Digital twins in supply chain management: Scope and methodological issues

This paper investigates the implementation of Digital Twins (DTs) in Supply Chain Management (SCM), highlighting the gap between their conceptual promise and practical applications. DTs are recognised for their potential to correct real-time deviations and anticipate and prevent disruptions as they emerge; however, operational deployments in SCM remain rare. Numerous studies mislabel simulation models or Digital Shadows (DSs) as DTs, blurring essential distinctions. To address this issue, this paper adopts a praxeological approach that aims to situate observed implementations within their decision-making context. From this perspective, we propose a novel methodological framework that integrates the historical evolution of supply chain information and decision systems with a multi-dimensional analysis grid, outlining technological progress from simulators to DSs and DTs. This grid evaluates core DT functionalities (simulation, detection, anticipation and correction) across 51 empirical case studies, providing granular insights into maturity levels and AI-enhanced patterns. The results show that most models support monitoring and decision-making, but only 16 % achieve closed-loop capabilities typical of fully functional DTs, mainly in closed systems. In contrast, open systems still depend on human intervention, although AI can increasingly support such contexts. This praxeological approach provides critical and evidence-based snapshots of actual implementations. It offers researchers a clarified conceptual lens and practitioners empirically grounded guidance, outlining avenues for future inquiry and a reflective framework to guide the development and governance of DTs in supply chains.