Streamline Control worked with a Fortune 500 enterprise to modernize how operational data was collected, integrated, and used across pipelines, refineries, and agricultural facilities. The organization faced increasing complexity as its operations scaled, with critical data trapped in disconnected systems. The engagement focused on creating a unified, real-time operational intelligence platform without disrupting ongoing operations. The result was a transformation that supported a $45.6B revenue business, improved forecast accuracy by 50%, and delivered enterprise-wide visibility with zero downtime.
The organization operated highly reliable industrial systems, but each business unit functioned in isolation. Pipeline operations, refinery controls, and agricultural facilities all generated valuable data, yet that data rarely moved beyond local environments. This fragmentation limited the organization’s ability to understand performance across assets, anticipate issues, or respond quickly to changing market and operational conditions.
Regulatory pressure intensified these challenges. Compliance with API 1165, PHMSA, and CER required extensive reporting that relied heavily on manual processes. This increased audit risk and consumed time better spent on optimization and planning.
At the same time, reporting cycles lagged behind operational reality. Business intelligence outputs often arrived weeks after events occurred, leaving leadership to make critical decisions without timely or complete information.
Streamline began by mapping the full operational landscape to understand system diversity, constraints, and opportunities for integration.
Rather than replacing proven control systems, Streamline designed a scalable architecture that unlocked their data safely and securely. The goal was to transform operational data into enterprise intelligence while preserving system stability and regulatory compliance.
The solution combined:
Streamline integrated a wide range of industrial technologies into a single data framework, including Rockwell ControlLogix, Honeywell Experion PKS, DeltaV DCS, and Schneider Electric PLCs. Secure protocols such as EtherNet/IP, OPC-UA, and Modbus TCP were used to normalize data and feed it into the MQTT ecosystem.
This approach ensured reliable, secure delivery of operational data into Snowflake, where it could be analyzed, visualized, and shared across the organization without compromising production systems.
The architecture was designed in alignment with the Purdue Model, maintaining clear separation between operational and enterprise layers. This ensured that safety, cybersecurity, and system integrity were never sacrificed in pursuit of analytics or visibility.
Value was delivered incrementally. Pipeline optimization capabilities were live within the first month. Agricultural analytics followed within three months, and full enterprise dashboards were in place within six months. This phased delivery reduced risk, built confidence, and ensured the organization saw results early and often.
Every organization’s path to enterprise intelligence looks different. For some, it starts with understanding what data already exists inside their operations. For others, it begins with connecting a single system, site, or asset and proving what’s possible. Streamline works alongside industrial teams to meet them where they are, designing solutions that respect operational realities while opening the door to new levels of insight, performance, and confidence.
Whether the goal is better forecasting, stronger compliance, improved asset performance, or simply clearer visibility across the business, the journey starts with a conversation grounded in real operational challenges. From there, Streamline helps organizations move forward at the right pace, building momentum through practical, measurable wins that grow over time.
For further information on Streamline Control’s cloud historian services, please contact Dan Lozie, Director of Analytics & Machine Learning, at dan.lozie@streamlinecontrol.com.