Artificial Intelligence and Automation in Occupational Risk Analysis at the Modern Manufacturing Operations: Literature Review

Research output: Other contributionpeer-review



Digital technologies play an important role in our daily lives as well as in our workplaces. Implementation of new systems and technologies, such as artificial intelligence (AI) or improved robotics, can change a number of aspects of human work planning and execution (European Agency for Safety and Health at Work, 2022). There is growing evidence of the use of artificial intelligence in the workplace in all sectors to simplify and / or automate tasks, but there is still limited understanding of the role of AI in occupational health and safety (OHS) system (Pishgar et al., 2021).
In order to be able to assess the new risks in the work environment in the manufacturing sector, it is necessary to understand the importance of new technologies, how it can change work environment and how affect employees at the work. Considering the development of process automation and the impact of Covid-19 on changes in work organization, the main challenges are connected with new forms of employee management through AI-based systems, work on an online platform, new systems for monitoring employee safety and health, improved robotics and AI-based systems for the automation of tasks and their interaction with the physical and mental health of workers, the assessment of new risks to the working environment, and possible problems related to changes in the qualifications of workers (European Agency for Safety and Health at Work, 2022; Goos & Manning, 2007; Goos et al., 2009).
The aim of the study is to analyze and point out main conclusions from the current scientific literature on artificial intelligence and automation in occupational risk analysis at the modern manufacturing operations.


An analysis of the literature was performed. Scientific articles by various authors as well as EU OHSA reports were analyzed. Such keywords were chosen for the initial analysis of the scientific literature: artificial intelligence, automation, manufacturing, occupational health and safety and altogether 19 articles from 45 were selected for the study.


The field of artificial intelligence (AI) is expanding rapidly and its use can be seen regularly in a number of well-known and important industries, such as healthcare, manufacturing and education. Although there is growing evidence of the use of AI in the workplace in all sectors to simplify and / or automate tasks, there is still a limited society understanding of the role of AI in occupational health and safety (OHS) system and risk analysis and prevention (Pishgar et al., 2021). The implementation of new systems and technologies that include artificial intelligence (AI) or advanced robotics has the potential to change a number of aspects of the way how human works. Workplace automation and employee interaction with these systems need to be further explored to address new risks and highlight health and safety impacts (OHS) and interaction of new risks in the work environment (The European Agency for Safety and Health at Work 2022). The development of artificial intelligence and automation is focused on the performance of specific tasks rather than changes in the entire work process (Parker & Grote, 2020). In the context of automation processes, workplaces are viewed from two perspectives, i.e., physical tasks and cognitive tasks, in which the risks associated with AI appear. Physical task analysis involves automation of equipment, robotics, sensors, actuators, use of materials, workload reduction (e.g. lifting weights), reduction of hazards in specific work areas now performed by the equipment, but this does not mean that the work process will not require employee presence (IFR, 2018; Goos & Manning, 2007; Goos et al., 2009). Traditionally this is understood that automation and robotics are types of work where the environment is hazardous or heavy loads must be lifted and are designed for speed and accuracy. These systems are mainly found in fully automated production. Systems with lower payloads as well as new generations of sensors and actuators have allowed the emergence of innovative types of robots (European Agency for Safety and Health at Work, 2022; Bauer et al., 2016). In addition, industries uses AI to solve a variety of other problems, such as decision-making (Akbar et al., 2016), environmental monitoring (Delabrida et al., 2015; Ding et al., 2011), lower operating costs (Shukla & Karki, 2016) and increasing productivity (Belforte et al., 2006). The second aspect is cognitive tasks, which are understood as exposure to the work process with information technology (e.g. 5G networks capable of providing incredible processing power), digitalization of equipment, use of software, learning, etc. aspects focus on different automation software. But the manufacturing sector is more focused on the physical approach - automation and robotics (Hämäläinen et al., 2018; European Agency for Safety and Health at Work, 2022). There are significant difficulties in production, as most day-to-day tasks are performed by automated systems, while assigning complex and changing tasks to employees, resulting in the increased cognitive workload, which can affect both human and production performance (Bannert, 2002; Lindblom & Thorvald, 2014; John, 2019). The integration of AI-based systems and advanced robotics can provide significant positive opportunities for workplace progress and productivity growth, as well as OHS. However, important OHS issues can also occur. Stress, discrimination in human resource decision-making and work intensification, as well as job insecurity and possible job losses, are some of the risks that employees may face (European Agency for Safety and Health at Work, 2022). The integration of new technologies and equipment in the workplace can increase these stressors and has already been shown that it especially poses psychosocial risks (Moore, 2018). These risks increase when artificial intelligence complements existing technological tools or is reintroduced in workplace management and design. Nowadays OHS recommends the use of a new system called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA), which highlights the role of AI in assessing new risks by anticipating and controlling exposure risks in the worker’s environment (Pishgar et al., 2021). Artificial intelligence (AI) is a broad and diverse field of research that has infiltrated all aspects of our lives and has become crucial over the years (Perrault et al., 2019). In general, artificial intelligence is the ability of a computer to process information and produce results that imitate how a person learns, makes decisions, and solves problems (Howard, 2019).


