Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing

3Data Analytics and Big Data Analytics

Neeta Nathani^* and Jagdeesh Kumar Ahirwar

Gyan Ganga Institute of Technology and Sciences, Jabalpur (M.P.), India

Abstract

Manufacturers have been under constant pressure to reduce expenses and boost efficiencies for many years. The increasing cost of raw materials, the desire for reduced pricing from consumers, and the complexity of supply chains have forced firms to look for innovative solutions to comply with these requirements. The need to improve efficiency and agility has become more pressing because to the COVID-19 pandemic. Employing data analytics may benefit manufacturing companies in many ways, such as improved inventory management, more accurate demand projections, improved quality control, and complete end-to-end visibility. After decades of exponential technological progress, commonly known as Industry 4.0, contemporary manufacturers are capable of collecting enormous volumes of data from each and every link in the supply chain, from sourcing raw materials to making the final delivery to the customer. Data platforms can receive real-time data feeds from IoT sensors and edge devices installed on machinery for processing. Sensor-equipped warehouses can collect data from forklift operations that can be utilized to improve operational efficiency. Future product design improvements can be informed by the analysis of data gathered from online consumer reviews, which can help detect quality control issues. Manufacturers have a ...