Big Data Analytics in Supply Chain Management: Theory and Applications

211 Pages·2020·9.582 MB·English

by  Amir H. Gandomi, Iman Rahimi, S. Fong, M. Ali Ülkü

Big Data Analytics in
Supply Chain Management

Big Data Analytics in
Supply Chain Management
Theory and Applications
Edited by
Iman Rahimi, Amir H. Gandomi,
Simon James Fong, and M. Ali Ülkü
First edition published 2021
by CRC Press
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Library of Congress Cataloging-in-Publication Data
Names: Rahimi, Iman, editor. | Gandomi, Amir Hossein, editor. |
Fong, Simon, editor. | Ülkü, Muhammed Ali, editor.
Title: Big data analytics in supply chain management : theory and
applications / edited by Iman Rahimi, Amir H. Gandomi, Simon James Fong
and M. Ali Ülkü.
Description: First edition. | Boca Raton : CRC Press, 2021. |
Includes bibliographical references and index.
Identifiers: LCCN 2020036449 (print) | LCCN 2020036450 (ebook) |
ISBN 9780367407179 (hbk) | ISBN 9780367816384 (ebk)
Subjects: LCSH: Business logistics. | Big data.
Classification: LCC HD38.5 .B53 2021 (print) | LCC HD38.5 (ebook) |
DDC 658.70285/57–dc23
LC record available at https://lccn.loc.gov/2020036449
LC ebook record available at https://lccn.loc.gov/2020036450
ISBN: 978-0-367-40717-9 (hbk)
ISBN: 978-0-367-81638-4 (ebk)
Typeset in Times
by codeMantra
This book
is
affectionately dedicated
to
all those in the front lines fighting against the COVID-19 pandemic
and also,
to my family – Dr. Rahimi
to Elnaz – Dr. Gandomi
to Mary-Helen Alexandra and Naomi Fatma-Anne – Dr. Ülkü

Contents
Preface.......................................................................................................................ix
Acknowledgments .....................................................................................................xi
Editors ....................................................................................................................xiii
Contributors .............................................................................................................xv
Chapter 1 Big Data Analytics in Supply Chain Management:
A Scientometric Analysis .....................................................................1
Iman Rahimi, Amir H. Gandomi, M. Ali Ülkü, and
Simon James Fong
Chapter 2 Supply Chain Analytics Technology for Big Data ...............................9
Sivagnanam Rajamanickam Mani Sekhar,
Swathi Chandrashekar, and Siddesh Gaddadevara Matt
Chapter 3 Prioritizing the Barriers and Challenges of Big Data
Analytics in Logistics and Supply Chain Management
Using MCDM Method .......................................................................29
Mehdi Keshavarz-Ghorabaee, Maghsoud Amiri,
Mohammad Hashemi-Tabatabaei, and Mohammad Ghahremanloo
Chapter 4 Big Data in Procurement 4.0: Critical Success Factors
and Solutions ......................................................................................45
Bernardo Nicoletti and Andrea Appolloni
Chapter 5 Recommendation Model Based on Expiry Date of Product
Using Big Data Analytics ...................................................................65
Abhisekh Kumar Singh, Maheswari Raja, and Azath Hussain
Chapter 6 Comparing Company’s Performance to Its Peers: A Data
Envelopment Approach ......................................................................79
Tihana Škrinjaric´
Chapter 7 Sustainability, Big Data, and Consumer Behavior: A Supply
Chain Framework .............................................................................109
Brianna A. Currie, Alexandra D. French, and M. Ali Ülkü
vii
viii Contents
Chapter 8 A Soft Computing Techniques Application of an Inventory
Model in Solving T wo-Warehouses Using Cuckoo Search
Algorithm .........................................................................................133
Ajay Singh Yadav, Anupam Swami, Navin Ahlawat, and
Srishti Ahlawat
Chapter 9 An Overview of the Internet of Things Technologies Focusing
on Disaster Response .......................................................................151
Reinaldo Padilha França, Ana Carolina Borges Monteiro,
Rangel Arthur, and Yuzo Iano
Chapter 10 Closing the Big Data Talent Gap ......................................................169
Curtis Breville
Index ......................................................................................................................189
Preface
Ever evolving, the main characteristics of Big Data (BD) are now expanded into “5V”
concept consisting of Volume, Velocity, Variety, Veracity, and Value. As BD has
been transitioning from an emerging topic to now an immensely growing research
field, it has become essential to classify the different types and examine the general
trends of this research area. The continuous efforts to create more sophisticated tech-
nology to gather data at different steps of supply chain operations and management
have engendered the new era of supply chain analytics. By utilizing BD, companies
such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery and com-
petitive advantage with their supply chains. Such optimized efficiencies and visibil-
ity of inventory levels, order fulfillments, material sourcing, and product delivery
are achieved by predictive data analytics to adjust supply with demand; leveraging
new planning strengths to optimize their sales channel strategies; optimizing supply
chain strategy and competitive priorities; even launching powerful new ventures. The
concurrence of events such as growth in the approval of supply chain technologies,
data inundation, and a shift in management focus from heuristics to data-driven deci-
sion-making have collectively resulted in the rise of the Big Data Analytics (BDA)
era. In spite of these opportunities, many supply chain operations are gaining little to
no value from BD. From sourcing to consumer purchasing behavior to logistics ser-
vices, if well-utilized, BD is poised to help management with more informed, timely,
and robust supply chain decisions. In this edited book, the contributors discuss the
outcomes of recent large-scale achievements on BDA topics in relation to Supply
Chain Management (SCM). That is, this book aims to showcase a diversity of SCM
issues that may benefit from BDA, both in theory and practice.
This book shows a representative sampling of real-world problems as well as dis-
cussing some diverse features related to the use of Big Data in real-world applications.
Big Data Analytics in Supply Chain Management provides a review of the state-
of-the-art progress in developing the field of Big Data and reveals the applicability of
Big Data approaches to tackle real-world supply chain problems.
It is tailored to the audiences of engineers in industries, researchers, students,
faculty members, and operations research and industrial engineering from academia,
who work mainly on Big Data in supply chain problems.
To facilitate this goal, Chapter 1 presents a scientometric analysis to analyze
scientific literature in Big Data analytics in supply chain management. Chapter 2
focuses on supply chain analytics technologies for Big Data that are capable of assist-
ing a firm, organization, or company in taking rapid, intelligent, cost-effective, and
more efficient decisions. Chapter 3 addresses the barriers and challenges of Big Data
analysis in supply chain management. Chapter 4 presents an exhaustive set of critical
success factors for procurement 4.0 based on a summary of the actual results and of
a survey in organizations undergoing digital transformation. Chapter 5 introduces
product recommendations based on expiry date using collaborative filtering and
content-based filtering and targets to minimize the unsold expired products using
data analytics.
ix