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Enterprise Layer Intelligence
Categories/IoT & Connected Hardware/ThinkIQ
ThinkIQ

ThinkIQ

Founded 2015

Track material lineage and factory operational data to support autonomous industrial decision-making

You can use ThinkIQ to organize factory data into a clear knowledge graph that maps how raw materials move and transform across your facilities. It unifies signals from sensors, enterprise resource planning, and manufacturing execution systems into a shared semantic model. This allows automated agents and engineering teams to trace batches, audit supply chain steps, detect cross-contamination risks, and resolve yield drops. By tracking decision provenance and material lineage from suppliers to finished goods, it provides reliable operational context for audits, recalls, and plant management.

What ThinkIQ does

Map material transformations across manufacturing linesConnect data feeds from legacy plant historians and sensorsTrace lot and batch lineage through all process stagesScore incoming supplier materials for quality varianceGenerate auditable decision records for compliance reviewsCorrelate operational parameter changes with final product yieldTranslate visual camera data into plant operational logsBuilds a material ledger showing how ingredients and parts move through plantsUnifies data across enterprise resource planning and manufacturing execution systemsTraces batch lineage to speed up product recalls and compliance checksMonitors yield fluctuations and correlates them with process constraintsConverts factory camera feeds into structured operational eventsRecords audit trails explaining the provenance behind every operational decisionScores incoming supplier batches based on historical quality and attributes

Frequently asked

CESMII SMIP, Microsoft Azure IoT, SAP S/4HANA, Oracle ERP, Rockwell Automation, Siemens MindSphere, Aveva PI System, MQTT Brokers

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ThinkIQ, material ledger, manufacturing knowledge graph, supply chain traceability, industrial IoT, batch tracking, provenance tracking, operational context, yield analysis, factory data