Post by account_disabled on Sept 14, 2023 10:26:25 GMT
AI is a core technology for digital transformation of the manufacturing industry. The Ministry of Trade, Industry and Energy has divided smart factories into four stages according to IT utilization, and AI technology is essential for the advanced stage where the entire manufacturing process is integrated and customized production is possible. ‘Smart Factory’, which symbolizes innovation in the manufacturing industry, is difficult to upgrade without AI.
ⓒGetty Images Bank
The area of AI use that is receiving Phone Number List the most attention in the manufacturing industry is advanced predictive analysis. Predictive analysis is a technology that predicts and prevents problematic situations through data, and is a representative method of supporting corporate decision-making and increasing efficiency and productivity.
Byeong-Wook Choi, Director of Global Technology Practice APAC at SAS, a leader in the advanced predictive analytics market, said at ‘ Future of Manufacturing 2023 ’ hosted by ITWorld and CIO Korea on May 25, “The use of analytics through SAS in the manufacturing industry is not only at customer contact points, but also at facilities and facilities. “It also has various advantages in terms of process,” he said.
Byungwook Choi, Director of SAS Global Technology Practice APAC, is giving a presentation at 'Future of Manufacturing 2023' held on May 25. ⓒITWorld
According to Director Choi Byeong-wook, the most active recent use case for advanced predictive analytics in the manufacturing industry is customer intelligence. It can be used to increase market share by better understanding and responding to the needs and preferences of customers and potential customers. In terms of product sales and utilization, it helps predict demand and optimize the supply chain by considering the volatility of external conditions such as weather, and reduces warranty costs by proactively responding to customer complaints.
By analyzing IoT data in real time, equipment performance can be maximized. Reduce costs due to downtime and unscheduled maintenance, or improve yield and field quality through AI-based insights into quality prediction and anomaly detection. Additionally, for smart connected products and facility assets, active and innovative business models can be created in real time by expanding products with telematics.
Director Choi Byeong-wook said, “The GUI-based AI self-service analysis platform provided by SAS supports smooth collaboration between engineers, data scientists, and developers, enabling easy and fast modeling. “We also support data governance and management decision-making through managed cloud services, providing benefits such as improved TCO, reduced deployment time, and increased elasticity,” he explained.
In particular, SAS supports solving various process and quality problems through so-called 'Composite AI', which is a mixture of various types of AI such as learning/language/visual/voice such as ML, deep learning, and computer vision. Director Choi Byeong-wook emphasized, “In terms of future manufacturing, complex AI will be used from various perspectives.
ⓒGetty Images Bank
The area of AI use that is receiving Phone Number List the most attention in the manufacturing industry is advanced predictive analysis. Predictive analysis is a technology that predicts and prevents problematic situations through data, and is a representative method of supporting corporate decision-making and increasing efficiency and productivity.
Byeong-Wook Choi, Director of Global Technology Practice APAC at SAS, a leader in the advanced predictive analytics market, said at ‘ Future of Manufacturing 2023 ’ hosted by ITWorld and CIO Korea on May 25, “The use of analytics through SAS in the manufacturing industry is not only at customer contact points, but also at facilities and facilities. “It also has various advantages in terms of process,” he said.
Byungwook Choi, Director of SAS Global Technology Practice APAC, is giving a presentation at 'Future of Manufacturing 2023' held on May 25. ⓒITWorld
According to Director Choi Byeong-wook, the most active recent use case for advanced predictive analytics in the manufacturing industry is customer intelligence. It can be used to increase market share by better understanding and responding to the needs and preferences of customers and potential customers. In terms of product sales and utilization, it helps predict demand and optimize the supply chain by considering the volatility of external conditions such as weather, and reduces warranty costs by proactively responding to customer complaints.
By analyzing IoT data in real time, equipment performance can be maximized. Reduce costs due to downtime and unscheduled maintenance, or improve yield and field quality through AI-based insights into quality prediction and anomaly detection. Additionally, for smart connected products and facility assets, active and innovative business models can be created in real time by expanding products with telematics.
Director Choi Byeong-wook said, “The GUI-based AI self-service analysis platform provided by SAS supports smooth collaboration between engineers, data scientists, and developers, enabling easy and fast modeling. “We also support data governance and management decision-making through managed cloud services, providing benefits such as improved TCO, reduced deployment time, and increased elasticity,” he explained.
In particular, SAS supports solving various process and quality problems through so-called 'Composite AI', which is a mixture of various types of AI such as learning/language/visual/voice such as ML, deep learning, and computer vision. Director Choi Byeong-wook emphasized, “In terms of future manufacturing, complex AI will be used from various perspectives.