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The purpose of this paper is to identify and model the evaluation criteria for the selection of third-party logistics service provider (3PLSP) by an interpretive structural modelling (ISM) approach in the pharmaceutical sector.

Delphi technique was used for identifying the most significant criteria, and the ISM method was employed for developing the interrelationship among the criteria. Also, the critical criteria for having high influential power were identified by using the Matrice d’Impacts Croisés Multiplication Appliqués à un Classement analysis.

The most significant factors, namely, capability of robust supply network/distribution network, quality certification and health safety, service quality and environmental quality certifications, were found to have a high driving power, and these factors demand the maximum attention of the decision makers.

As the ISM approach is a qualitative tool, the expert opinions were used for developing the structural model, and the judgments of the experts could be biased influencing the reliability of the model. The developed hierarchical concept is proposed to help the executives, decision and policy makers in formulating the strategies and the evaluation of sustainable 3PLSP.

It is an original research highlighting the association between the sustainable 3PLSP evaluation criteria by employing ISM tool in the pharmaceutical industry. This paper will guide the managers in understanding the importance of the evaluation criteria for the efficient selection of 3PLSP.

The purpose of this paper is to identify the critical factors influencing the cloud computing adoption (CCA) in the manufacturing micro, small and medium enterprises (MSMEs) by employing a decision-making trial and evaluation laboratory (DEMATEL) methodology.

Through literature review and expert opinions, 30 significant factors were identified, and then a DEMATEL approach was applied for exploring the cause–effect relationship between the factors.

The results of study highlighted that five factors, namely, “hardware scalability and standardisation”, “cost (subscription fees, maintenance cost and implementation cost (CS1)”, “innovation”, “installation and up gradation (CS28)”, and “quality of service” were the most significant factors influencing the CCA in the case sector.

The DEMATEL model was developed by considering expert inputs, and these inputs could be biased which can influence the reliability of the model. This study guides the organisational managers, cloud service providers and governmental organisations in formulating the new policies/strategies or modifying the existing ones for the effective CCA in the case sector.

For the first time. interdependency between the critical factors influencing CCA was discussed by employing the DEMATEL approach in the Indian manufacturing MSMEs context.

The issue of food security is one of the critical global challenges. The Government and the industries have begun apprehending the importance of green supply chain management (GSCM) implementation in their supply chains. There are various drivers or performance indicators (PIs) of GSCM in the agro-sector. This paper aims to analyse 14 PIs using an interpretive structural modelling (ISM) approach.

In this study, the PIs of GSCM were identified through a literature survey and opinions of field experts. The identified 14 PIs were modelled by applying an ISM methodology for establishing the interrelationship between the PIs and to identify the PIs having high influential power.

The result of the investigation underlined that three PIs, namely, environmental management (PI 1), regulatory pressure (PI 3) and competitive pressure (PI 2) are the significant PIs having high driving power.

The experts’ judgments were used for the development of the structural model, which could be biased influencing the reliability of the model. Also, only 14 significant PIs were considered for the analysis. This research is intended to help the policymakers, managers and supply chain designers in the food industry and in agribusiness in formulating the policies and strategies for achieving food security, conservation of the environmental resources and for improving the financial performance of the industry.

It is pioneering research focusing on the analysis of the PIs towards the implementation of GSCM in the Indian agro-industries context using an ISM approach. This research adds value to the existing knowledge base by identifying the crucial PIs, exploring their mutual relationship and highlighting their level of influence in the case sector.

In the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and distribution of various organizations have started adopting smart technologies globally. However, the adoption of smart technologies in the Indian warehousing industry is minimal. The study aims to identify the implementation barriers of smart technology in the Indian warehouse to achieve sustainability.

This study employs an integrated Delphi-ISM-ANP research approach. The study uses the Delphi approach to finalize the barriers identified from the detailed literature review and expert opinion. The finalized 17 barriers are modeled using interpretive structural modeling (ISM) to get the contextual relationship. The ISM method's output and analysis using the analytical network process (ANP) illustrate priorities.

The study's findings showed that the lack of government support, lack of vision and mission and the lack of skilled manpower are the most significant barriers restricting the organization from implementing smart and sustainable supply chain practices in the warehouse.

