Generative AI in the Manufacturing Process: Theoretical Considerations

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., & Denisi, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33, 606-659. doi: 10.1111/1748-8583.12524 Search in Google Scholar

Cappa, F., Oriani, R., Peruffo, E., & McCarthy, I. (2021). Big data for creating and capturing value in the digitalized environment: unpacking the effects of volume, variety, and veracity on firm performance. Journal of Production and Innovation Management, 38, 49-67. doi: 10.1111/jpim.12545 Search in Google Scholar

Cha, J.-H., Jeong, H.-G., Han, S.-W., Kim, D.-C., Oh, J.-H., Hwang, S.-H., & Park, B.-J. (2023). Development of MLOps Platform Based on Power Source Analysis for Considering Manufacturing Environment Changes in Real-Time Processes. In International Conference on Human-Computer Interaction (pp. 224–236). Springer. Search in Google Scholar

Chang, Y.-L., & Ke, J. (2023). Socially Responsible Artificial Intelligence Empowered People Analytics: A Novel Framework Towards Sustainability. Human Resource Development Review, 15344843231200930. Search in Google Scholar

Conboy, K., Mikalef, P., Dennehy, D., & Krogstie, J. (2020). Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. European Journal of Operational Research, 281(3), 656-672. doi: 10.1016/j.ejor.2019.06.051 Search in Google Scholar

De Mauro, A., Sestino, A., & Bacconi, A. (2022). Machine learning and artificial intelligence use in marketing: a general taxonomy. Italian Journal of Marketing, 439-457. doi: 10.1007/s43039-022-00057-w Search in Google Scholar

Dencheva, V. (2023). Share of marketers using generative artificial intelligence (AI) in their companies in the United States as of March 2023. Retrieved from https://www.statista.com/statistics/1388390/generative-ai-usage-marketing/ Search in Google Scholar

Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: a systematic literature review. Journal of Business Research, 121, 283-314. doi: 10.1016/j.jbusres.2020.08.019 Search in Google Scholar

Dwivedi, Y. K., Sharma, A., Rana, N. P., Giannakis, M., Goel, P., & Dutot, V. (2023). Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technological Forecasting and Social Change, 192. doi: 10.1016/j.techfore.2023.122579 Search in Google Scholar

Dworski, B. (2023). C-store retailers weigh in on automation, AI and data challenges. Retrieved from https://www.cstoredive.com/news/c-store-retailers-weighin-on-automation-ai-and-data-challenges/650008/ Search in Google Scholar

Ghosh, S. (2022). COVID-19, clean energy stock market, interest rate, oil prices, volatility index, geopolitical risk nexus: evidence from quantile regression. Journal of Economics and Development. doi: 10.1108/jed-04-2022-0073 Search in Google Scholar

Global Data. (2023). The impact of artificial intelligence in the consumer goods sector. Retrieved from https://just-drinks.nridigital.com/just_drinks_magazine_aug23/artificial-intelligence-impact-consumergoods-industry Search in Google Scholar

Głodowska, A., Maciejewski, M., & Wach, K. (2023). Navigating the digital landscape: A conceptual framework for understanding digital entrepreneurship and business transformation. International Entrepreneurship Review, 9(4), 7-20. doi: 10.15678/IER.2023.0904.01 Search in Google Scholar

Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119-132. doi: 10.1016/j.ijin.2022.08.005 Search in Google Scholar

Haponik, A. (2022). How AI improves productivity in manufacturing companies? Retrieved from https://addepto.com/blog/how-ai-improves-productivityin-manufacturing-companies/ Search in Google Scholar

Hartung, J., Dold, P. M., Jahn, A., & Heizmann, M. (2022). Analysis of AI-Based Single-View 3D Reconstruction Methods for an Industrial Application. Sensors, 22, 6425. doi: 10.3390/s22176425 Search in Google Scholar

Heuser, P., Letmathe, P., & Schinner, M. (2022). Workforce planning in production with flexible or budgeted employee training and volatile demand. Journal of Business Economics, 92, 1093-1124. doi: 10.1007/s11573-022-01090-z Search in Google Scholar

Hrnjica, B., & Softic, S. (2020). Explainable AI in Manufacturing: A Predictive Maintenance Case Study. In IFIP International Conference on Advances in Production Management Systems (APMS), (pp. 66–73). Novi Sad, Serbia. Search in Google Scholar

Hu, X., Liu, A., Li, X., Dai, Y., & Nakao, M. (2023). Explainable AI for customer segmentation in product development. CIRP Annals, 72(1), 89-92. doi: 10.1016/j.cirp.2023.03.004 Search in Google Scholar

