Technological Advancements and Barriers in Adopting Digital Twins for Supply Chain Management

Abstract

Digital twins are expected to improve supply chain decisions by linking physical operations with models that simulate and optimize them. This literature review, guided by PRISMA 2020, analyzes 104 studies (2016 to 2026) on the enabling technologies of supply chain digital twins, the barriers to their adoption and the algorithmic evidence behind them. The studies were classified into evidence tiers by application domain; twelve supply chain studies and six algorithm studies from other sectors were coded by decision problem, algorithm family, integration level and validation, and the methodological quality of fifteen of them was appraised. The Internet of Things, artificial intelligence, cloud and edge computing, extended reality and blockchain are the main enablers, but in the analyzed set the supply chain evidence is largely conceptual: only two supply chain studies apply an algorithm, none implements a bidirectional digital twin, and only one reports performance measures from a model run. State estimation, reduced-order models and reinforcement learning, which would allow real-time operation, have been evaluated only in other sectors. Technical, organizational and economic barriers appear to reinforce each other. We propose a three-stage implementation pathway and an agenda of seven research gaps, including closed-loop integration, shared benchmarks and multi-company data governance.

The authors' abstract, as published at the source. Algorithms, 2026 · DOI ↗