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@article{182378,
        author = {Omkar Deshmukh and Prof. Vishnupant Potdar and Dr. Nagnath Biradar},
        title = {AI-Driven Reverse Logistics Framework for Serialized Pharmaceutical Returns},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1654-1658},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182378},
        abstract = {Pharmaceutical reverse logistics – the process of returning drugs from end-users back to manufacturers – is critical for patient safety, regulatory compliance, and sustainability. This paper proposes an AI-powered framework to improve reverse logistics for serialized drug returns. Using a sample dataset of 1,000 pharmaceutical returns, we analyze data patterns (e.g. return reasons, cold-chain violations) and demonstrate how machine learning models (classification, predictive analytics, and optimization algorithms) can support decision-making. Key AI applications include automated anomaly detection, demand forecasting, and route optimization, all leveraging serialization data. Our results show that AI can help identify high-risk returns (such as temperature-compromised batches), forecast return volumes, and optimize transportation. The framework highlights real-world benefits like reduced costs, fraud prevention, and better regulatory compliance in drug recalls and returns.},
        keywords = {Reverse logistics, pharmaceutical returns, serialization, machine learning, predictive analytics, supply chain.},
        month = {July},
        }