-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] SPECIAL ISSUE ON Big Data Analytics in Operations Management and Supply Chain of the Annals of Operations Research (Extended April 15, 2016) Datum: Sat, 13 Feb 2016 21:37:08 +0000 Von: Samuel FOSSO WAMBA Samuel.FOSSO.WAMBA@neoma-bs.fr An: aisworld@lists.aisnet.org aisworld@lists.aisnet.org

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

SPECIAL ISSUE ON Big Data Analytics in Operations Management and Supply Chain of the Annals of Operations Research (Extended April 15, 2016)

Introduction: Big data analytics (BDA) is defined as a holistic approach to manage, process and analyze the “5 Vs” data-related dimensions (i.e., volume, variety, velocity, veracity and value) in order to create actionable insights for sustained value delivery, measuring performance and establishing competitive advantages (Fosso Wamba, Akter et al. 2015). Some scholars and practitioners even suggest that BDA is the “fourth paradigm of science” (Strawn 2012, p.34), or even “the next frontier for innovation, competition, and productivity” (Manyika, Chui et al. 2011, p.1), or the “new paradigm of knowledge assets” (Hagstrom 2012, p. 2). These statements are mainly driven by the pervasive adoption and use of BDA-enabled tools, technologies and infrastructure including social media, mobile device), automatic identification technologies enabling the Internet of Things, and cloud-enabled platforms to support intra- and inter-organizational operations to achieve competitive advantage. For example, BDA allows improved data-driven decision making and innovative ways to organise, learn and innovate (Yiu 2012; Kiron 2013), and thus, reinforcing customer relationship management, improving the management of operations risk, enhancing operational efficiency and overall firm performance (Kiron 2013). Hence, there is need for revisiting existing theories in operation management and supply chain management with data powered by 5Vs. Apart from technological efficiencies, Operations research models are the foundation to analyse big data and extract right information for making more accurate and timely decisions. Therefore, a special issue on big data analytics in operations management and supply chain would encourage scholars to promote and disseminate research on the role of operations research models in big data analytics.

Objectives: The aim of this special issue of the Annals of Operations Research is to attract manuscripts which are firmly grounded in operations management and supply chain theories, using BDA to create new business opportunities, to support suitable supply design and operations in the 21st century, improve performance, and gain sustainable competitive advantage. These findings will also provide some implications of BDA on operations management and supply chain management practices and strategies.

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

• Operations research models for big data analytics in supply chain management • BDA-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 • New theory development to explain the adoption and use of BDA in operations at the organizational and inter-organizational levels • Empirical studies assessing the business value of BDA in terms of quality management, new products and services design, improved internal and supply chain operations capabilities • Revisit current institutional theory, resource dependence theory, transaction cost economics theory, agency theory, resource based view theory and ecological modernization theory using BDA • Exploring social capital theory using BDA in supply chain network design • Redefining supply chain coordination mechanism using social actors network theory supported by big data • Building robust supply chain risk model using BDA • Assessing the impact of BDA on performance measurement systems in operation management and supply chain • Assessing the impact of BDA on predictive maintenance for industrial products

Important Dates: Submissions due date : Extended April 15, 2016 Reviewer first reports: June 15, 2016 Revised paper submission: September 15, 2016 Reviewer second reports: November 10, 2016 Final manuscript submissions to publisher: December 15, 2016

Submission Procedure: Prospective authors are invited to submit papers for this special thematic issue on “Big Data Analytics in Operations Management and Supply Chain” on or before February 15, 2016. 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 https://www.editorialmanager.com/anor/redirectToBanner.aspx?defaultTarget=Au... PRIOR TO SUBMISSION at: https://www.editorialmanager.com/anor/default.aspx .

All inquiries should be directed to the attention of: Professor Samuel Fosso Wamba, NEOMA Business School, France. E-mail: Samuel.FOSSO.WAMBA@neoma-bs.fr

All manuscript submissions to the special issue should be sent through the online submission system: https://www.editorialmanager.com/anor/default.aspx .

Guest Editors: Professor Samuel Fosso Wamba, NEOMA Business School, France Professor Angappa Gunasekaran, University of Massachusetts Dartmouth, USA Professor Dubey, Rameshwar, Symbiosis International University, India,

* * * * * * References: 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 0(0): xx-xx. Hagstrom, M. (2012). "High-performance analytics fuels innovation and inclusive growth: Use big data, hyperconnectivity and speed to intelligence to get true value in the digital economy." Journal of Advanced Analytics(2): 3-4. Kiron, D. (2013). "Organizational Alignment is Key to Big Data Success." MIT Sloan Management Review 54(3): 1-n/a. 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. Yiu, C. (2012). The Big Data Opportunity: Making Government faster, smarter and more personal. Policy Exchange. London: 36.

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