Designing a model for implementing operational decisions in the industry based on artificial intelligence

Mayyadah Mohammed Ridha Naser ¹ ( Department of Industrial Management , Isfahan Branch (Khorasgan), Islamic Azad University, Isfahan, Iran. )
Mohammad Jalali Varnamkhasti ² ( Department of Science, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran. )
Husam Jasim Mohammed ³ ( Al-Karkh University of Science, Baghdad, Iraq. )
Mojtaba Aghajani ⁴ ( Department of Management, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran. )

تاریخ ارسال : 1403/09/20 تاریخ تایید : 1403/11/23

کلید واژه: Data Mining, Classification, Decision Making, Technique, Improve, Processes.,

چکیده مقاله :

The increasing complexity and interconnectedness of industrial processes require innovative solutions to optimize operational decision-making. Artificial intelligence (AI) technology has gained attention for its potential to revolutionize operational decisions by harnessing vast amounts of data, analyzing it in real-time, and making informed decisions quickly and accurately. AI algorithms can automate decision-making processes, identify patterns and trends in data, and make predictions about future outcomes.  This study investigates the development of an efficient operating model for the deployment of large-scale artificial intelligence (AI) systems in a selected cement factory in Iraq.  The proposed methodology involves data collection, pre-processing, feature engineering, data visualization, and model selection. Key Performance Indicators (KPIs) were identified through partial least squares regression analysis to model the relationship between energy consumption, production rate, temperature, and moisture content on product quality in a cement factory in Iraq. The results of the study show that the machine learning model accurately predicted energy consumption, production rate, and product quality, with R-squared values of 0.95, 0.92, and 0.88, respectively. The optimized neural network model reduced energy consumption by 12% while maintaining production rate and product quality. Overall, the implementation of AI in the cement industry in Iraq has the potential to improve operational efficiency, reduce costs, and enhance product quality.

چکیده انگلیسی :

The increasing complexity and interconnectedness of industrial processes require innovative solutions to optimize operational decision-making. Artificial intelligence (AI) technology has gained attention for its potential to revolutionize operational decisions by harnessing vast amounts of data, analyzing it in real-time, and making informed decisions quickly and accurately. AI algorithms can automate decision-making processes, identify patterns and trends in data, and make predictions about future outcomes.  This study investigates the development of an efficient operating model for the deployment of large-scale artificial intelligence (AI) systems in a selected cement factory in Iraq.  The proposed methodology involves data collection, pre-processing, feature engineering, data visualization, and model selection. Key Performance Indicators (KPIs) were identified through partial least squares regression analysis to model the relationship between energy consumption, production rate, temperature, and moisture content on product quality in a cement factory in Iraq. The results of the study show that the machine learning model accurately predicted energy consumption, production rate, and product quality, with R-squared values of 0.95, 0.92, and 0.88, respectively. The optimized neural network model reduced energy consumption by 12% while maintaining production rate and product quality. Overall, the implementation of AI in the cement industry in Iraq has the potential to improve operational efficiency, reduce costs, and enhance product quality.

منابع و مأخذ:

[1] A. Ahmadi,, M. M. Moghadam and S. Ghasemi, The Model of Using Artificial Intelligence in Supply Chain Management in Product Production. Educational Administration: Theory and Practice, 30(6) (2024) 812-823.

[2] K. Alhosani, S. M. Alhashmi, Opportunities, challenges, and benefits of AI innovation in government services: a review. Discover Artificial Intelligence, 4(1) (2024) 18.

[3] A. Al-Surmi, M. Bashiri and I. Koliousis, AI based decision making: combining strategies to improve operational performance. International Journal of Production Research, 60(14) (2022) 4464-4486.

[4] K. Antosz, L. Pasko and A. Gola, The use of artificial intelligence methods to assess the effectiveness of lean maintenance concept implementation in manufacturing enterprises. Applied Sciences, 10(21) (2020)7922.

[5] B. C. Bizzo, G. Dasegowda, C. Bridge, B. Miller, J. M. Hillis, M. K. Kalra,, ... and K. J. Dreyer, Addressing the challenges of implementing artificial intelligence tools in clinical practice: principles from experience. Journal of the American College of Radiology, 20(3) (2023) 352-360.

[6] C. Bowles, L. Chen, R. Guerrero, P. Bentley, R. Gunn, A. Hammers, ... and D. Rueckert, Gan augmentation: Augmenting training data using generative adversarial networks. arXiv preprint arXiv:1810.10863 (2018).

[7] R. Cheng, A. Aggarwal, A. Chakraborty, V. Harish, M. McGowan, A. Roy, ... and B. Nolan, Implementation considerations for the adoption of artificial intelligence in the emergency department. The American Journal of Emergency Medicine (2024).

[8] D. Dadebo, D. Obura, N. Etyang and D Kimera, Economic and social perspectives of implementing artificial intelligence in drinking water treatment systems for predicting coagulant dosage: A transition toward sustainability. Groundwater for Sustainable Development, 23(2023) 100987.

