DOI: https://doi.org/10.32515/2664-262X.2024.10(41).2.95-103
Integration of Artificial Intelligence Into Decision Support Systems in Optimizing Production Processes of a Machine-Building Enterprise Using the Example of maChine Learning
About the Authors
Roman Boiko, post-graduate, Professor, Doctor in Technics (Doctor of Technic Sciences), e-mail: mr.r.boiko@gmail.com, ORCID ID: 0009-0007-3206-0533
Viktor Aulin, Professor, Doctor in Technics (Doctor of Technic Sciences), Professor, Doctor in Technics (Doctor of Technic Sciences), e-mail: AulinVV@gmail.com, ORCID ID: 0000-0003-2737-120X
Andrii Tykhyi, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Professor, Doctor in Technics (Doctor of Technic Sciences), e-mail: a.a.tihiy@gmail.com, ORCID ID: 0000-0001-5323-4415
Serhii Karpushyn, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Professor, Doctor in Technics (Doctor of Technic Sciences), e-mail: karp22.05.1972ksa@gmail.com, ORCID ID: 0000-0001-9035-9065
Oleh Koval, post-graduate, Professor, Doctor in Technics (Doctor of Technic Sciences), ORCID ID: 0009-0000-0678-1176
Abstract
This article provides a comprehensive overview of decision support systems based on artificial intelligence within the context of Industry 4.0. The integration approaches and data processing methods, as well as key machine learning and deep learning techniques, which form the technological foundation of such systems, are analyzed. The article outlines the architecture, typical implementation stages, and suggests a plan for artificial intelligence integration in a machine-building enterprise, including descriptions of the system components and deployment strategies. Potential challenges and recommendations for successful implementation are also discussed.
The outcomes of this integration can significantly reduce unplanned equipment downtimes, improve production efficiency, and provide enterprises with a substantial competitive advantage. Artificial intelligence, including machine learning and deep learning, enables the automation and acceleration of decision-making processes, detecting hidden patterns and preventing failures before they occur. The article highlights the importance of system architecture, data quality, and organizational factors in the successful deployment of artificial intelligence-based decision support systems. Furthermore, the article proposes a detailed integration plan, starting from data collection and unification to the selection of appropriate algorithms and the design of scalable, secure, and compatible architectures for real-time operations.
The integration of artificial intelligence-based decision support systems in machine-building enterprises can foster significant operational improvements, increase product quality, and optimize resource allocation. Future research may focus on explainable artificial intelligence, blockchain integration, and augmented reality to enhance transparency in supply chains and support personalized decision-making.
Keywords
artificial intelligence, machine learning, deep learning, decision support systems, predictive maintenance, quality control, production process optimization.
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References
1. J.M. Tien. (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4 149-178. https://link.springer.com/article/10.1007/s40745-017-0112-5
2. H. Hu, Y. Wen, T.-S. Chua, & X. Li. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652-687. https://ieeexplore.ieee.org/document/6842585
3. S. Gupta, S. Modgil, S. Bhattacharyya, & I. Bose. (2022) Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308 (1). 215-274. https://link.springer.com/article/10.1007/s10479-020-03856-6
4. E. Ananias, P.D. Gaspar, V.N. Soares, & J.M. Caldeira. (2021). Artificial intelligence decision support system based on artificial neural networks to predict the commercialization time by the evolution of peach quality. Electronics, 10 (19). 2394. https://www.mdpi.com/2079-9292/10/19/2394
5. M.I. Jordan, & T.M. Mitchell. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349 (6245). 255-260. https://www.science.org/doi/10.1126/science.aaa8415
6. N. Kriegeskorte. (2015). Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science, 1 417-446. https://www.annualreviews.org/content/journals/10.1146/annurev-vision-082114-035447
7. T. Taleb, C. Benzaïd, R.A. Addad, & K. Samdanis. (2023) AI/ML for beyond 5G systems: Concepts, technology enablers & solutions. Computer Networks, 237 article 110044. https://www.sciencedirect.com/science/article/abs/pii/S1389128623004899
8. I.H. Sarker. (2022). AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3 (2) 158. https://link.springer.com/article/10.1007/s42979-022-01043-x
9. B. Nathali Silva, M. Khan, & K. Han. (2017). Big data analytics embedded smart city architecture for performance enhancement through real‐time data processing and decision‐making. Wireless communications and mobile computing, https://onlinelibrary.wiley.com/doi/full/10.1155/2017/9429676
