Artificial intelligence in manufacturing - systematic literature review

• Autorzy: Tarasiuk Wojciech , Halicka Katarzyna

Identyfikatory

DOI

10.29119/1641-3466.2025.215.39

• Języki publikacji: EN

Abstrakty

EN

Purpose: The purpose of this paper is to explore the current state of research on artificial intelligence in manufacturing. The paper aims to identify key trends, leading authors, institutions and research topics, as well as to identify the main areas of scientific interest in this field. Design/methodology/approach: The research objectives were achieved by using a systematic literature review and bibliometric analysis. The study used Web of Science and Scopus databases, where searches were conducted according to specific keywords and inclusion criteria, such as document type, language and publication time range (2015-2024). The collected data was then analyzed for the distribution of documents by type, year of publication, country, institution, author, and co-occurrence of keywords, which made it possible to extract major thematic clusters and research trends. Findings: The analysis revealed five thematic clusters representing key research areas, alongside a rapid growth in publications from 2019, particularly in countries such as China, the United States, and India. These findings highlight an increasing global focus on AI's application in manufacturing. Originality/value: This article offers a comprehensive and up-to-date analysis of research on artificial intelligence in manufacturing, covering publications up to 2024. By identifying five key thematic clusters, it provides unique insights that will benefit researchers, industry practitioners, and decision-makers aiming to integrate AI into manufacturing processes. The study provides a better understanding of research trends and developments in the field, making it a valuable resource for researchers, industrial practitioners and decision makers interested in integrating AI into manufacturing processes.

Słowa kluczowe

EN

artificial intelligence; manufacturing; systematic literature review

PL

sztuczna inteligencja; produkcja; systematyczny przegląd literatury

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2025
• Tom: z. 215
• Strony: 677--701
• Opis fizyczny: Bibliogr. 77 poz.

