AI for predictive maintenance in manufacturing reduces downtime, and maintenance costs, and saves you from frequent repairs. It gives insights into your assets’ health in advance and tells when the machine part will fail or wear out and what caused its failure.
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Predictive maintenance in manufacturing minimizes downtime, keeping vehicles and machines operational for longer and boosting overall productivity.
It suggests optimal maintenance schedules of assets, thus reducing frequent repair and maintenance needs.
By predicting potential failures before they occur, AI helps prevent catastrophic breakdowns and costly warranty repairs, leading to significant cost savings.
It gives insights into machines’ performance helping you fix issues that majorly impact the machines’ lifespan.
Our client is a global leader in the commercial vehicle manufacturing industry. For over two decades, they have been manufacturing high-performing trucks, buses, engineering components, and power solutions in their country-wide facilities. These vehicles and solutions are fuelling the economies of many countries across the globe.
Reliable commercial vehicles are the lifeblood of many businesses’ operations. Even a single unexpected breakdown can lead to costly repairs and significant disruptions. To minimize these risks and reduce costs, our client relies on traditional telematics systems to monitor the health of their entire fleet.
While these systems provide valuable updates on vehicle performance and allow for scheduled maintenance, they primarily focus on identifying existing issues rather than predicting future failures. This reactive approach increased vehicle downtime, and technician workloads, and limited the personalized service offerings to the customers.
Softude created an AI solution for predictive maintenance in manufacturing that provides a real-time view of the health and performance of vehicles. It works on 10^8 records per day including both historical and real-time contextual data on various parameters such as engine temperature and fuel consumption.
Analyzing this huge amount of data gave insights into which vehicle models and their components or parts require immediate maintenance and also those that will wear out in the future. The maintenance team gets this info as early as 3 weeks so that they can prevent those issues from halting vehicles' operation.
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