The rapid advancement of digital technologies has significantly transformed not only individuals’ daily lives but also the operations of companies, manufacturers, and logistics firms. This transformation reflects the evolution of industrial revolutions, from Industry 1.0 to Industry 4.0. Initially, mechanization and cost-efficiency defined Industry 1.0. Industry 2.0 followed with electricity-powered assembly lines, enabling mass production. Industry 3.0 introduced digital control systems and automation, emphasizing quality, customization, and lean manufacturing principles. Over time, production systems advanced, and global awareness of sustainability and efficiency increased. Industry 4.0 responded by integrating smart technologies such as artificial intelligence (AI), big data, automation, and the Internet of Things (IoT). These technologies enabled real-time data processing, intelligent decision-making, and reduced human intervention in production processes. The focus shifted toward optimizing operations, minimizing errors, and ensuring sustainable resource use. Currently, the emergence of Industry 5.0 marks a new stage where human-machine collaboration takes precedence. This paradigm centers human input in digitalized environments, combining the cognitive abilities of humans with the computational power of AI. Industry 5.0 promotes the co-existence of automation and human creativity, fostering systems that are not only efficient but also responsive to individual needs. Technologies such as machine learning, natural language processing, generative AI, and predictive analytics enable personalized production and enhanced customer engagement. Additionally, AI-driven systems contribute to sustainability by addressing environmental concerns like energy consumption and waste reduction. This study examines the technological developments leading up to Industry 5.0, with a focus on AI’s transformative role. Far beyond automation, AI in Industry 5.0 functions as a strategic collaborator, enhancing decision-making, process optimization, and ecological responsibility. It represents a shift toward a more sustainable, inclusive, and intelligent industrial model.
Özbek et al. (Mon,) studied this question.