ABSTRACT. The research explores the transformative effect of generative artificial intelligence (AI) for smart manufacturing and addresses the most challenging productivity problems in the industry with breakthrough approaches. This detailed study analyses the use of AI-based solutions in different manufacturing domains and analyses the impact on operational efficiency, quality control and process optimisation. The results show generative AI can turn manufacturing operations remarkably for the better. In addition, the study examines critical success factors for implementing generative AI in manufacturing and provides recommendations for a more holistic and high-quality approach to generative AI in manufacturing that emphasizes cross-disciplinary collaboration and human-in-the-loop approaches. Organizational readiness, infrastructure compatibility, and workforce training requirements are challenges. Research indicates that AI manufacturers of the future will further move towards sustainability, cost-effectiveness and balanced automation solutions. The research provides manufacturing organizations with evidence-based strategies to integrate generative AI while protecting human expertise and oversight.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: generative artificial intelligence; smart manufacturing; industrial productivity; predictive maintenance, quality control systems; human-in-the-loop manufacturing

How to cite: Nica, E., Szpilko, D., Ștefan, V., Kalgi, M., Alexandru, B. (2023). “Reimagining Industrial Productivity: Generative Artificial Intelligence’s Breakthrough Approaches to Smart Manufacturing Challenges,” Economics, Management, and Financial Markets 18(3): 24–39. doi: 10.22381/emfm18320232.

Received 17 June 2023 • Received in revised form 22 September 2023
Accepted 27 September 2023 • Available online 30 September 2023