OEE & Asset Performance Management Solutions for Operational Excellence
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.​
< 70%
Industry average OEE vs. 85%+ world-class target
$10K – 100K+
Production revenue lost per unplanned downtime hour
50%
Downtime reduction via predictive maintenance (McKinsey)
400 – 1000%
ROI over 3 years
Production Loss​
5-15% of inventory is tied up in slow-moving or non-moving stock due to poor visibility and duplicate material records.​
Maintenance Leakage
2–5% production loss from inconsistent asset hierarchies and missing equipment context that prevents accurate root cause analysis.
Reliability Blind Spots
Unreliable MTBF/MTTR metrics from data gaps make it impossible to attribute full lifecycle costs across asset phases.
Wrong-Part Installations
Inaccurate BOMs and asset hierarchies cause wrong-part installations, shortened asset lifespans, and safety incidents.
The OEE Value Gap​
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
Global Cost of Unplanned Downtime
Trillions Annually
Source: McKinsey / Deloitte 2025
The OEEÂ Value Leak.
PiLog's Integrated Strategy
Clean Data Driving 85%+ OEE.
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.
Quantified Business Outcomes
From 70% to 85%+ OEE.
15 – 20%
Production capacity gain from OEE improvement
$5 – 20M
Annual savings from 30–50% unplanned downtime reduction
18 – 25%
Maintenance cost reduction through predictive approach
What Is Your OEE Gap Costing You?
Understand your production revenue loss from the OEE gap, then explore how PiLog's data-driven approach closes it.
