Accelerate Intralogistics Maintenance with Machine Learning at the Edge
Crosser and GEBHARDT Fördertechnik GmbH announce a solution to accelerate Predictive Maintenance Models for intralogistics by using Edge Analytics and Machine Learning.
Stockholm/Sinsheim, February 11th, 2020 – Today, Crosser, a leading provider of Intelligent Edge Analytics software for Industrial IoT and Industry 4.0, and GEBHARDT Fördertechnik, knowhow leader in the industry of intralogistics, announced a joint initiative to accelerate Predictive Maintenance Models for Intralogistics by using Machine Learning.
“We truly enjoy working with innovative companies like GEBHARDT“ says Martin Thunman, CEO and co-founder of Crosser. “By utilizing the intelligent edge with Crosser, GEBHARDT will enable great opportunities for their customers” Thunman continues.
The Crosser real-time platform empowers machine builders like GEBHARDT to apply intelligence, automation and integration of machine data faster and easier than before. The platform uses pre-built building blocks in the form of modules and a drag-anddrop interface which enables customers to self-service and innovate faster and at a significantly lower cost.
“The GEBHARDT team has proven that time to roll-out can be radically shortened by combining inhouse expertise and a modern edge analytics platform” says Kai Schwab, Regional Sales Director, Crosser.
”As a leading company in Industry 4.0, we want to work with our customers to reduce total cost of ownership while increasing plant availability. The GEBHARDT Galileo IoT platform uses business data in correlation with machine and performance data. In this way, we manage to digitalize customer experience completely and connect our intralogistics solution. Together with Crosser, we strive to pursue dynamic and future-oriented solutions in the field of digitaltransformation” says Marco Gebhardt, CEO GEBHARDT Fördertechnik.
The GEBHARDT solution enables predictive maintenance for intralogistics. By using machine learning and integration at the edge to detect and act upon anomalies surrounding their next generation warehouse solutions.
Learn more about how Crosser and GEBHARDT Fördertechnik use machine learning at the edge to avoid package misplacement and unwanted stops in their intralogistic solution. Read the Crosser and GEBHARDT Fördertechnik case study here: