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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In the fast-paced landscape of industrial operations, downtime stands as a formidable adversary, casting a shadow over productivity, efficiency, and profitability. Whether it’s a manufacturing facility, a power plant, or a fleet of vehicles, the cost of unplanned downtime can be staggering, both in terms of financial losses and disruptions to operations.
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Almost all businesses and government entities leverage different assets for daily operations. As these entities expand and diversify, the intricacies associated with handling these assets become increasingly complex. The role of asset operations management takes center stage here. From facilitating maintenance activities to streamlining safety protocols, it ensures that all organization assets are well equipped, increasing facility uptime.
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