In contemporary times, maintenance work, and even entire maintenance management systems, are usually supported by sophisticated computerized maintenance management system (CMMS) software and Enterprise Asset Management (EAM) software.
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Leveraging advanced technologies such as machine learning, AI, and IoT sensors, predictive maintenance promises to revolutionize how companies manage their assets, preempting equipment failures and optimizing maintenance schedules. However, beneath the benefits of predictive maintenance lie a set of challenges and drawbacks that cannot be overlooked. As organizations increasingly adopt predictive maintenance practices, it becomes imperative to shine a light on the less glamorous aspects of this approach.
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