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Global value changes continued to expand until the late 2000s. On the other hand, regional value chains have formed around major regional hubs due to the expansion of domestic demand in emerging economies, such as China, and strengthened trade protectionism since the global financial crisis. Such changes lead to the reorganisation of value chains, focusing on domestic markets (reshoring) or neighbouring countries (nearshoring). In particular, the importance of supply chain risk management has been highlighted following disruptions to the supply network due to the COVID-19 outbreak in December 2019. In this regard, major countries such as the USA and the EU are rapidly shifting to regional value chains for stable and sustainable production, rather than primarily aiming for production efficiency targeted at reducing costs. Industries in particular are more exposed to such supply chain risks under the existing structure and it now has become extremely important for businesses to take reaction to such risks. This is especially important for major industries in a country such as automobile or semiconductor manufacturing industries in South Korea. The aim of this study, therefore, is to establish the basis for the simultaneous growth of ports and linked industries by examining the existing structure of the global value chain for the automotive industry, which has a strong presence in South Korea’s domestic economy. In this regard, this research carries out a supply chain analysis focusing on the imports and exports of automotive parts. It also analyses the current structural risks and suggests risk management measures to secure a stable supply chain.

This study examines the motives and enabling factors regarding reverse factoring (RF) adoption in the automobile industry's supply chain.

This is a qualitative case study based on in-depth interviews with financial institutions in two countries having different statuses within the automobile industry global value chain: Mexico as a peripheral and Spain as a semi-peripheral country.

The RF is more widely deployed in Spain than in Mexico. The differences in the adoption of RF between the two countries stem from the availability of programs for suppliers at different supply levels, their efficient implementation and a robust regulatory framework, but especially from the cooperative approach adopted. The motives and enablers of RF adoption in the automobile industry can be explained under a framework of different supply chain management models. The RF programs driven by self-interest financial motives are characterized by an asymmetric distribution of benefits among supply chain participants. The RF programs that combine self-interest with cooperative motives are partially characterized by balanced benefits. In addition, they favor involvement practices and strengthen long-term relationships among supply chain participants. In this cooperative approach, trust, transparency and especially sharing information are considered relevant enablers. Finally, the specific automobile industry's features that determine RF adoption are linked to the structure and governance mode of the supply chain. The structure in terms of length – multiple supply levels – conditions the design of RF programs based on the buyer's position in the supply chain. The governance mode, particularly how the relationships are established, conditions the factors and requisites for efficient adoption of the RF programs.

This research analyzes the RF framed in the dynamics of buyer–supplier relationships and different models of supply chain management, allowing us to identify cooperation motives and their impact on RF adoption, beyond the traditional economic and financial motives highlighted by previous literature.

The arrival of global manufacturers has brought about new challenges such as environmental concerns and sustainability of supply chains. The purpose of this paper is to identify implementation level, major drivers, various practices and performance of environmentally and socially‐conscious supply chain management (SCM) in the context of the automobile industry in India.

The literature is reviewed to understand various challenges/barriers to the adoption of green supply chain management (GSCM) practices; statistical analysis of various drivers, practices and performance of environmentally and socially conscious supply chain is carried out in the case of an automobile cluster in central India. Personal interviews are conducted and a structured questionnaire is used for data collection from 30 organizations including original equipment manufacturers, first‐ and second‐tier suppliers.

Environmentally and socially responsive supply chains are in the early adoption stages in India. Companies studied in the auto cluster are not adequately addressing these measures in supply chain design and operations; though awareness and inclination to adopt has been on the rise – actual implementation lacks a holistic approach.

Investigation of GSCM along with social concerns is rarely done in the Indian context. This paper will offer valuable insights for managers in understanding the consequences of non‐compliance, especially with the Indian automobile industry recently becoming global.

The authors develop a methodology to select appropriate sustainable supply chain indicators (SSCIs) to measure Sustainable Development Goals (SDGs) in the global supply chain.

SSCIs are identified by reviewing the extant literature and topic modeling. Further, they are evaluated based on existing SDGs and ranked using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Notably, the evaluation of indicators is a multi-criteria decision-making (MCDM) process within a fuzzy environment. The methodology has been explained using a case study from the automobile industry.

The case study identifies appropriate SSCIs and differentiates them among peer suppliers for gaining a competitive advantage. The results reveal that top-ranked sustainability indicators include the management of natural resources, energy, greenhouse gas (GHG) emissions and social investment.

The study outcome will enable suppliers, specialists and decision makers to understand the criteria that improve supply chain sustainability in the automobile industry. The analysis provides a comprehensive understanding of the competitive package of indicators for gaining strategic advantage. This proactive sustainability indicator selection promotes and enhances sustainability reporting while fulfilling regulatory requirements and increasing collaboration potential with trustworthy downstream partners. This study sets the stage for further research in SSCIs’ competitive strategy in the automobile industry along with its supply chains.

This study is unique as it provides a framework for determining relevant SSCIs, which can be distinguished from peer suppliers, while also matching economic, environmental and social metrics to achieve a competitive advantage.

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.

This study proposes a methodology based on data source triangulation to measure the “strategic fit” for the automotive supply chain.

At first, the authors measured the responsiveness of the Indian automobile supply chain, encompassing the top ten major automobile manufacturers, using both sentiment and conjoint analysis. Second, the authors used data envelopment analysis to identify the frontiers of their supply chain. The authors also measured the supply chain's efficiency, using the balance sheet. Further, the authors analyzed the “strategic fit” zone and discussed the results.

The results indicate that both the proposed methods yield similar outcomes in terms of strategic fitment.

