lit magnetic resonnance imaging machine

Cost effective predictive Maintenance for Medical Device Manufacturer

Sogeti saved costs for the client by creating a predictive IoT maintenance solution.


Customer has a distributed install base of 10,000 devices and wanted to use machine data and logs collected to improve performance, forecast and anticipate issues. They wanted to provide support for multiple modalities CT, MRI and Ultrasound etc. and their versions and associated multiple log files of various formats and sizes ~100 MB. The company wanted to capitalize on it’s Knowledge by making it  available for easy diagnostic and training. 


The predictive maintenance allows transformation from a process of static maintenance to a dynamic maintenance. The solution is based on the PTC Axeda. It enables the to Collect data from various devices across modalities using Axeda foteam r connectivity, perform data analysis and calculation to identify added value with our analytics solutions comprising of Machine learning, correlation, clustering and provide alerts and notifications and plan for scheduled maintenance. It also allows service engineers to perform automated triaging and remote updates of the devices.


Sogeti helped in improving the machine availability and reduce unplanned down time. We also helped in reducing field services trips and thereby the maintenance costs.

  • Randhir Pandey
    Randhir Pandey
    Director at Capgemini, Bangalore
About the client

Global leader in Medical Devices and offers a full range of diagnostic medical imaging solutions including CT, MR, X-Ray, Ultrasound and Healthcare Informatics across the globe.