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'Beyond Smart to Intelligent Factory' CJ OliveNetworks to manage machinery and equipment lifespan with AI

A predictive maintenance system installed in Amorepacific Osan Beauty Park

CJ OliveNetworks (CEO Cha In-hyuk) has installed a predictive maintenance system in the Osan Beauty Park by Amorepacific, a global cosmetics giant.

The system predicts failures of machinery and equipment, helping extend their lifespan, in addition to increasing their productivity and reducing maintenance expenses.

When predictive maintenance is implemented, human workers no longer need to carry out predictive or maintenance activities themselves, leading to decreased exposure to accidents and risks, which is in line with ESG initiatives for a safer working environment.

Big data-intensive AI solution diagnoses facility defects without having to obtain current data

CJ OliveNetworks has implemented an AI solution for collecting and analyzing big data through deep learning in order to monitor status and perform predictive maintenance on robotics and facilities in Amorepacific’s cosmetics production plant.

This solution has built-in big data, which includes frequencies, shaft rotations, and vibrations of global bearing manufacturers, making it possible to diagnose equipment defects without having to obtain data from current, voltage, and vibration sensors installed in the equipment.

The solution visually displays results of the diagnosis at the location of the sensors on the equipment and provides an analysis and follow-up measures based on the type of defect, thereby contributing to a decrease in accident rate and an increase in productivity.

Leads to increased ESG management efficiency by reducing accident rate and improving worker safety

FactoryONE, the smart factory solution designed and developed by CJ OliveNetworks, includes various platforms for environmental safety, energy management, predictive maintenance, and facility management. The data accumulated in these platforms will be integrated with AI-based solutions for factory optimization and energy optimization.

According to Song Won-cheol, head of DT Business2 which oversees the AI factory business, “The initial cost of implementation is low because the solution offers real-time monitoring and status diagnosis of machinery and equipment without requiring input from big data or AI experts. This results in a significant decrease in facility defect rate, on top of securing worker safety and allowing workers to allot their time and effort towards tasks with higher value. With all this combined, the solution will lead to improved production efficiency.”