IoT-Driven Predictive Maintenance Dashboards in Industrial Operations

Industrial operations increasingly rely on Internet of Things (IoT) sensors to monitor machine health, process variables, and environmental conditions. This paper presents an end-to-end approach for deploying IoT-driven predictive maintenance dashboards that transform raw sensor streams into actionable maintenance decisions. We describe a scalable data architecture for real-time ingestion, processing, and storage; predictive models for remaining useful life (RUL) estimation and anomaly detection; a health-score framework that synthesizes multiple indicators; and a dashboard design that supports operators, maintenance planners, and line managers. A pilot deployment in a manufacturing setting demonstrates measurable improvements in asset uptime, reduced mean time to repair (MTTR), and more efficient maintenance scheduling. Key contributions include [1] an integrated IoT-to-dashboard framework bridging data science and operations, [2] a modular modeling approach combining time-series forecasting and anomaly detection with interpretable health scores, [3] a dashboard design guided by human factors and decision-support needs, and [4] practical guidelines for data governance, security, and deployment. The results indicate that well-designed predictive dashboards can shorten decision cycles, increase asset availability, and reduce maintenance costs while maintaining data quality and security.