Next-Generation AI-Driven Digital Twin Framework for Market Volatility Warning and Risk Detection in Global Logistics Supply Chains

Abstract

Serving as catalysts for real-time insight, predictive intelligence, and autonomous decision-making, digital twin (DT) systems are driving transformation in industrial domains. In global logistics supply chains, the coupling of DTs with AI/ML, edge computing, and 5G/6G connectivity offers powerful capabilities for market volatility warning and risk detection. However, these advantages also introduce profound cybersecurity, privacy, and trust challenges. Existing standards such as ISO/ IEC 27000, 3GPP TS 33.501, and ITU-T Y. 3172 partly address generic security and AI frameworks, but lack specificity toward dynamic, AI-driven DT ecosystems. This paper presents a next-generation AI-driven digital twin framework tailored for logistics supply chains, emphasizing security, interoperability, and standard alignment. We map cross-layer threats to curated countermeasures, and evaluate their alignment with international standards. We analyze standardization gaps and propose extensions and new research directions for DT-aware AI governance, cross-domain interoperability, and real-time assurance. A case study on logistics volatility detection demonstrates how this framework supports resilient, trustworthy operations. Finally, we delineate a roadmap for standardization-driven secure DT ecosystems, aiming to guide practitioners, standard bodies, and researchers.