Predictive maintenance is the largest use case for industrial AI
Industrial AI Market Report 2020-2025 was recently published by IoT Analytics', identifying a total of 33 different use cases that employ Artificial Intelligence tools and techniques on (predominantly) IoT-connected data sources and assets of industrial enterprises. These are the TOP 10 ranked by size and at the very top is Predictive Maintenance - one of TROIA's strongest area of expertise.
Predictive maintenance for industry 4.0 is a method of preventing asset failure by analyzing production data to identify patterns and predict issues before they happen.
Until now, factory managers and machine operators carried out scheduled maintenance and regularly repaired machine parts to prevent downtime. In addition to consuming unnecessary resources and driving productivity losses, half of all preventive maintenance activities are ineffective.
It is not a surprise therefore, that predictive maintenance has quickly emerged as a leading Industry 4.0 use case for manufacturers and asset managers. Implementing industrial IoT technologies to monitor asset health, optimize maintenance schedules, and gaining real-time alerts to operational risks, allows manufacturers to lower service costs, maximize uptime, and improve production throughput
Some of the benefits of predictive maintenance:
|Reduction in maintenance costs
|Reduction in machine failures
||Improved operator safety
|Reduced downtime for repairs
||Enhanced services for customers
|Reduced stock of spare parts
||Improved asset reliability
Offering more business benefits than corrective and preventative maintenance programs, predictive maintenance is a step ahead of preventive maintenance.
If you're still not convinced, read more HERE or let us help you with any questions.
previous article next article
see all articles