AI-based systems and advanced robotics are not entirely new, but the increase in computing power in recent years has led to a huge increase in the availability and performance of AI-based applications. The development of technology has a significant impact on OHS, as the work environment is changing and so are the risks at work. Employees are mostly subject to cognitive strain, as they need to be able to work with new equipment that is equipped with a variety of programs that employees often do not fully understand. This results in mental overload and a higher risk of accidents. It should also be mentioned that not all production facilities automate the entire work operations, but the automation takes place at one of the stages of production, which does not completely eliminate physical activity of a human. A new REDECA system is available to identify the risks associated with AI, which highlights the role of AI in anticipating and controlling worker exposure risks.

Acknowledgments. This research has been supported by the project “Ergonomic stress indicators in contemporary technological work environment and possibilities of its improvement in social-technical system “Human-Machine-Environment””, Agreement No. Nr.


Akbar, S.A., Chattopadhyay, S., Elfiky, N.M., Kak, A. 2016. A novel benchmark RGBD dataset for dormant apple trees and its application to automatic pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA, 27–30 June 2016; pp. 81–88.
Bannert, M. 2002. Managing cognitive load—Recent trends in cognitive load theory. Learn. Instr. 12, 139–146.
Bauer, W., Bender, M., Braun, M., Rally, P., & Scholtz, O. 2016. Lightweight robots in manual assembly—best to start simply. Examining companies’ initial experiences with lightweight robots, 1-32.
Belforte, G., Gay, P., Aimonino, D.R. 2006. Robotics for improving quality, safety and productivity in intensive agriculture: Challenges and opportunities. In Industrial Robotics: Programming, Simulationand Application; Low, K.H., Ed.; Intech Open: London, UK.
Delabrida, S.E., Angelo, T.D., Oliveira, R.A.R., Loureiro, A.A.F. 2015. Towards a Wearable Device for Monitoring Ecological Environments. In Proceedings of the 2015 Brazilian Symposium on Computing Systems Engineering (SBESC), Foz do Iguaçu, PR, Brazil, 3–6 November 2015; pp. 148–153.
Ding, J., Wang, J., Yuan, N., Pan, Q. 2011. The monitoring system of leakage accidents in crude oil pipeline based on ZigBee technology. In Proceedings of the 2011 Second International Conference on Mechanic Automation and Control Engineering, Inner Mongolia, China, 15–17 July 2011; pp. 1774–1777.
European Agency for Safety and Health at Work. 2022. Advanced robotics, artificial intelligence and the automation of tasks: definitions, uses, policies and strategies and Occupational Safety and Health. Report. p 70.
Goos, M., & Manning, A. 2007. Lousy and lovely jobs: The rising polarization of work in Britain. The review of economics and statistics, 89(1), 118-133.
Goos, M., Manning, A., & Salomons, A. 2009. Job Polarization in Europe. The American Economic Review, 99(2), 58–63.
Hämäläinen, R., Lanz, M., & Koskinen, K.T. 2018. Collaborative systems and environments for future working life: Towards the integration of workers, systems and manufacturing environments. The impact of digitalization in the workplace, 25-38, Springer, Cham.
Howard, J. 2019. Artificial intelligence: Implications for the future of work. Am. J. Ind. Med. 2019, 62, 917–926.
International Federation of Robotics (IFR). 2018. World Robotics 2018 - Industrial Robots and Service Robots. Federation of Robotics
John, K. 2019. How Augmentation Can Reduce Cognitive Load and Improve Decision Making in Manufacturing. Available online:
Lindblom, J.; Thorvald, P. 2014. Towards a framework for reducing cognitive load in manufacturing personnel. Adv. Cogn. Eng. Neuroergon. 11, 233–244. Available online:
Moore, P.V. 2018. The Threat of Physical and Psychosocial Violence and Harassment in Digitalized Work”, International Labour Organization, ACTRAV.
Parker, S.K., & Grote, G. 2020. Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology, 0 (0), 1-45.
Perrault, R., Shoham, Y., Brynjolfsson, E., Clark, J., Etchemendy, J., Grosz, B., Lyons, T., Manyika, J., Mishra, S., Niebles, J.C. 2019. The AI Index 2019 Annual Report; AI Index Steering Committee, Human-Centered AI Institute; Stanford University: Stanford, CA, USA.
Pishgar, M., Issa, S.F., Sietsema, M., Pratap, P., Darabi, H. 2021. REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health. International Journal of Environmental Research and Public Health, 18, 6705.
Shukla, A., Karki, H. 2016. Application of robotics in offshore oil and gas industry—A review Part II. Robot. Auton. Syst. 75, 508–524.
Original languageEnglish
Number of pages2
Publication statusPublished - 7 Jun 2022


  • artificial intelligence
  • automation
  • health
  • manufacturing
  • risks
  • safety

Field of Science*

  • 5.2 Economy and Business
  • 5.9 Other social sciences

Publication Type*

  • 6. Other publications


Dive into the research topics of 'Artificial Intelligence and Automation in Occupational Risk Analysis at the Modern Manufacturing Operations: Literature Review'. Together they form a unique fingerprint.

Cite this