Abstract
Artificial intelligence is one of the information and communication technologies that has promoted major transformations in organizations and their production processes, especially material handling. The growing number of published studies led to the development of this study, which aimed to describe the main characteristics of studies that report the application of artificial intelligence in material handling. The method used was the conceptual bibliographic, bibliometric design based on four stages: formulation of research questions, collection of bibliographic data, analysis, organization of the collected data, and generation of answers to the guiding questions. The results showed that the focuses of the applications are a) improvement and optimization of the logistics process, increased rationality of human-machine actions, and optimization of the decision-making process, b) use of several simultaneous methods and techniques, c) facing problematic situations aimed at problem-solving and generation of technologies, d) application of multiple artificial intelligence tools, e) successful results have increased competitiveness and rationality in material handling and f) opening for new and interconnected applications. The conclusion shows that using artificial intelligence has provided an environment for enhancing human cognitive capacity. The main contribution of this study to science is the finding that professional training in logistics needs to incorporate mastery of artificial intelligence.
Reference43 articles.
1. ASI, N. et al. Culturally distinctive features in journalistic text: a case study on students’ vs. ai-generated translations. Yavana Bhasha: Journal of English Language Education, v. 7, n. 1, p. 54-67, 2024.
2. BAR-GIL, O.; RON, T.; CZERNIAK, O. AI for the people? Embedding AI ethics in HR and people analytics projects. Technology in Society, in press, p. 102527, 2024. https://doi.org/10.1016/j.techsoc.2024.102527.
3. BORGHI, D. et al. High energy computed tomography of high-density alloys using a 6 MeV linear accelerator: Detectability and use of artificial intelligence. 13th Conference on Industrial Computed Tomography, Wels, Austria (iCT 2024), p. 1-11, 2024.
4. CASTILLO, O. D. D. et al. Supervised learning system for detection of cardiac arrhythmias based on electrocardiographic data. In: 2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2019. p. 1-4. https://doi.org/10.1109/HealthCom46333.2019.9009601.
5. CHIZOBA, C.; ISHOLA, R.; TEMITOPE, A. The economics and finance letters. Economics, v. 11, n. 1, p. 1-17, 2024. https^//doi.org/10.18488/29.v11i1.3596.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献