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:
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Data collection techniques
Data quality management
Data processing models and methods
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Data storage mechanisms
Data mining and Knowledge discovery
Data-driven decision support systems
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Data-based applications
Case studies on big data
Data analytics
Bioinformatics, healthcare informatics
Data tools
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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: gqhuang@hku.hk
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:
kltsui@cityu.edu.hk
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: zhongzry@gmail.com
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.