Risk Modeling Framework for Strategic and Operational Intervention to Enhance the Effectiveness of a Closed-Loop Supply Chain
IEEE Transactions on Engineering Management
; : 1-14, 2023.
Article
in English
| Scopus | ID: covidwho-2292273
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
Automobiles; Bayesian belief network (BBN); closed-loop supply chain (CLSC); Computational modeling; Costs; COVID-19; end-of-life vehicles (ELV) recovery program; Matriced Impacts Cruoses Multiplication Applique a un Classement (MICMAC); Planning; risk simulation; Supply chains; total interpretive structural modeling (TISM); Warranties; Bayesian networks; Commerce; Computer system recovery; Decision making; Risk assessment; Uncertainty analysis; Bayesian belief network; Closed-loop; Closed-loop supply chain; Computational modelling; End-of-life vehicle recovery program; End-of-Life Vehicles; Interpretive structural models; Matriced impact cruoses multiplication applique a un classement; Total interpretive structural modeling; Vehicle recoveries; Warranty
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
IEEE Transactions on Engineering Management
Year:
2023
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS