Modeling a Reverse Logistics Supply Chain for End-of-Life Vehicle Recycling Risk Management: A Fuzzy Risk Analysis Approach

• Published in: Sustainability Vol. 15; no. 3; p. 2142
• Main Authors: Omosa, Geoffrey Barongo, Numfor, Solange Ayuni, Kosacka-Olejnik, Monika
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Automobile industry; Closed loops; Competitive advantage; Efficiency; Engineering research; Forecasts and trends; Fuzzy algorithms; Fuzzy logic; Fuzzy sets; Fuzzy systems; Literature reviews; Logistics; Manufacturers; Methods; Modelling; Natural resources; Raw materials; Recycling; Recycling (Waste, etc.); Research methodology; Reverse logistics; Risk analysis; Risk assessment; Risk factors; Risk management; Supply chains; Sustainability; Transportation equipment industry; Vehicles; Waste management; Kenya
• DOI: 10.3390/su15032142
• ISSN: 2071-1050

The automotive industry is one of the largest consumers of natural resources, and End-of-Life Vehicles (ELVs) form bulky wastes when they reach the end of their useful life, hence environmental concerns. Efficiency in recycling ELVs is therefore becoming a major concern to address the number of ELVs collected and recycled to minimize environmental impacts. This paper seeks to describe several activities of a closed-loop reverse logistics supply chain for the collection and recycling of ELVs and to identify the related potential risks involved. This study further investigated the potential risks for managing the efficient recycling of ELVs by modeling and viewing the end-of-life vehicle (ELV) recycling system as a reverse logistics supply chain. ELV recycling steps and processes, including collection and transportation, as well as the laws and technologies, were analyzed for risk factor identification and analysis. The major aim of this research is to perform a unified hierarchical risk analysis to estimate the degree of risk preference to efficiently manage the ELV supply chain. This study also proposes a risk assessment procedure using fuzzy knowledge representation theory to support ELV risk analysis. As a result, the identified key risks were ranked in terms of their preference for occurrence in a reverse supply chain of ELV products and mapped into five risk zones, Very Low, Low, Medium-Low, Moderate, Serious, and Critical, for ease of visualization. Hence, with a step-by-step implementation of the presented solution, ELV recycling organizations will see benefits in terms of an improvement in their activities and thus reduced costs that may occur due to uncertainties in their overall ELV business.