Case Study: Schneider Electric achieves lower costs and safer operations with Microsoft Azure Machine Learning

Schneider Electric minimizes costs and worker risk with Azure Machine Learning service predictive maintenance

Schneider Electric, a global power management and industrial automation company, needed a safer, more cost-effective way for oil and gas producers to monitor remote equipment and prevent costly downtime. Using Microsoft Corporation’s Azure Machine Learning service and Azure IoT Edge as part of its Realift Rod Pump Control predictive IoT analytics solution, the company set out to help customers detect pump issues before crews had to travel to distant sites.

Microsoft Corporation helped Schneider Electric implement automated machine learning and an end-to-end Azure analytics pipeline to build, test, and deploy predictive models faster. The solution cut model development time from four months to one day for newer models and improved pump efficiency by 10% to 20% in just two days, while reducing field visits, lowering maintenance costs, and improving worker and environmental safety.

Schneider Electric

Matthieu Boujonnier

Analytics Application Architect and Data Scientist