ABSTRACT
In a closed-loop supply chain (CLSC), acquiring end-of-life vehicles (ELVs) and their components from both primary and secondary markets has posed a huge uncertainty and risk. Moreover, the constant supply of ELV components with minimization of cost and exploitation of natural resources is another pressing challenge. To address the issues, the present study has developed a risk simulation framework to study market uncertainty/risk in a CLSC. In the first phase of the framework, a total of 12 important variables are identified from the existing studies. The total interpretive structural model (TISM) is used to develop a causal relationship network among the variables. Then, Matriced Impacts Cruoses Multiplication Applique a un Classement is used for determining the nature of relationships (i.e., driving or dependence power). In the second phase, the relationship of TISM is used to derive a Bayesian belief network model for determining the level of risks (i.e., high, medium, and low) associated with the CLSC through the generation of conditional probabilities across 1) multi-, 2) single-, and 3) without-parent nodes. The study findings will help decision-makers in adopting strategic and operational interventions to increase the effectiveness and resiliency of the network. Furthermore, it will help practitioners to make decisions on change management implementation for stakeholders'performance audits on the attributes of the ELV recovery program and developing resilience in the CLSC network. Overall, the present study holistically contributes to a broader investigation of the implications of strategic decisions in automobile manufacturers and resellers. IEEE
ABSTRACT
The growth of Indonesia’s automotive sector has increased the number of end-of-life vehicles (ELVs), making ELV waste management a major issue. Most countries, such as Japan, China, and Europe, manage ELV waste well, but developing countries still do not. In developing countries, little is known about ELV social admissions. This study analyzes ELV management social acceptance in developing countries. Three hundred nine respondents from Jakarta, Bogor, Depok, Tangerang, and Bekasi (the district in Indonesia) were surveyed in a cross-sectional. A set of questions was designed to determine social acceptance (attitude, knowledge, social influence, institutional trust, health issues, and acceptance). After passing validity and reliability tests, the hypothesized research model was estimated using structural equitation. According to this study, social influence, attitude, knowledge, institutional trust, and health issues influenced public acceptance. The health issues variable was also a good moderator (Adj. R2 = 0.173, p < 0.001, average path coefficient = 0.299). The analysis of social acceptance models related to ELV management found that social influence, attitude, knowledge, and institutional trust play a role in one’s desire to accept a new rule, and health issues can strengthen a person in the admission process.
ABSTRACT
Environmental guidelines in the automotive industry greatly emphasize the recycling, remanufacturing, and recovering of end-of-life vehicles (ELVs). Given the principle of extended producer responsibility, developing an effective reverse logistics network is the most significant digit ahead of the industry. However, initial attempts addressing the reverse logistics network design (RLND) problem were short-sighted, focusing on cost minimization. Undoubtedly, the whole concept of recycling was founded on the pillars of sustainability. Accordingly, reverse logistics network design must be motivated by long-term environmental and societal benefits. This fact has become even more prominent in the current pandemic environment as COVID-19 has added serious uncertainties and risks to the supply chain processes. This paper reiterates the essence of sustainability goals and proposes a multi-objective fuzzy mathematical model to RLND problem for ELVs under such a fragile and fuzzy environment. The coverage of the proposed model is to optimally determine the locations and numbers of the facilities and the flows among them concerning environmental, social, and economic aspects. Hence, the model aims to reach a robust compromise solution that leads to a resilient network design. A real case study on the ELV market in Istanbul/Turkey proves the merit of the developed model. © 2021 University of Cincinnati. All rights reserved.