Know Which Machine
Will Fail Next.
Shift from reactive firefighting to planned, predictive maintenance. Combine IoT sensor data with AI-powered failure prediction to protect output, cut costs, and extend equipment life.
From reactive firefighting to predictive intelligence
Reactive maintenance costs 3–9x more than planned interventions. When a machine breaks down unexpectedly, the cost isn’t just the repair - it’s the lost output, scrapped work-in-progress, idle labour, and cascade delays across the production schedule. Unplanned downtime averages €240,000 per hour, yet 82% of maintenance is still reactive. Parts inventory is either overstocked (capital tied up) or understocked (longer downtime). And as experienced maintenance staff retire, decades of diagnostic knowledge walks out the door.
KFactory shifts maintenance from reactive to predictive by combining IoT sensor data with AI-powered failure prediction. The platform monitors equipment health in real time through energy, pressure, temperature, and vibration sensors. AI models detect degradation patterns and estimate remaining useful life (RUL) for critical assets, recommending optimal maintenance windows that respect production priorities. FMEA (Failure Mode & Effects Analysis) is integrated for structured risk assessment. Maintenance work orders are fully digital with photo capture, structured checklists, and sign-off workflows. Work orders sync bi-directionally with your existing CMMS, so maintenance teams work in their familiar system while KFactory handles the intelligence layer. Spare parts inventory is tracked with AI-powered reorder recommendations based on predicted demand - ensuring availability without overstocking.
The result: maintenance becomes a planned, data-driven activity rather than a crisis response. Critical machines get serviced at the right time - not too early (wasted parts and labour), not too late (unplanned breakdown). Every intervention is logged, traceable, and feeds back into AI models that get more accurate over time.