Skip to main content

Unleashing the Potential of Industry 5.0 Through AI: An AHP-Guided Roadmap

Issue Abstract

This research paper explores the profound impact of Artificial Intelligence (AI) on Industry 5.0, the era of intelligent and interconnected manufacturing. Leveraging the Analytic Hierarchy Process (AHP) as a guiding multi-criteria decision-making tool, the study develops a comprehensive roadmap for successful AI integration in Industry 5.0. The paper highlights AI's role in boosting production efficiency, enabling data-driven decision-making and facilitating predictive maintenance. The AHP method is introduced showcasing its suitability for complex decision-making with multiple criteria. The process and principles of AHP are outlined, emphasizing its usefulness in evaluating AI technologies for Industry 5.0 integration. A comparative analysis of AHP with other MCDM techniques is presented through real-world case studies demonstrating AHP's transparency, consistency and adaptability in prioritizing AI investments and deployment strategies. The empirical research section utilizes data-driven analysis to identify key factors influencing AI implementation in Industry 5.0. Stakeholder perspectives and expert opinions are integrated to derive meaningful criteria for the developed roadmap. Using AHP, the paper formulates a comprehensive AI integration roadmap for Industry 5.0. It includes identifying suitable AI applications, evaluating technological requirements, assessing workforce readiness and conducting risk analysis. The roadmap aims to guide industrial leaders, policymakers and decision-makers in effectively incorporating AI to unlock the full potential of Industry 5.0. The findings are discussed addressing potential challenges and limitations of the proposed roadmap. In conclusion, the research emphasizes the transformative power of AI in Industry 5.0 and its impact on industries. With AHP as a guiding tool, the paper offers a robust roadmap to navigate the complexities leading industries towards enhanced productivity, sustainable growth and heightened competitiveness in the Industry 5.0 landscape.
Keywords: Artificial Intelligence, Analytical Hierarchy Process, Industry 5.0, Multi-Criteria Decision Making (MCDM), Sustainable Growth. of AI integration,

Author Information
R Abishek Israel
Issue No
Volume No
Issue Publish Date
05 Sep 2023
Issue Pages

Issue References

1. Lee, J., & Kim, J. (2018). Industry 5.0: An Integrative Approach for Smart Manufacturing. Journal of Manufacturing Systems, 48, 144-169.
2. Chui, M., Manyika, J., & Miremadi, M. (2016). Where Machines Could Replace Humans—and Where They Can't (Yet). McKinsey Quarterly.
3. Jordan, M. I., & Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255-260.
4. Brown, C., Chui, M., & Manyika, J. (2017). Are Companies Ready for the Age of AI? McKinsey Global Survey.
5. Saaty, T. L. (1980). The Analytic Hierarchy Process. McGraw-Hill.
6. Saaty, T. L. (1990). How to Make a Decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48(1), 9-26.
7. Jin, X., Wah, B. W., & Cheng, X. (2015). Significance Analysis of Data in High Dimensions. In Encyclopedia of Biometrics (pp. 1326-1331). Springer, Boston, MA.
8. Gill, R. M., & Chang, K. T. (2018). The State of Artificial Intelligence in Manufacturing. Journal of Manufacturing Science and Engineering, 140(7), 070801.
9. Edwards, D. (2020). Artificial Intelligence and the Future of Work. Brookings Institution.
10. Karim, A., & Budiman, R. A. (2021). Artificial Intelligence and Its Implications in Industry 5.0: A Comprehensive Review. International Journal of Mechanical Engineering and Robotics Research, 10(3), 316-323.
11. Maheshwari, S., & Bector, C. R. (2020). Industry 4.0 and Industry 5.0: A Comparative Study. International Journal of Advanced Science and Technology, 29(5), 7692-7701.
12. Singh, R., Sharma, R., & Srivastava, S. (2019). Artificial Intelligence in Supply Chain Management: A Comprehensive Review. Transportation Research Part E: Logistics and Transportation Review, 130, 202-254.
13. Varshney, S., & Das, A. (2018). Artificial Intelligence: A Review and Analysis of Adoption in Business and Industrial Automation. International Journal of Business Information Systems, 27(1), 23-42.