Case Study: Schneider Electric achieves predictive edge analytics and reduced downtime with Microsoft Azure

Oil and gas experts use machine learning to deploy predictive analytics at the edge

Schneider Electric, a Paris‑based leader in energy management and automation, needed a way to make its Realift rod‑pump control system more proactive for oil and gas customers that operate thousands of distributed pumps. Many sites have limited cloud connectivity or prefer to keep data on‑premises, and traditional controllers were reactive—only responding after problems occurred—leading to costly downtime, equipment damage, and safety risks.

Schneider Electric integrated Azure Machine Learning with Azure IoT Edge to run predictive models on devices at the network edge, letting Realift analyze pump telemetry in real time and automatically adjust, shut down, or alert operators before faults occur. The edge deployment reduced unplanned downtime and maintenance costs, protected equipment and the environment, and improved worker safety by cutting site visits and enabling faster, local decision‑making.

Schneider Electric

Helenio Gilabert

Director for SCADA and Telemetry