Optimizing cost through predictive maintenance

Unexpected downtime is detrimental to any organization as the impact is wide-ranging and difficult to measure, irrespective of the building whether commercial or residential. When systems fail, the clock starts ticking and every minute costs. However, smart predictive maintenance systems (PdM) can foresee a vast majority of unexpected downtimes by alerting maintenance teams before the system fails. Predictive maintenance refers to the ability to forecast imminent breakdowns and faults of an appliance to proactively take necessary actions and ensure its availability and smooth operation. Traditionally, PdM for building systems involve scheduled servicing on a certain date post a certain number of operating hours. Despite regular servicing, building systems can and will break down every now and then, often at the worst times. 

Predictive maintenance
Predictive maintenance uses condition-monitoring equipment to evaluate an equipment’s performance in real-time. A key element in this process is the Internet of Things (IoT), which allows for different assets and systems to connect, work together, share, analyze, and analyse data. It relies on predictive maintenance sensors to capture and utilize information, and identify areas that needs attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. 

Benefits of implementing predictive maintenance
When predictive maintenance is working proficiently as a maintenance policy, maintenance is only performed on assets/equipment, when required. Precisely, just prior to break down is likely to happen. This brings quite a few cost savings, as given below:
•    Minimize the time, the machine is being maintained
•    Save the production hours lost to maintenance
•    Reduce the cost of spare parts and supplies

There have been cases where predictive maintenance plans have led to an increase in ROI i.e. about 25%-30% reduction in maintenance costs, 70%-75% decrease of break downs, and 35%-45% reduction in downtime. These cost savings come at a fee, however, some condition monitoring procedures are expensive and entail expert and qualified people for data scrutiny to be effective.

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