Twórcy

autor

Tarasiuk Wojciech

• Bialystok University of Technology

• https://orcid.org/0009-0005-7717-648X

autor

Halicka Katarzyna

• Bialystok University of Technology

• https://orcid.org/0000-0001-6370-8995

Bibliografia

• 1. Abbili, K.K. (2024). Explainable artificial intelligence (xai) and machine learning technique for prediction of properties in additive manufacturing. Journal of Advanced Manufacturing Systems, pp. 1-12, doi: https://doi.org/10.1142/S0219686725500118
• 2. Agrawal, R., Majumdar, A., Kumar, A., Luthra, S. (2023). Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities. Operations Management Research, Vol. 16, Iss. 4, pp. 1720-1741, doi: https://doi.org/10.1007/s12063-023-00383-y
• 3. Ahmmed, M.S., Isanaka, S.P., Liou, F. (2024). Promoting synergies to improve manufacturing efficiency in industrial material processing: A systematic review of industry 4.0 and AI. Machines, Vol. 12, Iss. 10, pp. 1-27, doi: doi.org/10.3390/machines12100681
• 4. Akinadewo, I.S., Dagunduro, M.E., Akinadewo, J.O., Oluwagbade, I.O., Akpan, J.U. (2024). How does artificial intelligence and accountability optimises energy management and cost reduction in Nigeria's manufacturing firms? International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG), pp. 1-12, doi: https://doi.org/10.1109/SEB4SDG60871.2024.10629858
• 5. Aktepe, A., Yanık, E., Ersöz, S. (2021). Demand forecasting application with regression and artificial intelligence methods in a construction machinery company. Journal of Intelligent Manufacturing, Vol. 32, Iss. 6, pp. 1587-1604, doi: https://doi.org/10.1007/ s10845-021-01737-8
• 6. Amini, M., Chang, S.I., Rao, P. (2019). A cybermanufacturing and AI framework for laser powder bed fusion (LPBF) additive manufacturing process. Manufacturing Letters, Vol. 21, pp. 41-44, doi: https://doi.org/10.1016/j.mfglet.2019.08.007
• 7. Aphirakmethawong, J., Yang, E., Mehnen, J. (2022). An overview of artificial intelligence in product design for smart manufacturing. 27th International Conference on Automation and Computing (ICAC), pp. 1-6, doi: https://doi.org/10.1109/ICAC55051.2022.9911089
• 8. Andres, B., Mateo-Casali, M.A., Fiesco, J.P., Poler, R. (2023). Artificial Intelligence Decision Systems to Support Industrial Equipment Manufacturing. International Conference on Industrial Engineering and Industrial Management (ICIEIM)-Congreso de Ingeniería de Organización, pp. 438-443, doi: https://doi.org/10.1007/978-3-031-57996- 7_75
• 9. Angelov, P.P., Soares, E.A., Jiang, R., Arnold, N.I., Atkinson, P.M. (2021). Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 11, Iss. 5, pp. 1-13, doi: https://doi.org/10.1002/widm.1424
• 10. Arias, C.R. (2022). An introduction to artificial intelligence. Seattle: SPU Works.
• 11. Arinez, J.F., Chang, Q., Gao, R.X., Xu, C., Zhang, J. (2020). Artificial intelligence in advanced manufacturing: Current status and future outlook. Journal of Manufacturing Science and Engineering, Vol. 142, Iss. 11, pp. 1-16, doi: https://doi.org/10.1115/1.4047855
• 12. Arora, A., Gupta, R. (2022). A Comparative Study on Application of Artificial Intelligence for Quality Assurance in Manufacturing. 4th International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 1200-1206, doi: https://doi.org/10.1109/ICIRCA54612.2022.9985522
• 13. Baskar, G., Sivakumar, R., Kadry, S., Dong, C.D., Singhania, R.R., Praveenkumar, R., Raja Sathendra, E. (2024). Comparative studies on modeling and optimization of fermentation process conditions for fungal asparaginase production using artificial intelligence and machine learning techniques. Preparative Biochemistry & Biotechnology, pp. 1-7, doi: https://doi.org/10.1080/10826068.2024.2367692
• 14. Boden, M.A. (2016). AI: Its Nature and Future. Oxford: Oxford University Press.
• 15. Castañé, G., Dolgui, A., Kousi, N., Meyers, B., Thevenin, S., Vyhmeister, E., Östberg, P.O. (2023). The ASSISTANT project: AI for high level decisions in manufacturing. International Journal of Production Research, Vol. 61, Iss. 7, pp. 2288-2306, doi: https://doi.org/10.1080/00207543.2022.2069525
• 16. Chauhan, A. (2024). Enhancing Yarn Quality in the Cotton Industry: AI- Based Nep Detection for Improved Manufacturing Processes. In: K.H., Rodriguez, R.V., Rege, M., Piuri, V., Xu, G., Ong, KL. (eds.), Artificial Intelligence and Knowledge Processing. AIKP 2023. Communications in Computer and Information Science (pp. 88-103). Cham: Springer, doi: https://doi.org/10.1007/978-3-031-68617-7_7
• 17. Doanh, D.C., Dufek, Z., Ejdys, J., Ginevičius, R., Korzynski, P., Mazurek, G., Ziemba, E. (2023). Generative AI in the manufacturing process: theoretical considerations. Engineering Management in Production and Services, Vol. 15, Iss. 4, pp. 76-89, doi: https://doi.org/10.2478/emj-2023-0029
• 18. Faccio, M., Cohen, Y. (2024). Intelligent cobot systems: human-cobot collaboration in manufacturing. Journal of Intelligent Manufacturing, Vol. 35, Iss. 5, pp. 1905-1907, doi: https://doi.org/10.1007/s10845-023-02142-z
• 19. Franke, F., Franke, S., Riedel, R. (2022). AI-based improvement of decision-makers' knowledge in production planning and control. IFAC-PapersOnLine, Vol. 55, Iss. 10, pp. 2240-2245, doi: https://doi.org/10.1016/j.ifacol.2022.10.041
• 20. Getachew, M., Beshah, B., Mulugeta, A., Kitaw, D. (2024). Application of artificial intelligence to enhance manufacturing quality and zero-defect using CRISP-DM framework. International Journal of Production Research, pp. 1-25, doi: https://doi.org/10.1080/00207543.2024.2407919
• 21. Goli, A., Zare, H.K., Moghaddam, R., Sadeghieh, A. (2018). A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry. Journal of Industrial and Systems Engineering, Vol. 11, pp. 190-203.
• 22. Grabarek, I., Stasiak-Cieślak, B. (2019). System ekspercki wspomagający dobór urządzeń adaptacyjnych dla kierowców z niepełnosprawnościami. WUT Journal of Transportation Engineering, Vol . 124, pp. 161-170.
• 23. Halicka, K. (2017). Main Concepts of Technology Analysis in the Light of the Literature on the Subject. Procedia Engineering, Vol. 182, pp. 291-298, doi: https://doi.org/10.1016/j.proeng.2017.03.196
• 24. Hrnjica, B., Softic, S. (2020). Explainable AI in manufacturing: a predictive maintenance case study. In: B. Lalic, V. Majstrovic, U. Marjanovic, G. von Cieminski, D. Romero (Eds.), Advances in Production Management Systems (pp. 66-73). Cham: Springer International Publishing, doi: https://doi.org/10.1007/978-3-030-57997-5_8
• 25. Huang, M.H., Rust, R.T. (2018). Artificial intelligence in service. Journal of service research, Vol. 21, Iss. 2, pp. 155-172, doi: https://doi.org/10.1177/1094670517752459
• 26. Jadhav, N.R., Bhutada, S., Sagavkar, S.R., Pawar, R., Kanwade, A.B., Mange, P. (2024). AI-driven pharmaceutical manufacturing: Revolutionizing quality control and process optimization. Journal of Statistics and Management Systems, Vol. 27, Iss. 2, pp. 405-416, doi: https://doi.org/10.47974/jsms-1265
• 27. Javaid, M., Haleem, A., Singh, R.P., Rab, S., Suman, R. (2022). Significant applications of Cobots in the field of manufacturing. Cognitive Robotics, Vol. 2, pp. 222-233, doi: https://doi.org/10.1016/j.cogr.2022.10.001
• 28. Kaab, A., Sharifi, M., Mobli, H., Nabavi-Pelesaraei, A., Chau, K.W. (2019). Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production. Science of the Total Environment, Vol. 664, pp. 1005-1019, doi: https://doi.org/10.1016/j.scitotenv.2019.02.004
• 29. Kasaraneni, R.K. (2021). AI-Enhanced Process Optimization in Manufacturing: Leveraging Data Analytics for Continuous Improvement. Journal of Artificial Intelligence Research and Applications, Vol. 1, Iss. 1, pp. 488-530.
• 30. Kondapaka, K.K. (2021). Advanced Artificial Intelligence Techniques for Demand Forecasting in Retail Supply Chains: Models, Applications, and Real-World Case Studies. African Journal of Artificial Intelligence and Sustainable Development, Vol. 1, Iss. 1, pp. 180-218.
• 31. Kozłowska, J. (2022). Methods of multi-criteria analysis in technology selection and technology assessment: a systematic literature review. Engineering Management in Production and Services, Vol. 14, Iss. 2, pp. 116-137, doi: https://doi.org/10.2478/emj2022-0021
• 32. Kumar, A., Dimitrakopoulos, R., Maulen, M. (2020). Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex. Journal of Intelligent Manufacturing, Vol. 31, pp. 1795-1811, doi: https://doi.org/10.1007/s10845- 020-01562-5
• 33. Lamsaf, A., Samale, P., Proença, H., Neves, J.C., Hambarde, K. (2024, March). Advancing Manufacturing Energy Efficiency: The Role of AI and Web-Based Tools. International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 1-6, doi: https://doi.org/10.1109/ESCI59607.2024.10497194
• 34. Leberruyer, N., Bruch, J., Ahlskog, M., Afshar, S. (2023). Toward Zero Defect Manufacturing with the support of Artificial Intelligence-Insights from an industrial application. Computers in Industry, Vol. 147, pp. pp. 1-12, doi: https://doi.org/10.1016/j.compind.2023.103877
• 35. Lee, J., Davari, H., Singh, J., Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing letters, Vol. 18, pp. 20-23, doi: https://doi.org/10.1016/j.mfglet.2018.09.002
• 36. Lina, R. (2022). Improving product quality and satisfaction as fundamental strategies in strengthening customer loyalty. AKADEMIK: Jurnal Mahasiswa Ekonomi & Bisnis, Vol. 2, Iss. 1, pp. 19-26, doi: https://doi.org/10.37481/jmeb.v2i1.245
• 37. Liu, C., Tian, W., Kan, C. (2022). When AI meets additive manufacturing: Challenges and emerging opportunities for human-centered products development. Journal of Manufacturing Systems, Vol. 64, pp. 648-656, doi: https://doi.org/10.1016/ j.jmsy.2022.04.010
• 38. Liu, Y., Yu, W., Dillon, T., Rahayu, W., Li, M. (2021). Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring. IEEE Transactions on Industrial Informatics, Vol. 18, Iss. 2, pp. 1345-1354, doi: https://doi.org/10.1109/TII.2021.3091774
• 39. Massaro, A., Dipierro, G., Selicato, S., Cannella, E., Galiano, A., Saponaro, A. (2021, June). Intelligent Quarry Production Monitoring Risks and Quality by Artificial Intelligence. IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0&IoT), pp. 242-247, doi: https://doi.org/10.1109/MetroInd4.0IoT51437.2021.9488469
• 40. Mediavilla, M.A., Dietrich, F., Palm, D. (2022). Review and analysis of artificial intelligence methods for demand forecasting in supply chain management. Procedia CIRP, Vol. 107, pp. 1126-1131, doi: https://doi.org/10.1016/j.procir.2022.05.119
• 41. Mirzaei, J., Parashkoohi, M.G., Zamani, D.M., Afshari, H. (2023). Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel. Results in Engineering, Vol. 20, pp. 1-15, doi: https://doi.org/10.1016/j.rineng.2023.101522
• 42. Młodzianowski, P., Rostkowski, R. (2021). Podstawy uczenia maszynowego dla menadżerów projektu IT. Management and Quality - Zarządzanie i Jakość, Vol. 3 Iss. 4, pp. 18-26.
• 43. Mmbando, G.S. (2024). The use of artificial intelligence in the production of genetically modified (GM) crops: a recent promising strategy for enhancing the acceptability of GM products? Discover Applied Sciences, Vol. 6, Iss. 11, pp. 1-12, doi: https://doi.org/10.1007/s42452-024-06212-6
• 44. Morales, E.F., Escalante, H.J. (2022). A brief introduction to supervised, unsupervised, and reinforcement learning. In: A.A. Torres Garcia, C.A. Reyes-Garcia, L. Villasenor-Pineda, O. Mendoza-Montoya (Eds.), Biosignal processing and classification using computational learning and intelligence (pp. 111-129). San Diego: Academic Press, doi: https://doi.org/10.1016/B978-0-12-820125-1.00017-8
• 45. Moosavi, S., Farajzadeh-Zanjani, M., Razavi-Far, R., Palade, V., Saif, M. (2024). Explainable AI in Manufacturing and Industrial Cyber-Physical Systems: A Survey. Electronics, Vol. 13, Iss. 17, pp. 1-28, doi: https://doi.org/10.3390/ electronics13173497
• 46. Netisopakul, P., Phumee, N. (2022). AI-enhanced predictive maintenance in manufacturing processes. 22nd International Conference on Control, Automation and Systems (ICCAS), pp. 1107-1112, doi: https://doi.org/10.23919/ICCAS55662.2022.10003774
