Here's what manufacturers see when they stop guessing and start measuring.
When manufacturers gain live OEE visibility — instead of end-of-shift paper logs or daily Excel reports — they consistently find performance losses they didn't know existed.
Companies that implement continuous OEE monitoring achieve an average 3:1 ROI within the first year of deployment. — Industry aggregate, multiple sources
Most OEE losses are invisible until you measure them continuously. A 2-minute micro-stoppage that happens 40 times per shift doesn't show up in a paper log — but it shows up clearly on a live Grafana dashboard. Once operators and supervisors can see it, they fix it.
Unplanned downtime is one of the most expensive recurring costs in manufacturing. Reactive maintenance — fixing things after they fail — is always more expensive than preventing the failure.
An industrial equipment manufacturer deployed predictive maintenance monitoring across 47 facilities and reduced unplanned downtime by 41%. — Published case study, 2025
Sensors already exist on most modern manufacturing equipment. The problem is that the data is trapped in the PLC — inaccessible to the people who need to act on it. Connecting that data stream to an anomaly detection system and alerting your maintenance team before a failure occurs is a straightforward engineering problem.
Manual data transfer — between SCADA and ERP, between paper logs and spreadsheets, between quality systems and reporting tools — is one of the highest-ROI automation targets in manufacturing.
Manufacturers that automate data collection and reporting free up an average of 1.5 FTE worth of productive capacity that was previously absorbed by manual processes. — Industry aggregate
Modern automation tools (n8n, Node-RED, Python pipelines) can bridge almost any two systems that have a data output. This work is rarely glamorous, but it consistently delivers the fastest ROI of any category of work we do.
AI agents work when the problem is well-defined, the data is available, and success can be measured. Predictive maintenance, process monitoring, and reporting automation meet all three criteria in most manufacturing environments.
74% of manufacturers report achieving AI ROI within the first year — when deployments are scoped to specific, measurable use cases. — Google Cloud Manufacturing AI Report, 2025
We don't deploy AI to impress you. We deploy it when the ROI calculation is clear.
Every Foundry Data Group engagement includes:
If the pilot doesn't deliver measurable improvement, we'll tell you — and we won't propose a retainer until we can justify one.
Benchmark-based illustrations. Updated as real client cases become available.
Situation
Manual paper logs. Shift supervisors spending 2 hrs/day compiling reports. No visibility into downtime causes or OEE by machine.
Approach
3-day Rapid Assessment → 8-week OEE monitoring pilot (Grafana + InfluxDB + MQTT data pull)
Results
Est. annual value: ~$180,000 in recovered capacity
Situation
Reactive maintenance culture. One packaging line averaging 4+ unplanned stops per week. Maintenance costs rising year-over-year with no trend data.
Approach
2-day Rapid Assessment → 10-week predictive maintenance pilot on top 5 highest-downtime assets
Results
Situation
Quality data on paper. End-of-week defect reports took 3 hrs to compile and were outdated before they were reviewed. No real-time visibility into defect rates by line or shift.
Approach
4-day Rapid Assessment → 6-week automated quality data pipeline (Python + n8n + Power BI dashboard)
Results
Run the numbers on your specific situation.
These estimates are based on industry benchmarks and your inputs. Actual results vary by facility, equipment, and implementation scope.
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Use our OEE ROI Calculator to run the numbers on your specific situation — or book a 30-minute call to talk through it directly.