Design/methodology/approach: A multi-phase qualitative approach was adopted, combining semi-structured interviews with eight automation providers and six grocery retailers, complemented by site visits. Data were analysed through the Gioia method to inductively derive recurring patterns and managerial logics. The emerging framework was then interpreted through the lens of the DCT. Findings: The study identifies six DC processes supported by distinct automation technologies, such as AS/RS systems, miniloads, shuttles, and robotic picking stations, and five strategic decision factors guiding automation decisions: selectivity, accessibility, expandability, scalability and resilience. Interpreted through the lens of dynamic capabilities theory, these dimensions show how the implementation of automation supports firms in sensing operational requirements, seizing technological opportunities and sustaining long-term adaptability and operational continuity. Originality/value: This research bridges the gap between theory and practice in grocery logistics by conceptualising warehouse automation implementation as a dynamic capability. It provides a validated framework for scholars and practitioners, supporting informed, future-oriented automation strategies in retail distribution.

Recent advancements in Artificial Intelligence (AI) have led to the emergence of AI Agents, autonomous systems performing complex tasks and pursuing defined objectives with minimal human interaction. Early research in AI Agents in supply chain (SC) management is pointing to their potential for enhancing visibility, resilience, and operational efficiency. However, as AI Agents are an emerging technology, several aspects of their application remain unclear. The literature lacks understanding of the SC processes they can support, the main drivers and constraints influencing their adoption, and practitioners’ perspectives on these aspects. Moreover, as their implementation inevitably reshapes human actors’ roles, scholars have called for further investigation into the nature of agent interaction with humans. This exploratory qualitative study addresses these gaps through semi-structured interviews with experienced practitioners in AI and SC. To the best of the authors’ knowledge, this is among the first studies to jointly address, through an integrated framework, the mapping of SC processes that AI Agents can support, the associated drivers and constraints to adoption ranked according to SC practitioners, and the human-AI Agent collaboration roles for each category of processes. The study reveals novel AI Agent-specific elements and introduces a conceptual framework alongside four testable propositions.

In fashion e‐commerce, about one in three orders is returned; however, firms keep offering free, fast returns driving these volumes even as they acknowledge the economic and environmental costs. Why do inefficient return practices persist? We argue that the answer is not a failure to optimize but a collective trap in which no single actor can afford to break away alone. Adopting a supply chain perspective and drawing on Extended Institutional Theory (EIT), through a qualitative multiple‐case study of Italian fashion e‐commerce firms, we examine how the interaction of legitimacy‐seeking and profit‐seeking logics across producers, retailers, and logistics and transport providers reproduces these practices. Upstream legitimacy‐driven decisions, aimed at maintaining high service levels, propagate downstream through contractual‐operational constraints, limiting the autonomy of logistics and transport providers. These interdependencies generate reinforcing decision‐making loops stabilizing dominant return practices, notwithstanding their inefficiencies. The study identifies outcome‐based imitation mechanisms, such as omnichannel return management, return consolidation, and differentiated return policies, as levers to disrupt these loops and enhance eco‐efficiency while maintaining legitimacy. The paper extends EIT to supply chain contexts and offers prescriptive insights. Practically, it highlights the need for sector‐based actions to rebalance legitimacy and profit motives for sustainable returns management.