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Supplier selection (SS) is one of the prime competencies in a sourcing decision. Taking into account the key role played by suppliers in facilitating the implementation of green supply chain management (GSCM), it is somewhat surprising that very little research attention has been imparted to the development of a strategic sourcing model for GSCM. This research aims to develop a strategic sourcing framework in which supplier organizations are prioritized and ranked based on their GSCM performance. Accordingly, the benchmark organization is identified and its strategy is explored for GSCM performance improvement.

The research develops an innovative GSCM performance evaluation framework using six parameters, namely, investment in corporate social responsibility, investment in research and development, utilization of renewable energy, total energy consumption, total carbon-di-oxide emissions and total waste generation. An integrated multicriteria decision-making (MCDM) approach is proposed in which the entropy method calculates criteria weights. The Complex Proportional Assessment (COPRAS) and the Grey relational analysis (GRA) methods are used to rank supplier organizations based on their performance scores. A real-world case of green supplier selection (GSS) is considered in which five leading India-based automobile manufacturing organizations (Supplier 1, Supplier 2, Supplier 3, Supplier 4 and Supplier 5) are selected. Surveys with industry experts at the strategic, tactical, and operational levels are carried out to collect relevant data.

The results reveal that total carbon dioxide emission is the most influential parameter, as it gains the highest weight. On the contrary, investment in research and development, and total waste generation have no significant impact on GSCM performance. Results show that Supplier 5 secures the top rank. Hence, it is the benchmark organization.

The proposed methodology offers an easy and comprehensive approach to sourcing decisions in the field of GSCM. The entropy weight-based COPRAS and GRA methods offer an error-free channel of decision-making and can be proficiently used to outrank various industrial sectors based on their GSCM performances. This research is specific to the automobile manufacturing supply chain. Therefore, research outcomes may vary across supply chains with distinct characteristics.

The basic propositions of this research are based on a real-world case. Hence, the research findings are practically feasible. The less significant parameters identified in this study would enable managers to impart more attention to vulnerable areas for improvement. This research may help policymakers identify the influential parameters for effective GSCM implementation. As this research considers all aspects of sustainability, the strategies of the benchmark supplier have a direct impact on organizations' overall sustainability. The study would enable practitioners to make various strategies for GSCM performance improvement and to develop a cleaner production system.

The originality of this research lies in the consideration of both economic, social, environmental and operational aspects of sustainability for assessing the GSCM performance of supplier organizations. Quantitative criteria are considered so that vagueness can be removed from the decision. The use of an integrated grey-based approach for developing a strategic sourcing model is another unique feature of this study.

High packaging density in the present VLSI era builds an acute power crisis, which limits the use of MOSFET device as a constituent block in CMOS technology. This leads researchers in looking for alternative devices, which can replace the MOSFET in CMOS VLSI logic design. In a quest for alternative devices, tunnel field effect transistor emerged as a potential alternative in recent times. The purpose of this study is to enhance the performances of the proposed device structure and make it compatible with circuit implementation. Finally, the performances of that circuit are compared with CMOS circuit and a comparative study is made to find the superiority of the proposed circuit with respect to conventional CMOS circuit.

Silicon–germanium heterostructure is currently one of the most promising architectures for semiconductor devices such as tunnel field effect transistor. Analytical modeling is computed and programmed with MATLAB software. Two-dimensional device simulation is performed by using Silvaco TCAD (ATLAS). The modeled results are validated through the ATLAS simulation data. Therefore, an inverter circuit is implemented with the proposed device. The circuit is simulated with the Tanner EDA tool to evaluate its performances.