The study outcomes facilitate measuring the strategic fit, thereby leveraging the resources available to align. The methodology proposed is both easy to use and practice. The methodology eases time and costs by eliminating hiring agencies to appraise the strategic fit. This valuable method to measure strategic fit can be considered feedback for strategic actions. This methodology could also be incorporated possibly as an operative measurement and control tool.

Data triangulation meaningfully enhances the accuracy and reliability of the analyses of strategic fit. Data triangulation leads to actionable insights relevant to top managers and strategic positioning of top managers within a supply chain.

In recent times, reverse logistics (RL) is gaining significant traction in various automobile industries to recapture returned vehicles’ value. A good RL program can lower manufacturing costs, establish a green supply chain, enhance customer satisfaction and provide a competitive advantage. However, reducing disruptions and increasing operational efficiency in the automobile RL requires implementing innovative technology to improve information flow and security. Thus, this manuscript aims to examine the hurdles in automobile RL activities and how they can be effectively tackled by blockchain technology (BCT). Merging BCT and RL provides the entire automobile industry a chance to generate value for its consumers through effective vehicle return policies, manufacturing cost reduction, maintenance records tracking, administration of vehicle information and a clear payment record of insurance contracts.

This research is presented in three stages to accomplish the task. First, previous literature and experts' opinions are examined to highlight certain factors that are an aggravation to BCT implementation. Next, this study proposed an interval-valued intuitionistic fuzzy set (IVIFS) – decision-making trial and evaluation laboratory (DEMATEL) with Choquet integral framework for computing and analyzing the comparative results of factor interrelationships. Finally, the causal outline diagrams are plotted to determine the influence of factors on one another for BCT implementation in automobile RL.

This study has categorized the barriers to BCT implementation into five major factors – operational and strategical, technical, knowledge and behavioral, financial and infrastructural, and government rules and regulations. The results revealed that disreputable technology, low-bearing capacity of IT systems and operational inefficiency are the most significant factors to be dealt with by automobile industry professionals for finer and enhanced RL processes utilizing BCT. The most noticeable advantage of BCT is its enormous amount of data, permitting automobile RL to develop client experience through real-time data insights.

This study reveals several factors that are hindering the implementation of BCT in RL activities of the automobile industry. The results can assist experts and policymakers improve their existing decision-making systems while making an effort to implement BCT into the automobile industry's RL activities.

Although there are several studies on the benefits of BCT in RL and the adoption of BCT in the automobile industry, individually, none have explicated the use of BCT in automobile RL. This is also the first kind of study that has used IVIFS-DEMATEL with the Choquet integral framework for computing and analyzing the comparative results of factor interrelationships hindering BCT implementation in automobile RL activities.

In today's competitive market subject to various uncertainties, supply chain flexibility (SCF) arose as a potential weapon to enhance the firm's performance. This paper model the effect of SCF on automobile industry performance during sales promotional schemes (SPS), which has turned out to be one of the most important marketing tools.

In view of the literature and the expert opinion taken from an automobile OEM, the SCFs and performance measures pertaining to the SPS environment have been identified. For the purpose of ranking the SCFs with respect to the performance measures, the Interpretive Ranking Process (IRP) has been applied using the direct contact method as a means for establishing the contextual relationships between SCFs and performance measures and to draw the interpreting reasons behind them.

The findings of this study along with the validity and stability of the results assessed through the system graphs and sensitivity analysis demonstrate that flexibility at the procurement end followed by the organizational end has the highest impact on the performance of the company during SPS.

Analysis of SCFs in regard to performance measures during SPS helps improve supply chain performance and offer valuable insights to the practicing managers in decision-making. This study augments the flexibility literature, by clubbing the two independent research streams, SCF and SPS.

The purpose of this paper is to explore and investigate the measurement of a carbon footprint and environmental program in supply chain management.

The study uses a case study methodology and employs the qualitative methods of interviews and document analysis to collect data on Hyundai Motors Co. (HMC) and its key first‐tier supplier, referred to here as Supplier A, in the Korean automobile industry.

The results of the study show that a key strategic action to implement carbon management is to identify and measure the carbon footprint of products and processes within the supply chain. A carbon footprint measurement framework and different levels of CO₂ adoption categories developed at HMC are presented. By monitoring and evaluating suppliers' CO₂ emissions performance, a focal company may avoid carbon‐related risk and retain competitiveness based on its supply chain.

Developing a carbon footprint measurement and evaluation program in the supply chain provides a track record to improve carbon and energy efficiency. This may lead companies to develop and exploit greater energy efficiency to tackle carbon emission challenges in the supply chain.

This paper provides academics and managers with a new approach to consider carbon management and green supply chain management.

The purpose of this paper is to develop a green supplier development (GSD) process model and validate the model with a case study in Indian automobile industry.

A literature survey of peer-reviewed journal articles, survey reports and paradigmatic books with managerial impact is done for the research. The process of GSD is modeled using stage-gate approach and KPIV and KPOV of the process are determined. The process model is implemented in an Indian automobile components manufacturing industry for validation.

The industry implemented the model with ten suppliers and was able to successfully convert seven of them into Green suppliers. Remaining three suppliers were asked to repeat the process again or terminate the contracts. Model implementation took around three years starting from planning of resources and finances to actual development of suppliers.

The model implementation was done with a small automobile industry and hence the validation and implications may be generalized by taking the case study further in different industries. It would be beneficial to test the model with case studies of large-scale industries.

The process model for implementing GSD activities will help managers in taking complex investment decisions. The stages and process inputs and outputs are clearly defined which helps the managers to successfully develop the suppliers.

This paper puts forward the process model that should be implemented for the successful development of green suppliers. It might represent new opportunities for rigorous and relevant research in the area of green supply chain.