The notion of Industry 5.0, which refers to the integration of artificial intelligence (AI) into human existence to enhance human skills, is gaining popularity because of the rising demands of end consumers for the personalization of acquired products. Hence this study investigates the evolution of digital production ton society from industry 4.0 to industry 5.0. This experimental study highlights the practical implications of integrating AI with Industry 5.0 digital society applications. An outstanding 32.9% increase in traffic flow is produced by using AI-driven smart traffic control, indicating a significant step forward in the direction of better transportation in cities. Reliable incident forecasting is provided by AI-enhanced security systems, suggesting better urban environments. The findings show that the organizational and supply chain tiers are where Industry 4.0 has had the greatest positive impact on ecological and financial sustainable values. But Industry 4.0 has negatively affected many micro and macro-social sustainable criteria.
TopIntroduction
Modern production sector has undergone a swift transformation as a result of the advent of artificial intelligence innovations and the quickly developing digital realm. The issue for companies everywhere is to boost output while maintaining human involvement in the production process. As emerging advancements like brain-machine links and AI emerge, the role of automation in the production stage grows, making this task harder. The impending industrial revolution, known as Industry 5.0, may be able to address these issues (Nahavandi 2019). Industry 5.0, to put it briefly, is the idea that robotics and humans should cooperate instead of compete.
Industry 1.0 began to take shape in the 18th era, with a focus on the following sectors: minerals extraction, transportation, glass, documents, fabrics, steam-powered iron, tools, concrete, chemicals, and gas. Benefits of this revolution include stable growth, jobs, expansion of farming, and transport. Industry 1.0 has a number of drawbacks, including contamination and the length of time needed to perform relevant procedures. Two statistical techniques utilized in Industry 1.0 were geometrical and linear algorithms (Vinitha et al 2020). Industries like rubber-based products, bicycles, automobiles, academic study of biological material, motors, generators, cellphone service, steel, train, electricity, paper, oil, chemical, and water-based innovations were the focus of Industry 2.0, which started in the 19th century.The development of the power system, telephones, telegraph, and inner combustion motors are among the innovations of this revolution. The expensive price of electrical power consumption is Industry 2.0's main disadvantage. Geography, differential formulas, and linear algebra were among the computational techniques used in Industry 2.0 (Madsen et .al 2016). Digital circuits, programmed circuits, home automation, wireless communications, cell phones, renewable energy, and semiconductors were the main areas of concentration for Industry 3.0 when it began in the 20th era. Robotics, renewable energy, automation industry, and telecommunication are among the innovations of this revolution. Since automated systems wouldn't function in some circumstances, this is Industry 3.0's main flaw. For instance, the implementation of Flexible Manufacturing Structures (FMS) was one of the main tenets of Industry 3.0. Unfortunately, certain firms could not afford the additional operating expenses and complexity of these systems. The intricacy and additional expenses discouraged numerous companies. The computational instruments of differentiation, linear programs, and logical regulators were used in Industry 3.0. The 21st era saw the emergence of Industry 4.0, which was centered on automated systems in all sectors of the economy. This revolution has produced completely automated systems and artificially intelligent systems that function in unpredictable environments. It has also positively impacted the 4th industrial revolution through machine learning. The fact that not all cloud data is secure and that industry-specific expertise platforms have not yet been fully established are the disadvantages of Industry 4.0. The concept of networks and optimization strategies are two examples of the computational instruments used by Industry 4.0 (McKee, & Gauch 2020).
Although Industry 4.0 won't be able to demonstrate its outcomes and accomplishments until 2020–2025, investigators are already discussing Industry 5.0, which will be built on self-education devices that resemble human behavior or that of other automated machines and continuously optimize production methods. The idea involves AI becoming ingrained in human existence (Khan et.al 2023).
Michael Rada is credited with coining the word “Industry 5.0” (Paschek et.al 2019). One of the main features of Industry 5.0 is the application of risk-reduction collaborative robots. These robots are able to perceive, sense, and comprehend the human driver together with the objectives and standards of the job they are carrying out. The idea is for these robots to assist human drivers in carrying out tasks by observing and learning from how a person completes them. Moreover, Industry 5.0 calls for the integration of AI with human existence in order to maximize human potential. For the advantage and convenient of human staff members, Industry 5.0 advanced IT innovations, IoT, robotics, AI, and augmented fact are extensively deployed in the sector (Skobelev, & Borovik 2017).