AI-Augmented Predictive Logistics in Industry 4.0 Supply Chains

  • Authors

    • Dr. Ahmed Hassan Professor, Cairo University, Egypt. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2018PI9R2X

    Published 02-04-2018

  • Predictive Logistics, Artificial Intelligence, Industry 4.0, Smart Supply Chains, Machine Learning, Digital Twin, Demand Forecasting, Route Optimization, Edge AI, Supply Chain Resilience

    Issue

    Vol. 1 No. 1 (2018)

    Section

    Articles

    How to Cite

    [1]
    A. Hassan, “AI-Augmented Predictive Logistics in Industry 4.0 Supply Chains”, IJMLPA, vol. 1, no. 1, pp. 01–12, Feb. 2018, doi: 10.67228/3142788X/IJMLPA-2018PI9R2X.

  • Abstract

    The advent of Industry 4.0 has redefined traditional supply chain operations through the integration of advanced digital technologies. Among these, Artificial Intelligence (AI) plays a pivotal role in transforming logistics into a predictive, autonomous, and adaptive ecosystem. This paper explores the role of AI-augmented predictive logistics in enhancing efficiency, responsiveness, and resilience across modern supply chains. By leveraging machine learning, real-time data analytics, and digital twins, predictive logistics enables dynamic forecasting of demand, proactive risk mitigation, route optimization, and inventory control. The paper presents an overview of enabling technologies, integration challenges, and practical applications in various industrial sectors. Case studies and simulation-based results demonstrate the performance improvements and cost reductions achievable through AI-driven approaches. Furthermore, the study highlights future directions, such as intent-based logistics orchestration and the integration of edge AI for decentralized decision-making. This research contributes to the understanding of how AI can drive next-generation supply chain strategies, aligning with the broader objectives of Industry 4.0.

  • References

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