Automobile Part Manufacturers


A leading automotive manufacturer, faced significant downtime and unexpected equipment failures, leading to production delays and increased maintenance costs. Traditional preventive maintenance schedules were not effectively addressing the root causes of equipment breakdowns.


The company implemented a predictive maintenance program utilizing advanced sensors, data analytics, and machine learning algorithms. Key steps included:

1. Sensor Installation: IoT sensors were deployed on critical machinery and equipment to collect real-time performance data.

2. Data Collection: The collected data from the sensors was seamlessly aggregated into a centralized database. This ensured a unified and holistic view of the equipment's health and performance.

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