Unlocking the potential of digital twins in supply chains: A systematic review

• Published in: Supply Chain Analytics 2024-09, Vol.7, p.100075, Article 100075
• Main Authors: Zaidi, Syed Adeel Haneef, Khan, Sharfuddin Ahmed, Chaabane, Amin
• Format: Article
• Language: English
• Subjects: Conceptual Framework; Data Analytics; Digital Twins; Enabling Technologies; Supply Chain Management
• ISSN: 2949-8635; 2949-8635
• DOI: 10.1016/j.sca.2024.100075

Digital Twins (DTs) developments are still in the pilot stages of deployment in supply chain management (SCM), and their full integration with real-time synchronization and autonomous decision-making poses many challenges. This paper aims to identify these common challenges and provide a conceptual framework for establishing a Digital Twin (DT) system to improve supply chain management performance. The paper presents a systematic literature review of 129 research papers on DT applications for SCM improvement. The selected papers were reviewed and classified into three categories: manufacturing and production, supply chain, and logistics. The development of digital technologies such as the Internet of Things (IoT), Radio Frequency Identification (RFID) devices, cloud computing, cyber-physical systems (CPSs), cybersecurity (CS), and simulation modeling has increased the opportunities to explore the creation of supply chain DTs. However, there are limitations and various challenges due to the complexity of most systems. The results indicate that DT for SCM should include external links (i.e. suppliers, distributors) and internal links (i.e. procurement, production, logistics) to deal with any disruption through data-driven modeling with real-time synchronization. Based on the review findings, this study proposes a three-layered conceptual framework to improve supply chain management performance. The proposed framework provides future directions for DT research in SCM. It provides a holistic and integrated approach to DT implementation, the common DT technologies, and data analytics techniques for improved supply chain performance.