Predictive maintenance has become an essential strategy for manufacturers seeking to optimise their operations, reduce downtime, and extend the life of their equipment. In recent years, machine learning has emerged as a powerful tool for predictive maintenance, enabling manufacturers to detect equipment failures before they occur and take preventive action.
According to a report by Deloitte, predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25% on average for manufacturing businesses.
These numbers seem miraculous, and the technical terms sound fascinating, but how are they practically useful? What impact do they bring to the businesses? And, more importantly, how can they be implemented?
In this blog, we have come up with a mock case study that showcases the effectiveness of machine learning for predictive maintenance in manufacturing. Consider this case study for the purpose of understanding the importance of predictive maintenance in the manufacturing business.
Challenges in the existing scenario
Without the assistance of machine learning and predictive maintenance, the manufacturer is fully relied on a reactive maintenance strategy for the business. Which meant that repairs were being performed only after a failure had occurred.
Such manufacturing businesses experience frequent breakdowns of their production lines and assembly units due to machine failures, which causes a delay in fulfilling customer orders and increases maintenance costs. This approach leads to unplanned downtime, and the business loses production time and revenue.
Implementing a predictive maintenance solution
To address this issue, the manufacturers can implement predictive maintenance solutions based on machine learning. The solution involves collecting and analysing data from various sources, including sensors, maintenance logs, and production data. The data will then be used to develop a predictive model to identify when a machine will likely fail.
The manufacturer can partner with a manufacturing software solutions provider like MSBC Group to build and develop the predictive model. The predictive model can be integrated with the manufacturer's existing systems and equipment, enabling it to monitor machine health and detect anomalies continuously.
The implementation of the machine learning solution for predictive maintenance can result in significant improvements in equipment reliability and uptime. The manufacturer can now predict equipment failures before they occur, enabling it to take proactive maintenance measures and prevent unplanned downtime. As a result of this, the manufacturer can reduce maintenance costs and improve production efficiency.
In addition, predictive maintenance assists the manufacturer in optimising its maintenance schedules. By predicting when the equipment would need maintenance, the manufacturer can schedule maintenance tasks during periods of low production, minimising disruption to operations and delivering final products on time.
The machine learning solution also provides the manufacturer with valuable insights into equipment performance. By analysing data from sensors, the manufacturer can identify trends and patterns in equipment behaviour, enabling it to make informed decisions about maintenance and optimisation.
We can conclude that machine learning has emerged as a powerful tool for predictive maintenance for the manufacturing industry. By collecting and analysing data from various sources, manufacturers can develop predictive models that enable them to detect equipment failures before they occur.
This proactive approach to maintenance can reduce downtime, improve equipment reliability, and increase production efficiency.
The case study discussed in this blog shows the effectiveness of machine learning for predictive maintenance in manufacturing. It serves as a model for other manufacturers seeking to optimise their operations through predictive maintenance. Do you want your manufacturing business to go under transformation, just like how we demonstrated in this blog? Then get in touch with us; our experts will be happy to help you improve your business efficiency and revenue.
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