Majicemi Co., Ltd.
Webinar held on the theme of “Challenges in utilizing factory operation data” ……
Magisemi Co., Ltd. will be holding a webinar on the theme of “Challenges in utilizing factory operation data.”
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(For details and to apply for participation, click here)
https://majisemi.com/e/c/uis-inf-20240621/M1D ■ With the progress of manufacturing industry DX, the utilization of factory operation data is progressing. In addition to a serious labor shortage, the company faces a difficult business environment, including intensifying global competition, soaring raw material prices, and efforts to decarbonize. Against this background, manufacturing companies are actively promoting various initiatives to make the most of factory operation data and realize efficiency and optimization of the entire factory. ■Challenges in utilizing factory operating data However, there are various issues to be addressed when promoting initiatives that utilize operational data of factory equipment. First of all, a high level of specialized knowledge and know-how is required to utilize operational data collected from equipment to build systems such as equipment abnormality detection. Specifically, in addition to knowledge of statistics and machine learning for data cleansing, analysis, and visualization, knowledge of equipment operating principles and failure patterns is also required. Additionally, a mechanism is needed to integrate data collection systems and analysis tools to process and analyze data in real time. In addition, simply reporting equipment abnormalities results in discussions about the frequency and impact of failures, making it difficult to clearly see the cost-effectiveness of investment. For this reason, it is necessary to go beyond simply using operational data to detect anomalies and make full use of detailed analysis results such as operating patterns and performance changes to realize a data-driven approach for preventive maintenance and optimal operation. ■ Promoting “Equipment diagnosis and operation evaluation” that fully utilizes the operating data of factory equipment Ube Information Systems has been promoting “visualization of equipment operating status” and “Using AI” using operating data from
manufacturing sites for many years. We are working on “failure prediction”. In this seminar, we will explain a method to quantify the difference between the optimal operating state of equipment and the current operating state as “abnormality level” through the AI predictive detection construction support service “SAILESS”. Furthermore, we will introduce in detail specific initiatives that have realized a data-driven approach, such as a case study that utilized this data to transition from time-based maintenance to condition-based maintenance of equipment. “SAILESS” is an anomaly sign detection system provided in a BTO (Build to Order) model that is customized according to customer requirements. This allows you to build an optimal anomaly detection system tailored to the target object and type of anomaly. Furthermore, since it can accumulate and analyze a variety of equipment data, it can also be used as a data platform to analyze the operating status of equipment from multiple angles. Especially: ・Those who have acquired operational data but are worried about how to utilize the data ・Those who want to utilize operational data to break away from dependence on the tricks and tips of maintenance technology ・Anomaly detection systems This is especially recommended for those who are considering implementing the system but are concerned about cost-effectiveness. (For details and to apply for participation, click here)
https://majisemi.com/e/c/uis-inf-20240621/M1D ■Sponsor: Ube
Information System Co., Ltd. ■Cooperation: Open Source Utilization Institute Co., Ltd. Majisemi Co., Ltd. We will also be holding webinars that will be useful to participants. ★For other webinar applications and videos and materials from past seminars, please check below★ https://majisemi.com?el=M1D ★If you would like to hold a webinar, please watch this video★ https: //youtu.be/pGj3koorgSY More details about this release:
https://prtimes.jp/main/html/rd/p/000002115.000054842.html