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.
Solution:
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.
