The interplay between artificial intelligence, production systems, and operations management resilience.

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Title: The interplay between artificial intelligence, production systems, and operations management resilience.
Authors: Fosso Wamba, Samuel1 (AUTHOR), Queiroz, Maciel M.2 (AUTHOR) maciel.queiroz@fgv.br, Ngai, Eric W. T.3 (AUTHOR), Riggins, Fred4 (AUTHOR), Bendavid, Ygal5 (AUTHOR)
Source: International Journal of Production Research. Aug2024, Vol. 62 Issue 15, p5361-5366. 6p.
Subjects: Artificial intelligence, Operations management, Electronic paper
Abstract: This editorial introduces the special issue "The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.' We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: The interplay between artificial intelligence, production systems, and operations management resilience.
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  Data: <searchLink fieldCode="AR" term="%22Fosso+Wamba%2C+Samuel%22">Fosso Wamba, Samuel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Queiroz%2C+Maciel+M%2E%22">Queiroz, Maciel M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> maciel.queiroz@fgv.br</i><br /><searchLink fieldCode="AR" term="%22Ngai%2C+Eric+W%2E+T%2E%22">Ngai, Eric W. T.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Riggins%2C+Fred%22">Riggins, Fred</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bendavid%2C+Ygal%22">Bendavid, Ygal</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Aug2024, Vol. 62 Issue 15, p5361-5366. 6p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Operations+management%22">Operations management</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+paper%22">Electronic paper</searchLink>
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  Label: Abstract
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  Data: This editorial introduces the special issue "The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.' We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/00207543.2024.2321826
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      – Code: eng
        Text: English
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      – SubjectFull: Operations management
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              Text: Aug2024
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