東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Big data analytics in supply chain m...

Rahimi, Iman.

Linked to FindBook

Google Book

Amazon

博客來

Big data analytics in supply chain management = theory and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data analytics in supply chain management/ edited by Iman Rahimi ... [et al.].
Reminder of title:	theory and applications /
other author:	Rahimi, Iman.
Published:	Boca Raton, FL :CRC Press, : 2021.,
Description:	1 online resource.
Subject:	Business logistics. -
Online resource:	https://www.taylorfrancis.com/books/9780367816384
ISBN:	9780367816384

Big data analytics in supply chain management = theory and applications /
Big data analytics in supply chain managementtheory and applications /[electronic resource] :edited by Iman Rahimi ... [et al.]. - 1st ed. - Boca Raton, FL :CRC Press,2021. - 1 online resource.

Includes bibliographical references and index.

"In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Business and Engineering students, scholars, and professionals, this book is a collection of the state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge on emerging supply chain problems"--

ISBN: 9780367816384

LCCN: 2020036450Subjects--Topical Terms:

648045
Business logistics.

LC Class. No.: HD38.5

Dewey Class. No.: 658.70285/57

Big data analytics in supply chain management = theory and applications /
LDR:01958cmm a2200301 a 4500 001 2311305
005 20210730171321.0
006 m o d
007 cr cnu---unuuu
008 230722s2021 flu ob 001 0 eng
010 $a 2020036450
020 $a 9780367816384 $q (electronic bk.)
020 $a 9781000326932 $q (EPUB)
020 $a 9781000326918 $q (PDF)
020 $a 9781000326925 $q (mobipocket)
020 $z 9780367407179 $q (hbk.)
035 $a 21669395
040 $a DLC $b eng $c DLC $d DLC
041 0 $a eng
050 0 0 $a HD38.5
082 0 0 $a 658.70285/57 $2 23
245 0 0 $a Big data analytics in supply chain management $h [electronic resource] : $b theory and applications / $c edited by Iman Rahimi ... [et al.].
250 $a 1st ed.
260 $a Boca Raton, FL : $b CRC Press, $c 2021.
300 $a 1 online resource.
504 $a Includes bibliographical references and index.
520 $a "In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Business and Engineering students, scholars, and professionals, this book is a collection of the state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge on emerging supply chain problems"-- $c Provided by publisher.
588 $a Description based on print version record.
650 0 $a Business logistics. $3 648045
650 0 $a Big data. $3 2045508
700 1 $a Rahimi, Iman. $3 3620764
856 4 0 $u https://www.taylorfrancis.com/books/9780367816384