Scaling Meter-Triggered Monitoring with Seeq Data Lab – Eli Lilly and Company

Caroline Orange Beimfohr, Digital Solutions Engineer, Eli Lilly and Company

Eli Lilly explain how they built a scalable meter-triggered maintenance solution in Seeq Data Lab to automatically convert equipment sensor data into maintenance work orders across global manufacturing sites. The presentation describes how the team used Data Lab to connect historian data, maintenance metadata, and API-based workflows into a single automated process that supports both continuous and gauge meters, while reducing manual effort and making maintenance decisions more data-driven. It also highlights the templated three-column onboarding model, custom configuration manager, and GMP-compliant review and audit controls that allowed the solution to scale across sites without forcing engineers to rebuild it locally. More importantly, the talk shows that scalable reliability programs are not just about automation, but about making smart architectural choices that balance compliance, usability, and global deployment so equipment data can drive timely action at enterprise scale.