Supply chains today are exposed to a lot of turbulence with outsized unforeseen penalties of seemingly contained events. It is significant for practitioners to identify and classify risks into various categories for the ease of monitoring. After the risks are classified, drivers for the particular risk need to be identified and addressed. A distinctive risk driver can be the instigating cause for many auxiliary drivers of risks; alternatively, a risk driver can be a secondary driver initiated as the effect of one or more primary drivers. This study contributes to ascertain and construct a causal effect diagram on predominant risk drivers archetypally seen in electronic supply chains. A combination of grey theory and DEMATEL methodologies has been employed in this study and a case evaluation was also done. The combined methodology is to deal with judgmental decisions and to transmute them to interpretable cause–effect diagraphs. It is evident from the results of proposed model that the risk drivers are interconnected and one risk driver can be the cause/effect of one or more risk drivers. Fluctuating exchange rates is found to be the principal causal driver initiating the effects of other risk drivers too, trailed by loss of information system and supply failure. Risk arising from the cost of capacity is the effect driver for most of the risk drivers, followed by bull whip effects and capacity inflexibility. In conclusion, the benefits of the research are of twofold, first to recognize and categorize the drivers of major supply chain risks and the other is to effectively identify the chief risk drivers, where the managers and practitioners could really concentrate on. A significant managerial implication of this research is that steps taken for curtailing the causal risk drivers can sequentially lead to dwindled effect risk drivers, leading to enhanced management of supply chain vulnerability.
The International Journal of Advanced Manufacturing Technology – Springer Journals
Published: Mar 9, 2017
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