Aug 2026· Digital Technologies Research and Applications· 0 citations· 36 references
TL;DR
The study explores how integrating AI, the Internet of Things (IoT), and big data analytics can enable the integration of DTs to support real-time operations management, informed decision-making, and the mitigation of vulnerabilities in logistics management.
Abstract
Digital twins (DTs) are transforming supply chain management and logistics operations by improving operational efficiency, enabling data-driven decision-making, and allowing for real-time. However, many current DTs implementations lack cybersecurity frameworks, predictive analytics, and dynamic risk assessment, relying instead on static data evaluation and key performance indicator (KPI) monitoring. This research addresses these gaps by suggesting an AI-based DT framework designed to enhance the resilience, security, and operational performance of logistics systems. The study explores how integrating AI, the Internet of Things (IoT), and big data analytics can enable the integration of DTs—virtual representations of physical resources and processes—to support real-time operations management, informed decision-making, and the mitigation of vulnerabilities in logistics management. The research employs a qualitative methodology and illustrative case studies, the research explores how the adoption of DTs aligns with the ongoing logistics transformations of Industry 4.0 and AI. The findings demonstrate a practical method that leads to boosting logistics efficiency, reducing delays, and promoting environmental sustainability. This work provides strategic guidance to promote the adoption this technology in the sector and offers valuable insights for researchers and practitioners seeking to leverage digital twins to deliver secure, adaptive, and efficient digital supply chain solutions.
Modern supply chains operate under increasing uncertainty, characterized by geopolitical risks, logistics disruptions, and volatile consumer demand. Digital twin technology serves as a tool for creating a virtual model of real supply chains, enabling real-time monitoring, predictive disruption modeling, and logistics process optimization. The study aims to provide an economic justification for digital twin implementation to enhance supply chain resilience. The methodological foundations of the digital twin concept are analyzed in the context of logistics system management, and empirical data on the technology's impact on operational and financial performance are systematized. It is found that digital twins can reduce disruption response time by 35–50%, decrease unplanned downtime by 25– 40%, and improve planning accuracy by 18–30%, collectively generating positive economic effects. The research findings can be applied to develop digital transformation strategies for logistics systems in industrial and commercial enterprises.
Anastasia M. Knyazeva, Ekaterina V. Kuznetsova, Arina A. Gracheva et al.· ACCOUNTING AND CONTROL· 0 citations
Smart Manufacturing Supply Chain Management (SM-SCM) is one of the most important parts of Industry 4.0. It uses smart digital technologies to make manufacturing networks more sustainable, resilient, and efficient. This review examines the fundamental concepts, data-driven supply chain functions, automation technologies, and core digital innovations that support modern manufacturing systems. It discusses the role of data in procurement, planning, production, inventory management, transportation, and customer service for enhancing real-time visibility and decision-making. The examination delves deeper into the topic of automation by way of smart factories, robots, cyber-physical systems (CPS), the Industrial Internet of Things (IIoT), PLCs, SCADA, and digital twin technologies. Analysis also takes a look at the ways in which important supporting technologies like blockchain, AI, ML, cloud, and the Internet of Things have impacted predictive analytics, safe data sharing, and smart manufacturing. The paper also highlights practical application scenarios in automotive, aerospace, electronic equipment, manufacturing equipment, and energy and process industries, demonstrating how these technologies improve productivity, flexibility, quality, and resource utilization. Finally, the review identifies current challenges and future opportunities for developing sustainable, resilient, and autonomous manufacturing supply chains, providing valuable insights for researchers, industry practitioners, and policymakers working toward next-generation smart manufacturing ecosystems.
