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During pre-vaccine era, pharmaceutical supplies [self-care essentials (SCEs)] have been proved to be a major deflector, protector and safety guard against novel coronavirus disease (COVID-19). Hence, the objective of the study is to provide a comprehensive socio-technological decision-making framework based on multiple criteria for selecting the suppliers of pharmaceuticals, such as SCEs, by multi-brand enterprises (distributors) in the pandemic environment.

A hybrid methodology of Bayesian best worst method (BWM) and multi-attributive border approximation area comparison (MABAC) method has been applied for carrying out the study. Bayesian BWM has been applied for computing the importance of criteria identified for the selection of SCEs' suppliers during pandemic environment and MABAC method evaluated the suppliers of the SCEs.

In the study, the authors have identified eight criteria such as disinfection and sanitization of vehicles, social conscience of suppliers, brand (Technological recognition) of SCEs and logistics and distribution network, among others, which are critical to the selection of a supplier for the supply of SCEs. The application of the proposed hybrid model revealed that lead time and quality of SCEs are of utmost concern for pharmacies in a pandemic environment. Among the ten suppliers, results showed that Suppliers 2, 4 and 5 have been ranked first for supplying hand wash, hand sanitizer and face mask, respectively.

The proposed model has helped the multi-brand distributors of pharmaceuticals in selecting suppliers during the ongoing crisis of COVID-19. In addition to that, in future the outcomes of the study would be helpful for multi-brand distributors as well as pharmacies and hospitals in selecting the best suppliers. Policy makers will be able to make and revise the policies immediately with the help of the proposed decision-making framework.

The paper makes a novel contribution towards theory with the criteria identified for selecting best suppliers during the pandemic COVID-19. Additionally, the proposed hybrid model helps multi-brand distributors of pharmaceuticals in making decisions that lead to a huge social and economic success in pandemic time.

The purpose of this paper is to explore the impact of stakeholder pressures on firm’s circular economy (CE) initiatives. The organizational responses are quite heterogeneous even when the firms face similar pressure. The authors have tried to explain this heterogeneity by using innovative capability as mediating variables.

Empirical survey data from Indian manufacturing firms are obtained to test the proposed hypotheses. The hypotheses are grounded in resource-based view of the firm. The authors used structural equation modeling approach with maximum likelihood methods of approximation.

The results indicate that exploratory innovation positively influences the firms to adopt CE practices. Whereas, exploitative innovation capability inhibits the adoption of CE practices.

This study guides business managers to focus on develop exploratory innovative capabilities before the adoption of CE practices. This study also provides guidance to policy maker about the role of regulatory mechanism plays to encourage/inhibits firms for adoption of CE practices.

This is first study to analyze the idiosyncratic behavior of the firms when subjected to stakeholder pressure for CE practices adoption. Innovative capabilities (exploratory/exploitative) are able to explain the reason for diverse response to stakeholder response.

Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.

Using bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.

This research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.

This research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.

This research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

From last few decades, logistics management (LM) constitutes a global concern among organization’s supply chain (SC) to improve their business effectiveness. The purpose of this paper is to uncover and analyze the critical factors (CFs) related to the implementation of effective LM concept and benchmark the SC performance.

The most common (16) CFs were identified and selected through literature and use of the Delphi method. Subsequently, the selected most common CFs were analyzed to distinguish their causal relations using the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) technique under unclear surroundings. A case example of Indian Logistics Company is also discussed to reveal the practical applicability of the research.

Provision of the effective information communication and technological developments in the system and Management dedication, support and involvement CFs are found to have the top most influences in the effective implementation of LM. This paper also groups the CFs into cause and effect relationship which provides valuable insights for analyzing the factors in successful implementation of LM.

This work attempts to understand the different CFs, their relative position and the importance rating in the system, due to which, managers can differentiate the factor which greatly affects the concepts of implementing LM, and thus, improvements can be made accordingly.

First, this work offers 16 CFs to LM implementation from a SC scenario. Second, in the context of contributing to the theory, the combined Delphi and fuzzy DEMATEL-based model is provided that helps in managing the logistic related issues effectively.

Over the past two decades, the hub location domain has witnessed remarkable growth, yet no prior study reviewed and synthesised problem formulation and solution methodologies to address real-life challenges.

The current study conducts a comprehensive bibliometric literature review to develop a thematic framework that describes and presents hub location problems. The work employs cluster, bibliometric, and social network analyses to delve into the essential themes.

Key themes include cooperation, coopetition, sustainability, reshoring, and dynamic demand, contributing to the complex challenges in today’s hub location problems. As the first work in this field, the study serves as a valuable single-source reference, providing scholars and industry practitioners with key insights into the evolution of hub location research, prominent research clusters, influential authors, leading countries, and crucial keywords.

Findings have significant implications since they highlight the current state of hub location research and set the stage for future endeavours. Specifically, by identifying prominent research clusters, scholars can explore promising directions to push the boundaries of knowledge in this area.

This work is a valuable resource for scholars in this domain and offers practical insights for industry practitioners seeking to understand the hub location problems. Overall, the study’s holistic approach provides a solid foundation for advancing future research work in the hub location field.

The purpose of this paper is to design and develop a balanced scorecard (BSC)-based index for quantifying the suitability of suppliers to mature to the next level of collaboration with the buyer company.

A BSC-based decision framework was developed using factors and factor indicators affecting collaboration with the supplier, using exploratory research. The proposed decision framework was implemented as a real-world case study in an Indian manufacturing organisation by using the Analytic Hierarchy Process. An 11-step methodology was developed to quantify supplier suitability to mature to the next level of collaboration. This is referred to as the “supplier collaborative performance index” (SCPI).

