Big Data for Service and Manufacturing Supply Chain Management George Q. Huang1, Kwok Leung Tsui2, Ray Y. Zhong1 1 2 HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong .Department of Systems Engineering and Engineering Management, Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong Big data refers to a data set collection which is so large and complex that it is difficult to process using current database management tools or traditional data processing approaches. Big data is initially driven from the service supply chain management (SCM) such as healthcare, finance, tourism, telecommunication, information technology, etc. It is reported that, since 1980s, the percapita capacity to store information has approximately doubled every forty months (Manyika, Chui et al. 2011). Take the year of 2012 for example, it was estimated that 2.5 Q bytes of data were created every day (IBM, 2013). Manufacturing SCM may largely involve in a range of human activities from high-tech products such as space craft to daily necessities like toothbrush. Manufacturing is regarded as the “hard” parts of economy using labors, machines, tools, and raw materials to produce finished goods for different purposes. (Hill and Hill 2009; Terziovski 2010; Eichengreen and Gupta 2013). According to a report from Mckinsey & Company, in 2010, manufacturing and service sector stored about 2 exabytes of new data, which is more than any other sectors (http://www.geip.com/library/detail/13170). Recently, Auto-ID technology (e.g. RFID, Barcode) has been widely used in supply chain. Such automatic data collection approach brings new challenges which could be summarized from horizontal and vertical dimensions. Horizontal dimensions indicate the dynamics of big data, which means the interaction and intertransverse feature of data among manufacturing, logistics, and retailing phases. Vertical dimension describes the characteristics of big data in supply chain, which are highlighted as “5V”- volume, velocity, variety, verification, and value. This special issue of the International Journal of Production Economics is devoted to publish emerging technologies and significant insights related to big data in service and manufacturing SCM, aiming to upgrade and transfer these two sectors into a level that is more efficient and smarter. Typical topics include, but not limited to, the following dimensions: Data collection techniques Data quality management Data processing models and methods Data storage mechanisms Data mining and Knowledge discovery Data-driven decision support systems Data-based applications Case studies on big data Data analytics Bioinformatics, healthcare informatics Data tools Data modeling Data-based optimization Data-based control and automation Multi-dimensional data technology Industrial informatics and control All submissions will be judged for their appropriateness to the journal’s remit and the novelty of their theoretical and practical research contributions. While quantitative research is preferred, relevant qualitative research studies as well as case studies are also welcomed. Manuscript Preparation and Submission In preparing manuscripts, authors are required to follow the “Instructions to Authors” that are presented at the back of any recent issue of the International Journal of Production Economics. Authors should submit their papers via the EES http://www.ees.elsevier.com/ijpe and select “Special Issue: Big Data for Service and Manufacturing Supply Chain Management” when asked to indicate the “Article Type” in the submission process. Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Manuscripts will be refereed according to the normal IJPE standards and procedures. Publication Schedule Manuscript submission: 31 December 2013 Reviewer reports: 30 April 2014 Revised paper submission: 31 July 2014 Final manuscript submissions to publisher: 30 October 2014 Special Issue Guest Editors George Q. Huang, Professor HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong. Tel: 852-28592591, E-mail: [email protected] Kwok Leung Tsui, Chair Professor Department of Systems Engineering and Engineering Management, Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Tel: 852-34422177, E-mail: [email protected] Ray Y. Zhong, PhD HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong. Tel: 852-28592579, E-mail: [email protected] References: Eichengreen, B. and Gupta, P. (2013). "The two waves of service-sector growth." Oxford Economic Papers 65(1): 96-123. Hill, T. and Hill, A. (2009). Manufacturing strategy: text and cases, Palgrave Macmillan. IBM, (2013). “What is big data? – Bringing big data to the enterprise”, www. IBM.com. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011). "Big data: The next frontier for innovation, competition, and productivity." McKinsey Global Institute: 1-137. Terziovski, M. (2010). "Innovation practice and its performance implications in small and medium enterprises (SMEs) in the manufacturing sector: a resource‐based view." Strategic Management Journal 31(8): 892-902.