Dr. Prathviraj Singh Rathore· International Journal of Nex...· 0 citations
Digital technologies are transforming modern supply chains by improving efficiency, visibility, and responsiveness while helping organizations address disruptions caused by natural disasters, geopolitical conflicts, pandemics, cyberattacks, and market uncertainties. Supply chain resilience has evolved from traditional reactive risk management to proactive and adaptive approaches supported by technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, Big Data Analytics, Cloud Computing, and Digital Twins. These technologies enhance real-time visibility, predictive capabilities, collaboration, and resource optimization, enabling organizations to identify risks and respond effectively to disruptions. This study explores key resilience strategies in digitally enabled supply chains, focusing on technological enablers, organizational capabilities, and resilience dimensions including visibility, flexibility, agility, adaptability, and recovery capability. A conceptual framework is developed to examine the relationship between digital technologies and supply chain resilience. Using qualitative and quantitative assessment methods, the study evaluates resilience performance under various disruption scenarios through resilience metrics and performance indicators. The findings reveal that organizations adopting integrated digital resilience strategies achieve superior disruption management, risk mitigation, and recovery performance compared to traditional supply chain systems. The study highlights the critical role of digital technologies in strengthening supply chain resilience and provides practical insights for managers, policymakers, and researchers seeking to build sustainable and resilient supply chain ecosystems.
Suresh Babu Reddy· International Journal of Com...· 0 citations
In today’s rapidly evolving business landscape, the efficient management of supply chains and resources is paramount for ensuring competitiveness and sustainability. Data analytics has emerged as a transformative tool, enabling enterprises to make informed decisions, optimize operations, and achieve greater cost-efficiency. This paper explores the role of data analytics in streamlining supply chain and resource management, highlighting the key technologies such as big data, machine learning, artificial intelligence, and the Internet of Things (IoT). By leveraging advanced data-driven techniques, businesses can improve demand forecasting, inventory management, and logistics while reducing operational costs. However, challenges such as data privacy concerns, high implementation costs, and the need for skilled personnel must be addressed to fully realize the potential of these technologies. Through a series of case studies, this paper also demonstrates the successful application of data analytics by leading enterprises. The future of supply chain and resource management lies in the integration of innovative data analytics tools that will drive sustainability, operational efficiency, and enhanced customer satisfaction.
B. Anna· International Journal of App...· 0 citations
Manufacturing systems increasingly require real-time performance monitoring and data-driven optimization to reduce downtime, stabilize quality, and support flexible production. Although Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies have been widely discussed in smart manufacturing, existing studies often treat sensing, key performance indicators (KPIs), analytics, and decision support as separate concerns. This paper presents a structured literature review and conceptual synthesis of IoT-enabled performance monitoring and optimization in manufacturing systems, with emphasis on recent work in IIoT architectures, edge and cloud analytics, digital twins, predictive maintenance, and manufacturing KPIs. The main contribution is an integrated five-layer conceptual framework that connects physical sensing and data acquisition, edge computing and connectivity, data management and integration, analytics and intelligence, and application-level decision support. The framework clarifies how shop-floor data can be transformed into KPI-oriented insights and optimization actions while accounting for cybersecurity, interoperability, data governance, scalability, and human-in-the-loop decision-making. An illustrative automotive parts/CNC manufacturing scenario demonstrates the framework's potential application; however, no simulation, pilot deployment, or quantitative validation is claimed. The review concludes by outlining implementation considerations and a validation roadmap for future empirical studies, including digital-twin simulation, pilot testing, baseline KPI comparison after implementation, and cost-benefit assessment.
Sami Gazem Abdullah Thabet, M. Amrani· 2026 6th International Confe...· 0 citations
The automotive industry is evolving rapidly, but many supply chains still operate through disconnected systems that limit visibility, delay decisions, and reduce resilience. This research presents a practical blueprint for transforming traditional supply chains into an AI-native synchronized ecosystem.
Readers will gain insights into how Multi-Agent Artificial Intelligence (MAAI), Digital Twins, Reinforcement Learning, predictive analytics, and mathematical optimization can work together to synchronize production, supplier collaboration, inventory, warehousing, yard operations, and transportation within a unified enterprise architecture.
The paper goes beyond theory by introducing a vendor-neutral reference architecture, implementation roadmap, governance framework, and technology stack that organizations can adapt to their own digital transformation journey. It also identifies key operational inefficiencies observed across global automotive OEMs and demonstrates how AI can improve visibility, resilience, planning agility, and enterprise-wide decision-making.
I look forward to engaging with researchers, supply chain professionals, and industry leaders to exchange ideas and shape the future of intelligent automotive supply chains.
P. Mishra· International Journal For Mu...· 0 citations
