Wu, K.-J., Liao, C.-J., Tseng, M.-L., Lim, M. K. , Hu, J. and Tan, K. (2017) Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties. Journal of Cleaner Production, 142(2), pp. 663-676. (doi: 10.1016/j.jclepro.2016.04.040)
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Abstract
Rapid market changes aimed at sustainability have led to supply chain risks and uncertainties in the Taiwanese light-emitting diode industry. These risks and uncertainties can be captured by social media, quantitative and qualitative data (referred to herein as big data), but the industry has been unable to manage this information boom to respond to customer needs. These various types of data have their own characteristics that affect decision making about developing firm capabilities. This study aggregates the various data to undertake an extensive investigation of supply chain risks and uncertainties. Specifically, this study proposes using the fuzzy and grey Delphi methods to identify a set of reliable attributes and, based on these attributes, transforming big data to a manageable scale to consider their impacts. Subsequently, both the fuzzy and grey Decision Making Trial and Evaluation Laboratories applied to determine the causal relationships for supply chain risks and uncertainties. The results reveal that capacity and operations have greater influence than other supply chain attributes and that risks stemming from triggering events are difficult to diagnose and control. The implications, conclusions and findings are addressed.
| Item Type: | Articles |
|---|---|
| Keywords: | Big data, Supply chain risks and uncertainties, Sustainability indicators, Decision Making Trial and Evaluation Laboratory (DEMATEL), Delphi method |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Lim, Professor Ming |
| Authors: | Wu, K.-J., Liao, C.-J., Tseng, M.-L., Lim, M. K., Hu, J., and Tan, K. |
| College/School: | College of Social Sciences > Adam Smith Business School > Management |
| Journal Name: | Journal of Cleaner Production |
| Publisher: | Elsevier |
| ISSN: | 0959-6526 |
| Published Online: | 20 April 2016 |
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