• 47. Obaidullah, A.J. (2023). Advanced AI modeling and optimization for determination of pharmaceutical solubility in supercritical processing for production of nanosized drug particles. Case Studies in Thermal Engineering, Vol. 49, pp. 1-10, doi: https://doi.org/10.1016/j.csite.2023.103199
• 48. Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, Vol. 37, pp. 879-910, doi: https://doi.org/10.17705/1CAIS.03743
• 49. Patalas-Maliszewska, J., Pająk, I., Skrzeszewska, M. (2020). AI-based Decision-making Model for the Development of a Manufacturing Company in the context of Industry 4.0. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-7, doi: https://doi.org/10.1109/FUZZ48607.2020.9177749
• 50. Patel, S., Mekavibul, J., Park, J., Kolla, A., French, R., Kersey, Z., Lewin, G.C. (2019). Using machine learning to analyze image data from advanced manufacturing processes. Systems and information engineering design symposium (SIEDS), pp. 1-5, doi: https://doi.org/10.1109/SIEDS.2019.8735603
• 51. Pracucci, A. (2024). Designing Digital Twin with IoT and AI in warehouse to support optimization and safety in Engineer-to-Order manufacturing process for prefabricated building products. Applied Sciences, Vol. 14, Iss. 15, pp. 1-20, doi: https://doi.org/10.3390/app14156835
• 52. Radanliev, P., De Roure, D. (2023). Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0. Health and Technology, Vol. 13, Iss. 1, pp. 11-15, doi: https://doi.org/10.1007/s12553-022- 00722-2
• 53. Rathore, A.S., Nikita, S., Thakur, G., Mishra, S. (2023). Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends in Biotechnology, Vol. 41, Iss. 4, pp. 497-510.
• 54. Rentsch, R., Heinzel, C., Brinksmeier, E. (2015). Artificial intelligence for an energy and resource efficient manufacturing chain design and operation. Procedia CIRP, Vol. 33, pp. 139-144, doi: https://doi.org/10.1016/j.procir.2015.06.026
• 55. Rezaei, A., Richter, J., Nau, J., Streitferdt, D., Kirchhoff, M. (2023). Transparency and Traceability for AI-Based Defect Detection in PCB Production. In: D. Simian, L.F. Stoica, (eds.), Modelling and Development of Intelligent Systems. MDIS 2022. Communications in Computer and Information Science (pp. 54-72). Cham: Springer, doi: https://doi.org/10.1007/978-3-031-27034-5_4
• 56. Rojek, I., Mikołajewski, D., Mroziński, A., Macko, M. (2024). Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence-A Review. Electronics, Vol. 13, Iss. 16, pp. 1-18, doi: https://doi.org/10.3390/electronics13163338
• 57. Rossini, R., Prato, G., Conzon, D., Pastrone, C., Pereira, E., Reis, J., Ferreira, A. (2021, September). AI environment for predictive maintenance in a manufacturing scenario. 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1-8, doi: https://doi.org/10.1109/ETFA45728.2021.9613359
• 58. Rymarczyk, J. (2020). Technologies, opportunities and challenges of the industrial revolution 4.0: Theoretical considerations. Entrepreneurial Business and Economics Review, Vol. 8, Iss. 1, pp. 185-198.
• 59. Schreiber, M., Klöber-Koch, J., Bömelburg-Zacharias, J., Braunreuther, S., Reinhart, G. (2019). Automated quality assurance as an intelligent cloud service using machine learning. Procedia Cirp, Vol. 86, pp. 185-191, doi: https://doi.org/10.1016/j.procir.2020.01.034
• 60. Shanmuganathan, S. (2016). Artificial neural network modelling: An introduction. In: S. Shanmuganathan, S. Samarasinghe (Eds.), Artificial Neural Network Modelling (pp. 1-14). Cham: Springer International Publishing, doi: https://doi.org/10.1007/978-3- 319-28495-8_1
• 61. Shi, Z., Ferrari, G., Ai, P., Marinello, F., Pezzuolo, A. (2023). Artificial Intelligence for Biomass Detection, Production and Energy Usage in Rural Areas: A review of Technologies and Applications. Sustainable Energy Technologies and Assessments, Vol. 60, pp. 1-14, doi: https://doi.org/10.1016/j.seta.2023.103548
• 62. Singh, L., Singh, M., Soni, A. (2023). Intelligent Manufacturing: Harnessing the Power of Artificial Intelligence for Product Design and Development. International Conference on Scientific and Technological Advances in Materials for Energy Storage and Conversions, pp. 527-538, doi: https://doi.org/10.1007/978-981-97-3173-2_35
