Highlights
  • Complex shop floor with legacy protocols and strict security requirements
  • Objective is to gain visibility into OEE and production metrics
  • Deployed Litmus Edge on gateways and Litmus Edge Manager in central data center
  • Reduced downtime and scrap with data visualizations and pre-built analytics
  • Integrated with MindSphere to build data models and then deploy them at the edge

A multinational engineering company that designs and manufactures power systems for transportation is adopting an Industrial IoT platform to gain visibility into OEE and other production parameters. They face several challenges including a complex shop floor with data locked in legacy machines, strict security protocols and no method to push and manage data models at the edge.

Corporate requirements for data collection, centralized application management and security influenced the company to look at Litmus for flexibility of deployment. They opted to deploy Litmus Edge on gateways to provide additional security segregation and Litmus Edge Manager in a regional data center for centralized management. They also took advantage of Litmus’ native integration with MindSphere to collate data there.

Within days the customer was able to connect and collect OEE and downtime data from data-locked legacy systems with pre-built drivers and KPIs. The IT team was able to provide visualizations at the equipment, cell, line and factory level to both executives and plant floor managers to improve operations. As a result, they reduced downtime and scrap.

The customer also enabled edge applications in a way that fit into their IT requirements. They developed edge applications centrally and then deployed them securely to edge devices on the shop floor for engineer usability while maintaining structure and security guidelines. They also generated data models in MindSphere and used Litmus to push those models to the edge.