How to Build a Reliable AIoT Pipeline for Pharmaceutical Manufacturing

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Pharmaceutical manufacturing facilities generate data from diverse sources including sensors, RFID readers, BLE trackers, and enterprise systems like MES, ERP, and LIMS, making data integration a core engineering challenge. Experts recommend designing such systems as event-processing architectures, where each data point carries identity, timestamp, and context to remain meaningful. Edge computing plays a key role by filtering invalid sensor readings, removing duplicates, and standardising protocols before data reaches central systems. A master-data mapping layer is also essential to reconcile inconsistent equipment identifiers across platforms, preventing AI models from misidentifying the same physical asset. Specialists advise establishing a clean, trusted data foundation before selecting any machine learning model, and treating AI-generated scores as inputs to a broader decision layer rather than standalone conclusions.

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