• 63. Stavropoulos, P., Papacharalampopoulos, A., Christopoulos, D. (2023) . Use of Artificial Intelligence at the Level of Manufacturing Processes. European Symposium on Artificial Intelligence in Manufacturing, pp. 157-166, doi: https://doi.org/10.1007/978-3-031-57496- 2_16
• 64. Szpilko, D., Naharro, F.J., Lăzăroiu, G., Nica, E., de la Torre Gallegos, A. (2023). Artificial intelligence in the smart city-a literature review. Engineering Management in Production and Services, Vol. 15, Iss. 4, pp. 53-75, doi: https://doi.org/10.2478/emj-2023-0028
• 65. Szum, K. (2021). IoT-based smart cities: A bibliometric analysis and literature review. Engineering Management in Production and Services, Vol. 13, Iss. 2, pp. 115-136, doi: https://doi.org/10.2478/emj-2021-0017
• 66. Szymańska, E., Berbel Pineda, J.M. (2024). Innovation processes: from linear models to artificial intelligence. Engineering Management in Production and Services, Vol. 16 Iss. 3, pp. 15-28, doi: https://doi.org/10.2478/emj-2024-0021
• 67. Trakadas, P., Simoens, P., Gkonis, P., Sarakis, L., Angelopoulos, A., Ramallo-González, A. P., Karkazis, P. (2020). An artificial intelligence-based collaboration approach in industrial iot manufacturing: Key concepts, architectural extensions and potential applications. Sensors, Vol. 20, Iss. 19, pp. 1-20, doi: https://doi.org/10.3390/s20195480
• 68. Ukwaththa, J., Herath, S., Meddage, D.P.P. (2024). A review of machine learning (ML) and explainable artificial intelligence (XAI) methods in additive manufacturing (3D printing). Materials Today Communications, Vol. 41, pp. 110294, doi: https://doi.org/10.1016/ j.mtcomm.2024.110294
• 69. Ulkir, O., Bayraklılar, M.S., Kuncan, M. (2024). Energy consumption prediction of additive manufactured tensile strength parts using artificial intelligence. 3D Printing and Additive Manufacturing, Vol. 11, Iss. 5, pp. e1909-e1920, doi: https://doi.org/10.1089/ 3dp.2023.0189
• 70. Umer, M.A., Belay, E.G., Gouveia, L.B. (2024). Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0. Electronics, Vol. 13, Iss. 3, pp. 1-20, doi: https://doi.org/10.3390/electronics13030660
• 71. Urgo, M., Terkaj, W., Simonetti, G. (2024). Monitoring manufacturing systems using AI: A method based on a digital factory twin to train CNNs on synthetic data. CIRP Journal of Manufacturing Science and Technology, Vol. 50, pp. 249-268, doi: https://doi.org/10.1016/j.cirpj.2024.03.005
• 72. Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S., Tegmark, M., Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, Vol. 1, Iss. 1, pp. 1-10, doi: https://doi.org/10.1038/s41467-019-14108-y
• 73. Vyskočil, J., Douda, P., Novák, P., Wally, B. (2023). A digital twin-based distributed manufacturing execution system for industry 4.0 with ai-powered on-the-fly replanning capabilities. Sustainability, Vol. 15, Iss. 7, pp. 1-27, doi: https://doi.org/10.3390/su15076251
• 74. Wan, J., Li, X., Dai, H.N., Kusiak, A., Martinez-Garcia, M., Li, D. (2020). Artificialintelligence-driven customized manufacturing factory: key technologies, applications, and challenges. Proceedings of the IEEE, Vol. 109, Iss. 4, pp. 377-398, doi: https://doi.org/10.1109/JPROC.2020.3034808
• 75. Wang, J., Wen, Y., Long, H. (2024). Evaluating the mechanism of AI contribution to decarbonization for sustainable manufacturing in China. Journal of Cleaner Production, Vol. 472, pp. 1-12, doi: https://doi.org/10.1016/j.jclepro.2024.143505
• 76. Wyskwarski, M. (2015). Metody sztucznej inteligencji w organizacji inteligentnej. Scientific Papers of Silesian University of Technology. Organization and Management Series, Vol. 86, pp. 159-168.
• 77. Xiao, Y., Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of planning education and research, Vol. 39, Iss. 1, pp. 93-112, doi: https://doi.org/10.1177/0739456X17723971

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).