A grey-layered ANP based decision support model for analyzing strategies of resilience in electronic supply chains. (January 2020)

• Record Type: Journal Article
• Authors: Rajesh, R.
• Is Part Of: Engineering applications of artificial intelligence. Volume 87(2020)
• Publication Date: 2020-01
• Subjects: Supply chain resilience -- Supply chain risk -- Resilient strategies -- Grey theory -- Analytic network process; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.103338
• Languages: English
• ISSNs: 0952-1976

Abstract

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. Graphical abstract: Highlights: Ascertain supply chain strategies for resilience in electronic supply chains. Decision support using grey theory and layered ANP is implemented. Importance relations among risks and strategies were simultaneously considered. Sensitivity analysis was conducted and the input ratings were found to be unbiased. Top management can prioritize resilient strategies for implementation and practice.