********************* CALL FOR PAPERS *********************

 

SUBMISSION DUE DATE: February 15, 2015

Reviewer first reports: June 15, 2015

Revised paper submission: September 15, 2015

Reviewer second reports: December 10, 2015

Final manuscript submissions to publisher: March 15, 2016

 

SPECIAL ISSUE ON Big Data and Business Analytics Adoption and Use: A Step toward Transforming Operations and Production Management?

 

International Journal of Operations & Production Management

 

Guest Editors:

Dr Samuel Fosso Wamba, Associate Professor, NEOMA Business School, France

Dr Andrew Taylor, Professor, Bradford University School of Management, UK

Dr Eric Ngai, Professor, The Hong Kong Polytechnic University, Hong Kong

Dr Fred Riggins, Associate Professor, North Dakota State University, USA

 

Introduction:

Big  data analytics is defined as “a collection of data and technology that accesses, integrates, and reports all available data by filtering, correlating, and reporting insights not attainable with past data technologies” (APICS 2012). It is an emerging phenomenon which reflects the ever increasing significance of data in terms of its growing volumes, variety and velocity (the speed with which it is being created and processed) (Department for Business- Innovation and Skills 2013). While data has always been a part of the Information and Communication Technology (ICT) agenda, it is the scale and scope of change which big data is bringing that has attracted so much attention. Like many new phenomena it is sometimes over-sold because of hype or misunderstanding, yet there are tangible case studies of the power of big data to generate value and competitive advantage, albeit such examples remain comparatively small in number to-date. Its applications have been strong in the financial services, insurance, retailing and healthcare sectors, while in manufacturing, companies such as Rolls Royce and Ford have been reported to derive success from big data in predicting engine failures before they occur and in managing supplier risk (Goodwin 2013).

 

For Operations Management, big data has the potential to enable more sophisticated data-driven decision making and new ways to organise, learn and innovate (Yiu 2012; Kiron 2013). Its impact may be manifest in strengthening customer relationships, managing operations risk, improving operational efficiency or by improving product or service delivery or whatever the key business drivers may be (Kiron 2013). Operations in many organisations are experiencing much more voluminous and unstructured data environments because of real-time information from sensors and RFID tags which facilitate asset and business process monitoring (Davenport, Barth et al. 2012), end-to-end supply chain visibility, improved manufacturing and industrial automation (Wilkins 2013), manufacturing efficiency and effectiveness (Zelbst, Green et al. 2011). Ford, for example, is reported to be scouring “the metrics from the company's best processes across myriad manufacturing efforts and through detailed outputs from in-use automobiles--all to improve and help transform its business.” (Gardner 2013). However, despite some reported successes, OM researchers need to retain a healthy scepticism until rigorous research has been done in operations contexts. That is why this new phenomenon should have the attention of OM researchers, and hence this call for papers.

 

Given its high operational and strategic potential, notably in generating business value within various industries, big data has recently become the focus of a variety of scholars and practitioners. Some researchers have recently suggested that “big data” is the “next big thing in innovation” (Gobble 2013, p.64), “the fourth paradigm of science” (Strawn, (2012)), or “the next frontier for innovation, competition, and productivity” (Manyika, Chui et al. 2011, p.1). As a result, challenges related to big data have confronted businesses and organizations. In the operations and service contexts, big data also holds tremendous potential. In a recent survey study on third-party logistics services (3PL), (Langley 2014) found that 97% of shippers and 93% of 3PLs “feel strongly that improved, data-driven decision-making is essential to the future success of their supply chain activities and processes” (p. 4), whereas approximately 50% of each group disagrees that “big data fuels these decisions”  which shows how much potential for big data has still to be realized (p. 4).  Big retailers are currently leveraging big data capabilities for  improved customer experience, fraud reduction, and just-in-time recommendations (Tweney 2013).

In addition, big data technologies can be implemented in a range of applications including industrial automation tools, building management systems, production equipment, sales force information systems, and power plan conditions tools. For example, big data enabled-automation and manufacturing facilitates real-time detection and diagnosis of production issues, and thus reduces significantly downtime costs. Similarly, insights from big data analytics allows real-time process monitoring and measurement for improved quality management, logistics and order fulfilment cycles (Wilkins 2013). In short, “by observing causal factors for quality issues, process variability and energy efficiency through the manufacturing process, big data analysis becomes the basis for gaining a competitive advantage”(Wilkins 2013).

 

Even if big data holds the capability of transforming competition and thus competitive advantage, many managers are still struggling to understand the concepts related to big data, consequently failing to capture business value from big data. In addition, very few empirical studies have been conducted on the real value from big data.

 

Objective:

The main objective of this special issue is to fill this knowledge gap. Specifically, this special issue aims to invite OM scholars and practitioners to look at the ways and means to co-create and capture business value from big data in terms of new business opportunities, improved performance, and competitive advantage. The results will in turn reveal the implications of big data on operations management practices and strategies.

 

Recommended Topics:

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

 

·         Assessment of the effect of big data on operations and production management systems

·        Assessment of the effect of big data on the decision-making processes in operations

·        Assessment of facilitators and inhibitors of big data adoption for logistics, order fulfilment, distribution and supply chain management

·         Big data-enabled business analytics at the plant location , organizational, and supply chain levels

·         In-depth & longitudinal case studies and pilot studies on the implementation of IT infrastructure to support big data initiatives for improved operations management, lean & agile operations, quality management in operations and supply chain management

·        Facilitation of innovative electronic business models and operations by using big data in various sectors (e.g., healthcare, retail industry, and manufacturing)

·         New theory development to explain the adoption and use of big data in operations at the organizational and inter-organizational levels

·        Empirical studies assessing the  business value of big data in terms of quality management, new products and services design, improved internal and supply chain operations capabilities

·        Social media and big data in cloud for services, operations and production management transformation

·         Placement of data analytics and big data in cloud for services, operations and production management transformation

 

 

Submission Procedure

Prospective authors are invited to submit papers for this special thematic issue on “Big Data Adoption and Use: A Step toward Transforming Operations and Production Management” on or before February 15, 2015. 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 http://www.emeraldinsight.com/products/journals/author_guidelines.htm?id=ijopmPRIOR TO SUBMISSION at: http://mc.manuscriptcentral.com/ijopm.

