Maintenance Matters Focus on: CMMS 14 | Plant & Works Engineering www.pwemag.co.uk April/May 2025 Bridging the gap between CMMS & emerging technologies CMMS platforms are evolving, but integrating AI, IoT, and machine learning remains a major challenge. Compatibility issues, data inconsistencies, and a lack of expertise hinder adoption. To bridge the gap, businesses must prioritise modular systems, data standardisation, and phased implementation—ensuring technology enhances, rather than complicates, maintenance operations. PWE reports. In recent years, the role of Computerised Maintenance Management Systems (CMMS) has evolved dramatically. What was once a straightforward digital logbook for scheduling and tracking maintenance tasks is now expected to be an intelligent, predictive tool that helps organisations optimise their asset management. The shift towards predictive maintenance, driven by technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML), is a logical progression. However, for many maintenance teams, this shift is proving to be a significant challenge. The problem is not the technology itself—AI, IoT, and ML have been around for some time and are already making a huge impact in various industries. The challenge is integrating these technologies effectively with existing CMMS platforms. Many organisations, particularly those with legacy systems, are finding that their CMMS simply isn’t built to handle the influx of real-time data from IoTenabled sensors or to apply machine learning algorithms to detect patterns in equipment failure. Instead of becoming more efficient, maintenance teams often find themselves bogged down by compatibility issues, inconsistent data, and unreliable system performance. For businesses that have already invested heavily in their CMMS, the idea of scrapping the system and starting from scratch is both impractical and expensive. Upgrading is often the preferred route, but even that comes with its own set of hurdles. Many CMMS providers are rushing to offer ‘AI-powered’ or ‘IoT-ready’ features, yet in practice, these capabilities often fall short of what’s needed in real-world maintenance operations. Systems that claim to be fully integrated often require extensive customisation, additional middleware, or workarounds that undermine the very efficiency gains they promise. Compounding the problem is a general lack of expertise in implementing and managing these new technologies. Maintenance professionals are experts in machinery and equipment, but they aren’t necessarily data scientists or software engineers. The result is a disconnect between the teams that develop CMMS software and the people who use it on the ground. Training is essential, but without the right tools and support, even the most skilled maintenance teams can struggle to make sense of vast amounts of data and extract meaningful insights. Implementation This isn’t to say that the integration of AI, IoT, and ML into CMMS is a lost cause. Far from it. There are businesses that have successfully implemented predictive maintenance strategies, using data-driven insights to reduce downtime and improve asset reliability. However, these success stories often involve organisations that have either built their CMMS with integration in mind from the outset or have been willing to invest significantly in bridging the gap between old and new technologies. So what’s the solution? First and foremost, CMMS providers need to prioritise seamless integration with emerging technologies rather than simply adding features as an afterthought. Open Application Programming Interfaces
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