Digital Twins for Smart Supply Chain Transformation: A Multidisciplinary Review

Ogochukwu Chinazaekpele Nwosu¹, Charles Chikwendu Okpala², Nkemakonam Chidiebele Igbokwe³

¹ Researcher, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.

² Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.

³ Associate Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.   

Abstract

Digital Twins (DTs) are increasingly recognized as a cornerstone technology for smart supply chain transformation under the Industry 4.0 paradigm, as they enable real-time visibility, predictive intelligence, and closed-loop optimization across complex global networks. Despite rapid growth in digital twin research, existing studies remain fragmented across operations research, industrial engineering, artificial intelligence, cyber–physical systems, and sustainability science, which limits systematic understanding of measurable sustainability outcomes. This article presents a data-driven and multidisciplinary review of digital twins in smart supply chains, through the integration of a PRISMA-guided systematic screening protocol, bibliometric science-mapping analysis, and a novel sustainability impact quantification framework that links DT-enabled optimization to standardized environmental, economic, and social performance indicators. The review proposes a reusable taxonomy of DT scopes, computational architectures, optimization intelligence, and Industry 4.0 integration pathways, thereby highlighting how IoT-enabled monitoring, AI-enhanced decision intelligence, blockchain-supported traceability, and edge–cloud hybrid systems generate quantifiable benefits such as carbon emission reductions (8–20%), energy intensity improvements (5–15%), spoilage minimization (15–30%), and enhanced disruption recovery performance. Key research gaps are identified in interoperability standards, lifecycle sustainability accounting, explainable AI integration, and scalable adoption in emerging economies. Through the provision of methodological innovation, measurable KPI structures, and a high-impact future research agenda, this review establishes digital twins as critical enablers of resilient, low-carbon, and sustainable supply chain ecosystems.

Keywords: Digital twins, Smart supply chains, Industry 4.0, Real-time optimization, Sustainability metrics, Decision intelligence, Bibliometric review

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