AI-Driven Autonomous Decision Intelligence for Smart Food Manufacturing Cyber-Physical Systems
TL;DR
The role of AI-driven autonomous system and decision intelligence in advancing smart food manufacturing CPS is explored to provide an insight into the development of efficient, reliable and a sustainable next-generation food manufacturing ecosystem.
Abstract
The food manufacturing industry is undergoing a rapid transformation through the adoption of Industry 4.0 technologies, which include Cyber Physical System (CPS), Artificial Intelligence (AI) and Industrial Internet of Things (IIOT). Despite of these developments, modern food manufacturers continue to face challenges which are related to quality consistency, food safety assurance, process optimization and real-time decision making. The large amount of data generated by numerous sensors and production systems often remain underutilized because of the limited intelligent decision-making capability of the system. Simultaneously, the manufacturers identify production inefficiencies, resource wastage, increased operational cost and difficulties in maintaining a uniform product quality. To address these challenges, Autonomous System, Artificial Intelligence (AI) and Decision Intelligence are introduced as transformative technologies which are capable of converting a raw data into an actionable insight. This process includes by integrating the Machine Learning, Computer Vision and Predictive Analytics enables real-time monitoring, predictive maintenance and adaptive process control. Therefore, these capabilities support a data-driven decision making, improve the resource utilization and enhance food-safety and sustainability. Furthermore, the autonomous system can dynamically respond to the changing production conditions enabling a flexible manufacturing operation. This paper explores the role of AI-driven autonomous system and decision intelligence in advancing smart food manufacturing CPS. It discusses all the key enabling technologies, implementation challenges and industrial application while highlighting their potential to transform traditional manufacturing environments into an intelligent and a self-optimizing system. This paper aims to provide an insight into the development of efficient, reliable and a sustainable next-generation food manufacturing ecosystem