Digital Twins for Logistics and Supply Chain Systems: Literature Review, Conceptual Framework, Research Potential, and Practical Challenges

• URL: http://arxiv.org/abs/2311.17317v1
• Date: Wed, 29 Nov 2023 02:15:16 GMT
• Title: Digital Twins for Logistics and Supply Chain Systems: Literature Review, Conceptual Framework, Research Potential, and Practical Challenges
• Authors: Tho V. Le and Ruoling Fan
• Abstract summary: This paper introduces the background of the logistics and supply chain industry, the DT and its potential benefits, and the motivations and scope of this research. The literature review indicates research and practice gaps and needs that motivate proposing a new conceptual DT framework for LSCS. Ideas on the next steps to deploy a transparent, trustworthy, and resilient DT for LSCS are presented.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To facilitate an effective, efficient, transparent, and timely decision-making process as well as to provide guidelines for industry planning and public policy development, a conceptual framework of digital twins (DTs) for logistics and supply chain systems (LSCS) is needed. This paper first introduces the background of the logistics and supply chain industry, the DT and its potential benefits, and the motivations and scope of this research. The literature review indicates research and practice gaps and needs that motivate proposing a new conceptual DT framework for LSCS. As each element of the new framework has different requirements and goals, it initiates new research opportunities and creates practical implementation challenges. As such, the future of DT computation involves advanced analytics and modeling techniques to address the new agenda's requirements. Finally, ideas on the next steps to deploy a transparent, trustworthy, and resilient DT for LSCS are presented.

Related papers

• Designing a Collaborative Platform for Advancing Supply Chain Transparency [0.0]
  Supply chain transparency (SCT) is essential for regulatory compliance and meeting sustainability standards. A minority of companies are currently publishing supply chain information. This work contributes to SCT research by providing insights into the challenges and opportunities of implementing multi-tier SCT.
  arXiv  Detail & Related papers  (2024-09-12T14:55:45Z)
• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry [1.1060425537315088]
  Digital Twins (DTs) are virtual replicas of physical manufacturing systems that combine sensor data with sophisticated data-based or physics-based models, or a combination thereof, to tackle a variety of industrial-relevant tasks like process monitoring, predictive control or decision support. The backbone of a DT, i.e. the concrete modelling methodologies and architectural frameworks supporting these models, are complex, diverse and evolve fast, necessitating a thorough understanding of the latest state-of-the-art methods and trends to stay on top of a highly competitive market.
  arXiv  Detail & Related papers  (2024-07-02T14:05:10Z)
• Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry [0.0]
  This work proposes a digital twin framework for optimal and autonomous decision-making applied to a gas-lift process in the oil and gas industry. The framework combines Bayesian inference, Monte Carlo simulations, transfer learning, online learning, and novel strategies to confer cognition to the DT.
  arXiv  Detail & Related papers  (2023-11-21T18:02:52Z)
• Online Advertisements with LLMs: Opportunities and Challenges [51.96140910798771]
  This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems. We introduce a general framework for LLM advertisement, consisting of modification, bidding, prediction, and auction modules.
  arXiv  Detail & Related papers  (2023-11-11T02:13:32Z)
• Towards a General Framework for Continual Learning with Pre-training [55.88910947643436]
  We present a general framework for continual learning of sequentially arrived tasks with the use of pre-training. We decompose its objective into three hierarchical components, including within-task prediction, task-identity inference, and task-adaptive prediction. We propose an innovative approach to explicitly optimize these components with parameter-efficient fine-tuning (PEFT) techniques and representation statistics.
  arXiv  Detail & Related papers  (2023-10-21T02:03:38Z)
• Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Levels [47.009401895405006]
  Recent global disruptions, such as the pandemic and geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains. Recent global disruptions, such as the pandemic and geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains. Autonomous supply chains (ASCs) have emerged as a potential solution, offering increased visibility, flexibility, and resilience in turbulent trade environments.
  arXiv  Detail & Related papers  (2023-10-13T22:09:52Z)
• Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey [11.18312489268624]
  We propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives. We also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs.
  arXiv  Detail & Related papers  (2022-02-15T15:44:00Z)
• Digital Twin: From Concept to Practice [1.3633989508250934]
  This paper proposes a framework to help practitioners select an appropriate level of sophistication in a Digital Twin. Three real-life case studies illustrate the application and usefulness of the framework.
  arXiv  Detail & Related papers  (2022-01-14T17:41:26Z)
• Wizard of Search Engine: Access to Information Through Conversations with Search Engines [58.53420685514819]
  We make efforts to facilitate research on CIS from three aspects. We formulate a pipeline for CIS with six sub-tasks: intent detection (ID), keyphrase extraction (KE), action prediction (AP), query selection (QS), passage selection (PS) and response generation (RG) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.
  arXiv  Detail & Related papers  (2021-05-18T06:35:36Z)
• Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions [62.67593386796497]
  This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin. The major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics.
  arXiv  Detail & Related papers  (2020-11-02T19:08:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.