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The impact of climate change (CC) has become the most defining issue of the present times. From an increase in temperature to food and water scarcity, the adverse effect of CC is faced globally by all countries on an unprecedented level. Environmentally polluting industries are considered one of the primary contributors to the problem of CC. This chapter aims to provide an understanding of the emerging global threat of CC and various strategies that environmentally polluting firms could undertake to tackle the climate change crisis.

Based on the review of the literature, this chapter presents deliberation on the climate change crisis and various strategies for environmentally polluting industries to tackle CC issues.

This chapter presents a bird's eye view of the CC issue and various strategies that could be undertaken by environmentally polluting industries to address the CC crisis.

This chapter should be very useful for policymakers, practitioners and corporations to understand the issue of climate change and global warming and its implications. Further, it will also help polluting companies to reduce greenhouse gas (GHG) emissions and address the adverse impact of their business activities.

Considering the dearth of sector-specific studies based on climate change, this chapter is one of the few studies that explore CC strategies in the context of polluting industries.

This paper aims to systematically review the managerial literature on Industry 4.0 (I4.0) in emerging markets (EMs) through bibliometric analyses to identify incipient research streams and literature gaps and recommend avenues for future research.

This research uses bibliographic coupling analysis (BCA) to obtain a comprehensive view of the intellectual contours within the addressed literature. The primary source utilised is the Web of Science database. A total of 345 peer-reviewed journal articles were retrieved. Complementing BCA, we use social network analysis and the content analysis of articles to study the resulting literature clusters.

The results reveal four thematic clusters: (1) Adoption of I4.0 in EMs; (2) impact of I4.0 on organisational aspects and financial performance of companies and supply chains in EMs; (3) I4.0, lean management and operational performance in EMs and (4) I4.0 and the development of sustainable practices in EMs. We supplement these results with the proposal of different future avenues of research, both general and specific, for each identified cluster.

The current study has certain limitations arising from using the bibliometric method and techniques employed in the analyses.

To the best of the authors’ knowledge, there is no comprehensive literature review article on this subject. This research is deemed valuable for future scholars as it facilitates the identification of research fronts that define the forefront of knowledge, reveals current trends and sets the stage for further exploration of key issues in the field. This, in turn, can offer valuable insights to academics, policymakers and practitioners.

Recent years have witnessed a spike in Industry 4.0 initiatives among manufacturing organizations, particularly in the automotive sector. This acceleration aims to enhance competitiveness by addressing various aspects, from efficiency and workforce productivity to safety and insightful decision-making. However, merely adopting technological solutions in isolation may not suffice. Automotive companies need a holistic approach that integrates the antecedents of Industry 4.0 into their overall strategy. This study aims to identify and analyse key antecedents for Industry 4.0 adoption in the Indian automotive sector.

The study follows a structured six-stage methodology, which includes a systematic literature review, expert consultations and best–worst method (BWM) analysis. The research identifies, validates and systematically ranks 16 antecedents that are pivotal for Industry 4.0 adoption.

The study categorizes 16 antecedents into four dimensions: regulatory framework (RF), technology infrastructure (TI), operational optimization (OO) and performance dynamics (PD). The findings emphasize the significance of “Government policies to support smart factories”, “Support from top management”, “Financial performance” and “Technology readiness” as crucial antecedents for Industry 4.0 implementation in the Indian automotive sector.

These findings provide valuable guidance for industry practitioners and policymakers in strategically planning the Industry 4.0 deployment in the automotive sector.

This study contributes to the limited body of research on the identification and analysis of key antecedents for Industry 4.0 adoption in the automotive sector, particularly in emerging economies such as India. By using the BWM, it offers a structured and efficient approach to determining the priority order of these antecedents.

This study investigates the barriers impeding the adoption of Industry 4.0 in the Indian textile sector.

It identifies and prioritizes 15 significant barriers based on thorough research and expert opinions. The Decision-Making Trial and Evaluation Laboratory method is used to assess the influence and interdependencies of these barriers. A sensitivity analysis further validates the robustness of the findings.

The research uncovers significant barriers to I4.0 adoption, emphasizing their cause-and-effect dynamics. Key barriers include high implementation costs, inadequate government support, lack of managerial commitment, difficulties with technology integration, concerns about security and privacy and employee resistance due to job insecurity. These challenges are ranked to help decision-makers develop effective strategies for overcoming them.

This study focuses solely on the Indian textile sector, indicating the need for further research in other countries to enhance the general applicability of the findings.

The insights provided can assist policymakers and industry leaders in understanding the key barriers hindering I4.0 adoption. This knowledge supports the development of sector-specific policies and strategies that promote I4.0 practices and facilitate smooth integration within the supply chain.

This research is among the first to employ the Decision-Making Trial and Evaluation Laboratory method framework to analyse the interrelations among barriers affecting I4.0 adoption in the Indian textile sector, offering valuable empirical insights.

This study examined the role of artificial intelligence (AI) tools in facilitating the accessibility and usability of electronic resources (e-resources) in academic libraries.

This study employed a quantitative descriptive survey to collect data from library users. The population targeted was sampled using a purposive sampling technique. A total of 427 (58%) participated in this study.

Most respondents preferred electronic journals (e-journals) among the e-resources stored in academic libraries. Chatbots were identified as preferred AI tools for accessing and enhancing the usability of these resources. Strategies mentioned included the potential for integrating AI tools across various e-resources. However, among the challenges reported was the inability to integrate AI tools with the existing library management systems. Improving e-resource discovery and access can significantly enhance the effectiveness of AI tools in academic libraries.

