Using data analysis to forecast when maintenance should be performed, predictive maintenance is heralded by some as the future of facilities management. But do the challenges outweigh the benefits? Two FMs debate it out: CBRE’s Micah Jacob and Jaco Van Heerden.
For predictive maintenance
Micah Jacob: At present, the facilities management industry practises two types of maintenance:
- reactive maintenance (RM), which is determined by failure of an asset (leaks and breakdowns, for example), and
- planned preventive maintenance (PPM), which is determined by legislative and regulatory bodies (Australian standards, compliance and so on).
Predictive maintenance (PdM) is a smarter way of managing assets that uses the condition of the asset in real time and determines required actions. Used effectively, PdM can help FMs save costs and it also helps with real-time asset monitoring, proactive operations, data decentralisation, reducing asset downtime and optimising performance.
Some examples of PdM are thermal imaging, infrared, sound level measurements, vibration analysis, leak detections and temperature monitoring using wireless sensors.
Like any new practice, the greatest challenge for PdM today is acceptance from clients and organisations, as it is unregulated. But there are plenty of benefits to be excited about.
Unplanned call outs for breakdowns impact the majority of the reactive costs and increase workload for the facilities manager. PdM eliminates unplanned call outs as it provides data analytics around failure rates. As most PdM is completed without any shutdown to equipment, this also reduces downtime, which can be impactful for assets that cannot afford any downtime.
PdM has already shown to increase ROI tenfold. The applicability of PdM across various FM sectors such as commercial, retail and industrial makes it extremely effective. Various forms of PdM pin point accuracy using predictive algorithms, and data derived from sensors can help FMs avoid critical incidents.
Against predictive maintenance
Jaco Van Heerden: In the realm of FM, predictive maintenance stands as a beacon of operational optimisation. Yet, beneath the sheen of its benefits, a nuanced landscape of challenges emerges.
Confronted with the decision to transition from traditional preventive maintenance to the more forward-looking predictive approach, we can easily get sold by the claims of efficiency and savings, but let’s shed some light on the practical drawbacks of this transformative strategy.
Initial investment: The allure of predictive maintenance’s long- term gains is tempered by the upfront financial commitment. These systems predominantly operate using cutting-edge sensors and software, which is expensive.
Complex implementation: The reality of integrating predictive maintenance within an existing ecosystem is far from seamless, and can lead to unforeseen delays and adjustments to accommodate the system.
Skill requirements: The vast amount of data generated by predictive maintenance calls for a shift in the skill set of the existing FM team. This shift will result in having to either hire new team members or train existing to add the required knowledge to your team.
Data accuracy: These systems’ predictive alerts provide invaluable insights, but not without occasional misfires. There will inevitably be instances where false alarms lead to operational disruptions.
Limited applicability: Predictive maintenance might not be suitable for all types of equipment or industries, making its benefits limited in certain contexts.
Ongoing maintenance: Finally, predictive maintenance isn’t a one-time solution. Regular system maintenance and updates are essential to sustain accuracy, underlining the need for ongoing commitment and resource allocation.