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1707022 Vol 8 · Issue 7 Download Paper

The Role of AI in Optimizing Processes and Reducing Costs Across Sectors

Carolina Araujo Moreira

Subject area: Science,Engineering and Technology  ·  Area of research: Engineering

Abstract

Artificial Intelligence (AI) is proving to be a transformative tool in optimizing processes and reducing costs across a wide array of industries, including construction, manufacturing, logistics, and computing. By utilizing AI to simulate alternative scheduling scenarios, project managers can explore a variety of possibilities, adjusting resource allocations, mitigating risks, and optimizing execution timelines with greater precision. AI?s ability to process vast amounts of data and predict the outcomes of various decisions provides project managers with a more informed perspective on the impact of schedule adjustments. Studies by Filippini et al. (2023) and Thatcher et al. (2022) highlight AI's effectiveness in optimizing machine learning clusters, managing industrial resources, and enhancing logistics operations, ultimately leading to improved efficiency and cost reductions. Additionally, the integration of AI into planning and resource allocation systems offers a high degree of flexibility, allowing real-time adjustments to address unforeseen challenges such as delays or supply chain disruptions. This proactive management prevents issues from escalating and helps keep the project within budget while providing a clearer understanding of decision-making consequences. The synergy of machine learning, optimization, and predictive analytics shows that AI is not only improving operational efficiency but also facilitating informed decisions, cost savings, and sustainable solutions. As AI continues to advance, its potential to optimize processes and provide strategic management across diverse sectors is increasingly promising, offering new opportunities to enhance both performance and sustainability.

Keywords

Artificial Intelligence; Process Optimization; Cost Reduction; Resource Allocation; Sustainability.

References

[1] Dittakavi, R. S. S. (2023). AI-optimized cost-aware design strategies for resource-efficient applications. Journal of Science & Technology, 4(1), 1-10.

[2] Filippini, F., Anselmi, J., Ardagna, D., & Gaujal, B. (2024). A Stochastic Approach for Scheduling AI Training Jobs in GPU-Based Systems. IEEE Transactions on Cloud Computing, 12, 53-69. https://doi.org/10.1109/TCC.2023.3336540.

[3] Filippini, F., Ardagna, D., Lattuada, M., Amaldi, E., Ciavotta, M., Riedl, M., Materka, K., Skrzypek, P., Magugliani, F., & Cicala, M. (2021). ANDREAS: Artificial intelligence traiNing scheDuler foR accElerAted resource clusterS. 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud), 388-393. https://doi.org/10.1109/FiCloud49777.2021.00063.

[4] Krause, T. (2020). AI-Based Discrete-Event Simulations for Manufacturing Schedule Optimization. Proceedings of the 4th International Conference on Algorithms, Computing and Systems. https://doi.org/10.1145/3423390.3426725.

[5] Thatcher, J., Eldred, M., Suboyin, A., Rehman, A., & Maya, D. (2022, October). Optimizing Rig Scheduling Through AI. In Abu Dhabi International Petroleum Exhibition and Conference (p. D021S056R004). SPE.

[6] Tu, M., Liang, R., & Zheng, F. (2024). An AI Planning Approach to Supply Chain Logistics Planning and Scheduling. 2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI), 446-450. https://doi.org/10.1109/IoTAAI62601.2024.10692976.

[7] SANTOS , Hugo; PESSOA, Eliomar Gotardi. Impacts of digitalization on the efficiency and quality of public services: A comprehensive analysis. LUMEN ET VIRTUS, [S. l.], v. 15, n. 40, p. 4409–4414, 2024. DOI: 10.56238/levv15n40-024. Disponível em: https://periodicos.newsciencepubl.com/LEV/article/view/452. Acesso em: 25 jan. 2025. [Crossref]

[8] Freitas, G. B., Rabelo, E. M., & Pessoa, E. G. (2023). Projeto modular com reaproveitamento de container maritimo. Brazilian Journal of Development, 9(10), 28303–28339. https://doi.org/10.34117/bjdv9n10-057

