Tracing Path from Industry 4.0 to Industry 5.0: History
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Contributor:
Marina Marinelli

Industry 4.0 is a recent trend representing the vision for the integration of information, objects and people in cyber-physical scenarios in order to transform factories into intelligent environments. Although this transition is still ongoing, the corresponding vision of Industry 5.0 has already emerged. Industry 5.0 aims to bring the human factor back into the production system, with the collaborative work paradigm of human–robot collaboration (HRC) at its core. 

  • human–robot
  • robots
  • digital

1. Introduction

Industry 5.0 is a new concept, so there is little agreement on how it is defined [1], but it is based on the observation that Industry 4.0 overfocuses on digitalization and AI-driven technologies at the expense of the original principles of social fairness and sustainability [2]. Along the same lines, Maddikunta et al. [3] note that Industry 5.0 can bring back the human force to factories and promote more skilled jobs. This occurs through replacing the limited machine–human interaction present in the Industry 4.0 context with highly collaborative, adaptable and personalized scenarios in the context of Industry 5.0 [4]. Nahavandi (2019) [5] confirms that the fundamental difference between Industry 4.0 and Industry 5.0 concerns the use of robots and, specifically, the fact that in Industry 5.0, robots are expected to become ‘cobots’, i.e., collaborators with the human operators. The same perspective is also supported by Adel [6], who describes Industry 5.0 as a changing paradigm based on collaboration between humans and machines. Along the same lines, Akundi et al. [1] observe that the primary trend of Industry 5.0 is the introduction of human–robot co-working environments and the creation of a smart society.

2. Adoption Drivers and Concerns for Industry 5.0

2.1. Adoption Drivers

Industry 4.0 is a digital and technological revolution set to fundamentally change production and manufacturing. According to the IFR [7], the impact of automation on employment is not necessarily different from the previous waves of technology-driven change over the centuries. While media articles often reflect fears about automation and job displacement, there is no concrete evidence to support these concerns. In fact, automation is driving job creation; what may indeed be different this time is the pace of change in job profiles and skills requirements, which appears to be occurring faster than in the past. Furthermore, automation enables large companies to increase their competitiveness through faster product development and delivery as well as to bring back to their domestic base parts of the supply chain that they previously outsourced to sources of cheaper labor [7].
Industry 5.0, as a value-driven approach, makes a bold focus shift from individual technologies to a systematic approach, which empowers the industry to achieve societal goals beyond jobs and growth and places the wellbeing of the industry worker at the center of the production process [2]. Nahavandi [5] envisages Industry 5.0 as the era where humans trust cobots as effective partners and this collaboration leads to increased efficiency, improved production and reduced waste and costs. IFR [8] also confirms that cobots are tools to support employees in their work, relieving them of many heavy, unergonomic and tedious tasks. De Simone et al. [9] and Marinelli [10] express similar views regarding the enhancement of ergonomics and productivity, while Kim [11] also mentions the expected benefits of affordability, improved performance and creativity, task flexibility and improved safety. Furthermore, automation can create rewarding job profiles with a higher variety of tasks and worker autonomy in task and process planning and decision making as a result of a focus on effective human-cyber-physical systems [12].

