About this Research Topic
Digital manufacturing plays a vital role in the Fourth Industrial Revolution, under which physical equipment, data, and people must efficiently collaborate to transform the manufacturing process. While large-scale production and standardization were essential in traditional manufacturing, Digital Manufacturing (DM) places emphasis on mass customization and cooperation. Through the use of digital technology, businesses can improve all aspects of their operations, including faster production and the provision of data intelligence to adapt different capabilities and outputs.
Today, the Internet of Things (IoT) is being used in nearly every industry to connect systems and share data. This provides companies with access to valuable big data insights that can be utilized through artificial intelligence and machine learning (AI/ML) capabilities to transform their pricing, efficiency, product, and service quality. The significance of this technology lies in its ability to increase the overall effectiveness of a manufacturing operation, from predicting maintenance to automating various processes. Businesses that adopt digital manufacturing are becoming more competitive and agile than those that rely heavily on traditional manufacturing, with quicker production, reduced costs, real-time inventory monitoring, and the ability to foresee market trends and potential success.
This article collection aims to highlight the latest advancements in the use of AI/ML-supported data analytics as part of the digital manufacturing revolution. Topics of interest include, but are not limited to:
• AI / ML in material flow and assembly line processes
• Monitoring of manufacturing processes using IOT/AI
• Detection of issues and quality of products using data analytics
• Software-related technology for Visibility of end-to-end operations for employee and management efficiencies
• Application of AI/ML in supply chain management
• Maintenance prediction using data analytics
• Planning for consumer demand and responsiveness using data analytics
• Increased innovation and collaboration opportunities
• Optimization of equipment and workstations
Today, the Internet of Things (IoT) is being used in nearly every industry to connect systems and share data. This provides companies with access to valuable big data insights that can be utilized through artificial intelligence and machine learning (AI/ML) capabilities to transform their pricing, efficiency, product, and service quality. The significance of this technology lies in its ability to increase the overall effectiveness of a manufacturing operation, from predicting maintenance to automating various processes. Businesses that adopt digital manufacturing are becoming more competitive and agile than those that rely heavily on traditional manufacturing, with quicker production, reduced costs, real-time inventory monitoring, and the ability to foresee market trends and potential success.
This article collection aims to highlight the latest advancements in the use of AI/ML-supported data analytics as part of the digital manufacturing revolution. Topics of interest include, but are not limited to:
• AI / ML in material flow and assembly line processes
• Monitoring of manufacturing processes using IOT/AI
• Detection of issues and quality of products using data analytics
• Software-related technology for Visibility of end-to-end operations for employee and management efficiencies
• Application of AI/ML in supply chain management
• Maintenance prediction using data analytics
• Planning for consumer demand and responsiveness using data analytics
• Increased innovation and collaboration opportunities
• Optimization of equipment and workstations
Keywords: Smart Manufacturing, Artificial Intelligence, Machine Learning, Data Analytics, IOT, Optimization, Sensors, Supply chain management
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
