HSINCHU, Taiwan – AUO today announced that its Taichung Fab 3 has received recognition from the ROI-EFESO INDUSTRIE 4.0 AWARD in its Smart Automation category.
AUO has integrated AIoT to promote automation and high-efficient management systems with its strong fundamentals in smart manufacturing to solve process pain points in manufacturing and improve production efficiency. Owing to its abundant energy for digital transformation, AUO has become the first corporation in Taiwan, and the first display manufacturing corporation recognized worldwide by the ROI-EFESO INDUSTRIE 4.0 AWARD.
Established in 2013, the ROI-EFESO INDUSTRIE 4.0 AWARD is one of the most renowned awards in the Germanosphere and a significant benchmark for the digital transformation of industries that recognizes corporations with outstanding contributions and digital transformation solutions.
“For a long time, AUO has dedicated itself to improving the resilience of its factory production lines.” Said Ting-Li Lin, Senior Vice President of the Manufacturing Group at AUO. “Onwards, AUO will accelerate the cultivation of omni-talented professionals, exert its potential in green manufacturing, and quickly duplicate the success of Taichung Fab 3 to other plants in providing smart solutions that are more diversified and customized. In working toward an intelligentized, smart automated, and smartified sustainable smart factory.”
ROI-EFESO INDUSTRIE 4.0 AWARD jury member Dr. Niels Syassen, Member of the Executive Board at SICK AG, stated: “AUO's Industry 4.0 solution is a lodestar for contemporary consumer goods manufacturing in technologically demanding and highly volatile markets. It combines multiple smart automation technologies: AIoT and Big Data analytics support store floor management, for example, while Autonomous Mobile Robots comprehensively automate material provisioning and setup changes. The result is a unique Industry 4.0 infrastructure for economically and ecologically oriented display manufacturing.”
AUO gradually develops toward full automation manufacturing processes and human-robot collaboration to improve production efficiency. It integrates manufacturing sites with the Cyber-Physical System (CPS) through the six major smart technologies of smart automation, Internet-of-Things (IoT), predictive maintenance, smart imaging, data science, and self-feedback, which is used to support decision-making and to grasp information in making proper corrections in real-time accurately. The CPS improves production efficiency and satisfies the needs for high-mix, low-volume, and highly customized products. AUO steadily works toward an autonomously running and self-adaptive Industry 4.0 sustainable smart factory.
AUO combines its production lines with IoT, big data, optical identification technology, automated guided vehicles (AGV), and autonomous mobile robots (AMR). Using human-robot collaboration replaces conventional technologies in its operations and in reviewing automated integrations such as automatic data comparison and parameter adjustment. These replacements have simplified the changeover process in manufacturing and reduced the overall manual working items by 80% from 2018 to 2021, significantly improving production efficiency.
In the production process, the inspection of product information and machine status in production lines is time- and labor-consuming. Digital twin-driven management systems integrate data for analysis, simulation, prediction, and control for anomaly alerts and troubleshooting, achieving system integration and full-time monitoring for effective enhancement of data processing. The systems are the key facilitators in the high-efficiency management of smart factories, responsible for the 16% improvements in factory equipment efficiency and the 36% increase in overall production yield from 2018 to 2021.
Facing extreme weather challenges, AUO uses sensors to collect data for energy consumption prediction analysis, integrating data and transferring the prediction results back to the energy management center to monitor and respond to anomalous energy and resource consumption in real time. This procedure has successfully reduced water usage by 27% and material waste by 30% in the four years from 2018 to 2021, moving toward the goal of net-zero carbon emissions.
