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Predictive and Prescriptive Logistics Optimization Using Hybrid AI, Time-Series Analytics, and Synthetic Data: A Case Study

Author

Listed:
  • Lakshmi Devi Pujari

    (Professor, Dept. of ECE, St. Peter's Engineering College(A), Hyderabad -500014.)

  • Sridhar C. Naga Venkata

    (Product Innovation Manager, Hyderabad)

  • Saayee Saahit CNV

    (Scholar, Dept. of Computer Science, NW Missouri State University, Missouri)

  • Swetha Reddy Ravula

    (Visiting Asst., Professor, Bucknell University)

Abstract

Global logistics networks face increasing volatility driven by geopolitical tensions, climate disruptions, demand variability, and operational uncertainty. Although artificial intelligence has improved predictive capabilities in logistics, classical and standalone learning models remain limited by data sparsity, non-stationarity, and scalability constraints. This study proposes a hybrid logistics intelligence framework that integrates time-series forecasting, synthetic data generation, and AI-based optimization. The framework is designed to enhance forecasting robustness and translate predictions into actionable operational decisions. A FedEx case study demonstrates how historical shipment data, real-time telemetry, and synthetically generated disruption scenarios can be jointly leveraged to improve demand forecasting, routing efficiency, and service reliability. Performance is evaluated across real, simulated, and hybrid datasets. Results show that the proposed approach consistently outperforms traditional statistical and machine-learning methods in accuracy, robustness, and operational scalability.

Suggested Citation

  • Lakshmi Devi Pujari & Sridhar C. Naga Venkata & Saayee Saahit CNV & Swetha Reddy Ravula, 2026. "Predictive and Prescriptive Logistics Optimization Using Hybrid AI, Time-Series Analytics, and Synthetic Data: A Case Study," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(14), pages 68-77, January.
    • Handle: RePEc:bcp:journl:v:10:y:2026:i:14:p:68-77
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    References listed on IDEAS

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