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Generative AI in the Manufacturing Process: Theoretical Considerations

Author

Listed:
  • Doanh Doung Cong

    (National Economics University, Hanoi, Vietnam)

  • Dufek Zdenek

    (Brno University of Technology, Czechia)

  • Ejdys Joanna

    (Bialystok University of Technology, Poland)

  • Ginevičius Romualdas

    (Mykolas Romeris University, Lithuania)

  • Korzynski Pawel

    (Kozminski University, Poland)

  • Mazurek Grzegorz

    (Kozminski University, Poland)

  • Paliszkiewicz Joanna

    (Warsaw University of Life Sciences, Poland)

  • Wach Krzysztof

    (Krakow University of Economics, Poland)

  • Ziemba Ewa

    (University of Economics in Katowice, Poland)

Abstract

The paper aims to identify how digital transformation and Generative Artificial Intelligence (GAI), in particular, affect the manufacturing processes. Several dimensions of the Industry 4.0 field have been considered, such as the design of new products, workforce and skill optimisation, enhancing quality control, predictive maintenance, demand forecasting, and marketing strategy. The paper adopts qualitative research based on a critical review approach. It provides evidence of the GAI technology support in the mentioned areas. Appropriate use of emerging technology allows managers to transform manufacturing by optimising processes, improving product design, enhancing quality control, and contributing to overall efficiency and innovation in the industry. Simultaneously, GAI technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks, improve a marketing strategy and identify market trends.

Suggested Citation

  • Doanh Doung Cong & Dufek Zdenek & Ejdys Joanna & Ginevičius Romualdas & Korzynski Pawel & Mazurek Grzegorz & Paliszkiewicz Joanna & Wach Krzysztof & Ziemba Ewa, 2023. "Generative AI in the Manufacturing Process: Theoretical Considerations," Engineering Management in Production and Services, Sciendo, vol. 15(4), pages 76-89, December.
    • Handle: RePEc:vrs:ecoman:v:15:y:2023:i:4:p:76-89:n:3
    DOI: 10.2478/emj-2023-0029
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    References listed on IDEAS

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    1. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    2. Zhu, Yimin & Zhang, Jiemin & Wu, Jifei & Liu, Yingyue, 2022. "AI is better when I'm sure: The influence of certainty of needs on consumers' acceptance of AI chatbots," Journal of Business Research, Elsevier, vol. 150(C), pages 642-652.
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