Streaming machine and PLC data through Oden's Data Engine, AI models deliver live process-setting recommendations to operators and flag quality drift before it becomes scrap.
Production lines depend on conditions and equipment behavior that change between scheduled inspections. Quality drift, slowdowns, or unplanned downtime may emerge without a clear indication of which line, machine, or process step needs attention. Plant teams often rely on manual readings, spreadsheets, and disconnected historian systems, making it hard to compare current conditions with normal operation. This delays response and increases the chance of downtime, scrap, safety exposure, or missed production targets.
Oden Process AI collects streaming process signals, machine states, and quality data across production lines and sends the readings to a secure cloud application. Dashboards and configurable rules show performance against target, quality risk, and downtime causes, retaining history by site, line, or asset. This gives operators, process engineers, and plant leadership the context to adjust process settings in real time, helping them hit speed, cost, and quality targets together and document the response.
1. Sensors, PLCs, and machine controllers on the plant floor stream process, quality, and machine-state data continuously. 2. Oden's Data Engine ingests these streams alongside contextual production data, cleaning, labeling, and aligning it in real time. 3. Embedded AI models analyze the unified dataset to detect drift from optimal operating windows and predict quality outcomes. 4. Process AI pushes specific, actionable setting recommendations directly to the operator's screen. 5. Factory Analytics dashboards let engineers and plant leaders investigate root causes and track OEE.
Vendor documentation: vendor website
Evidence level: L3 · Case-documented: how evidence levels work
Performance gains: Achieve OEE gains of over 20% and lift productivity by up to 30% without added headcount
Rapid ROI: See up to 5x return on investment within 12 months
Faster operator ramp-up: Get new operators performing like veterans from their first shifts
Less scrap and rework: Reduce scrap and quality escapes with predictive quality alerts
Real-time process setting guidance delivered directly to operator screens
Flags quality drift before product falls out of spec
Unified collection, cleaning, and contextualization of machine and production data
Pre-built and configurable visualizations for OEE, downtime, and root cause analysis
Configurable triggers notify teams the moment metrics deviate from target
Compare lines, shifts, and plants on one unified data foundation
Factory Analytics and Process AI empowered frontline operators across ink and coatings manufacturing
Real-time analytics cut quality troubleshooting from 3–5 days to 20 minutes
Ask the AI Solution Architect: compare this solution against alternatives, check connectivity for your region, and get integration guidance. Included with IoT Apps Enterprise.
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