Knowledge Centre​

With maintenance often exceeding 30% of operating budgets, controlling expenses is crucial. This article analyzes how CMMS saves operational costs by shifting from reactive repairs to proactive strategies. We detail four digital pathways—including downtime reduction and resource optimization—and share real-world cases (up to 1100% ROI) to prove that CMMS is a strategic investment for sustainable growth, not just an expense.

This article explores Enterprise Smart Maintenance, a data-driven paradigm leveraging AI and IoT to shift industrial operations from passive repair to active prediction. It details key benefits such as reduced downtime and cost optimization, outlines scalable SaaS solutions for SMEs, and forecasts future trends powered by digital twins and AI models.

Routine equipment maintenance is the bedrock of enterprise growth: it strengthens safety through meticulous checks and rapid closure of hazards, prevents costly unplanned downtime by catching early fault signals and shifting from reactive repairs to proactive prevention, and extends asset life while stabilizing quality. By building data-driven “health records,” it can cut repair costs by 20%–30%. Paired with systems like SAMEX EAM, routine equipment maintenance turns inspection data into actionable insights for full life-cycle control and stronger compliance.

Administrative asset management is often overlooked, causing duplicate procurement, asset loss, and chaotic inventory—especially in multinational operations. The remedy is a three-part upgrade: build company-wide life-cycle awareness, standardize closed-loop processes (procurement → registration “one item, one code” → usage/maintenance → disposal), and deploy digital tools for centralized, real-time management. Results cited include sharp cuts in idle/loss (e.g., 5% to 0.5%), faster approvals and inventories (72 hours to 30 minutes; 21 days to 4), and lower maintenance and spare-part costs (35% and 40%), making it a core lever for cost reduction and digital transformation.