RESUMO
Textile industries are among the most polluting and demand urgent management measures to mitigate their negative environmental impact. Thus, it is imperative to incorporate the textile industry into the circular economy and to foster sustainable practices. This study aims to establish a comprehensive, compliant decision framework to analyse risk mitigation strategies for circular supply chain (CSC) adoption in India's textile industries. The Situations Actors Processes and Learnings Actions Performances (SAP-LAP) technique analyses the problem. However, interpreting the interacting associations between the SAP-LAP model-based variables is somewhat lacking in this procedure, which might skew the decision-making process. As a result, in this study, the SAP-LAP method is accompanied by a novel ranking technique, namely, the Interpretive Ranking Process (IRP), which reduces decision-making issues in the SAP-LAP method and aids in evaluating the model by determining the ranks of variables; furthermore, the study also offers causal relationships among the various risks and risk factors and various identified risk-mitigation actions by constructing Bayesian Networks (BN) based on conditional probabilities. The study's originality represents the findings using an instinctive and interpretative choice approach to address significant concerns in risk perception and mitigation techniques for CSC adoption in the Indian textile industries. The suggested SAP-LAP and the IRP-based model would assist firms in addressing risk mitigation techniques for CSC adoption concerns by providing a hierarchy of the various risks and mitigation strategies to cope with. The simultaneously proposed BN model will help visualise the conditional dependency of risks and factors with proposed mitigating actions.
RESUMO
The whole world is faced with the COVID-19 epidemic that causes major disruptions in global supply chains. The aim of study is to evaluate the effects of COVID-19 on energy efficient global supply chains (SCs) and to model the global supply chain resilience and energy management affected during the COVID-19 considering trade between Turkey and China, and Turkey and the EU. In this study, firstly using System Dynamics (SD) model, the behavior of countries against COVID-19 for a certain period of time is observed, subsequently the increase in complexity is analyzed with entropy measurement to determine whether the systems are resilient or not and to mark the differences arising from reporting in the first and second wave of the pandemic in the developed model. It is determined that the second wave reporting differences is less than first wave reporting differences except Turkey. From the learning effect perspective, it has been seen that the effect on the economy and foreign trade are less than first wave of pandemic even though the number of patients originating in the second wave are higher. It means that countries responded to the second wave of COVID-19 in a more resilient way. It is found that as a major finding of this study, perceived complexity of the system decreases in the second wave because of the resilience of supply chain considering learning effect and centralized decision making ensure increasing resilience and resilience measure in global supply chains. The study is highly helpful for governments, decision makers and managers to understand and manage the impacts of COVID-19 on global supply chains being resilient and energy efficient.
