How AI-driven Predictive Tools Can Reduce Factory Downtime

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In October 2017, Nodent; a popular paint maker within the Uyo metropolis suffered an unforeseen downtime at its factory that cost it some bucks. The firm’s entire production was shut down for three consecutive days at such a critical time of the year when paint makers experience peak production.

Nodent's only power-generating set broke down leaving it with no backup system, not even power from the national grid in the face of a tall log of production orders to clear. Customers were disappointed and the firm lost a lot in earnings. The thing is, Nodent didn't foresee that breach coming. If they did, some sort of preventive maintenance would have been implemented.

Factory downtimes are anathema to smooth business operations and often lead to revenue loss in terms of deferred production and should be properly managed.

Though Nodent failed at it, scheduled preventive maintenance over the years has been the best way to ensure our engines keep humming safely for the most times possible but today, there seems to be a potential replacement and a better maintenance paradigm from that. It is AI-driven predictive maintenance.

The predictive and generative characteristics of Artificial Intelligence (AI) technology have called for a reassessment of the conventional ways of operations in almost all sectors of modern life. An AI-driven predictive maintenance model works by using real-time data gathered via a network of sensors to accurately predict what is wrong, where something is wrong or what will likely go wrong, and inform system operators early enough to make proactive adjustments or solutions.

If you've heard of digital twins, then what I just described above is no stranger or different. These AI models can play a huge role in stemming unnecessary factory downtime and the corresponding loss in revenues for business operators. Howbeit, it requires the Internet of Things (IoT) to be able to get the most accurate reading possible, as well as buy-in from a cross-section of experts involved in the overall operation concerned.

Comments

Anonymous said…
The last 2 sentences speaks so much volume and shows where the bulk of the work needs to be done. Good work with the write up
Tue May 30, 06:54:00 PM
Anonymous said…
Exactly
Tue May 30, 11:26:00 PM
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