Explainable artificial intelligence supply von chen tin chih (3 Ergebnisse)
Sprache: Englisch
Verlag: Springer Nature Switzerland Ag, 2026
Serie: SpringerBriefs in Applied Sciences and Technology, Buch 469 von 472. Buch 469 von 472 - SpringerBriefs in Applied Sciences and Technology
- Softcover
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Paperback. Zustand: Brand New. 124 pages. 6.10x0.29x9.25 inches. In Stock.
Sprache: Englisch
Verlag: Springer, 2026
Serie: SpringerBriefs in Applied Sciences and Technology, Buch 469 von 472. Buch 469 von 472 - SpringerBriefs in Applied Sciences and Technology
- Softcover
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Taschenbuch. Zustand: Neu. Explainable Artificial Intelligence in Supply Chain Management | Methodology, System Architecture, and Applications | Tin-Chih Toly Chen | Taschenbuch | SpringerBriefs in Applied Sciences and Technology | ix | Englisch | 2026 | Springer | EAN 9783032225634 | Verantwortliche Person für die EU: Springer…Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer, Berlin, Springer, 2026
Serie: SpringerBriefs in Applied Sciences and Technology, Buch 469 von 472. Buch 469 von 472 - SpringerBriefs in Applied Sciences and Technology
- Softcover
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book systematically reviews XAI techniques and introduces how these XAI techniques can be systematically applied to SCM, including methodology, system architecture, and applications. Relevant references, examples, or cases are also used as suppo…rting evidence.So far, artificial intelligence (AI) technologies have been widely used in the field of supply chain management (SCM) for supply chain design, production and transportation planning, demand and sales forecasting, cell manufacturing, just-in-time (JIT) control, etc. Some applications of AI technologies in SCM are not easy to understand or communicate, especially for supply chain stakeholders with insufficient background knowledge of AI, which undoubtedly limits the practicality and credibility of these applications. To solve this problem, explainable artificial intelligence (XAI) is considered as a feasible strategy. However, most of the relevant research results are scattered in various journals or conference proceedings, and there is an urgent need to systematically integrate these results. In addition, although there have been many reviews on the possible applications of XAI in SCM, there are few systematic introductions, including methodology, system architecture, and case studies. This book answers this need.
