Case Study: Motor Oil Group achieves predictive maintenance accuracy and avoids costly shutdowns with SAP HANA

How Motor Oil Hellas Maximizes their Predictive Maintenance Capabilities

Motor Oil Hellas, operator of the large Corinth Refinery in Greece, needed a more accurate way to predict equipment failures and avoid costly production shutdowns despite continuous sensor streams across the plant. With high stakes—processing 185,000 barrels per stream day—the company wanted to extract value from four years of historical data, detect abnormal events earlier, and get real-time alerts to the right technicians.

Working with Accenture Greece, Motor Oil deployed a solution on SAP HANA Cloud and SAP Analytics Cloud that uses machine learning and SAP’s Predictive Analysis Library to prepare, model, and forecast sensor data. The system achieves over 70% accuracy for 24‑hour sensor forecasts and explains abnormal events with >77% accuracy up to 20–120 hours in advance, delivers intuitive dashboards and automated email alerts, reduces unexpected downtime and maintenance costs, and improves technician safety by enabling repairs instead of replacements.

Motor Oil Group

Dimitrios Michalopoulos

Industrial Applications Head of IT Division