Supply Chain Risk Management in Indian Manufacturing Industries: An Empirical Study and a Fuzzy Approach

V. Viswanath Shenoi (), T. N. Srikantha Dath () and Chandrasekharan Rajendran ()
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V. Viswanath Shenoi: Amrita College of Engineering and Technology
T. N. Srikantha Dath: M S Ramaiah University of Applied Sciences
Chandrasekharan Rajendran: Indian Institute of Technology Madras

A chapter in Supply Chain Management in Manufacturing and Service Systems, 2021, pp 107-145 from Springer

Abstract: Abstract Risks are inevitable in supply chains, and they need to be detected early and appropriately addressed. This chapter primarily attempts to identify early warning signals and implement suitable mitigation decisions to meet exigencies related to the management of supply chain risks. Further, the chapter also presents an approach that addresses the risks in an integrated manner. First, a framework is essential to understand the relationships between the independent and dependent variables. An empirical study is undertaken by developing a questionnaire that captures the perceptions of the supply chain practitioners on risks perceived in their supply chains, and the framework is subjected to validity tests. Secondly, the data obtained from these surveys is utilized to develop a fuzzy model for identifying and predicting all plausible risks based on the instantaneous risk vector. Fuzzy Cognitive Map (FCM) is used to represent the overall behavior of the dynamical system of the supply chain. The instantaneous risk vector is passed on to the dynamical system to identify all plausible risks that may appear in near future. The resultant vector obtained suggests that ignoring the initially perceived risks eventually lead to possible disruptions in the supply chain. The resultant vector thus obtained is useful for decision making to alleviate the impact of various types of risks. Finally, the relative comparison of the mitigation strategies’ ranking was made for the results obtained from regression, FCM, and Fuzzy TOPSIS.

Date: 2021
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DOI: 10.1007/978-3-030-69265-0_4

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