Generative AI in the Manufacturing Process: Theoretical Considerations-Reference-Cited by-同舟云学术

Generative AI in the Manufacturing Process: Theoretical Considerations

Published:2023-12-01 Issue:4 Volume:15 Page:76-89
ISSN:2543-912X
Container-title:Engineering Management in Production and Services
language:en
Short-container-title:

Author:

Doanh Doung Cong¹^ORCID,Dufek Zdenek²^ORCID,Ejdys Joanna³^ORCID,Ginevičius Romualdas⁴^ORCID,Korzynski Pawel⁵^ORCID,Mazurek Grzegorz⁶^ORCID,Paliszkiewicz Joanna⁷^ORCID,Wach Krzysztof⁸^ORCID,Ziemba Ewa⁹^ORCID

Affiliation:

1. 2 National Economics University , Hanoi , Vietnam

2. 3 Brno University of Technology , Czechia

3. 1 Bialystok University of Technology , Poland

4. 4 Mykolas Romeris University , Lithuania

5. 5 Kozminski University , Poland

6. 6 Kozminski University , Poland

7. 7 Warsaw University of Life Sciences , Poland

8. 8 Krakow University of Economics , Poland

9. 9 University of Economics in Katowice , Poland

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Management of Technology and Innovation,Industrial and Manufacturing Engineering,Strategy and Management,Management Information Systems

Link

https://www.sciendo.com/pdf/10.2478/emj-2023-0029

Reference82 articles.

1. Abolghasemi, M., Hurley, J., Eshragh, A., & Fahimnia, B. (2020). Demand forecasting in the presence of systematic events: Cases in capturing sales promotions. International Journal of Production Economics, 230. doi: 10.1016/j.ijpe.2020.107892

2. Achouch, M., Dimitrova, & M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences, 12(16), 8081. doi: 10.3390/app12168081

3. Adel, A. (2022). Future of industry 5.0 in society: Humancentric solutions, challenges and prospective research areas. Journal of Cloud Computing, 11, 1-15. doi: 10.1186/s13677-022-00314-5

4. Allahloh, A. S., Sarfraz, M., Ghaleb, A. M., Al-Shamma’a, A. A., Hussein Farh, H. M., & Al-Shaalan, A. M. (2023). Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance. Sustainability 15(11), 8808. doi: 10.3390/su15118808

5. Arena, F., Collotta, M., Luca L., Ruggieri, M., & Termine, F. G. (2022). Predictive Maintenance in the Automotive Sector: A Literature Review. Mathematical and Computational Applications 27(1), 2. doi: 10.3390/mca27010002