The gap between your current overall equipment effectiveness (OEE) and the world-class 85%+ benchmark is not a maintenance problem – it is a data problem. Clean asset master data enables reliable OEE calculation and unlocks predictive maintenance ROI.
Industry average OEE vs. 85%+ world-class target
Production revenue lost per unplanned downtime hour
Downtime reduction via predictive maintenance (McKinsey)
ROI over 3 years
5-15% of inventory is tied up in slow-moving or non-moving stock due to poor visibility and duplicate material records.
2-5% production loss from inconsistent asset hierarchies and missing equipment context that prevents accurate root cause analysis.
Unreliable MTBF/MTTR metrics from data gaps make it impossible to attribute full lifecycle costs across asset phases.
Inaccurate BOMs and asset hierarchies cause wrong-part installations, shortened asset lifespans, and safety incidents.
A 13-point OEE gap (70% → 83%) at a mid-size facility represents $10–15M in annual lost production. The root cause? Inaccurate asset data driving wrong maintenance decisions.
“Effective APM and OEE depend on clean, contextualized asset master data and standardized failure/event models. MDM for the asset domain enables reliability engineering, predictive maintenance, and total cost transparency.”
— Gartner Research, 2025
Accurate equipment hierarchies, maintenance history consolidation, clean BOMs with correct specs, and real-time SAP PM/EAM integration across Maximo and Infor systems.
Reliability Centered Maintenance (RCM) implementation, PM schedule optimization, criticality analysis, risk-based prioritization, and FMEA with quality data inputs.
Clean data foundation for IoT/sensor integration, anomaly detection and early warning, and AI-ready hierarchies for ML predictive maintenance models.
Production capacity gain from OEE improvement
Annual savings from 30–50% unplanned downtime reduction
Maintenance cost reduction through predictive approach
Extended asset lifespan = deferred capex of millions
Understand your production revenue loss from the OEE gap, then explore how PiLog’s data-driven approach closes it.
