In recent years, big data and predictive analytics have attracted burgeoning interest among academia and practitioners (see Wamba et al. 2015; Dubey et al. 2015; Dubey and Gunsekaran, 2015). In current practices, data is generally collected to test the theoretical models derived after extensive literature review, opinion from experts and experience.
This traditional approach has several limitations. Moreover, social media and online data collection and analysis have created opportunities to utilise data for making real-time and more accurate decisions in supply chain management. Hence, various scholars have expressed the need for robust techniques. Waller and Fawcett (2013) have argued for redefining existing theories in supply chains as big data possesses immense potential in terms of volume, velocity and variety (3Vs). In the past, due to limitations of data in terms of volume and variety, researchers have most of the time had to limit the scope of their studies into supply chain. Hence, there is need for revisiting existing theories in supply chain management with data powered by 3Vs.
What is less understood in supply chain literature is the adequate use of big data in supply chain management (Hazen et al. 2014). Contributions to the supply chain literature using big data are scant. In spite of its popularity among industry professionals and practitioners, there is lack of clarity in term of understanding and applications of big data in supply chains. There is an urgent need for contributions in the field of supply chain management in which big data has been used extensively to explain the complex supply theories in supply chains which are still underdeveloped.
The aim of this special issue is to attract manuscripts which are firmly grounded in supply chain theories, using big data and predictive analytics to take current supply chain theory and practice to the next level of excellence in terms of supporting suitable supply design and operations in the context of 21st century organisational competitiveness.
References:
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2015). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology, 1-15.
Dubey, R., & Gunasekaran, A. (2015). Education and Training for Successful Career in Big Data and Business Analytics. Industrial and Commercial Training, 47(4),174-181.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (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.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics.(DOI:10.1016/j.ijpe.2014.12.031).
Suitable topics include, but are not limited to, the following:
Important Dates
Full manuscript submission: 30 June, 2016
This traditional approach has several limitations. Moreover, social media and online data collection and analysis have created opportunities to utilise data for making real-time and more accurate decisions in supply chain management. Hence, various scholars have expressed the need for robust techniques. Waller and Fawcett (2013) have argued for redefining existing theories in supply chains as big data possesses immense potential in terms of volume, velocity and variety (3Vs). In the past, due to limitations of data in terms of volume and variety, researchers have most of the time had to limit the scope of their studies into supply chain. Hence, there is need for revisiting existing theories in supply chain management with data powered by 3Vs.
What is less understood in supply chain literature is the adequate use of big data in supply chain management (Hazen et al. 2014). Contributions to the supply chain literature using big data are scant. In spite of its popularity among industry professionals and practitioners, there is lack of clarity in term of understanding and applications of big data in supply chains. There is an urgent need for contributions in the field of supply chain management in which big data has been used extensively to explain the complex supply theories in supply chains which are still underdeveloped.
The aim of this special issue is to attract manuscripts which are firmly grounded in supply chain theories, using big data and predictive analytics to take current supply chain theory and practice to the next level of excellence in terms of supporting suitable supply design and operations in the context of 21st century organisational competitiveness.
References:
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2015). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology, 1-15.
Dubey, R., & Gunasekaran, A. (2015). Education and Training for Successful Career in Big Data and Business Analytics. Industrial and Commercial Training, 47(4),174-181.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (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.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics.(DOI:10.1016/j.ijpe.2014.12.031).
Suitable topics include, but are not limited to, the following:
- Supply chain network design
- Re-defining existing supply chain theories
- Applications of big data in disaster relief supply chain network design
- Suppliers selection
- Improving coordination in supply chain networks
- Applications in natural resource-constrained supply chain networks
- The design of ethical supply chains using big data
- Explaining complex theories surrounding performance measurement systems (PMS)
- Improving forecasting models using big data and business analytics
- Explaining crucial properties of supply chain networks
- Dynamic supply chain alignment
- Supply chain resilience in supply chain network design
- Addressing supply chain skill gap issues using big data
- Explaining assimilation of big data
Important Dates
Full manuscript submission: 30 June, 2016
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