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Make better data-driven decisions and respond faster to shop floor events to seize competitive advantage.
Download White PaperMake better data-driven decisions and respond faster to shop floor events to seize competitive advantage.
Download White PaperThe Critical Manufacturing IoT Data Platform is a complete, highly scalable solution that combines IoT, equipment integration, data processing and analytics with contextual intelligence from MES to help manufacturers generate transformative insights to improve performance, efficiency and innovation.
The Critical Manufacturing IoT Data Platform provides complete coverage and native integration with MES and equipment, using the Connect IoT module. The added value of using MES to enrich and contextualize data, to close output loops, ensures that the data pipeline is unified, standardized and comprehensive.
The Critical Manufacturing IoT Data Platform is a complete data management solution that comes with all the building blocks to ingest, store, process, transform and analyze data in real-time. The application is modular, open and extensible, allowing new functional blocks to be added and seamlessly plugged-in to the application.
The Critical Manufacturing IoT Data Platform, combined with MES, is an end-to-end solution for processing manufacturing data, providing support for a wide range of valuable use-case scenarios that are available immediately. Manufacturers can achieve quick wins with fast ROI and build upon results as more data becomes available. The possibilities are limitless.
Store equipment and device data for traceability and analysis: augment equipment data with MES data for full contextualization; correlate two or more variables over a given period of time.
Understand the effect and influence of different variables on a certain outcome (e.g.: yield, performance).
Monitor a set of parameters for specific conditions to automatically trigger alerts and actions (e.g. identify abnormal WIP build-up at a certain process step, put the lot on hold or put an equipment down).
Compare the performance of different equipment across a series of indicators (OEE, MTTR, MTBF, Uptime, etc.).
Analyze conditional monitoring sensor data in real-time to determine the probability of failure or the remaining useful life of a given equipment.
Monitor equipment parameters and adjust parameters on subsequent steps for the same material (feedforward) or on the same equipment for next materials (feedback).
