Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics
Taofeeq Durojaye Moshood, Gusman Nawanir, Shahryar Sorooshian, Okfalisa Okfalisa
Universiti Malaysia Pahang Al-Sultan Abdullah University of Gothenburg Universitas Islam Negeri Sultan Syarif Kasim Riau
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
The supply chains shaping their distribution networks become more diverse as companies respond to global markets’ stringent criteria. This is also counterproductive to the visibility of the supply chain within the company and can adversely affect the organization’s core business. This paper attempts to evaluate how organizations can benefit from introducing Digital Twins to enhance their logistics supply network visibility. Additionally, deployment issues and technologies supporting Digital Twins were reviewed. This study used ATLAS.ti 9 software tools to save, classify, and evaluate the data for this analysis to systematically review the literature. We reviewed, compiled, and sorted papers from 227 publications for this article and then recognized 104 as critical to the work scope; this analysis’ quest date was set from 2002 to 2021. This article represents the first attempt at dealing with the issue of supply chain visibility through the Digital Twins in the logistics field. The research outcomes found that Digital Twins would help companies develop predictive metrics, diagnostics, projections, and physical asset descriptions for their logistics. This study also suggested some steps to overcome the challenges in implementing a Digital Twins in the logistics industry. For researchers, this review offers the possibility to unify and expand existing solutions and to identify links and interfaces that are still needed. As for managerial implications, this study can be used to identify future strategies and technologies to fulfil certain logistics tasks and develop new technological solutions for current and future demands.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
工程Digital Transformation in Industry
Supply Chain Resilience and Risk Management · Quality and Supply Management
参考文献 109
此处列出前 3 条
引用本文 169
按被引量排序,此处列出前 3 条