Predictive Maintenance Savings Estimator
Estimate savings from moving HVAC and equipment from reactive to predictive maintenance.
Estimate only, based on typical reactive-to-predictive ratios. A puniq audit ties this to your work orders, assets, and failure history.
About this tool
Most GCC buildings still fix equipment after it breaks. Reactive maintenance means emergency call-outs, premium parts, overtime, and unplanned downtime, all of which cost far more than catching the same fault early. This estimator shows what you could save by shifting that reactive spend toward predictive maintenance driven by live data from your BMS and meters.
Enter your annual maintenance budget, the share that is currently reactive (70% is typical for buildings without good fault detection), and how aggressively predictive analytics can cut it. The tool returns your annual savings, the reactive spend it comes from, and a rough estimate of downtime hours avoided.
How to read the result
Predictive maintenance does not remove maintenance, it removes surprises. The savings figure is the slice of today's reactive spend that early fault detection and condition-based scheduling can avoid. The downtime estimate is indicative only, scaled from the cost saved. A puniq audit grounds these numbers in your real work orders and asset history.
Frequently asked
What counts as reactive maintenance?
Any repair triggered by a failure or breakdown rather than a schedule or a sensor warning. It is the most expensive way to maintain a building because it includes emergency and downtime costs.
Is 25% a realistic reduction factor?
Yes. Buildings adding fault detection and condition-based scheduling commonly cut 15 to 35% of reactive spend. The exact figure depends on asset age, instrumentation, and team discipline.
How is downtime hours avoided estimated?
It is a rough indicator scaled from the savings, not a measured figure. Use it for direction, not for an SLA. Real downtime depends on which assets fail and how critical they are.
Do I need new sensors for this?
Often not. A lot of predictive value comes from data your BMS and meters already produce, once it is unified and analyzed. puniq flags where extra instrumentation actually pays back.
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