Hull, B. (2011). Manufacturing Best Practices: Optimizing Productivity and Product Quality. Hoboken, New Jersey, USA: John Wiley & Sons. Search in Google Scholar

Hyun Baek, T., & Kim, M. (2023). Ai robo-advisor anthropomorphism: The impact of anthropomorphic appeals and regulatory focus on investment behaviors. Journal of Business Research, 164. doi: 10.1016/j.jbusres.2023.114039 Search in Google Scholar

Iansiti, M., & Lakhani, K. R. (2020). Competing in the Age of AI. Boston, MA. Search in Google Scholar

Katreddi, S., Kasani, S., & Thiruvengadam, A. (2022). A Review of Applications of Artificial Intelligence in Heavy Duty Trucks. Energies, 15(20), 7457. doi: 10.3390/en15207457 Search in Google Scholar

Khang, A., Rani, S., Gujrati, R., Uygun, H., & Gupta, S. K. (2023). Designing Workforce Management Systems for Industry 4.0: Data-Centric and AIEnabled Approaches (1st ed.). CRC Press. doi: 10.1201/9781003357070 Search in Google Scholar

Koole, G. M., & Li, S. (2023). A practice-oriented overview of call center workforce planning. Stochastic Systems. doi: 10.1287/stsy.2021.0008 Search in Google Scholar

Korzynski, P., Kozminski, A. K., & Baczynska, A. (2023). Navigating leadership challenges with technology: Uncovering the potential of ChatGPT, virtual reality, human capital management systems, robotic process automation, and social media. International Entrepreneurship Review, 9(2), 7-18. doi: 10.15678/IER.2023.0902.01 Search in Google Scholar

Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Wach, K., & Ziemba, E. (2023). Generative artificial intelligence as a new context for management theories: analysis of Chat- GPT. Central European Management Journal, 31(1). doi: 10.1108/CEMJ-02-2023-0091 Search in Google Scholar

Kshetri, N., Dwivedi, Y. K., Davenport, T. H., & Panteli, N. (2023). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. International Journal of Information Management, 102716. doi: 10.1016/j.ijinfomgt.2023.102716 Search in Google Scholar

Kumar, A., Gupta, N., & Bapat, G. (2023). Who is making the decisions? How retail managers can use the power of ChatGPT. Journal of Business Strategy. doi:10.1108/jbs-04-2023-0067 Search in Google Scholar

Kwong, C. K., Jiang, H., & Luo, X. G. (2016). AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products. Engineering Applications of Artificial Intelligence, 47(10), 49-60. doi: 10.1016/j.engappai.2015.04.001 Search in Google Scholar

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21-32. Search in Google Scholar

Lei, Y., Vyas, S., Gupta, S., & Shabaz, M. (2022). AI based study on product development and process design. International Journal of System Assuring Engineering Management, 13(1), 305-311. doi: 10.1007/s13198-021-01404-4 Search in Google Scholar

Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., & Wang, L. (2022). Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems, 65, 279-295. doi: 10.1016/j.jmsy.2022.09.017 Search in Google Scholar

Li, X., Pan, L., Zhou, Y., Wu, Z., & Luo, S. (2022). A Temporal– Spatial network embedding model for ICT supply chain market trend forecasting. Applied Soft Computing, 125. doi: 10.1016/j.asoc.2022.109118 Search in Google Scholar

Liu, B., Song, C., Liang, X., Lai, M., Yu, Z., & Ji, J. (2023). Regional differences in China’s electric vehicle sales forecasting: Under supply-demand policy scenarios. Energy Policy, 177. doi: 10.1016/j.enpol.2023.113554 Search in Google Scholar

Liu, C., Tian, W., & Kan, Ch., (2022). When AI meets additive manufacturing: Challenges and emerging opportunities for human-centered products development. Journal of Manufacturing Systems, 64, 648-656. doi: 10.1016/j.jmsy.2022.04.010 Search in Google Scholar

Liyanage, S., Abduljabbar, R., Dia, H., & Tsai, P.-W. (2022). AI-based neural network models for bus passenger demand forecasting using smart card data. Journal of Urban Management, 11(3), 365-380. doi: 10.1016/j.jum.2022.05.002 Search in Google Scholar

Ma, S., Fildes, R., & Huang, T. (2016). Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information. European Journal of Operational Research, 249(1), 245-257. doi: 10.1016/j.ejor.2015.08.029 Search in Google Scholar

Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623. doi: 10.1016/j.technovation.2022.102623 Search in Google Scholar

Mathur, S., Kumar, D., Kumar, V., Dantas, A., Verma, R., & Kuca, K. (2023). Xylitol: Production strategies with emphasis on biotechnological approach, scale up, and market trends. Sustainable Chemistry and Pharmacy, 35. doi: 10.1016/j.scp.2023.101203 Search in Google Scholar

Mazumdar, T., Raj, S. P., & Sinha, I. (2005). Reference price research: Review and propositions. Journal of Marketing, 69, 84-102. doi: 10.1509/jmkg.2005.69.4.84. Search in Google Scholar

Mazurek, G. (2018). Internet Rzeczy a cyfrowa transformacja – implikacje dla marketingu B2C [The Internet of Things and digital transformation - implications for B2C marketing]. In L. Sułkowski, & D. Kaczorowska-Spychalska (Eds.). Nowy paradygmat rynku [A new market paradigm], (pp. 33–57), Warsaw, Poland: Difin. Search in Google Scholar

Nadira, K. (2023). Implementing AI-Automation in Manufacturing for Product Quality Assurance. Retrieved from https://gleematic.com/implementing-ai-automation-in-manufacturing-for-product-qualityassurance/ Search in Google Scholar

Narasimhan, S. (2023). How AI & ML are Revolutionizing Product Quality Control. Retrieved from https://www.hurix.com/how-ai-ml-are-revolutionizingproduct-quality-control/ Search in Google Scholar

Njomane, L., & Telukdarie, A. (2022). Impact of COVID-19 food supply chain: Comparing the use of IoT in three South African supermarkets. Technology in Society, 71, 102051. doi: 10.1016/j.techsoc.2022.102051 Search in Google Scholar

Nosalska, K., Piatek, Z. M., Mazurek, G., & Rzadca, R. (2018). Industry 4.0: coherent definition framework with technological and organizational interdependencies. Journal of Manufacturing Technology Management, 31(5), 837-862. doi: 10.1108/JMTM-08-2018-0238 Search in Google Scholar

Ooi, K. B., Wei-Han Tan, G., Al-Emran, M., Al-Sharafi, M., Capatina, A., Chakraborty, A., Dwivedi, Y. K., Huang, T.-L., Kumar Kar, A., Lee, V. H., Loh, X.-M., Micu, A., Mikalef, P., Mogaji, E., Pandey, N., Raman, R., Rana, N. P., Sarker, P., Sharma, A., Teng, Ch., Wamba F. S., & Wong, L.-W. (2023). The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions. Journal of Computer Information Systems. doi: 10.1080/08874417.2023.2261010 Search in Google Scholar

Open AI. (2023). Introducing ChatGPT and Whisper APIs. Retrieved from https://openai.com/blog/introducing-chatgpt-and-whisper-apis Search in Google Scholar

Palmatier, R. W., Houston, M. B., & Hulland, J. (2018). Review articles: Purpose, process, and structure. Journal of the Academy of Marketing Science, 46, 1-5. doi: 10.1007/s11747-017-0563-4 Search in Google Scholar

Pandey, R., Uziel, S., Hutschenreuther, T., & Krug, S. (2023) Towards Deploying DNN Models on Edge for Predictive Maintenance Applications. Electronics, 12(3), 639. doi. 10.3390/electronics12030639 Search in Google Scholar

Plantec, Q., Deval, M.-A., Hooge, S., & Weil, B. (2023). Big data as an exploration trigger or problem-solving patch: Design and integration of AI-embedded systems in the automotive industry. Technovation, 124, 102763. doi: 10.1016/j.technovation.2023.102763 Search in Google Scholar

Raja, A. (2023). How Generative AI can enhance the Manufacturing Industries? Retrieved from https://www.linkedin.com/pulse/how-generative-ai-canenhance-manufacturing-industries-raja/ Search in Google Scholar

Rossini, R., Prato, G., Conzon, D., Pastrone, C., Pereira, E., Reis, J., Gonçalves, G., Henriques, D., Santiago, A. R., & Ferreira, A. (2021). AI environment for predictive maintenance in a manufacturing scenario. In2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), (pp. 1-8). Vasteras, Sweden. doi: 10.1109/ETFA45728.2021.9613359 Search in Google Scholar

Rymarczyk, J. (2021). The impact of Industrial Revolution 4.0 on international trade. Entrepreneurial Business and Economics Review, 9(1), 105-117. doi: 10.15678/EBER.2021.090107 Search in Google Scholar

