SERVICE & MAINTENANCE
Embracing data and AI A
Steve McGregor, executive chairman at DMA Group, discusses the future of HVAC servicing and maintenance, and how the latest digital solutions are transforming the effi cacy of a predictive approach.
£49 billion maintenance backlog across UK public services isn’t just a statistic—it’s a call to action. The National Audit Offi ce (NAO) has warned that ineffi cient
Steve McGregor
and ineff ective maintenance practices, chronic underfunding, and ageing stock in critical public sector buildings jeopardise essential services and value for money. Aside from replacement and upgrade where required, the solution is a shift towards more proactive strategies that align with predictive maintenance, powered by real-time data, using monitoring tools and artifi cial intelligence (AI). By tracking asset conditions and responding to anomalies before they escalate, facilities management (FM) professionals can drive better outcomes, extend asset life, and reduce maintenance costs. Historically, HVAC maintenance relied heavily on preventative
practices – scheduled servicing at regular intervals to reduce the risk of unplanned breakdowns. While this approach has obvious benefi ts when adequately deployed, without skilled interpretation, it can lead to unnecessary intervention, resulting in avoidable costs if carried out when not required. Reactive maintenance, addressing issues as they arise,
remains common in an underfunded public sector. However, this is ultimately a false economy. Emergency repairs and unplanned downtime cause shortened asset life, expensive repairs, and disruption to building operations and businesses, which erode any initial cost savings. The result isn’t just a fi nancial problem. HVAC downtime can have serious consequences. In healthcare settings, for instance, if essential climate control and ventilation break down, critical services may become unavailable, putting patient care under threat.
16 May 2025 •
www.acr-news.com The future lies in proactive and predictive maintenance.
By leveraging smart-tech with machine learning models and real-time data collection, predictive maintenance monitors and analyses system performance, identifi es potential failures, and triggers corrective actions before issues escalate. This optimises asset management, minimises downtime, and reduces operational expenses.
Predictive maintenance A data-driven approach to M&E maintenance transforms asset
management by providing actionable insights into equipment performance. Technologies such as IoT sensors, building management and Computer Aided Facilities Management (CAFM) systems, alongside embedded or cloud-based analytics platforms, can collect and process real-time data, including: ■ Temperature and humidity monitoring: Identifying ineffi ciencies or risks.
■ Occupancy patterns: Adjusting HVAC output based on the number of people in a room.
■ Air quality conditions: Triggering ventilation or fi ltration adjustments when needed.
■ Water quality: Monitoring the condition of closed-system water for signs of corrosion, which could indicate leaks.
■Weather adaptation: Sensors factor in outdoor conditions to adjust indoor climate control dynamically.
■Energy usage: Tracking energy usage to identify high energy users, which can inform sustainability upgrades.
This granular information lets facilities teams streamline their maintenance schedules based on live insights, avoid
Download the ACR News app today
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44