The proposed decision-making framework helps quantify the extent of collaboration with each supplier. It serves as an index by using the perspectives that are significant for the strategic performance of the buyer company, the factors that affect the collaboration and their specific factor indicators. The initial results of the implementation of the case study were found to be useful in judging supplier suitability in order to mature in their relationship.

A comprehensive BSC-based framework for enhancing relationships with suppliers, SCPI will be instrumental in deciding, managing and improving the level of collaboration with suppliers in manufacturing companies, depending on priorities.

This approach provides a single index to establish the supplier’s suitability to mature to the next level of collaboration with a buyer company. The higher the value of the Collaboration Index for a supplier, the better is the chance to move to the next level of maturity.

Buyer companies’ engagement with suppliers varies according to their specific business needs. Prior models of supplier classification are based on the dimensions like product specification or criticality of purchase. As the supply chains are embracing collaboration among partners to respond to the dynamic market conditions, it is prudent to study the changes adopted by buyer companies for supplier classification. The paper aims to discuss these issues.

This is an exploratory research for identifying the criteria used for supplier classification based on the interviews of practitioners from buyer multinational manufacturing companies operating in India. The content analysis of the subjective responses led to the identification of distinctive criteria.

Although, many of the practitioners were unaware of the model per se, they labelled their suppliers as per the types stated in the Kraljic model. They mentioned twenty six criteria for supplier classification; five of which do not have any reference in the extant literature. The degree of presence of these criteria provides a multi-criteria framework for supplier classification that has been further extended based on prior models.

The exploratory nature of the study, its confinement to Indian milieu and small sample size could limit the generality and exhaustiveness of the findings.

The framework and its extension can be used by practitioners in assessing and classifying their suppliers for strengthening supply chain collaboration.

This research unveils five new criteria used by buyer companies for supplier classification. The multi-criteria framework for supplier classification and its extension give new insights into the supplier characteristics significant for supply chain collaboration.

The purpose of this paper is to facilitate green supply chain (GSC) managers and planners to model and access GSC risks and probable failures. This paper proposes to use the fuzzy failure mode and effects analysis (FMEA) approach for assessing the risks associated with GSC for benchmarking the performance in terms of effective GSC management adoption and sustainable production.

Initially, different failure modes are defined using FMEA analysis, and in order to decide the risk priority, the risk priority number (RPN) is determined. Such priority numbers are typically acquired from the judgment decisions of experts that could contain the element of vagueness and imperfection due to human biases, and it may lead to inaccuracy in the process of risk assessment in GSC. In this study, fuzzy logic is applied to conventional FMEA to overcome the issues in assigning RPNs. A plastic manufacturer GSC case exemplar of the proposed model is illustrated to present the authenticity of this method of risk assessment.

Results indicate that the failure modes, given as improper green operating procedure, i.e. process, operations, etc. (R6), and green issues while closing the loop of GSC (R14) hold the highest RPN and FRPN scores in classical as well as fuzzy FMEA analysis.

The present research work attempts to propose an evaluation framework for risk assessment in GSC. This paper explores both sustainable developments and risks related to efficient management of GSC initiatives in a plastic industry supply chain context. From a managerial perspective, suggestions are also provided with respect to each failure mode.

To assess the state of supply chain management (SCM) research in India and to understand the research trends and methodologies used. The present study also aims to create a taxonomy of the subject areas researched in India.

The present study employed the systematic literature review methodology. Literature from 395 peer journal papers in 67 leading journals over a 20-year period (2000–2020 Quarter-1) was comprehensively reviewed and assessed.

SCM research in India started around the year 2000. The quantum of research was low (single digit) until 2010. There has been steady growth over the last decade, and over 50% of the total papers up until now has been published in the last four years. The present study created a three-tiered taxonomy of the subject areas and classified the papers as per it. The first tier (level-1) has seven categories (SCM strategy, network design, SCM processes and integration, IT systems, skills, performance measurement and others). A perusal of the newly created taxonomy revealed that, except for a few areas under level-1 categories (such as SCM processes and SCM strategy), the other level-1 categories have not seen much research. Similarly, there is little or no research in a large number of level-2 categories (such as outsourcing strategy, channel strategy, demand management, demand fulfillment, customer relationship management, integrated supply chain planning, new product development, returns, supply chain orientation, performance monitoring, performance improvement, SCM adoption process, SCM implementation issues and quantified benefits of SCM). Methodologically, the rigor of SCM research in India needs improvement.

A comprehensive taxonomy of SCM subject areas researched in India at three cascading levels was created for the first time in the present study. The taxonomy will help provide researchers with a clear understanding of the structure of the subject areas and help in identifying areas where research has been carried out and the subject areas where gaps exist for future research to proceed. The present study also provides an overview of the methodological rigor of SCM research in India and points out some of the limitations that researchers should avoid in future studies.

The main aim of the study is to develop a structural framework that includes e-business processes and sustainability-oriented enablers to improve supply chain performance.

To improve supply chain performance, the present study conducts a detailed literature review to explore the key enablers based on e-business processes and sustainability aspects of various recognized databases. An automotive case organization is chosen to conduct a case study for developing the structural framework. The structural framework is developed by adopting an interpretive structural modeling approach. Furthermore, the fuzzy-MICMAC approach is applied to compute the driving and the dependence power of each selected enabler.

This study develops a structural framework that indicates the improved supply chain performance is achieved by “development of supply chain web system,” “strong customer relationship management,” and “enhancement in control over cost quality and sustainability” as the most critical enablers.

The enablers for the development of framework are obtained through the inputs of an expert panel. However, the researchers may conduct large scale surveys to strengthen the input components of the framework.

This is one of the unique studies that list down a set of 27 most critical e-business processes and sustainability based enablers to improve supply chain performance.