The proposed optimized device geometry delivers exceptionally low OFF current (order of 10^−18 A/um), fairly high ON current (5x10^−5 A/um) and a steep subthreshold slope (20 mV/decade) followed by excellent ON–OFF current ratio (order of 10^13) compared to the similar kind of heterostructures. With a very low threshold voltage, even lesser than 0.1 V, the proposed device emerged as a good replacement of MOSFET in CMOS-like digital circuits. Hence, the device is implemented to construct a resistive inverter to study the circuit performances. The resistive inverter circuit is compared with a resistive CMOS inverter circuit. Both the circuit performances are analyzed and compared in terms of power dissipation, propagation delay and power-delay product. The outcomes of the experiments prove that the performance matrices of heterojunction Tunnel FET (HTFET)-based inverter are way ahead of that of CMOS-based inverter.

Germanium–silicon HTFET with stack gate oxide is analytically modeled and optimized in terms of performance matrices. The device performances are appreciable in comparison with the device structures published in contemporary literature. CMOS-like resistive inverter circuit, implemented with this proposed device, performs well and outruns the circuit performances of the conventional CMOS circuit at 45-nm technological node.

The prime objective of this paper is to design a green supply chain management (GSCM) framework to evaluate the performance of environmental-conscious suppliers using multi-criteria decision-making (MCDM) approach.

The literature survey reveals critical factors for implementing GSCM, adopted methodologies and the result obtained by several researchers. Data have been collected by conducting surveys and interviews with strategic-level personnel of five esteemed organizations in automobile manufacturing sectors. A GSCM framework is developed in which a mathematical tool entropy–the technique for order of preference by similarity to ideal solution (TOPSIS) has been used to analyze the six parameters of automobile manufacturing unit. Initially, entropy is used to find the weights of each of the parameters that influence the decision matrix of the TOPSIS method. Secondly, the proposed GSCM framework ranks the supplier. Finally, sensitivity analysis of the model satisfies the GSCM framework and benchmarked the supplier.

The result shows that “Total CO₂ emission” has an influential role for GSCM sustainability, and hence, firms should put more effort to reduce emissions to improve overall performance. Again, the parameters like investment in R&D and total waste generation may be ignored in the selection process. The result reveals the benchmarked supplier and its strategies for effective sourcing, which would have an indirect effect on organizations' overall sustainability.

This research entirely focuses on sustainability within supply chain considering economic, social and environmental paradigms. The mathematical modeling of the proposed work considers many influential parameters and provides an easy and comprehensive decision-making technique.

The methods may be adopted by the industries for sustainable supply chain management. This study benchmarks the supplier organizations and explores the adopted policies by benchmarked organizations. Other organizations should follow the policies followed by benchmarked organization for enhancing environmental, social and economic performance. Organizations striving for sustainable development can adopt this framework for evaluation of supplier performance and benchmark with better accuracy.

The design of the GSCM framework explores both the qualitative and quantitative data based on environmental, social and economic parameters simultaneously in the evaluation of environmentally conscious suppliers. The research also investigates the constraints of the system to implement the GSCM in automobile manufacturing unit. Additionally, the sensitivity analysis justifies the benchmarked supplier and the adopted strategies to be followed by other manufacturing unit.

This study aims to measure the competitiveness of top apparel exporting nations competing with China in different apparel product categories across the global environment.

Compound annual growth rate, trade competitiveness, market share percentages, revealed comparative advantage and its variant normalized revealed comparative advantage using two-, four- and six-digit harmonized system codes for the period of 2016–2021 were used to understand the comparative advantage of competing apparel exporting nations.

The findings revealed that China still holds a more decisive comparative advantage than its competitors over the majority of the product categories within the knitted or not knitted apparel and clothing accessories. The other competing nations hold better export competitiveness over China in specific categories. However, that is not sufficient to be the “Next China.”

The study has important implications for different stakeholders of the global apparel industry, such as governments, industry officials, policymakers, investors, researchers and students. The study’s limitations arise from using product categories as competitiveness indicators, notably relying on a macro level approach for measurement while the micro level perspective is not analyzed, which constitutes a significant limitation of the study.

This research thoroughly analyzes the competitive position of the top ten apparel-exporting countries in the global market.