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According to a study done by Aberdeen, unplanned equipment failure can cost up to 260,000 dollars an hour. Equipment failures may have associated health and safety risks, which can be mitigated with a model that predicts an impending issue. Existing best practices, such as scheduled preventative maintenance, can mitigate failure, but will not catch unusual, unexpected failures. Scheduled maintenance can also be excessively conservative, resulting in excessive downtime and maintenance costs. Using AI to automate the process allows operators to identify subtle or unknown issues with equipment operation in the collected sensor data, schedule maintenance when maintenance is truly needed, or be automatically notified to intervene when a sudden failure is imminent.
AI will allow your organization to predict whether parts failure will occur. By leveraging AI’s collected data via sensors, your model will be able to signal your maintenance crew when an impending issue is likely to occur. Not only will this save your organization unintended down time, this proactive approach to maintenance will allow you to prevent unintended consequences of equipment failure. Some of these intended consequences include health and safety risks along with inventory damages. AI will empower your maintenance staff with the information they need to keep operations running without any disruptions.
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Manufacturers use AI to deliver the best products on the market as quickly and ethically as possible, while increasing productivity and profits. They can significantly improve demand forecasting, supply chain management, predictive maintenance, and many other operational areas with the help of artificial intelligence.