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Roadmap to Implement Industry 5.0 and the Impact of This Approach on TQM

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Smart Applications and Data Analysis (SADASC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1677))

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Abstract

The fifth Industrial Revolution (Industry 5.0) encompasses the transition from a digital-driven to a sustainable, human-centric, and resilient industry. Industry 5.0 recognize and value the role of workers in the production system. Therefore, it sets the health and safety of employees as a priority. Workers will be empowered and aided by robots and advanced technologies in order to improve work processes and work areas, hence improving companies’ productivity and efficiency. However, the shift from Industry 4.0 to Industry 5.0 will de- pend on how employees will embrace the new vision and on how prepared they are to work alongside machines, especially since advanced technologies have developed the fear of loss of jobs among employees. In this context, we propose a roadmap to implement the industry 5.0 vision and build interest among workers for change by merging two concepts ADKAR and Quality Circles. Furthermore, we discussed the impact of industry 5.0 on Total Quality Management.

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References

  1. Garouani, M., Ahmad, A., Bouneffa, M., Hamlich, M.: AMLBID: an auto-explained automated machine learning tool for big industrial data. SoftwareX 17, 100919 (2022)

    Article  Google Scholar 

  2. Hamlich, M., Ramdani, M.: Data classification by SAC “Scout Ants for Clustering” algorithm. J. Theor. Appl. Inf. Technol. 55(1), 66–73 (2013)

    Google Scholar 

  3. Chaabi, M., Hamlich, M.: A sight on defect detection methods for imbalanced industrial data. In: ITM Web of Conferences, vol. 43, p. 01012 (2022)

    Google Scholar 

  4. Govindarajan, U.H., Trappey, A.J., Trappey, C.V.: Immersive technology for human-centric cyberphysical systems in complex manufacturing processes: a comprehensive overview of the global patent profile using collective intelligence. Complexity. 2018 (2018)

    Google Scholar 

  5. Kaasinen, E., et al.: Empowering and engaging industrial workers with operator 4.0 solutions. Comput. Ind. Eng. 139, 105678 (2020)

    Article  Google Scholar 

  6. Yu, K., Luo, B.N., Feng, X., Liu, J.: Supply chain information integration, flexibility, and operational performance: an archival search and content analysis. Int. J. Logist. Manag. 29 (2018)

    Google Scholar 

  7. Garouani, M., Ahmad, A., Bouneffa, M., Hamlich, M., Bourguin, G., Lewandowski, A.: Towards big industrial data mining through explainable automated machine learning (2021). https://doi.org/10.21203/rs.3.rs755783/v1

  8. Xu, X., Lu, Y., Vogel-Heuser, B., Wang, L.: Industry 4.0 and industry 5.0—inception, conception and perception. J. Manuf. Syst. 61, 530–535 (2021)

    Article  Google Scholar 

  9. Maddikunta, P.K.R., et al.: Industry 5.0: a survey on enabling technologies and potential applications. J. Ind. Inf. Integr. 26, 100257 (2022)

    Google Scholar 

  10. Sundelin, N.: Guidelines for implementing collaborative robots in industrial application (2021)

    Google Scholar 

  11. Vu, H.T., Lim, J.: Effects of country and individual factors on public acceptance of artificial intelligence and robotics technologies: a multilevel SEM analysis of 28-country survey data. Behav. Inf. Technol. 41, 1–14 (2021)

    Google Scholar 

  12. Cheng, W.-J., Pien, L.-C., Cheng, Y.: Occupation-level automation probability is associated with psychosocial work conditions and workers’ health: a multilevel study. Am. J. Ind. Med. 64(2), 108–117 (2021)

    Article  Google Scholar 

  13. Pinzone, M., et al.: A framework for operative and social sustainability functionalities in Human-Centric Cyber-Physical Production Systems. Comput. Ind. Eng. 139, 105132 (2020)

    Article  Google Scholar 

  14. Mattsson, S., Ekstrand, E., Tarrar, M.: Understanding disturbance handling in complex assembly: analysis of complexity index method results. Procedia Manuf. 25, 213–222 (2018)

    Article  Google Scholar 

  15. Rožanec, J.M., et al.: Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications. arXiv preprint arXiv:2203.10794 (2022)

  16. Rowlands, H., Milligan, S.: ‘Quality-driven industry 4.0’, in key challenges and opportunities for quality, sustainability and innovation in the fourth industrial revolution: quality and service management in the fourth industrial revolution—sustainability and value co-creation. In: World Scientific, pp. 3–30 (2021)

    Google Scholar 

  17. Romero, D., et al.: Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. In: 46th International Conference on Computers & Industrial Engineering, pp. 1–11 (2016), ISSN 2164–8670 CD-ROM, ISSN 2164–8689

    Google Scholar 

  18. Anand Jayakumar, A., Krishnaraj, C.: Quality circle–formation and implementation. Int. J. Emer. Res. Eng. Sci. Technol. 2(2) (2015)

    Google Scholar 

  19. Boca, G.D.: Adkar model vs. quality management change (2013)

    Google Scholar 

  20. Charantimath, P.M.: Total Quality Management. Pearson Education India (2011)

    Google Scholar 

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Correspondence to Meryem Chaabi .

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Chaabi, M. (2022). Roadmap to Implement Industry 5.0 and the Impact of This Approach on TQM. In: Hamlich, M., Bellatreche, L., Siadat, A., Ventura, S. (eds) Smart Applications and Data Analysis. SADASC 2022. Communications in Computer and Information Science, vol 1677. Springer, Cham. https://doi.org/10.1007/978-3-031-20490-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-20490-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20489-0

  • Online ISBN: 978-3-031-20490-6

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