دورية أكاديمية
Performance Optimization of Industrial Supply Chain Using Artificial Intelligence.
| العنوان: | Performance Optimization of Industrial Supply Chain Using Artificial Intelligence. |
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| المؤلفون: | Alomar MA; Department of Industrial Engineering, Faculty of Engineering-Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia. |
| المصدر: | Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jul 30; Vol. 2022, pp. 9306265. Date of Electronic Publication: 2022 Jul 30 (Print Publication: 2022). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Hindawi Pub. Corp Country of Publication: United States NLM ID: 101279357 Publication Model: eCollection Cited Medium: Internet ISSN: 1687-5273 (Electronic) NLM ISO Abbreviation: Comput Intell Neurosci Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New York, NY : Hindawi Pub. Corp. |
| مواضيع طبية MeSH: | Artificial Intelligence* , Commerce*, Female ; Humans ; Industry |
| مستخلص: | Nowadays, organized retailing has witnessed a newer trend in the upcoming generations. Globally, these changes are attributed to growing family income, increased female participation, the transformation from joint to nuclear family structure, and technological advancements. Moreover, other variables such as lower supply chain costs, growing sales, rising consumer demands, changing market structure, and increasing competition also influenced supply chain networks. It is observed that the organizational nonlivestock supply chain performance is affected by strategic, operational, and environmental aspects. AI is helping to deliver powerful optimization capabilities, which are required for more accurate capacity planning, improved productivity, high quality, lower costs, and greater output, all while fostering safer working conditions. These benefits are all made possible thanks to the introduction of AI in supply chains. By conducting a comprehensive analysis of the relevant previous research, the purpose of this work is to determine the specific contributions that artificial intelligence (AI) has made to supply chain management. This research attempted to discover the present as well as possible AI strategies that may increase both the study of Supply Chain Management as well as the practice of it. This was done in order to solve the current scientific gap of AI in Supply Chain Management. It was also found that there are holes in the existing study that need to be filled by more scientific investigation. To be more exact, the following four facets were discussed: (1) the AI approaches that are most often used in Supply Chain Management; (2) the AI techniques that have the potential to be used in Supply Chain Management; (3) the Supply Chain Management subfields that have benefited from the application of AI so far; and (4) the subfields that have a high potential to be improved by AI. Identifying and evaluating articles from the four supply chain management domains of logistics, marketing, supply chain management, and manufacturing require the use of a predetermined set of inclusion and exclusion criteria. In this study, insights are provided via the use of methodical analysis and synthesis. A better understanding of these parameters not only improves the nonlivestock supply chain processes but also ensures competitive advantage. The present research aims to test the following elements that including supply chain speed, customer retention, supply chain management integration, and various management. The proposed work categorizes the performance of the supply chain using the Improved Feed Forward Network with Particle Swarm Optimization technique. Results indicate that inventory management, customer happiness, profitability, and client base identification are listed as competitive advantage elements. On the other hand, stakeholder satisfaction, innovation and learning, market performance, customer satisfaction, and financial success are the six recognized organizational performance criteria. Resultantly, the overall performance metrics of the proposed work is 94.12%, while accuracy rate, specificity, and sensitivity rate are found to be 94.12%, 92.15%, and 89.14%, respectively. The research can be helpful for industrial managers to optimize the performance of supply chain systems using artificial intelligence. Competing Interests: No potential conflicts of interest were reported by the author. (Copyright © 2022 Madani Abdu Alomar.) |
| References: | Polymers (Basel). 2021 Sep 09;13(18):. (PMID: 34577940) IEEE Trans Neural Netw. 2001;12(2):424-30. (PMID: 18244397) Polymers (Basel). 2021 Dec 23;14(1):. (PMID: 35012073) IEEE Trans Neural Netw. 2006 Sep;17(5):1116-25. (PMID: 17001974) Comput Intell Neurosci. 2022 Apr 28;2022:9023478. (PMID: 35528332) |
| تواريخ الأحداث: | Date Created: 20220809 Date Completed: 20220810 Latest Revision: 20220810 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC9356809 |
| DOI: | 10.1155/2022/9306265 |
| PMID: | 35942447 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1687-5273 |
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| DOI: | 10.1155/2022/9306265 |