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          Charting the Path: Taiwan's 2024 breakthrough in AI integration

          Source:Profet AI Release Date:2024-05-07 333
          Semiconductor / Electronic Chip AutomationElectronic chip manufacturing
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          The New Paradigm of Enterprise AI: Comprehensive Expansion and Twin Transformation for an Intelligent Green Future

          Group photo of the guest speakers (from left to right): Mr. Jonathan Yu, General Manager of Global Sales of Profet AI; Mr. Jerry Huang, CEO of Profet AI; Dr. Shu-Kai Fan, Chairman of the Chinese Institute of Industrial Engineers and Dean of the College of Management at National Taipei University of Technology; Mr. Eddie Lin, Vice President of Kinsus Interconnect Tech; Mr. Chih-Hung Tsai, General Manager of Shang Mu Glass; Mr. Jing-Lun Jiang, Factory Manager of AUO Longtan Fab; Mr. Daniel Yang, General Manager of the Intelligent Services Business Group (ISBG) at AUO; Mr. Shin-Min Chao, Executive Vice President of AUO Digitech

           

          Challenges in Supply Chain Transformation: Dr. Shu-Kai Fan Advocates for Enterprises to Courageously Experiment, Leading Digital Green Transformation through Twin Transformation

           

          With the rise of economic cycles and the focus on domestic economies, the supply chain is confronted with challenges of restructuring and localization. Within the manufacturing industry, continuous innovation and improvement are imperative to maintain competitiveness. Dr. Fan pointed out, "In the era of AI, we need the courage to venture into uncharted territories like Zheng He exploring the unknown, daring to try, believing in ourselves, and adopting the attitude of 'To believe is to see' to face the challenges with AI."

           

          "One Data" is a crucial core concept, where enterprises achieve Twin Transformation through the collection, cleansing, visualization, and digitization of data. This transformation aims to concurrently undertake both digital and green transformations in order to meet the digital demands of modern society and achieve sustainability goals. Employing AI for corporate governance, enterprises establish an intelligent management system that spans form the workshop to MES and then to ERP. On the factory management side, utilizing AI to monitor the effectiveness of greenhouse gas emission and optimizing parameters enable the fulfillment of low-carbon operation. In response to future trends and EU policies, the green account is considered a crucial performance indicator.

           

          The Manufacturing Industry Fully Embraces AI: Successfully Creating the Golden Line (Benchmark Production Line) Leading the Intelligent Revolution in PCB Processes

           

          Eddie Lin, Vice President of Kinsus Interconnect Tech, stated that AI is not just a future prospect but a present reality. He urged the need to embrace AI at the moment. Using the manufacturing industry as an example, customers expect to utilize AI to standardize decisions. In this case, model scores would reflect process and management quality, while also allowing customers to standardize their own practices.

           

          During digital transformation, emphasis should be placed on identifying solutions that best suit the company, rather than blindly pursuing the latest technologies. By introducing AI technology, data acquisition set-top boxes, and other intelligent technologies into aging factories, the "Golden Line" has successfully been created, integrating various machines and technologies. Through the successful integration of the fully automated virtual measurement (AVM) system, AIoT has been achieved.

           

          In the PCB manufacturing process, the introduction of AI has addressed the challenges of manual product quality inspection, particularly in the judgment of Multiple-Defect. AI offers significant benefits in predicting machine conditions, establishing models for different locations, and explaining issues within the original process. This enables the prediction and resolution of problems within the manufacturing process.

           

          Intelligizing Traditional Industries: Leading Intelligent Energy to Drive Automation in Factories

           

          Energy management has consistently been a challenge for traditional industries, frequently resulting in fines as a consequence of excessive power consumption in the past. Shang Mu Glass has introduced smart meters and the Smart Grid energy management system from AUO Digitech, enabling real-time monitoring and automatic shutdown. This has effectively prevented energy waste and enhanced production efficiency. Internally, through talent training and education, the company instills the true concepts of AI, encouraging senior workers to embrace AI without fear. The commitment is towards achieving the goal of digital literacy for all, aspiring to become the TSMC (Taiwan Semiconductor Manufacturing Company) of the glass industry and realizing the vision of an automated factory.

           

          Intelligent Manufacturing Advances towards a Green Future: Digital Transformation of Old Factories Sets a New Paradigm

           

          Mr. Ching-Lun Jiang, Factory Manager of AUO Longtan Fab, shares his 25-year journey of digital transformation in the old factory. To address the challenges of competition arising from external globalization and internal issues such as outdated equipment, scattered data, and energy concerns, it is imperative to take proactive measures to improve manufacturing efficiency and manage the rising challenge of electricity costs.

           

          Firstly, to overcome the issue of downtime, AUO Digitech has developed an innovative data acquisition approach by intelligizing traditional old machines. They have independently created the SPIIDER set-top box, enabling data retrieval without disrupting machine operations, while also saving costs associated with equipment replacement. Furthermore, by utilizing the Profet AI AutoML analysis tool, engineers are assisted in rapidly visualizing prediction results for decision-makers. This not only reduces their workload but also enables them to concentrate on addressing critical issues in the factory. Through 4 major steps, namely data digitization, data application visualization, intelligent data analysis, and equipment automation, the advantage of AI lies in its ability to continuously adjust parameters for optimized management. Despite its initial suboptimal results, this accelerates the traditional Design of Experiments (DOE) process, eventually finding the most suitable solutions. Taking the example of utilizing AI to improve power consumption analysis, it successfully replaces manual judgment, resulting in reduced discrepancies in machine power usage. This achievement not only realizes carbon reduction benefits but also lowers the company's electricity costs.

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