[9] Freitas, G. B., Rabelo, E. M., & Pessoa, E. G. (2023). Projeto modular com reaproveitamento de container maritimo. Brazilian Journal of Development, 9(10), 28303–28339. https://doi.org/10.34117/bjdv9n10-057

[10] Pessoa, E. G., Feitosa, L. M., e Padua, V. P., & Pereira, A. G. (2023). Estudo dos recalques primários em um aterro executado sobre a argila mole do Sarapuí. Brazilian Journal of Development, 9(10), 28352–28375. https://doi.org/10.34117/bjdv9n10-059

[11] PESSOA, E. G.; FEITOSA, L. M.; PEREIRA, A. G.; E PADUA, V. P. Efeitos de espécies de al na eficiência de coagulação, Al - residual e propriedade dos flocos no tratamento de águas superficiais. Brazilian Journal of Health Review, [S. l.], v. 6, n. 5, p. 24814–24826, 2023. DOI: 10.34119/bjhrv6n5-523. Disponível em: https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/63890. Acesso em: 25 jan. 2025.

[12] SANTOS , Hugo; PESSOA, Eliomar Gotardi. Impacts of digitalization on the efficiency and quality of public services: A comprehensive analysis. LUMEN ET VIRTUS, [S. l.], v. 15, n. 40, p. 4409–4414, 2024. DOI: 10.56238/levv15n40-024. Disponível em: https://periodicos.newsciencepubl.com/LEV/article/view/452. Acesso em: 25 jan. 2025.

How to cite this paper

Carolina Araujo Moreira "The Role of AI in Optimizing Processes and Reducing Costs Across Sectors" Iconic Research And Engineering Journals Volume 8 Issue 7 2025 Page 710-713
Carolina Araujo Moreira "The Role of AI in Optimizing Processes and Reducing Costs Across Sectors" Iconic Research And Engineering Journals, vol. 8, no. 7, Jan. 2025
Carolina Araujo Moreira (2025). The Role of AI in Optimizing Processes and Reducing Costs Across Sectors. Iconic Research And Engineering Journals, 8(7).
Carolina Araujo Moreira "The Role of AI in Optimizing Processes and Reducing Costs Across Sectors" Iconic Research And Engineering Journals, vol. 8, no. 7, Jan. 2025.
@article{1707022,
      author = {Carolina Araujo Moreira },
      title = {The Role of AI in Optimizing Processes and Reducing Costs Across Sectors},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {7},
      pages = {710-713},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707022.pdf},
      abstract = {Artificial Intelligence (AI) is proving to be a transformative tool in optimizing processes and reducing costs across a wide array of industries, including construction, manufacturing, logistics, and computing. By utilizing AI to simulate alternative scheduling scenarios, project managers can explore a variety of possibilities, adjusting resource allocations, mitigating risks, and optimizing execution timelines with greater precision. AI?s ability to process vast amounts of data and predict the outcomes of various decisions provides project managers with a more informed perspective on the impact of schedule adjustments. Studies by Filippini et al. (2023) and Thatcher et al. (2022) highlight AI's effectiveness in optimizing machine learning clusters, managing industrial resources, and enhancing logistics operations, ultimately leading to improved efficiency and cost reductions. Additionally, the integration of AI into planning and resource allocation systems offers a high degree of flexibility, allowing real-time adjustments to address unforeseen challenges such as delays or supply chain disruptions. This proactive management prevents issues from escalating and helps keep the project within budget while providing a clearer understanding of decision-making consequences. The synergy of machine learning, optimization, and predictive analytics shows that AI is not only improving operational efficiency but also facilitating informed decisions, cost savings, and sustainable solutions. As AI continues to advance, its potential to optimize processes and provide strategic management across diverse sectors is increasingly promising, offering new opportunities to enhance both performance and sustainability.},
      keywords = {Artificial Intelligence; Process Optimization; Cost Reduction; Resource Allocation; Sustainability.},
      month = {January},
  }