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ABSTRACT. This research explores how generative artificial intelligence (AI) and predictive intelligence transform manufacturing operations and supply chain resilience. The study analyses how recent implementations and outcomes have been reshaped through extensive analysis of recent implementations and outcomes on how AI technologies impact traditional manufacturing paradigms. The study outlines the main factors affecting successful AI adoption: ethical considerations, system integration capabilities and workforce development needs. The findings show that generative AI has vast potential to drive manufacturing operations but to truly maximize the gains, the balance between the technical and organizational aspects is key. The study adds to the existing body of literature on AI implementation in manufacturing and offers practical implications for industry practitioners looking to benefit from these technologies.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: generative artificial intelligence; predictive maintenance; supply chain resilience; manufacturing operations; reinforcement learning; Industry 4.0

How to cite: Pârvu, I. A., Szydło, J., Sedlarski, T., Dumitrescu, C.-O., Satnoianu, B. M. (2023). “Predictive Intelligence in Manufacturing: Generative Artificial Intelligence’s Impact on Supply Chain and Operational Resilience,” Economics, Management, and Financial Markets 18(3): 40–55. doi: 10.22381/emfm18320233.

Received 4 June 2023 • Received in revised form 23 September 2023
Accepted 28 September 2023 • Available online 30 September 2023

1Bucharest University of Economic Studies, Bucharest, Romania, This email address is being protected from spambots. You need JavaScript enabled to view it. (corresponding author); This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it..
2Bialystok University of Technology, Bialystok, Poland, This email address is being protected from spambots. You need JavaScript enabled to view it..
3St Kliment Ohridski University of Sofia, Bulgaria, This email address is being protected from spambots. You need JavaScript enabled to view it..

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