10. S. Sun, X. Zheng, J. Villalba-Díez, & J. Ordieres-Meré. (2020). Data handling in industry 4.0: Interoperability based on distributed ledger technology. Sensors., 20 (11) 3046. https://www.mdpi.com/1424-8220/20/11/3046
11. A. Krtalić, A. Kuveždić Divjak, & A. Miletić. (2023), Toward Data Lakes for Crisis Management. The International Archives of the Photogrammetry, Remote Sensing and Spatial. Information Sciences, 48 539-546. https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/539/2023/
12. H.-Y. Cheng,b & Y.-C. Wu. (2020). Applying machine learning models with an ensemble approach for accurate real-time influenza forecasting in Taiwan: Development and validation study. Journal of medical Internet research, 22 (8) article e15394. https://www.jmir.org/2020/8/e15394
Citations
1. J.M. Tien. Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4 (2017), 149-178. URL: https://link.springer.com/article/10.1007/s40745-017-0112-5
2. H. Hu, Y. Wen, T.-S. Chua, X. Li. Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2 (2014), 652-687. URL: https://ieeexplore.ieee.org/document/6842585
3. S. Gupta, S. Modgil, S. Bhattacharyya, I. Bose. Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308 (1) (2022), 215-274. URL: https://link.springer.com/article/10.1007/s10479-020-03856-6
4. E. Ananias, P.D. Gaspar, V.N. Soares, J.M. Caldeira. Artificial intelligence decision support system based on artificial neural networks to predict the commercialization time by the evolution of peach quality. Electronics, 10 (19) (2021), 2394. URL: https://www.mdpi.com/2079-9292/10/19/2394
5. M.I. Jordan, T.M. Mitchell. Machine learning: Trends, perspectives, and prospects. Science, 349 (6245) (2015), 255-260. URL: https://www.science.org/doi/10.1126/science.aaa8415
6. N. Kriegeskorte. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science, 1 (2015), 417-446. URL: https://www.annualreviews.org/content/journals/10.1146/annurev-vision-082114-035447
7. T. Taleb, C. Benzaïd, R.A. Addad, K. Samdanis. AI/ML for beyond 5G systems: Concepts, technology enablers & solutions. Computer Networks, 237 (2023), article 110044. URL: https://www.sciencedirect.com/science/article/abs/pii/S1389128623004899
8. I.H. Sarker. AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3 (2) (2022), 158. URL: https://link.springer.com/article/10.1007/s42979-022-01043-x
9. B. Nathali Silva, M. Khan, K. Han. Big data analytics embedded smart city architecture for performance enhancement through real‐time data processing and decision‐making. Wireless communications and mobile computing, 2017. URL: https://onlinelibrary.wiley.com/doi/full/10.1155/2017/9429676
10. S. Sun, X. Zheng, J. Villalba-Díez, J. Ordieres-Meré. Data handling in industry 4.0: Interoperability based on distributed ledger technology. Sensors., 20 (11) (2020), 3046. URL: https://www.mdpi.com/1424-8220/20/11/3046
11. A. Krtalić, A. Kuveždić Divjak, A. Miletić. Toward Data Lakes for Crisis Management. The International Archives of the Photogrammetry, Remote Sensing and Spatial. Information Sciences, 48 (2023), 539-546. URL: https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/539/2023/
12. H.-Y. Cheng, Y.-C. Wu. Applying machine learning models with an ensemble approach for accurate real-time influenza forecasting in Taiwan: Development and validation study. Journal of medical Internet research, 22 (8) (2020), article e15394. URL: https://www.jmir.org/2020/8/e15394
Copyright (c) 2024 Roman Boiko, Viktor Aulin, Andrii Tykhyi, Serhii Karpushyn, Oleh Koval
Integration of Artificial Intelligence Into Decision Support Systems in Optimizing Production Processes of a Machine-Building Enterprise Using the Example of maChine Learning
About the Authors
Roman Boiko, post-graduate, Professor, Doctor in Technics (Doctor of Technic Sciences), e-mail: mr.r.boiko@gmail.com, ORCID ID: 0009-0007-3206-0533
Viktor Aulin, Professor, Doctor in Technics (Doctor of Technic Sciences), Professor, Doctor in Technics (Doctor of Technic Sciences), e-mail: AulinVV@gmail.com, ORCID ID: 0000-0003-2737-120X
Andrii Tykhyi, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Professor, Doctor in Technics (Doctor of Technic Sciences), e-mail: a.a.tihiy@gmail.com, ORCID ID: 0000-0001-5323-4415
Serhii Karpushyn, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Professor, Doctor in Technics (Doctor of Technic Sciences), e-mail: karp22.05.1972ksa@gmail.com, ORCID ID: 0000-0001-9035-9065
Oleh Koval, post-graduate, Professor, Doctor in Technics (Doctor of Technic Sciences), ORCID ID: 0009-0000-0678-1176
Abstract
Keywords
Full Text:
PDFReferences
1. J.M. Tien. (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4 149-178. https://link.springer.com/article/10.1007/s40745-017-0112-5
2. H. Hu, Y. Wen, T.-S. Chua, & X. Li. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652-687. https://ieeexplore.ieee.org/document/6842585
3. S. Gupta, S. Modgil, S. Bhattacharyya, & I. Bose. (2022) Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308 (1). 215-274. https://link.springer.com/article/10.1007/s10479-020-03856-6
4. E. Ananias, P.D. Gaspar, V.N. Soares, & J.M. Caldeira. (2021). Artificial intelligence decision support system based on artificial neural networks to predict the commercialization time by the evolution of peach quality. Electronics, 10 (19). 2394. https://www.mdpi.com/2079-9292/10/19/2394
5. M.I. Jordan, & T.M. Mitchell. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349 (6245). 255-260. https://www.science.org/doi/10.1126/science.aaa8415
6. N. Kriegeskorte. (2015). Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science, 1 417-446. https://www.annualreviews.org/content/journals/10.1146/annurev-vision-082114-035447
7. T. Taleb, C. Benzaïd, R.A. Addad, & K. Samdanis. (2023) AI/ML for beyond 5G systems: Concepts, technology enablers & solutions. Computer Networks, 237 article 110044. https://www.sciencedirect.com/science/article/abs/pii/S1389128623004899
8. I.H. Sarker. (2022). AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3 (2) 158. https://link.springer.com/article/10.1007/s42979-022-01043-x
9. B. Nathali Silva, M. Khan, & K. Han. (2017). Big data analytics embedded smart city architecture for performance enhancement through real‐time data processing and decision‐making. Wireless communications and mobile computing, https://onlinelibrary.wiley.com/doi/full/10.1155/2017/9429676
10. S. Sun, X. Zheng, J. Villalba-Díez, & J. Ordieres-Meré. (2020). Data handling in industry 4.0: Interoperability based on distributed ledger technology. Sensors., 20 (11) 3046. https://www.mdpi.com/1424-8220/20/11/3046
11. A. Krtalić, A. Kuveždić Divjak, & A. Miletić. (2023), Toward Data Lakes for Crisis Management. The International Archives of the Photogrammetry, Remote Sensing and Spatial. Information Sciences, 48 539-546. https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/539/2023/
12. H.-Y. Cheng,b & Y.-C. Wu. (2020). Applying machine learning models with an ensemble approach for accurate real-time influenza forecasting in Taiwan: Development and validation study. Journal of medical Internet research, 22 (8) article e15394. https://www.jmir.org/2020/8/e15394
Citations
1. J.M. Tien. Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4 (2017), 149-178. URL: https://link.springer.com/article/10.1007/s40745-017-0112-5
2. H. Hu, Y. Wen, T.-S. Chua, X. Li. Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2 (2014), 652-687. URL: https://ieeexplore.ieee.org/document/6842585
3. S. Gupta, S. Modgil, S. Bhattacharyya, I. Bose. Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308 (1) (2022), 215-274. URL: https://link.springer.com/article/10.1007/s10479-020-03856-6
4. E. Ananias, P.D. Gaspar, V.N. Soares, J.M. Caldeira. Artificial intelligence decision support system based on artificial neural networks to predict the commercialization time by the evolution of peach quality. Electronics, 10 (19) (2021), 2394. URL: https://www.mdpi.com/2079-9292/10/19/2394
5. M.I. Jordan, T.M. Mitchell. Machine learning: Trends, perspectives, and prospects. Science, 349 (6245) (2015), 255-260. URL: https://www.science.org/doi/10.1126/science.aaa8415
6. N. Kriegeskorte. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science, 1 (2015), 417-446. URL: https://www.annualreviews.org/content/journals/10.1146/annurev-vision-082114-035447
7. T. Taleb, C. Benzaïd, R.A. Addad, K. Samdanis. AI/ML for beyond 5G systems: Concepts, technology enablers & solutions. Computer Networks, 237 (2023), article 110044. URL: https://www.sciencedirect.com/science/article/abs/pii/S1389128623004899
8. I.H. Sarker. AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3 (2) (2022), 158. URL: https://link.springer.com/article/10.1007/s42979-022-01043-x
9. B. Nathali Silva, M. Khan, K. Han. Big data analytics embedded smart city architecture for performance enhancement through real‐time data processing and decision‐making. Wireless communications and mobile computing, 2017. URL: https://onlinelibrary.wiley.com/doi/full/10.1155/2017/9429676
10. S. Sun, X. Zheng, J. Villalba-Díez, J. Ordieres-Meré. Data handling in industry 4.0: Interoperability based on distributed ledger technology. Sensors., 20 (11) (2020), 3046. URL: https://www.mdpi.com/1424-8220/20/11/3046
11. A. Krtalić, A. Kuveždić Divjak, A. Miletić. Toward Data Lakes for Crisis Management. The International Archives of the Photogrammetry, Remote Sensing and Spatial. Information Sciences, 48 (2023), 539-546. URL: https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/539/2023/
12. H.-Y. Cheng, Y.-C. Wu. Applying machine learning models with an ensemble approach for accurate real-time influenza forecasting in Taiwan: Development and validation study. Journal of medical Internet research, 22 (8) (2020), article e15394. URL: https://www.jmir.org/2020/8/e15394