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Big Data Analytics in Logistics and Supply Chain Management

Special issue call for papers from International Journal of Logistics Management


SPECIAL ISSUE ON Big Data Analytics in Logistics and Supply Chain Management

International Journal of Logistics Management

Guest Editors:
Professor Samuel Fosso Wamba, Toulouse Business School, France
Professor Angappa Gunasekaran, University of Massachusetts Dartmouth, USA
Professor Eric Ngai, Hong Kong Polytechnic University, Hong Kong
Professor Thanos Papadopoulos, Kent Business School, University of Kent, UK

Big  data analytics (BDA) is a holistic approach to managing, processing and analyzing the 5V data-related dimensions (i.e., volume, variety, velocity, veracity and value) to create actionable insights for delivering sustained value, measuring performance and establishing competitive advantage (Fosso Wamba, Akter et al. 2015). The high business potential of BDA has been acknowledged by scholars (Mayika, Chui et al. 2011; Strawn, 2012; Gobble, 2013). For example, big data analytics can improve firm operational and strategic capabilities (Hazen, Skipper et al. 2016), enhance supply chain processes (Hazen, Boone et al. 2014) and, thus, overall supply chain performance (Hazen, Boone et al. 2014; Hazen, Skipper et al. 2016). Also, big data may transform manufacturing activities through automation, real-time process monitoring and measurement as well as detection and diagnosis of production issues, and thus leading to improved firm performance (e.g., low downtime costs, improved quality management, logistics and order fulfilment cycles) (George, Haas et al. 2014).  However so far very few studies have examined how and why BDA impacts on operational-, firm- and supply chain-level outcomes.

Recommended Topics:
The topics to be discussed in this special issue include but are not limited to the following:

• evaluation of the impact of BDA on logistics and supply chain management processes and performance
• evaluation of inhibitors and facilitators of BDA for supply chain management
• evaluation of the impact of BDA on different organizational and supply chain levels
• longitudinal case studies and pilot studies on the implementation and use of IT to support BDA for improved operations and supply chain management
• emergence of new business models based on the use of BDA in supply chain management
• empirical studies on the business value of BDA in operations and supply chains
• development and use of alternative theories to explain BDA adoption and use in operations and supply chain management
• empirical studies on the use of resources and capabilities for BDA in operations and supply chain management
• empirical studies on the use of BDA to analyze social media data (e.g., Twitter, Facebook) for supply chain management optimization
• Talent management in the context of BDA

Submission Procedure
Prospective authors are invited to submit papers for this special thematic issue on “Big Data Analytics in Logistics and Supply Chain Management” on or before June 15, 2017. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at PRIOR TO SUBMISSION at:  

About International Journal of Logistics Management
Editorial objectives
• To provide executives and teachers with reports of current developments in the field of logistics and supply chain management.
• To facilitate the interchange of information about logistics and supply chain management among business planners and researchers on a world-wide basis.
• To provide a platform for new thinking on the problems and techniques of logistics and supply chain management.

Editor-in-Chief: Dr Benjamin Hazen
Air Force Institute of Technology, USA
[email protected]m

All inquiries should be directed to the attention of:

Samuel Fosso Wamba
Guest Editor
E-mail: [email protected]

All manuscript submissions to the special issue should be sent through the online submission system:

Samuel Fosso Wamba is a Professor in the Department of Information, Operations and Management Sciences at The Toulouse Business School. Prior, he was Associate Professor at NEOMA Business School, France and Senior lecturer in the School of Information Systems & Technology (SISAT), University of Wollongong, Australia. He earned an MSc in mathematics, from the University of Sherbrooke in Canada, an MSc in e-commerce from HEC Montreal, Canada, and a Ph.D. in industrial engineering, from the Polytechnic School of Montreal, Canada. His current research focuses on business value of IT, business analytics, big data, inter-organisational system (e.g., RFID technology) adoption and use, e-government (e.g., open data), supply chain management, electronic commerce and mobile commerce. He has published papers in a number of international conferences and journals. He is organizing special issues on IT related topics for many top journals.

Angappa Gunasekaran is a Professor and Dean at the Charlton College of Business, University of Massachusetts, Dartmouth.  Dr. Gunasekaran has held academic positions in UK, Australia, Finland, India and Canada. He has over 250 articles published in peer-reviewed journals. He has presented about 50 papers and published 50 articles in conferences and given a number of invited talks in many countries. He is on the Editorial Board of several journals. He has organized several international workshops and conferences in the emerging areas of operations management and information systems.  He is currently interested in researching logistics and supply chain management. Dr. Gunasekaran has been the founding Director of Business Innovation Research Center (BIRC) since 2006.

Eric W. T. Ngai, PhD
Prof. Eric Ngai is a Professor in the Department of Management and Marketing at The Hong Kong Polytechnic University. His current research interests are in the areas of E-commerce, Supply Chain Management, Decision Support Systems and RFID Technology and Applications. He has over 100 refereed international journal publications including MIS Quarterly, Journal of Operations Management, Decision Support Systems, IEEE Transactions on Systems, Man and Cybernetics, Production & Operations Management, and others. He is an Associate Editor of European Journal of Information Systems and Information & Management. He serves on editorial board of four international journals. Prof. Ngai has attained an h-index of 20, and received 1190 citations, ISI Web of Science.

Thanos Papadopoulos is a Professor and Co-Director of the MBA in Kent Business School at the University of Kent, UK. Previously, he was Associate Professor in Sussex School of Business, Management, and Economics at the University of Sussex, UK. He holds a PhD from Warwick Business School, UK, an MSc from Athens University of Economics and Business, Greece, and a Diploma (Equivalent to MEng) from the Computer Engineering and Informatics Department at the University of Patras, Greece. His research areas lie in Big Data, and the deployment of information systems/technology within organizations and supply chains. His articles have been published in, inter alia, British Journal of Management, International Journal of Operations and Production Management, International Journal of Production Economics, Journal of Strategic Information Systems, Journal of Business Research, IEEE Transactions on Engineering Management, and Production Planning and Control.

Fosso Wamba, S., S. Akter, et al. (2015). "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study." International Journal of Production Economics 165: 234-246.
George, G., M. R. Haas, et al. (2014). BIG DATA AND MANAGEMENT. Academy of Management Journal, Academy of Management. 57: 321-326.
Gobble, M. M. (2013). "Big Data: The Next Big Thing in Innovation." Research Technology Management 56(1): 64-66.
Hazen, B. T., C. A. Boone, et al. (2014). "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications." International Journal of Production Economics 154: 72-80.
Hazen, B. T., J. B. Skipper, et al. (2016). "Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda." Computers & Industrial Engineering.
Manyika, J., M. Chui, et al. (2011). Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute.
Strawn, G. O. (2012). "Scientific Research: How Many Paradigms?" EDUCAUSE Review 47(3): 26.