释放数字孪生在供应链中的潜力:系统回顾

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引用次数: 0

摘要

数字孪生(DT)的发展仍处于供应链管理(SCM)部署的试验阶段,其与实时同步和自主决策的全面整合带来了许多挑战。本文旨在找出这些共同的挑战,并为建立数字孪生(DT)系统以提高供应链管理绩效提供一个概念框架。本文对 129 篇有关 DT 应用于改进供应链管理的研究论文进行了系统的文献综述。所选论文经审查后分为三类:制造与生产、供应链和物流。物联网 (IoT)、射频识别 (RFID) 设备、云计算、网络物理系统 (CPS)、网络安全 (CS) 和仿真建模等数字技术的发展增加了探索创建供应链 DT 的机会。然而,由于大多数系统的复杂性,存在着局限性和各种挑战。研究结果表明,供应链管理的 DT 应包括外部链接(即供应商、分销商)和内部链接(即采购、生产、物流),以通过数据驱动建模和实时同步应对任何中断。根据综述结果,本研究提出了一个改善供应链管理绩效的三层概念框架。所提出的框架为供应链管理领域的 DT 研究提供了未来方向。它为 DT 的实施、常见的 DT 技术和数据分析技术提供了一个整体的综合方法,以提高供应链绩效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unlocking the potential of digital twins in supply chains: A systematic review

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.

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