Toward Sustainability: Using Big Data to Explore Decisive Supply Chain Risk Factors Under Uncertainty

Kuo-Jui Wu; Ching-Jong Liao; Ming-Lang Tseng; Ming Lim; Jiayao Hu; Kimhua Tan

doi:10.1016/j.jclepro.2016.04.040

Toward Sustainability: Using Big Data to Explore Decisive Supply Chain Risk Factors Under Uncertainty

Kuo-Jui Wu, Ching-Jong Liao, Ming-Lang Tseng, Ming Lim, Jiayao Hu, Kimhua Tan

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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.

Original language	English
Pages (from-to)	663-676
Number of pages	14
Journal	Journal of Cleaner Production
Volume	142
Issue number	2
Early online date	20 Apr 2016
DOIs	https://doi.org/10.1016/j.jclepro.2016.04.040
Publication status	Published - 20 Jan 2017

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Cleaner Production. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Cleaner Production, [142, 2, (2016)] DOI: 10.1016/j.jclepro.2016.04.040

© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

Big data
Supply chain risks and uncertainties
Sustainability indicators
Decision Making Trial and Evaluation Laboratory (DEMATEL)
Delphi method

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10.1016/j.jclepro.2016.04.040

Post-PrintAccepted author manuscript, 1 MBLicence: CC BY-NC-ND

Cite this

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title = "Toward Sustainability: Using Big Data to Explore Decisive Supply Chain Risk Factors Under Uncertainty",

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. ",

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note = "NOTICE: this is the author{\textquoteright}s version of a work that was accepted for publication in Journal of Cleaner Production. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Cleaner Production, [142, 2, (2016)] DOI: 10.1016/j.jclepro.2016.04.040 {\textcopyright} 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/",

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AU - Hu, Jiayao

AU - Tan, Kimhua

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N2 - 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.

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Toward Sustainability: Using Big Data to Explore Decisive Supply Chain Risk Factors Under Uncertainty

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