Connecting operations technology (OT) and IT has been an ongoing goal for manufacturers to drive greater insights across their operations through the ability to share data between plant floor and enterprise systems. To aid the connection of IT and OT systems, Google Cloud has updated it Manufacturing Data Engine (MDE)—Google Cloud’s software for acquiring, processing and analyzing factory data.
This update establishes the initial technical foundation extensions for MDE to integrate with Google’s Cortex Framework, a software package of reference architectures, deployment accelerators and integrated services designed to speed up cloud deployments and help users accelerate business insights into their enterprise IT data. The combination of MDE and Cortex Framework enables manufacturers to have a more comprehensive view of their factory operations, uncover hidden insights, and drive intelligent decision-making by more easily collecting and processing multi-modal 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.
By using MDE and Cortex Framework—in combination with supporting services like BigQuery ML, Vertex AI, Gemini and Timeseries Insights API—manufacturers can:
- Link enterprise to factory floor insights: Contextualize shop floor data with enterprise data sources (e.g., production, supply chain, customer service and marketing) with MDE and Cortex Framework to identify new insights whether from marketing, sales, distribution, production or finance.
- Gain end-to-end process insights: Connect sales orders to production orders and then to overall equipment effectiveness and purchase orders for a holistic view of end-to-end processes.
- Drive accurate overall equipment effectiveness (OEE) analytics: Monitor and optimize equipment and plant performance, availability and quality at scale with actionable insights to drive production improvements and meet business requirements.
- Operate more sustainably: Analyze telemetry data for utility consumption and waste to reduce costs and meet environmental, social and governance (ESG) goals. Combine transaction data from ERP with ESG data to elevate vendor performance management processes to new levels.
- Get 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.
- Enact 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 to help reduce downtime and maintenance costs.
- Deploy 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.
Praveen Rao is director of manufacturing industry strategy and solutions at Google Cloud, and John Studdert is solutions lead, manufacturing and supply chain, at Google Cloud Cortex Framework.
