Future Workforce for Industry 5.0
Huzina Saheal, Sheikh Sajid Mohammad
- Year
- 2024
- Citations
- 3
- Access
- Open access
Abstract
Industry 5.0 refers to an arrangement whereby humans work alongside smart machines and robots. It involves utilizing cutting-edge technology to enable robots to assist people in working more efficiently. The transition from Industry 4.0 to Industry 5.0 will create new industrial sectors and job opportunities. The World Economic Forum 2020 has predicted that, by 2025, 85 million jobs currently performed by humans will be automated. Subsequently, about 97 million new jobs might be required for the occupations of the future, which are anticipated to be performed in collaboration with people, robots, and computers. These forecasts suggest that employment in the upcoming years will be significantly impacted by the new industrial revolution. As a result, the vast majority of people in the labor market of today will need to acquire new skills, as there will be a significant change in nearly every job. Employees should have self-management abilities and must also learn new skills or enhance existing ones in order to assess, compare, and comprehend information. In order to overcome the obstacles in the transition to Industry 5.0, personnel must be resilient, stress tolerant, and adaptable. The chapter will offer critical reflections about the benefits to workforce in Industry 5.0, challenges that workers will have to face due to transition to Fifth Industrial Revolution, and the in-demand skills that workers should possess to be prepared for Industry 5.0. The chapter also entails the issues related to integration of technology and human workforce and considerations that managers should keep in mind before integrating technology and workforce. Finally, recommendations for preparing workers for future of work by reskilling and upskilling them are also presented.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991