Manufacturing Data Engine and Manufacturing Connect
Add edge to cloud data visibility across manufacturing operations and empower your manufacturing engineers to optimize production at scale using accessible data analytics and AI.
Fuel team productivity
Turns all your manufacturing engineers into data scientists with easy-to-use implementations.
Drive data transformation
Delivers real-time, edge to cloud data access and visibility across manufacturing operations.
Deploy AI in production
Helps factories run faster and smoother by using AI and data to optimize cost and production.
AI-driven operation optimization use cases
Quickly identify root causes of quality fluctuations and defects in order to pinpoint beneficial initiatives to increase quality and consistency, eliminate sources of defects, optimize maintenance schedules, improve machine calibration, and offer targeted operator training.
Analyze time-series machine data, fueled by the same powerful AI engine that powers Google products, like Search. Tap into a real-time stream of machine sensor data provided by the Manufacturing Data Engine. Detect trends and anomalies as they unfold across multiple sensor dimensions such as noise, vibration, and temperature. Rely on a fully managed, serverless API service to drive various use cases from root cause analysis to OEE optimization.
Deploy a solution in weeks without compromising on prediction accuracy using pre-built ML models. Direct integration with Manufacturing Data Engine helps you to tap into live and historic data and empower your own engineers to develop their own pipelines with our pre-built components. You have global visibility and can shift from facility-by-facility maintenance strategies to a global view on machine health.
Monitor in-line quality control by aggregating sensor and visual data and simulating parameter changes, combining inspection visual data with in-line sensor data. Leverage imaging data to detect and flag quality issues. Combine it with real-time sensor data to get a view of quality against defined parameters. Use your in-line quality parameter data model and AI tools to model parameter changes and understand the impact on your product quality.