2.2. Challenges and Concerns

Safety and wellbeing: One of the biggest challenges in the development of collaborative systems between humans and robots is to ensure operators’ psychophysical well-being in terms of Occupational Health and Safety (OHS) while preserving high robot performance [9]. The operator must accept that the working space is shared and there are no fences between them and the cobot, which can be perceived as a safety problem. Especially when the cobot needs to adjust to the motion of the worker, its movements are not completely predictable, and this raises technical challenges and safety limitations [8].
The human–robot collaboration (HRC) field recognizes the importance of protecting workers from accidents and injuries and has placed significant emphasis on developing reliable and secure operational environments and systems. This includes adhering to safety guidelines outlined in ISO standards that are specific to industrial settings, personal use and collaborative robots [11]. Additionally, the industry is focused on developing systems, such as control systems and motion/collision detection, which allow for collaboration while ensuring operation speed and separation from robots. Gualtieri et al. [13] and Proia et al. [14] have presented relevant reviews of the AI applications developed to control safety and ergonomic performance of the interface between the robot and the human operator. Furthermore, indicative examples of more focused relevant research include the work by Ibrahim et al. [15], who have thoroughly reviewed robotic control systems and Jantch et al. [16], Han et al. [17] and Xiao et al. [18], who studied robotic self–awareness, kinematic coordination for collision detection and kinaesthetic flexibility, respectively.
Factors widely accepted as impactful to an operator’s performance in HRC include stress, workload and trust [9]. Increased mental stress for humans working with robots can be caused by the physical attributes of the robot, including its size, shape and motion, especially when it moves close at high speeds and without warning [19]. Furthermore, although the introduction of collaborative robots involves a decrease in the physical workload, the mental workload can significantly increase. This can be due to misinterpretation of the cobot’s intentions, especially in cases where its degree of autonomy is high [20] or in cases where the operator has to undertake tasks involving cobot monitoring and supervision, which entail a significant cognitive cost [9]. Additionally, according to Gervasi et al. [21], trust plays a crucial role in achieving optimal HRC. When trust is lacking, operators may not fully utilize the robot, resulting in decreased performance or even non-use. The design of a cobot can also affect trust; for instance, a robot that is too large may discourage humans from collaborating, while a smaller robot or one with social cues may increase the operator’s comfort level. Kim [11] notes that employees who have little trust in robots should not be forced to work in HRC settings. Instead, organizations should provide guidance to employees on how to effectively treat and manage robots and educate them on their own human rights and responsibilities in the context of HRC.
Employment insecurity: The issue of machines replacing human labor is not a new concern, but the current smart technological revolution has advanced it to an unprecedented level. According to Bughin et al. for the global consultancy firm McKinsey [22], job profiles that involve monotonous tasks and necessitate minimal digital skills may witness the most significant decline in terms of overall employment, dropping from roughly 40% to nearly 30% by 2030. Conversely, nonrepetitive tasks and those that require advanced digital skills are projected to experience the greatest increase in employment, rising from approximately 40% to more than 50%. These employment shifts will likely have an impact on wages with employees in categories that entail repetitive tasks and low digital skills experiencing stagnant or reduced wages. There is no doubt that when some jobs are at risk of disappearing completely and the content of others is changing significantly, workers are expected to develop job insecurity and present hostility and suspicion toward the implementation of new technologies. According to Kozak et al. [23], low education is the strongest individual predictor of automation insecurity, while Schneiders and Papachristos [24] note that workers who already work with robots are more convinced that the widespread use of robots can create more job opportunities in the future compared to workers who have not yet been exposed to robots. Therefore, early exposure to industrial robots is crucial in changing workers’ perceptions of them. Moreover, workers from countries that have already adopted advanced technologies are less likely to fear robots as the labor market has already undergone changes and workers have adjusted their skills accordingly [23].
Ethical, training and legal issues: It is important for organizations to be mindful of unethical uses of robots, including programming them for malevolent purposes, hacking into other robots, sharing sensitive information with robots unnecessarily and encouraging social loafing among employees by shifting their responsibilities to robots [11]. Organizations must ensure that the data generated and collected by robots are stored and used ethically, with rigorous protocols to protect privacy [11][25].
Furthermore, tighter collaboration between industry, government and educational institutions is necessary to prepare existing and future employees with both the soft skills as well as the practical expertise needed for the new jobs [22]. HR departments should take the lead in ensuring that employees have adequate information about robots and are fully aware of the code of behavior and potential risks associated with HRC [11]. Moreover, a clear strategy and suitable training to make sure that the company has the required know-how and skills is crucial for employee acceptance and the success of technological transformation [25]. The role of governments is particularly important in terms of the above, as appropriate policy incentives and funding initiatives can encourage corporate investment in training [22].
In addition, cooperation and communication between different departments and across company boundaries is essential, with particular emphasis on the legal aspects concerning data access beyond departmental and company boundaries [25].

This entry is adapted from the peer-reviewed paper 10.3390/systems11030152

References

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