Originality in the context of AI applications in academic libraries refers to the unique approaches, innovative tools and creative solutions that enhance the accessibility and usability of electronic resources. By focusing on unique solutions that enhance the accessibility and usability of e-resources, these libraries can better serve their diverse user populations and adapt to the evolving landscape of information needs.

A vaccination strategy to cover the susceptible population is key to containing the spread of any virus during a healthcare emergency. This study quantifies the susceptibility of a region based on initial infection rates to prioritize optimal vaccine distribution strategies. The authors propose a metric, the regional vulnerability index (RVI), that identifies the degree of susceptibility/vulnerability of a region to virus infections for strategically locating hubs for vaccine storage and distribution.

A two-phase methodology is used to address this problem. Phase 1 uses a modified Susceptible-Infected-Recovered (SIR) model, ModSIR, to estimate the RVI. Phase 2 leverages this index to model a P-Center problem, prioritizing vulnerable regions through a Mixed Integer Quadratically Constrained Programming model, along with three variations that incorporate the RVI.

Results indicate a weighting scheme based on the population-to-RVI ratio fosters fair distribution and equitable coverage of vulnerable regions. Comparisons with the public distribution strategy outlined by the Government of India reveal similar zonal segregations. Additionally, the network generated by our model outperforms the actual distribution network, corroborated by network metrics such as degree centrality, weighted degree centrality and closeness centrality.

This research presents a novel approach to prioritizing vaccine distribution during pandemics by applying epidemiological predictions to an integer-programming framework, optimizing COVID-19 vaccine allocation based on historical infection data. The study highlights the importance of strategic planning in public health response to effectively manage resources in emergencies.

This study aims to reveal and prioritize the main barriers and challenges in front of the Logistics 4.0 transformation, which is the extension of Industry 4.0. Also, this study presents a roadmap for a company operating in developing countries to reduce and eliminate challenges and hurdles for each link in their supply chain.

A two-stage methodology was used in this study. First, a detailed literature review was conducted to identify the barriers to innovations compatible with Industry 4.0. Hence, barriers have been identified, including nine from the literature review. The best–worst method (BWM) is then used to determine these barriers’ weights and order of importance. To implement BWM, two-stage e-surveys are applied to experts.

The “Managerial and Economic Challenges” dimension is the most important, and “Regulatory and social challenges” is the least essential dimension among the main dimension. Moreover, financial constraints or capitals are the most critical barriers among the sub-barriers. This study gives the reader a comprehensive insight into how detected barriers affect digitalization performance. Therefore, this framework is a roadmap designed with a holistic view to guide manufacturers, logistics parties and even policy and decision-makers.

Theoretically and empirically identifies the potential barriers and challenges in the digital transformation of logistics is already missing at the desired level. From this point of view, to the best of the authors’ knowledge, this study is the first research that determines barriers based on the Logistics 4.0 model with an industrial perspective. One of the most important limitations of this study is that a total of nine dimensions were examined under only three basic barriers. Different alternatives can be identified for future studies.

The study aims to identify the essential competencies for Industry 4.0 within the manufacturing sector, to prioritise developing them among the workforce in creating a competitive advantage for the organization.

The study employs the Analytic Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) methodology to prioritize competencies. Literature review and expert input guided the identification of competencies, which were ranked by experts for their relevance, through pairwise comparisons.

Seven competency groups, encompassing 21 sub-groups, were identified as essential for the Industry 4.0 workforce. Digital-technical and industry-specific competencies emerged as the most prominent to be developed on priority, followed by cognitive and business competencies. Despite their smaller representation, core/generic competencies remain the foundation for developing the newer and more specialised competencies.

Recognising the need for empirical studies in early-adopting organisations of Industry 4.0, future research should explore competencies across industries as well as talent development mechanisms, for a nuanced understanding of competency requirements.

The study informs organisations, educators and policymakers guiding workforce training, talent management and development, educational curriculum aligned with the demands of Industry 4.0 to bridge the competency gaps. It can support India’s strategic initiatives like “Make in India” by fostering a digitally ready and competent workforce.

This research provides an empirically validated, structured framework for Industry 4.0 competency prioritization specific to the manufacturing sector in India. It integrates expert inputs with AHP to rank competencies, offering a contextual understanding of competency requirements. It also contributes to human capital theory by advancing competency mapping for Industry 4.0.

In this research, experimental and numerical methods were used to study the effect of pore geometry on residual stress and mechanical behavior of 3D-printed parts. In this regard, samples with circular, rhombic and hexagonal pore geometries were printed using fused deposition modeling (FDM), and their residual stress was measured through the mechanical strain release method. The finite-element method (FEM) was utilized to study the strength and natural frequency of the samples.

As a modern method of part manufacturing and repair, 3D printing has been highly regarded in industrial arenas for its ability to offer high precision without the need for different dies. Porosity has been studied as a solution for reducing weight in structures, and its effect on the mechanical behavior of a structure depends on the loading conditions and applications.

The results of the investigation showed that the rhombic pore geometry had the highest residual stress, while the sample with circular pores exhibited the lowest residual stress. Stress distribution and modal analyses indicated that the sample with rhombic pore geometry had the lowest displacement coupled with the highest strength and natural frequency. However, considering the total of external load-induced stress and residual stresses, the sample with hexagonal pore geometry outperformed the other samples and showed the longest fatigue life.

According to the literature review, residual stress is one of the key factors influencing the performance of 3D-printed parts. However, the effects of pore geometry on residual stress and structural strength in 3D-printed components remain underexplored. Therefore, this study investigates the impact of hexagonal, rhombic and circular pore geometries on residual stress and structural strength through both experimental and numerical analyses.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.