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Agile Supply Chain Management Collaboration Based on Artificial Intelligence Traceability System | International Journal of Engineering Intelligent Systems
<!DOCTYPE html> <html lang="en-US" xml:lang="en-US"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Agile Supply Chain Management Collaboration Based on Artificial Intelligence Traceability System | International Journal of Engineering Intelligent Systems </title> <meta name="generator" content="Open Journal Systems 3.3.0.7"> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Jie Wei"/> <meta name="DC.Date.created" scheme="ISO8601" content="2024-09-01"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2024-11-05"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-11-05"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-11-05"/> <meta name="DC.Description" xml:lang="en" content="With the rapid development of the market economy and the manufacturing industry, the market has already reached saturation. The fierce competition between enterprises is reflected in their products and services, which is fundamentally a matter of the competition between supply chain management modes. In recent years, artificial intelligence (AI) has developed rapidly. The application of human-computer interaction technology to supply chain management (SCM) systems can improve the efficiency of SCM. Although the traditional SCM mode is adequate for product manufacturing, transportation and sales, it is unable to rapidly customize the customer’s product demand. The traceability function is a production control system that can track companies’ products. This paper compared the collaboration mode of agile SCM based on AI traceability system with the traditionalSCM mode. The experimental results showed that the responsiveness of the traditional SCM mode and the agile SCM collaboration mode based on AI traceability system were 76% and 89.8% respectively in a small supply chain. In large supply chains, the responsiveness of the traditional SCM mode and the agile SCM collaboration mode based on AI traceability system were 70.8% and 87.6% respectively. Therefore, human-computer interaction in the collaboration mode of agile SCM based on AI traceability system can improve the responsiveness of the supply chain to customer needs.Keywords: Supply chain (SC), agile supply chain, manage collaboration, traceability system, artificial intelligence, multimodal learningCite As J. Wei, "Agile Supply Chain Management Collaboration Based on Artificial Intelligence Traceability System",&nbsp;Engineering Intelligent Systems, vol. 32 no. 5, pp. 401-410, 2024."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="1885"/> <meta name="DC.Identifier.URI" content="https://website-eis.crlpublishing.com/index.php/eis/article/view/1885"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2024 International Journal of Engineering Intelligent Systems"/> <meta name="DC.Rights" content=""/> <meta name="DC.Source" content="International Journal of Engineering Intelligent Systems"/> <meta name="DC.Source.ISSN" content="2753-9806"/> <meta name="DC.Source.Issue" content="5"/> <meta name="DC.Source.Volume" content="32"/> <meta name="DC.Source.URI" content="https://website-eis.crlpublishing.com/index.php/eis"/> <meta name="DC.Title" content="Agile Supply Chain Management 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Engineering Intelligent Systems </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Article </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> Agile Supply Chain Management Collaboration Based on Artificial Intelligence Traceability System </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Jie Wei </span> <span class="affiliation"> Business Management Department, ChongQing Vocational College of Transportation, Jiangjin 402247, Chongqing, China </span> </li> </ul> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>With the rapid development of the market economy and the manufacturing industry, the market has already reached saturation. The fierce competition between enterprises is reflected in their products and services, which is fundamentally a matter of the competition between supply chain management modes. In recent years, artificial intelligence (AI) has developed rapidly. The application of human-computer interaction technology to supply chain management (SCM) systems can improve the efficiency of SCM. Although the traditional SCM mode is adequate for product manufacturing, transportation and sales, it is unable to rapidly customize the customer’s product demand. The traceability function is a production control system that can track companies’ products. This paper compared the collaboration mode of agile SCM based on AI traceability system with the traditional<br>SCM mode. The experimental results showed that the responsiveness of the traditional SCM mode and the agile SCM collaboration mode based on AI traceability system were 76% and 89.8% respectively in a small supply chain. In large supply chains, the responsiveness of the traditional SCM mode and the agile SCM collaboration mode based on AI traceability system were 70.8% and 87.6% respectively. Therefore, human-computer interaction in the collaboration mode of agile SCM based on AI traceability system can improve the responsiveness of the supply chain to customer needs.<br><br>Keywords: Supply chain (SC), agile supply chain, manage collaboration, traceability system, artificial intelligence, multimodal learning<br><br>Cite As</p> <p>J. Wei, "Agile Supply Chain Management Collaboration Based on Artificial Intelligence Traceability System", <br><em>Engineering Intelligent Systems,</em> vol. 32 no. 5, pp. 401-410, 2024.<br><br><br><br><br><br></p> </section> </div><!-- .main_entry --> <div class="entry_details"> <div class="item galleys"> <h2 class="pkp_screen_reader"> Downloads </h2> <ul class="value galleys_links"> <li> <a class="obj_galley_link pdf" href="https://website-eis.crlpublishing.com/index.php/eis/article/view/1885/287"> Chain Management - Click to Download PDF </a> </li> </ul> </div> <div class="item published"> <section class="sub_item"> <h2 class="label"> Published </h2> <div class="value"> <span>2024-09-01</span> </div> </section> </div> <div class="item issue"> <section class="sub_item"> <h2 class="label"> Issue </h2> <div class="value"> <a class="title" href="https://website-eis.crlpublishing.com/index.php/eis/issue/view/37"> Vol. 32 No. 5 (2024): International Journal of Engineering Intelligent Systems </a> </div> </section> <section class="sub_item"> <h2 class="label"> Section </h2> <div class="value"> Article </div> </section> </div> <div class="item copyright"> <h2 class="label"> License </h2> <span>The submission of a paper implies that, if accepted for publication, it will not be published elsewhere in the same form, in any language, without the prior consent of the publisher. 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