Shin, W., Han, J., & Rhee, W. (2021). AI-assistance for predictive maintenance of renewable energy systems. Energy, 221, 119775. doi: 10.1016/j.energy.2021.119775. Search in Google Scholar

Sigov, A., Ratkin, L., Ivanov, L. A., & Xu, L. D. (2022). Emerging Enabling Technologies for Industry 4.0 and Beyond. Information Systems Frontiers. doi: 10.1007/s10796-021-10213-w Search in Google Scholar

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. doi: 10.1016/j.jbusres.2016.08.001 Search in Google Scholar

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, Elsevier, 104(C), 333-339. doi: 10.1016/j.jbusres.2019.07.039 Search in Google Scholar

Sohrabpour, V., Oghazi, P., Toorajipour, R., & Nazarpour, A. (2021). Export sales forecasting using artificial intelligence. Technological Forecasting and Social Change, 163. doi: 10.1016/j.techfore.2020.120480 Search in Google Scholar

Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems, 3, 192-204. doi: 10.1016/j.iotcps.2023.04.006 Search in Google Scholar

Srivastava, S. (2023). How AI is Proving as a Game Changer in Manufacturing – Use Cases and Examples. Retrieved from https://appinventiv.com/blog/ai-inmanufacturing/ Search in Google Scholar

Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3. doi: 10.1016/j.sca.2023.100026 Search in Google Scholar

Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3. doi: 10.1016/j.sca.2023.100026 Search in Google Scholar

Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, 215, 107864. doi: 10.1016/j.ress.2021.107864 Search in Google Scholar

Vaddi, K., & Khan, M. (2023). A New Era of Quality Assurance – Role of Generative AI in Reshaping Software Testing. Retrieved from https://www.encora.com/insights/a-new-era-of-qa-role-of-generative-ai-inreshaping-software-testing Search in Google Scholar

Vaid, S., Puntoni, S., & Khodr, A. (2023). Artificial intelligence and empirical consumer research: A topic modeling analysis. Journal of Business Research, 166. doi: 10.1016/j.jbusres.2023.114110 Search in Google Scholar

Villar, A., Paladini, S., & Buckley, O. (2023). Towards Supply Chain 5.0: Redesigning Supply Chains as Resilient, Sustainable, and Human-Centric Systems in a Post-pandemic World. Operational Research Forum, 4, 60. doi: 10.1007/s43069-023-00234-3 Search in Google Scholar

Viverit, L., Heo, C. Y., Pereira, L. N., & Tiana, G. (2023). Application of machine learning to cluster hotel booking curves for hotel demand forecasting. International Journal of Hospitality Management, 111. doi: 10.1016/j.ijhm.2023.103455 Search in Google Scholar

Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7-30. doi: 10.15678/EBER.2023.110201 Search in Google Scholar

Wang, G., Ledwoch, A., Hasani, R. M., Grosu, R., & Brintrup, A. (2019). A generative neural network model for the quality prediction of work in progress products. Applied Soft Computing, 85, 105683. doi: 10.1016/j.asoc.2019.105683 Search in Google Scholar

Wang, T., & Wu, D. (2024). Computer-Aided Traditional Art Design Based on Artificial Intelligence and Human-Computer Interaction. Computer-Aided Design and Applications, 21(S7), 59-73. doi: 10.14733/cadaps.2024.S7.59-73 Search in Google Scholar

Wlodarczyk, S. (2023). How Generative AI will transform manufacturing. Retrieved from https://aws.amazon.com/blogs/industries/generative-ai-in-manufacturing/ Search in Google Scholar

Xu, Q., Dong, J., Peng, K., & Yang, X. (2024). A novel method of neural network model predictive control integrated process monitoring and applications to hot rolling process. Expert Systems With Applications, 237, 121682. doi: 10.1016/j.eswa.2023.121682 Search in Google Scholar

Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18. doi: 10.1016/j.iswa.2023.200235 Search in Google Scholar

Zeng, W., Wang, J., Zhang, Y., Han, Y., & Zhao, Q. (2022). DDPG-based continuous thickness and tension coupling control for the unsteady cold rolling process. The International Journal of Advanced Manufacturing Technology, 120(11-12), 7277-7292. doi: 10.1007/s00170-022-09239-4 Search in Google Scholar

Zhu, Y., Zhang, J., Wu, J., & Liu, Y. (2022). AI is better when I’m sure: The influence of certainty of needs on consumers’ acceptance of AI chatbots. Journal of Business Research, 150, 642-652. doi: 10.1016/j.jbusres.2022.06.044 Search in Google Scholar