The IoT Data Platform for Manufacturers

Make better data-driven decisions and respond faster to shop floor events to seize competitive advantage.

Download White Paper

The IoT Data Platform for Manufacturers

Make better data-driven decisions and respond faster to shop floor events to seize competitive advantage.

Download White Paper

Transform manufacturing data intoactionable insights

The 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.

Visualize, report and interact with data

  • Automatically identify and resolve issues as early as possible with a highly scalable event ingestion mechanism that can route events to multiple real-time stream processing instances.
  • Make faster time-critical data analysis with batch-processing.
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How does the IoT Data Platform differentiate?

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.

Analyze data with their full context, enabling faster decisions and timely problem solving.

Enrich IoT data received from the shop floor equipment with MES contextual data for deeper insights.

Reduce the cost of hardware for processing, storage and solution deployment on-premises, in the cloud or in hybrid mode.

Avoid high IT costs by using open and universal technology for developers, Apache Kafka™ streaming data platform and Apache Spark™ unified analytics engine.

Drive results with a complete end-to-end IoT Data Platform

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.

    • Collect and process all your manufacturing IoT data close to where it is generated and solve the issues of bandwidth and latency created in data transfer.
    • Reduce security risks with encryption of data over the network compared to processing data in the cloud.
    • Use Connect IoT, a Critical Manufacturing MES module, to make IoT integration with any type of legacy or IoT device fast and easy, utilizing the largest library of interfaces in the industry.
    • Avoid isolated and disconnected silos of data. The IoT Data Platform ingests all your sensor, device, equipment and application data, such as ERP, PLM and MES in a consistent and unified way.
    • The IoT Data Platform can ingest both structured and unstructured data payloads, such as images, documents and test results.
    • Using strong security and event validation, the IoT Data Platform stores events in an Apache Kafka™ event stream, which provides a durable, distributed and highly scalable unified analytics platform for large scale online or offline data processing across a wide range of simultaneous workloads. Apache Kafka™ also allows any event stream to be replayed from any given point in time onwards.
    • The IoT Data Platform supports multiple consumer data processing subscriptions to one or more events, allowing data to be processed in real-time using statistical, machine learning or other types of algorithms to respond rapidly to conditions as they arise.
    • For scheduled data, it is possible to use batch processing for larger volumes of data, for example, to create predictive models to be used by the stream processing applications.
    • Data consumers can perform other data operations, such as data augmentation, data transformation and also persist the data into data stores for later visualization and analysis. New events can be injected into the platform using the ingestion layer or trigger applications in the Serving and Output layer.
    • Give your Data Engineers and Scientists the tools they need to generate prescriptive analytics to recommend future actions for better outcomes. With Apache Spark™, data can be processed in real-time using statistical, machine learning or other types of algorithms to respond rapidly to conditions as they arise.
    • Data outputs become useful in the serving and output layer, where they can be visualized, analyzed and explored to gain actionable insights.
    • Applications can automatically respond to close the loop, such as sending an email to schedule equipment maintenance, based on a predictive maintenance model.
    • Any third-party business intelligence application can be used to access the data persisted in one of the data stores in the Serving layer.

Address a wide range of manufacturing use cases

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 sensor data

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.

Correlate variables and outcomes

Understand the effect and influence of different variables on a certain outcome (e.g.: yield, performance).

Trigger alerts

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 equipment performance

Compare the performance of different equipment across a series of indicators (OEE, MTTR, MTBF, Uptime, etc.).

Predict equipment failure

Analyze conditional monitoring sensor data in real-time to determine the probability of failure or the remaining useful life of a given equipment.

Adjust equipment parameters

Monitor equipment parameters and adjust parameters on subsequent steps for the same material (feedforward) or on the same equipment for next materials (feedback).

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