This study would help the practitioners enable the sustainable warehousing system or convert the existing warehouse into a smart and sustainable warehouse by developing an appropriate strategy. This study would also help reduce the impact of different barriers that would strengthen the chance of technology adoption in the warehouses.

The literature related to adopting smart and sustainable practices in the warehouse is scarce. Modeling of adoption barrier for smart and sustainable warehouse using an integrated research approach is the uniqueness of this study that have added value in the existing scientific knowledge.

This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context.

20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used.

The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties.

This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care.

This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP.

The purpose of this study is to determine the significant factors affecting the adoption of Cloud of Things (CoT) by Indian small and medium-sized enterprises, using exploratory and confirmatory factor analysis.

Significant factors that impact CoT implementation were identified through a detailed literature survey. A conceptual framework and hypotheses were proposed for linking the significant factors so identified, namely, cost saving, relative advantage, sharing and collaboration, reliability, security and privacy, technical issues and adoption intention. The data were collected from 270 Indian SMEs using an online survey. Structural equation modelling (SEM) was used to test the proposed model.

It was observed that factors such as “sharing and collaboration”, “cost saving” and “relative advantage” had a positive influence on CoT adoption. Findings of the study also supported the hypothesis that “security and privacy” were the prime concerns for CoT adoption.

Sample coverage across different geographical areas with qualitative data can be helpful. The SEM methodology is only capable of verifying linear relationships; to counter this, a hybrid approach with tools such as artificial neural network and multiple linear regression can be used.

This study intends to guide the managers of SMEs, cloud service providers and regulatory organisations for formulating an effective strategy to adopt CoT. It may be noted that CoT is the prime building block of Industry 4.0 and SMEs will benefit from government support for the same.

This paper highlights the influence of factors on the adoption intention of CoT with a focus on the SMEs of a developing country like India.

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

Firms have been implementing lean manufacturing to improve their business performances. However, they have difficulties in the implementation due to the many barriers. In view of the lack of research and the importance in understanding them, the purpose of this paper is identify and evaluate the lean barriers with respect to their levels of importance in implementation.

As lean barriers are scattered in the literature and a variety of performance measures are used in practice, an extensive literature review is first carried out to identify the lean barriers and performance measures. A novel ranking technique – interpretive ranking process (IRP) – is adopted in the evaluation. In the IRP-based evaluation approach, a group discussion technique, where five Indian lean experts are involved, is applied to determine the most important lean barriers and performance measures. Several matrices are developed step by step for calculating the ranks of the selected lean barriers. Upon validating the ranks, an IRP-based lean barrier evaluation model is developed.

The IRP-based lean barrier evaluation model can help firms better understand lean barriers and their levels of importance in lean implementation. In the light of this model, to successfully implement lean, firms should provide sufficient management time and training to employees, develop a right culture, develop effective communication, carry out low-cost production, and obtain external funding.

The evaluation results provide the practitioners with a realistic framework to deal with many problems, especially those related to resource allocation, in lean implementation. Based on the framework, practitioners can prioritize lean barriers during implementation in accordance with performances targeted.

This is the first study that provides a comprehensive review of lean barriers available in the literature and evaluates them in accordance with performance measures. The combined use of literature review and experts in the evaluation approach justifies the value of the study.

Textile and Apparel (T&A) sector significantly influences socio-economic and environmental dimensions of the sustainability. The purpose of this paper is proposed to establish the interrelationship among the critical barriers to the sustainable development of T&A supply chains by using a multi-criteria decision-making approach and to obtain a ranking of the barriers.

In the present investigation through literature review and from expert opinions, 14 significant challenges to the sustainable growth of T&A sector have identified. For establishing the interrelationship and for developing a structural model of the identified challenges, interpretive structural modelling (ISM) methodology is employed.

The results of the investigation revealed that lack of effective governmental policies (B8), poor infrastructure (B4), lack of effective level of integration (B6), low foreign investment (B13) and demonetization (B12) are the top most significant challenges.

The model development based on the expert inputs from the industry and academia, these inputs could be biased influencing the accuracy of the model. Also, inclusion more factors for the analysis will improve the reliability of the model.

This research is intended to guide the policy and decision makers for improving overall the growth of the T&A supply chain.

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.