Predictive maintenance offers an opportunity to use routinely generated cold-chain data to identify emerging equipment risks earlier and support more timely maintenance decisions. This evidence-informed narrative review examines how temperature monitoring, equipment inventories, maintenance records, power data and other operational information can be better linked and used for cold-chain equipment management. It finds that direct immunization evidence for advanced predictive models remains limited, and proposes a practical four-stage pathway: strengthening data quality and linkage; introducing explainable rule-based alerts; applying condition-based maintenance and risk prioritization; and progressing to predictive analytics only where data quality, governance, technical capacity, response systems and validation are adequate. The framework is intended to complement, rather than replace, established preventive and corrective maintenance approaches.

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• Equipment monitoring systems (EMS)

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