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Industry 4.0 (I4.0) not only turns traditional industrial activities upside down but also demonstrates its potential to enhance industrial competitiveness and productivity. In this context, technological advancement and I4.0 is a strategy to be pursued. This study aims to consider different I4.0 technologies by analysing Indian small and medium enterprises (SMEs).

Key factors and promising I4.0 technologies were selected using literature analysis and experts’ panel. The appropriate I4.0 technology for Indian SMEs is recommended using the fuzzy complex proportional assessment (COPRAS) method.

Results reveal that ability to expand IT infrastructure, change in the organization’s structure and the capacity to analyse key performance indicators as three crucial key factors in I4.0 implementation. In particular, the smart factory is identified as a better I4.0 for Indian SMEs.

This work has analysed Indian SMEs, but it is appropriate for other developing economies with limited technical resources, financial resources and inadequate skill sets. This work identifies a gap in the current literature, and the findings proposed by this work are oriented to assist decision makers, industrial managers and practitioners in selecting I4.0 technology and enhancing the industrial infrastructure. At the same time, cooperation between the government and industrial community is required to develop programmes for imparting the knowledge of I4.0 among SMEs. The framework used in this study will arm the industrial management in adopting I4.0.

This study aims to find and evaluate the impact of COVID-19 in India’s mining activities. From the literature survey and input from experts, 14 important factors and ten green recovery strategies are identified. These 14 factors and 10 strategies are examined in a real industrial environment.

In this research study, fuzzy-complex proportional assessment is used to compare the identified strategies with the selected factors. Based on the outcome, this study identifies the optimum strategy that could assist the mining industry in handling both COVID-19 and sustainability issues.

The study’s outcome reveals that a large group of workers, collapse of demand and disruption, and suffered contractual workers are the top three factors that need to be considered regarding COVID-19 and sustainability issues. Effective strategies for eco-innovation practices, an emphasis on health and safety, and environmental awareness, education and training are the three primary strategies to be implemented for the greatest impact.

This research study is the first of its kind to coordinate both issues of COVID-19 and sustainability. In this regard, this study sheds a timely light on the precautions that need to be taken in tackling COVID-19 and sustainability issues. This study’s outcome will enhance the managerial capability in developing robust and effective strategies for handling difficult situations.

The transportation sector is frequently criticized for its adverse environmental impacts. Industrial managers and decision-makers in the transportation sector have difficulty embracing sustainable transportation. Taking this difficulty into consideration, this study intends to locate and evaluate the enablers of sustainable transportation.

Using the Pareto analysis, the study finalized 20 enablers from the list of 25 enablers. The finalized enablers are prioritized, and their causal inter-relationships are revealed using the grey-decision-making trial and evaluation laboratory (grey-DEMATEL) method. A sensitivity analysis is performed to determine the influence of experts' weight in the final results.

Findings reveal enhancement of cross-departmental cooperation, decision synchronization, technology cooperation, policy cooperation and route optimization as the five most critical enablers of sustainable transportation. Based on the results, the study proposes some policy implications for the decision-makers to proactively incorporate sustainability concepts.

This paper is the first of its kind to evaluate the enablers of sustainable transportation in the Indian leather industry. This paper proposes a comprehensive list of enablers of sustainable transportation and reveals their causal interrelationships using grey-DEMATEL. The weight of the provided enablers will help the industrial managers to more proactively take sequential actions to incorporate sustainable transportation.

With the rapid change that has taken place with digitalization and data-driven approaches in supply chains, business environment become more competitive and reaching sustainability in supply chains become even more challenging. In order to manage supply chains properly, in terms of considering environmental, social and economic impacts, organizations need to deal with huge amount of data and improve organizations' data management skills. From this view, increased number of stakeholders and dynamic environment reveal the importance of data-driven technologies in sustainable supply chains. This complex structure results in new kind of risks caused by data-driven technologies. Therefore, the aim of the study to analyze potential risks related to data privacy, trust, data availability, information sharing and traceability, i.e. in sustainable supply chains.

A hybrid multi-criteria decision-making (MCDM) model, which is the integration of step-wise weight assessment ratio analysis (SWARA) and TOmada de Decisao Interativa Multicriterio (TODIM) methods, is going to be used to prioritize potential risks and reveal the most critical sustainability dimension that is affected from these risks.

Results showed that economic dimension of the sustainable supply chain management (SSCM) is the most critical concept while evaluating risks caused by data-driven technologies. On the other hand, risk of data security, risk of data privacy and weakness of information technology systems and infrastructure are revealed as the most important risks that organizations should consider.

The contribution of the study is expected to guide policymakers and practitioners in terms of defining potential risks causes by data-driven technologies in sustainable supply chains. In future studies, solutions can be suggested based on these risks for achieving sustainability in all stages of the supply chain causes by data-driven technologies.