 

About International Journal of Operations & Production Management Journal

The International Journal of Operations & Production Management exists to provide a communication medium for all those working in the operations management field. This includes:

• Private and public sectors 

• Manufacturing and service settings

• Academic institutions  

• Consultancies.

The content of the Journal focuses on topics which have a substantial management (as opposed to technical) content. A double-blind review process ensures the journal content's high quality, validity and relevance.

 

Editor-in-Chief: Professor Steve Brown

University of Exeter Business School, UK

 

All inquiries should be directed to the attention of:

Samuel Fosso Wamba

Guest Editor

E-mail: samuel.fosso.wamba@neoma-bs.fr

 

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

http://mc.manuscriptcentral.com/ijopm

* * * * * *

Samuel Fosso Wamba, PhD., is Associate Professor at NEOMA Business School, France. Prior, he was a Senior lecturer at 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 including European Journal of Information Systems, Production Planning and Control, International Journal of Production Economics, Information Systems Frontiers, Business Process Management Journal, Proceedings of the IEEE, AMCIS, HICSS, ICIS, and PACIS. He is organizing special issues on IT related topics for the Business Process Management Journal, Pacific Asia Journal of the Association for Information Systems, Journal of Medical Systems, Journal of Theoretical and Applied Electronic Commerce Research, Journal of Organizational and End User Computing, and Production Planning & Control.

 

Andrew Taylor, PhD

Andrew Taylor is Professor of Operations and Information Systems at Bradford School of Management, Andrew teaches World Class Operations, Resource Planning for Operations and Environmental Management & Quality Systems. He specialises in research relating to organisational performance improvement approaches such as Lean Systems, Performance Measurement and applications of new technologies such as Data Mining, Knowledge Management and 3D Printing. Professor Taylor has professional experience in aerospace, public utilities and government organisations, having worked in Short Brothers (now part of the Bombardier group), Northern Ireland Electricity and the Northern Ireland Training Authority. He has consulted widely. As a graduate of The Queen’s University of Belfast, Andrew holds a BSc in electronics and information systems, an MSc in industrial engineering and a PhD in manufacturing management. Previously Andrew Taylor was Professor of Information Management at Queen’s, Belfast where he worked for 12 years before coming to Bradford in 1996. His research work has been published in Omega, International Journal of Operations and Production Management, International Journal of Production Economics, Expert Systems with Applications, European Journal of Information Systems, Communications of the ACM, Information Systems Management, Production Planning and Control and the International Journal of Production Research.

 

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.

 

Fred Riggins, PhD

Fred Riggins is Associate Professor in the College of Business at North Dakota State University.  His research focuses on e-commerce, inter-organizational systems, RFID, and microfinance.  He has published in leading journals including Management Science, Journal of Management Information Systems, Journal of the Association for Information Systems, International Journal of RF Technologies, Electronic Commerce Research and Applications, and Communications of the ACM.  In a 2009 AIS publication, he ranked #9 on the list of top IS researchers from 2003-2007 based on number of publications and outlets.  According to Google Scholar he has an h-index of 19 and over 2,500 citations. 

 

 

References:

APICS (2012). APICS 2012 Big Data Insights and Innovations Executive Summary.

Davenport, T. H., P. Barth, et al. (2012). "How Big Data Is Different." MIT Sloan Management Review 54(1): 43-46.

Department for Business- Innovation and Skills (2013). Seizing the data opportunity: A strategy for UK data capability

Gardner, D. (2013). "Ford scours for more big data to bolster quality, improve manufacturing, streamline processes."   Retrieved 19th February 2014, from http://www.zdnet.com/ford-scours-for-more-big-data-to-bolster-quality-improve-manufacturing-streamline-processes-7000010451/.

Gobble, M. M. (2013). "Big Data: The Next Big Thing in Innovation." Research Technology Management 56(1): 64-66.

Goodwin, G. (2013). Takeaways from the MIT/Accenture Big Data in Manufacturing Conference. MIT/Accenture Big Data in Manufacturing conference Cambridge, USA.

Kiron, D. (2013). "Organizational Alignment is Key to Big Data Success." MIT Sloan Management Review 54(3): 1-n/a.

Langley, J. C. J. (2014). 2014 THIRD-PARTY LOGISTICS STUDY: The State of Logistics Outsourcing. Capgemini Consulting: 56pp.

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.

Tweney, D. (2013). "Walmart scoops up Inkiru to bolster its ‘big data’ capabilities online."   Retrieved 15 October, 2013, from http://venturebeat.com/2013/06/10/walmart-scoops-up-inkiru-to-bolster-its-big-data-capabilities-online/.

Wilkins, J. (2013). "Big data and its impact on manufacturing."   Retrieved 17 February, 2014, from http://www.dpaonthenet.net/article/65238/Big-data-and-its-impact-on-manufacturing.aspx.

Yiu, C. (2012). The Big Data Opportunity: Making Government faster, smarter and more personal. Policy Exchange. London: 36.

Zelbst, P. J., K. W. J. R. Green, et al. (2011). "Radio Frequency Identification Techonology Utilization and Organizational Agility." The Journal of Computer Information Systems 52(1): 24-33.