Source URL: https://cloud.google.com/blog/topics/manufacturing/manufacturing-bridging-it-ot-data-engine-cortex-framework/
Source: Cloud Blog
Title: Google Cloud helps manufacturers bridge IT and OT data with Manufacturing Data Engine and Cortex Framework
Feedly Summary: Connecting operational technology (OT) and information technology (IT) has long been a goal for manufacturers to drive greater insights across their operations. Today, at the International Manufacturing Technology Show, Google Cloud is announcing an update to our Manufacturing Data Engine (MDE) to help bridge this divide and deliver greater productivity, innovation, and profitability to the industry.
We have now established initial technical foundation extensions for the Manufacturing Data Engine, Google Cloud’s signature manufacturing solution, to integrate with Cortex Framework, a packaged solution of reference architectures, deployment accelerators, and integrated services designed to speed up cloud deployments. Connecting IT data from Cortex Framework to OT data in MDE will provide manufacturers a holistic view of their factory and business operations that’s built on AI-enabled analytics and insights. This release will provide an initial set of features for MDE to support technical interoperability required for broader IT and OT use cases.
Historically, manufacturers have faced a disconnect between their physical machines (OT) and the data they generate (IT). This separation has led to siloed teams and inefficiencies. Bringing the two together provides the opportunity to apply analytical and AI tools to industrial data, which can help unlock new levels of automation and valuable insights for enterprises.
Connecting OT and IT data faster
Today, MDE is Google Cloud’s cornerstone solution for acquiring, processing and analyzing factory OT data. Cortex Framework helps customers accelerate business insights into their enterprise IT data, enabling better business outcomes, with less risk, complexity, and cost. With MDE joining the Cortex Framework solutions portfolio, manufacturers have access to a powerful combination of solutions to connect and harness the full potential of IT and OT information on a consolidated Google Cloud data and AI platform.
This powerful combination enables manufacturers to drive a more comprehensive view of their factory operations, uncover hidden insights, and drive intelligent decision-making by more easily collecting and processing multimodal data from machines, sensors, and cameras using MDE and then contextualizing it with data from core enterprise applications like SAP, Oracle, and Salesforce — as well as other external datasets via Cortex Framework.
Manufacturers can now achieve a holistic view of their entire operations, perform data visualization and analysis, and more effectively leverage AI on and off the factory floor.
This combined offering builds on successes like those achieved at companies like Tyson Foods, where MDE is already processing and contextualizing factory OT data. With Cortex Framework, manufacturers are enabled with access to additional IT data.
Accelerating operational excellence
With OT and IT data consolidated on Google Cloud, manufacturers also have the opportunity to accelerate operational excellence with smarter data- and AI-driven insights. By building on MDE and Cortex Framework — in combination with supporting services like BigQuery ML, Vertex AI, Gemini, and Timeseries Insights API — customers can tackle pressing industry challenges such as:
Linking the enterprise to factory floor insights: Contextualize shop floor data with enterprise data sources (e.g. production, supply chain, customer service, marketing) with MDE and Cortex Framework to identify new insights whether from marketing, sales, distribution, production, finance, or more.
Gaining end-to-end process insights: Connect sales orders to production orders, and then to overall equipment effectiveness and purchase orders, and get a holistic view of end-to-end processes.
Driving accurate and timely overall equipment effectiveness analytics: Monitor and optimize equipment and plant performance, availability, and quality at scale with actionable insights to drive production improvements and meet business requirements.
Operating more sustainably: Analyze telemetry data for utility consumption and waste to reduce costs and meet environment, social, and governance (ESG) goals. Combine transaction data from ERP with ESG data from partners like Dun & Bradstreet to elevate vendor performance management processes to new levels.
Innovating with AI on top
Faster root cause analysis with machine-level anomaly detection: Analyze telemetry data streams with self-training anomaly-detection machine-learning models to quickly understand where anomalous data was created by specific machines and/or processes providing a critical head start on root-cause analysis for faster corrective action.
Proactive and automated maintenance activity: Inform plant maintenance processes on transactional maintenance systems with AI-generated predictions for machine service needs via integrated telemetry and sensor data – helping to reduce downtime and maintenance costs.
Flexible and scalable visual quality control: Train and continuously improve vision AI models on Google Cloud, deploying them on the edge and ingesting the data back to the cloud for scalable and flexible analysis of quality assurance trends and easy access to details of specific defects and component quality.
By harnessing the power of data and AI, manufacturers can unlock new levels of agility, resilience, and competitiveness.
Come visit our Google Cloud at booth #236709 at IMTS to learn more.
AI Summary and Description: Yes
Summary: The text discusses Google Cloud’s updates to its Manufacturing Data Engine (MDE) aimed at integrating operational technology (OT) with information technology (IT) to enhance manufacturing insights and efficiency. This integration enables organizations to leverage AI and analytics for better decision-making and operational excellence.
Detailed Description:
The announcement highlights the critical importance of merging IT and OT to streamline manufacturing processes. The key points include:
– **MDE Update**: Google Cloud is enhancing its Manufacturing Data Engine to bridge the gap between OT and IT, fostering greater productivity and innovation in manufacturing.
– **Cortex Framework Integration**: The MDE will integrate with the Cortex Framework, a set of solutions designed to expedite cloud deployments, allowing for holistic data analysis across IT and OT.
– **Addressing Historical Disconnect**: Manufacturers have traditionally struggled with the separation of physical machinery (OT) and data (IT), resulting in inefficiencies. The integration aims to eliminate these silos.
– **Analytical and AI Tools**: By bringing IT and OT data together, manufacturers can utilize advanced analytical tools and AI to derive valuable insights from industrial data, unlocking automation and improved decision-making capabilities.
– **Enhanced Data Visualization**: The combined solution will enable manufacturers to visualize and analyze data more effectively, leading to actionable insights across all operations.
– **Operational Excellence**: Key benefits for manufacturers include:
– Linking shop floor data to enterprise data sources to unveil new insights.
– Achieving end-to-end visibility of processes from sales orders to production and overall equipment effectiveness.
– Driving sustainability by analyzing utility consumption and waste, aiding in ESG compliance.
– Utilizing AI for faster root cause analysis and proactive maintenance, reducing downtime and improving efficiency.
– Scalable visual quality control using AI, enhancing quality assurance processes.
The integration facilitates a comprehensive view of operations, leveraging machine data, enterprise applications, and external datasets for more informed decision-making. By initiating this convergence of IT and OT, Google Cloud is positioning manufacturers to benefit from a data-driven approach to operational excellence. As such, this initiative holds substantial implications for AI adoption and industrial AI security.