A field guide for data leaders: How General Mills transformed their manufacturing analytics and decision-making with Materialize
Introduction: From minutes to microseconds
Manufacturing has always relied on data. But that data has often lived in silos, hidden in systems built for batch reports and slow feedback loops.
At General Mills, data has long been in the company’s DNA — dating back to the origins of dimensional modeling in the 1960s. But in today’s landscape, the demands on data infrastructure have shifted dramatically. The modern plant floor now generates hundreds of thousands of signals in real time, and responding to issues in minutes rather than days is critical.
This guide provides an inside look at how General Mills is transitioning from traditional batch-based analytics to real-time, streaming analytics — powered by Materialize — to optimize manufacturing, improve quality, and empower plant operators.
You’ll find six detailed use cases drawn directly from General Mills’ transformation journey, including how they use real-time SQL, digital twins, and streaming architectures to enhance decision-making, enable rapid iteration, and even begin to automate line optimization.
Use Case #1: Real-time line disruption alerts
When a production line goes out of spec, every minute lost translates to material waste, lost throughput, and additional labor. General Mills wanted operators to respond in real time to faults, not rely on post-mortem batch reports.
Using Materialize, they stream data directly from PLCs and time-series systems to create always-fresh SQL views. Operators on the floor now get alerts and context fast enough to respond immediately.
Key Outcomes:
- Downtime response cut from hours to minutes.
- No need for separate streaming pipelines.
- Operators empowered with immediate insight.
Use Case #2: Ingredient variability and adaptive process control
Raw materials like flour, sugar, or cornmeal are not uniform. Each batch has slightly different physical and chemical properties. These variations impact how machines need to run.
General Mills uses streaming analytics to detect these differences in real time and feed recommendations to operators on how to adjust setpoints, bake time, and process parameters. Eventually, this feedback will be automated.
Key Outcomes:
- Reduced product variability.
- Less manual tuning across lines.
- Foundation for closed-loop automation.
Use Case #3: Dynamic digital twins
General Mills is building real-time digital representations of their manufacturing lines. These digital twins allow engineers and analysts to experiment rapidly with new settings, monitor transient losses, and run localized optimization tests without affecting global systems.
Materialize enables this agility by letting teams create streaming SQL views — no need for complex bespoke code. More people can explore line behavior and optimization opportunities with lower overhead.
Key Outcomes:
- Local teams can run independent experiments.
- Faster iteration cycles.
- Broad access to real-time data via SQL.
Use Case #4: Human-in-the-loop optimization → autonomy
General Mills is progressing from enabling humans to make better decisions — to automating those decisions. This begins by sending recommendations to the plant floor, then moving toward machines making those changes autonomously.
Streaming data powers the contextual awareness needed to close this loop safely and confidently.
Key Outcomes:
- Clear roadmap to full automation.
- Safety guardrails layered into feedback systems.
- Earning the right to automate through data-driven confidence.
Use Case #5: Unified semantic layer (no more “two clocks”)
One of the biggest problems in modern analytics: batch and streaming systems that tell different stories. General Mills aims to eliminate that split by using a unified SQL layer on top of streaming data — no separate pipelines, no dual definitions.
Materialize provides a consistent, always-updating view of key metrics so that analytics, operations, and automation all align.
Key Outcomes:
- One source of truth for operational and analytical data.
- Faster decision-making without semantic drift.
- Simpler architecture, lower maintenance.
Use Case #6: Real-time operator messaging via reverse ETL
Sometimes the best use of real-time data is simply telling someone to act. General Mills uses Materialize to trigger alerts and recommendations to the right person at the right time — with minimal latency.
Instead of waiting for reports or dashboards, operators receive immediate, contextual messages informed by live data.
Key Outcomes:
- Higher operator responsiveness.
- Reduced dependence on dashboards.
- Scalable alerting architecture powered by SQL.
The future of manufacturing analytics happens in the moment
The future of manufacturing analytics is real time — but not because real-time is a buzz word. It’s because in manufacturing, problems don’t wait, things shift at the speed of production, and optimization is a constant process that happens in the moment.
Materialize enables data teams to move from observing to acting — and eventually automating — without needing a team of distributed systems experts.
As Nathan Bean of General Mills put it, ‘We want to go from art to science — and Materialize lets us do that with agility, safety, and speed.’