[9] M. A. Davis, D. Ramakrishnan, M. Sala and Aboian, M. Local Economic Considerations in Selecting Artificial Intelligence Tools for Implementation. Journal of the American College of Radiology, 20(10) (2023) 981-984.

[10] R. Deivanathan, A review of artificial intelligence technologies to achieve machining objectives. Cognitive social mining applications in data analytics and forensics, (2019)138-159.

[11] Y. Duan, J. S. Edwards and Y. K. Dwivedi, Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48 (2019) 63-71.

[12] O. Dudnik, M. Vasiljeva, N. Kuznetsov, M. Podzorova, I. Nikolaeva, L. Vatutina, ... and M. Ivleva, Trends, impacts, and prospects for implementing artificial intelligence technologies in the energy industry: the implication of open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2) (2021) 155.

[13] A. El Rhatrif, B. Bouihi and M. Mestari, Challenges and Limitations of Artificial Intelligence Implementation in Modern Power Grid. Procedia Computer Science, 236 (2024) 83-92.

[14] H. Feng, The application of artificial intelligence in electrical automation control. In Journal of Physics: Conference Series 1087(6) (2018) 062008. IOP Publishing.

[15] R. Gandhi, Support Vector Machine – Introduction to Machine Learning Algorithms, Medium, (2018). https://towardsdata science.com/support-vector-machine-introduction-to-machine -learning-algorithms-934a444fca47

[16] R. Geissbauer, S. Schrauf, P. Berttram, F. Cheraghi, Digital Factories: Shaping the Future of Manufacturing, Price waterhouse Coopers, (2017).
https://www.pwc.de/de/digitaletransformation/digital-factories-2020-shaping-the-future-ofmanufacturing.pdf.

[17] M. G. Gomes, V. H. C. da Silva, L. F. R. Pinto, P. Centoamore, S. Digiesi, F. Facchini and G. C. Neto, Economic, environmental and social gains of the implementation of artificial intelligence at dam operations toward Industry 4.0 principles. Sustainability, 12(9) (2020) 3604.

[18] S. Gupta, S. Modgil, S. Bhattacharyya and 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.

[19] N. Haefner, V. Parida, O. Gassmann and J. Wincent, Implementing and scaling artificial intelligence: A review, framework, and research agenda. Technological Forecasting and Social Change, 197 (2023) 122878.

[20] J. Heier, J. Willmann and K. Wendland, Design intelligence-pitfalls and challenges when designing AI algorithms in B2B factory automation. In Artificial Intelligence in HCI: First International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 22 (2020) 288-297. Springer International Publishing.

[21] I. Jackson, D. Ivanov, A. Dolgui and J. Namdar, Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation. International Journal of Production Research, (2024) 1-26.

[22] O. S. Joel, A. T. Oyewole, O. G. Odunaiya and O. T. Soyombo, Leveraging artificial intelligence for enhanced supply chain optimization: a comprehensive review of current practices and future potentials. International Journal of Management & Entrepreneurship Research, 6(3) (2024) 707-721.

[23] D. T. Kearns, Machine Learning in the Mining Industry – A Case Study, Medium, (2017). https://medium.com/sustainabledata/machine-learning-in-the-mining-industry-a-case-study33b771729eb2

[24] M. Lahlali, N. Berbiche and J. El Alami, Artificial Intelligence Operating Model: A Proposal Framework for AI Operationalization and Deployment (2022) .

[25] J. Lee, H. Davari, J. Singh and V. Pandhare, Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing letters, 18 (2018) 20-23.

[26] M. C. Lee, H. Scheepers, A. K. Lui and E. W. Ngai, The implementation of artificial intelligence in organizations: A systematic literature review. Information & Management, (2023) 103816.

[27] S. Madhavan, M. T. Jones, Deep Learning Architectures – IBM Developer, IBM Developer Articles, 2017. https://developer. ibm.com/articles/cc-machine-learning-deep-learning-architec tures/.

[28] S. Mao, B. Wang, Y. Tang and F. Qian, Opportunities and challenges of artificial intelligence for green manufacturing in the process industry. Engineering, 5(6) (2019) 995-1002.

[29] J. Marcus, Challenges and frontiers in implementing artificial intelligence in process industry. Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry: Ongoing Applications, Perspectives and Future Trends, 1338(1) (2021).

[30] J. H. Marler, Artificial intelligence, algorithms, and compensation strategy: Challenges and opportunities. Organizational Dynamics, (2024) 101039.

[31] M. I. Merhi, A process model of artificial intelligence implementation leading to proper decision making. In Responsible AI and Analytics for an Ethical and Inclusive Digitized Society: 20th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2021, Galway, Ireland, September 1–3, 20 (2021) 40-46. Springer International Publishing.

[32] M. I. Merhi and A. Harfouche, Enablers of artificial intelligence adoption and implementation in production systems. International Journal of Production Research, (2023) 1-15.

[33] S. Mithas, Z. L. Chen, T. J. Saldanha and A. De Oliveira Silveira, How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?. Production and Operations Management, 31(12) (2022) 4475-4487.

[34] Y. Narukawa, and M. Daumas, Modeling Decisions for Artificial Intelligence. Springer Berlin Heidelberg (2010).

[35] E. V. Orlova, Design technology and AI-based decision making model for digital twin engineering. Future Internet, 14(9) (2022) 248.

[36] R. S. Peres, X. Jia, J. Lee, K. Sun, A. W. Colombo and J. Barata, Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE access, 8 (2020) 220121-220139.

[37] E. Peretz-Andersson, S. Tabares, P. Mikalef and V. Parida, Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach. International Journal of Information Management, 77 (2024) 102781.

[38] W. Reim, J. Åström and O. Eriksson, Implementation of artificial intelligence (AI): a roadmap for business model innovation. 1 (2) (2020) 180–191. https://doi.org/10.3390/ai1020011

[39] S. Robinson, T. Alifantis, J. S. Edwards, J. Ladbrook and A. Waller, Knowledge-based improvement: Simulation and artificial intelligence for identifying and improving human decision-making in an operations system. Journal of the Operational Research Society, 56 (2005) 912-921.

[40] C. Sanderson, Q. Lu, D. Douglas, X. Xu, L. Zhu and J. Whittle, Towards implementing responsible AI. In 2022 IEEE International Conference on Big Data (2022) 5076-5081. IEEE.

[41] S. Schlögl, C. Postulka, R. Bernsteiner and C. Ploder, Artificial intelligence tool penetration in business: Adoption, challenges and fears. In Knowledge Management in Organizations: 14th International Conference, KMO 2019, Zamora, Spain, July 15–18, 14 (2019) 259-270. Springer International Publishing.

[42] J. C. C. Sin and V. Kathiarayan, The Role of Artificial Intelligence in Strategic Decision-Making Opportunities, Challenges, and Implications for Managers in the Digital Age (2023).

[43] J. Shaw, F. Rudzicz, T. Jamieson and A. Goldfarb, Artificial intelligence and the implementation challenge. Journal of medical Internet research, 21(7) (2019) e13659.

[44] S. C. Shelmerdine, D. Togher, S. Rickaby and G. Dean, Artificial Intelligence (AI) Implementation within the NHS: The South West London AI Working Group Experience. Clinical Radiology(2024).

[45] M. Sofia, F. Fraboni, M. De Angelis, G. Puzzo, D. Giusino and L. Pietrantoni, The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science: The International Journal of an Emerging Transdiscipline, 26 (2023) 39-68.

[46] T. A. S. Srinivas, R. Yadav, V. Gowri, K. Chandraprabha, S. Ponnusamy and D. Mavaluru, Development and implementation of unmanned vehicles through artificial intelligence involving communication system with sensors and control parameters. Measurement: Sensors, 33 (2024) 101136.

[47] K. Soni, N. Kumar, A. S. Nair, P. Chourey, N. J. Singh and R. Agarwal, Artificial Intelligence: Implementation and obstacles in industry 4.0. In Handbook of Metrology and Applications (2022)1-23. Singapore: Springer Nature Singapore

[48] M. U. Tariq, M. Poulin and A. A. Abonamah, Achieving operational excellence through artificial intelligence: Driving forces and barriers. Frontiers in psychology, 12 (2021) 686624.

[49] D. Tchuente, J. Lonlac and B. Kamsu-Foguem, A methodological and theoretical framework for implementing explainable artificial intelligence (XAI) in business applications. Computers in Industry, 155 (2024) 104044.

[50] Z. Tekic, I. Cosic and B. Katalinic, Manufacturing and the Rise of Artificial Intelligence: Innovation Challenges. Annals of DAAAM & Proceedings, 30 (2019)..

[51] M. Vogel, G. Strina, C. Said and T. Schmallenbach, The Evolution of Artificial Intelligence Adoption in Industry. Artificial Intelligence and Social Computing, 72(72) (2023)..

[52] Vortarus Technologies LLC. Evaluating a Manufacturing Decision with a Decision Tree, (2017). Retrieved from https://vortarus.com/manufacturing-decision-decisiontree/

[53] M. Willenbacher, C. Kunisch and V. Wohlgemuth, Application of Methods of Artificial Intelligence for Sustainable Production of Manufacturing Companies. In From Science to Society: New Trends in Environmental Informatics (2018) 225-236. Springer International Publishing

[54] B. Xu, Z. Cai, E. T. K. Lim, X. Song, A. Chong, C. W. Tan and J. Yu, Artificial Intelligence or Augmented Intelligence: A Case Study of Human-AI Collaboration in Operational Decision Making. In D. Vogel, K. N. Shen, & P. S. Ling (Eds.), PACIS 2020 Proceedings Article 147 (2020), Association for Information Systems. AIS Electronic Library

[55] B. Yang, On the Influence and Application of Artificial Intelligence on China's Commercial Sports Industry—Empirical Analysis Based on Artificial Intelligence Coupling Model. In Proceedings of the 2022 7th International Conference on Multimedia Systems and Signal Processing (2022) 40-45.

[56] T. M. Zubkova, Automated industrial design based on artificial intelligence. Russian Engineering Research, 38 (2018) 394-398.