Social and environmental risk management in resilient supply chains: A periodical study by the Grey-Verhulst model

As sustainability and allied concerns are at present gaining greater than before attention amongst stakeholders, enterprises are enforced to consider social and environmental risk assessments along with conventional economic risk assessments. Hence to advance sustainable competitive advantages, the...

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Bibliographic Details
Published in:	International journal of production research 2019-06, Vol.57 (11), p.3748-3765
Main Author:	Rajesh, R.
Format:	Article
Language:	eng
Subjects:	Data analysis Environmental management grey theory Resilience Risk assessment Risk management social and environmental risks supply chain risks Supply chains Sustainability Verhulst model
Online Access:	Get full text
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Summary:	As sustainability and allied concerns are at present gaining greater than before attention amongst stakeholders, enterprises are enforced to consider social and environmental risk assessments along with conventional economic risk assessments. Hence to advance sustainable competitive advantages, the property of resilience is becoming a success factor for enterprises. Resilience is the property of enterprises or their supply chains to resume operations after disruptions and to regain its sustainable competitive advantages quickly and effectively. This study essentially focuses on identifying drivers of social and environmental risk management (SERM) in resilient supply chains and to acknowledge the importance of these drivers towards the implementation of SERM practices of enterprises. Representative case studies of three electronics manufacturing firms were also considered in this research to gain practical insights. Periodical data analysis has been piloted for the collected datasets from these companies. Since the sequences of the collected data show saturated sigmoidal tendencies, the Verhulst model fits best with the data sequences. A Grey-Verhulst model has been implemented in this research and was practically tested for case firms to exemplify the data sequences of prediction and to effectually improve the SERM performances of firms.
ISSN:	0020-7543 1366-588X