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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.

This study investigates the overall publications of The TQM Journal since its inception with an aim to identify the trending topics and emerging trends.

The quantitative bibliometric and social network analysis techniques composed of keywords, co-occurrence network and keyword cluster detection are employed to conduct the investigation. A total of 968 papers published in The TQM Journal till August 2022 were sourced from the SCOPUS database to conduct the analysis.

The research identifies five themes from the published articles namely, customer service experience and satisfaction; quality management and organizational performance; quality measurement tools and models; quality and sustainable development; and quality and competitive advantage. The study also identifies the most significant articles, authors and countries published in the journal and shows that Industry 4.0 is the trending topic and quality 4.0 the new emerging trend in the journal.

The analysis is carried out only for papers published in The TQM Journal till August 2022; those after this month are not included in the analysis. The outcome of this study is dynamic in nature and subject to change over time as more papers, citations and collaborations are added to the list.

This is the first article of its kind to explore The TQM Journal publications with an aim to identify trending and emerging topics and also the most valuable authors based on the number of publications and citations through the bibliometric analysis.

This study aims to comprehend the application of analytics in the supply chain during the ongoing COVID-19 crisis and identify the emerging themes.

The author downloaded a list of research articles on the application of analytics to the supply chain from SCOPUS, conducted a systematic literature review for exploratory analysis and proposed a framework. Notably, the author used the topic modeling technique to identify research themes published during the ongoing COVID-19 crisis and thereby underscore some future research directions.

The author found that artificial intelligence, machine learning, internet of thing and blockchain are trending topics. Additionally, the author identified five themes by topic modeling, including the theme “Social Media information in Supply chain.”

The results were derived from a data set extracted from SCOPUS. Thus, the author excluded all studies not listed in SCOPUS from the analysis. Future research with articles indexed in other databases should be investigated to get a more holistic perspective of specific themes.

This study provides a deeper understanding and proposes a framework for applications of analytics in the supply chain that researchers could use for future research and industry practitioners to implement in their organizations to make a more sustainable and resilient supply chain.

This study provides exploratory information from published articles on the use of analytics in the supply chain during the COVID-19 crisis and generates themes that help understand the emerging and underpinned area of research.

The purpose of this study is to examine various data sources to identify trends and themes in humanitarian supply chain management (HSCM) in the digital age.

In this study, various data sources such as published literature and social media content from Twitter, LinkedIn, blogs and forums are used to identify trending topics and themes on HSCM using topic modelling.

The study examined 33 published literature and more than 94,000 documents, including tweets and expert opinions, and identified eight themes related to HSCM in the digital age namely “Digital technology enabled global partnerships”, “Digital tech enabled sustainability”, “Digital tech enabled risk reduction for climate changes and uncertainties”, “Digital tech enabled preparedness, response and resilience”, “Digital tech enabled health system enhancement”, “Digital tech enabled food system enhancement”, “Digital tech enabled ethical process and systems” and “Digital tech enabled humanitarian logistics”. The study also proposed a framework of drivers, processes and impacts for each theme and directions for future research.

Previous research has predominantly relied on published literature to identify emerging themes and trends on a particular topic. This study is unique because it examines the ability of social media sources such as blogs, websites, forums and published literature to reveal evolving patterns and trends in HSCM in the digital age.

The metaverse is a virtual world where users can communicate with each other in a computer-generated environment. The use of metaverse technology has the potential to revolutionize the way businesses operate, interact with customers, and collaborate with employees. However, several obstacles must be addressed and overcome to ensure the successful implementation of metaverse technology. This study aims to examine the implementation of metaverse technology in the management of an organization's supply chain, with a focus on predicting potential barriers to provide suitable strategies.

Covariance-based structural equation modeling (CB-SEM) was used to test the model. In addition, artificial neural network modeling (ANN) was also performed.

The CB-SEM results revealed that a firm's technological limitations are among the most significant barriers to implementing metaverse technology in the supply chain management (SCM). The ANN results further highlighted that the firm's technological limitations are the most crucial input factors, followed by a lack of governance and standardization, integration challenges, poor diffusion through the network, traditional organizational culture, lack of stakeholder commitment, lack of collaboration and low perception of value by customers.

Because metaverse technology has the potential to provide organizations with a competitive advantage, increase productivity, improve customer experience and stimulate creativity, it is crucial to discuss and develop solutions to implementation challenges in the business world. Companies can position themselves for success in this fascinating and quickly changing technological landscape by conquering these challenges.

This study provides insights to metaverse technology developers and supply chain practitioners for successful implementation in SCM, as well as theoretical contributions for supply chain managers aiming to implement such environments.

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.

This study aims to formulate a conceptual sustainable framework for developing a trusted, reliable, scalable, transparent, traceable and sustainable agri-food supply chain in a developing country so that it minimizes wastage and increases the efficiency levels of agri-produce and its usage.

This study uses a rigorous review of extant literature, case studies and the interview method for theory building, using blockchain technology (BT) as a subject. Further, the study builds a framework to relate blockchain solutions to the challenges faced by the agri-supply chain. Notably, the use of BT in the agri-food supply chain is a relatively new area of study.

Limitations of using BT 3.0 in a diverse supply chain like the food sector, especially in a developing country such as India, may be overcome by adopting BT 4.0 and could change the country’s face by controlling inefficiencies and ensuring transparency, helping in good governance, improving the humanitarian supply chain and integrating the bottom of the pyramid to the main economy. Based on the findings, this study proposes BT 4.0 for the agri-supply chain in India to deal with the current issues of demand-supply gap, wastages of agri-produce, unequal distribution of profit among agriproduct producers and logistics suppliers and ensuring sustainability.

Results in this study have been derived from a specific demographic condition in India. Future research with other demographic conditions may be replicated. BT is a new technology product, and its effectiveness is yet to be established.

The outcome of this study provides the application of BT 4.0 in the area of the agri-food supply chain. The BT 4.0 framework has been developed on studying a few cases that either implemented BT or were in the testing phase. The benefits of the agri-food supply chain vis-à-vis its overall social well-being may be achieved on the successful implementation of the framework, despite existing complexities in the food supply chain. Further research on this subject may help the other dimensions of the complexity of adopting BT 4.0. Both practitioners and policymakers from developing countries can, therefore, use the findings of this study to analyze BT 4.0 and address the concerns of the agri-food supply chain.

This research paper has proposed a conceptual framework of BT 4.0, which is a completely new technology. It is fairly transparent, and therefore sustainable supply chain practices may be achieved easily.