A grey-layered ANP based decision support model for analyzing strategies of resilience in electronic supply chains

Abstract

Augmented globalizationand vertical integration have made contemporary supply chains an intricate network subject to a number of vulnerabilities. Preemptive measures are needed for dealing with mutable risks and vulnerabilities to safeguard robust supply chain systems. Supply chain risk management (SCRM) connotes a set of risk management responses essentially instigated to confront supply chain risks. As supply chain risks are intertwined, one resilient strategy for risk mitigation can moderate several supply chain risks. A complex decision making problem involving twelve major supply chain risks and twenty one resilient strategies for risk mitigation have been acknowledged in this research with archetypal focus on electronics manufacturing supply chains. A combination of Multi criteria decision aid (MCDA) and artificial intelligence (AI) is increasingly used in decision making of complex real world problems. A decision support model incorporating an amalgamation of grey theory and layered analytic network process (ANP) has been employed for quantifying various resilient strategies for risk mitigation. The proposed model was also applied in a practical setting taking a case study of an electronics manufacturing company. Sensitivity analysis was also conducted to ensure the robustness of obtained results. The combined methodology proposed in this research could be effectively used by top management, to pigeonhole the resilient supply chain strategies for better managing their supply chains.