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proactive maintenance

As we step into 2025, ready for another year of new challenges and opportunities, we’ll still find some things stay the same. Demanding customers, expecting more for less; trying to stay on top of a workload in the face of a string of urgent requests and understaffed teams; and keeping the building operational in addition to dealing with added emphasis on sustainability, compliance and data management. But, in the face of all this, there is an opportunity to do things differently over the year ahead through better use of CMMS software.

While the majority of SMEs and large enterprises are now using computerized maintenance management system (CMMS) software, not all are equal. Service Works Global (SWG) has achieved the highest score for its asset and maintenance management capabilities from independent analyst firm Verdantix for the second consecutive year, meaning we’re well placed to share our top five time saving tips for maintenance and asset management.

Automating work dispatch

One of the fastest and easiest ways to save time within maintenance and asset management is automating the administration and, in some cases, the identification of work. CMMS can do this in several ways. At a basic level, it can assign priorities and deadlines, and identify duplicate requests. More advanced systems have smart technician-dispatching capabilities, automatically sending work to the mobile app of the most suitable technician based on the required skills, shift patterns and workload. FMs can get the most benefit by integrating their CMMS with a BAS, which can trigger the automatic creation of a work order when a fault is detected.

Maintenance scheduling

A well-planned maintenance schedule is immeasurably more practical than dealing with asset breakages and malfunctions, and the knock-on impact of downtime. CMMS makes light work of scheduling maintenance for multiple assets, providing graphical tools to help plan PPMs in line with expected team resource; a high degree of configurability to ensure schedules fit around site hours and avoid pre-planned events such as student exam time; and automation to eliminate the manual element of creating an annual schedule for each asset.

Preventative vs predictive maintenance

Facilities and maintenance managers can take PPMs one step further by implementing a network of sensors. Sensors can be placed next to any asset to monitor temperature, pressure, humidity, vibration frequency, and more. As these readings start to show deterioration or produce an abnormal result, then maintenance can be scheduled before any breakage occurs. Such predictive, or condition-based maintenance, can minimize spare parts costs, asset downtime and time spent on the work.

Quick, insightful data

52% of FMs in a recent UK survey with i-FM said that data and analytics would be the game changer reshaping FM over the next few years, more so than AI, more powerful CMMS systems and the increasing integration of building systems. This should come as no surprise, and we’re seeing much smarter data use from our clients. Commonly, our customers are now identifying poorly performing assets across multiple sites, allowing them to take action across all of them simultaneously to prevent further time-consuming breakdowns. They are also saving money and reducing downtime through better planning of operative visits. Performance-based FM contracts are gaining more traction in recent years due to the greater levels of accountability and quality. CMMS makes it quick and easy to understand which work should take priority, as well as providing access to asset history and manuals for ease of repair.

AI and chatbots

While not yet a widely adopted tool in FM, AI or artificial intelligence is creeping into operations. ChatGPT has certainly made AI more accessible within both personal and professional lives, and has paved the way for wider AI usage. Building on the popularity of ChatGPT, FM software is now starting to include natural language processing (NLP) based assistants. Technicians can use the ‘chat’ feature to input the problem they are facing with a piece of equipment, and the AI will go through vendor-provided documents to identify the recommended fix. Further to this, AI can analyse data from sensors and external sources to identify potential faults in seconds. For example, in the winter, AI could take maintenance data combined with weather data and anticipate escalator breakdown based on the amount of grit trampled into the mechanism. This level of forewarning means maintenance can take place with minimal disruption to building users and be conducted by the FM team in a more planned, efficient way.

 

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