Boulder, 80303, CO,
United States
Self Ed
Self Ed, a prominent reseller, system integrator, and consulting company, that plays a vital role in numerous system integration and digital transformation initiatives. Self Ed collaboration with software players such as Software AG empowers organizations to embrace disruptive technologies and accelerate their journey to the cloud, thus reshaping their business models.
| Reseller and SI | Vendor | Application | Category | Market |
|---|---|---|---|---|
| Self Ed | Software AG | Software AG TrendMiner | Analytics and BI | Analytics and BI |
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Product | Category | When | Insight |
|---|---|---|---|---|---|---|---|---|---|---|
|
|
Bayer | Life Sciences | 92815 | $55.5B | Germany | Software AG | Software AG TrendMiner | Analytics and BI | 2021 |
In 2021, Bayer deployed Software AG TrendMiner at Crop Science sites in Germany. The AWS-hosted deployment was intended to democratize process data analytics and to support deeper root-cause analysis on reactor viscosity and other production anomalies, aligning the Software AG TrendMiner implementation with Analytics and BI use cases for time-series operational data.
The implementation emphasized search-based time-series analytics, pattern detection, and interactive dashboards to enable engineers to investigate anomalies without heavy data science handoffs. Configuration work focused on indexing process signals, creating analytic workspaces for reactor viscosity, and packaging repeatable investigative workflows for production and process engineering teams.
Deployment was piloted and then expanded across Crop Science production reactor sites, with rollout work beginning in 2021 and major outcomes reported by 2022. Bayer executed the program with partner eschbach while maintaining internal delivery oversight, reflecting a hybrid delivery model for operational analytics adoption.
Using Software AG TrendMiner, Bayer identified specific parameter changes linked to viscosity shifts and reported an approximate 10 percent increase in reactor production capacity, an outcome documented by 2022. The implementation impacted production operations and process engineering workflows, embedding analytics-driven root-cause analysis into routine operational practice.
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