Predictive Quality

Seebo Predictive Quality is used in discrete manufacturing to minimize quality rejects that lead to costly production rework and scrap – for example automotive Tier 1 and OEMs, data storage systems, and more.

The solution aggregates data from the production line, including data from automated quality inspection systems, and applies process-based machine learning to anticipate process and asset issues that drive scrap.

Predictive Waste

Seebo Predictive Waste reduces production waste in process manufacturing, where quality inspection of product samples is regularly performed, typically in a lab environment – e.g. food and beverages, pharmaceuticals, and cosmetics.

The solution employs supervised machine learning models and automated root cause analysis to alert process engineers to process and asset disturbances that will drive quality failures and waste.

Production Optimization

Seebo Production Optimization leverages process-based machine learning to deliver continuous improvements in production line processes – translating to an increase in production profitability.

The solution enables production teams to continually predict and prevent process inefficiencies – such as undesired side products, losses during purification, losses during separation, process instability, and more – to improve production yield and quality.

Predictive Maintenance

Seebo Predictive Maintenance is a turnkey solution that is easily customized to suit the precise needs of manufacturing processes.

The solution combines machine learning algorithms and intuitive digital twin dashboards for maintenance and operations managers to understand how to prevent machine failure in their production lines, how to optimize maintenance scheduling, and the best way to fix an issue for reduced service time and costs.

Digital Twin

Seebo Digital Twin delivers manufacturing teams actionable insights into quality, availability, and performance of the production line. The insights are provided in the context of the production line by presenting them within the digital representation of  line’s processes and assets.

Users navigate through the digital twin to drill into processes and assets of interest, while gaining critical production performance insights at every level of the digital twin.