Falling Metrics, Rising Opportunity: How to Reboot Maintenance Productivity Through Smart Optimization

More than 55% of maintenance resources and activities in an average facility are still reactive, according to industry benchmarks. This over-reliance on reactive maintenance is often rooted in poor asset data and misaligned preventive maintenance strategies. When asset records are inaccurate or incomplete, planners struggle to schedule effective PMs, and technicians are forced to respond to issues after failures occur. This pattern reduces maintenance productivity and reflects a larger trend across industries: when data integrity slips, performance quickly follows.

Understanding Maintenance Productivity

Maintenance productivity measures how efficiently teams complete tasks. Common KPIs include wrench time, MTTR (mean time to repair), and schedule compliance. In platforms such as IBM Maximo or HxGN EAM, these metrics rely heavily on good asset data.

When data is missing or incorrect, work orders are delayed or misrouted. Technicians waste time, and planned maintenance becomes reactive. Therefore, optimization efforts fall short of their goals.

The Role of Smart Optimization

Smart optimization uses analytics, AI models, and digital twins to improve maintenance. However, smart does not mean superficial. Optimization only works when the foundation is sound, and that foundation is asset data integrity.

For instance, consider a manufacturing firm using IFS who might have faced issues with predictive maintenance. Alerts were not triggering repairs. An audit showed 15% of sensor mappings were incorrect due to errors during migration. In the end, a data verification project corrected the records and restored system performance.​

Turning the Tide: From Decline to Improvement

Instead of viewing declining productivity as a sign of failure, companies can reframe it as a valuable opportunity for improvement. A drop in wrench time or a rise in reactive maintenance is often not just a performance issue, but a symptom of deeper data-related problems. These dips frequently point to inaccurate, outdated, or incomplete asset records within EAM or CMMS platforms. 

By prioritizing asset verification and data validation, organizations can uncover and correct these root causes. Verified asset data lays the groundwork for more precise preventive maintenance scheduling. It also improves planning and resource allocation. In addition, it enhances spare parts forecasting and strengthens digital twin synchronization. In this way, what initially appears as a setback can become the starting point for smarter, more strategic maintenance operations.

The Future of Maintenance Efficiency

Verified asset data improves more than metrics. It enhances system performance, builds technician trust, and enables digital transformation. As more organizations adopt IoT sensors and mobile CMMS apps, the underlying asset data must be constantly validated. AI-based asset verification tools are becoming the enablers of this next-gen maintenance landscape.

Conclusion

The recent drop in maintenance productivity isn’t the end of the story. It is a chance to start fresh. By verifying asset data, companies can optimize maintenance and boost performance. Tools such as AI Walkdown and P&ID extraction offer a fast and scalable solution. For those using EAM platforms, verified data is more than helpful. It is a requirement for success.