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@article{182417,
        author = {Nitish Bendre and Prof. Vishnupant Potdar and Dr. Nagnath Biradar},
        title = {AI-Driven Anomaly Detection in Pharmaceutical Serialization Events},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {2321-2326},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182417},
        abstract = {the process of assigning unique identifiers to each drug package – has become mandatory under regulations like the US DSCSA and EU FMD, creating massive streams of data about each product’s lifecycle. Detecting anomalies in these serialization events (for example, duplicate or missing scans, out-of-sequence movements, or unexpected location appearances) is critical to ensuring supply-chain integrity and regulatory compliance. Recently, artificial intelligence (AI) and machine learning (ML) techniques have been proposed to analyze serialization data for early anomaly detection. This paper surveys the application of AI/ML methods to pharmaceutical serialization event data, outlining typical anomaly types, specific AI techniques (such as isolation forests, neural autoencoders, and time-series models), and end-to-end system designs. We review the advantages of AI-driven monitoring – e.g. real-time fraud alerts, reduced manual auditing, and predictive risk mitigation – as well as challenges like data quality, model explainability, and integration with legacy systems. Case examples and frameworks (from GS1 EPCIS data standards to vendor solutions) are discussed, highlighting how AI can enhance track-and-trace processes, improve counterfeiting detection, and support regulatory compliance. Finally, we identify future research directions in scalable anomaly detection for the pharmaceutical supply chain.},
        keywords = {AI, ML, Anomaly Detection, Pharmaceutical Industry, supply chain},
        month = {July},
        }