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Industry 4.0 (I4.0), the Fourth Industrial Revolution, integrates Big Data analytics, blockchain, cloud computing, digitisation and the Internet of Things to enhance supply chain (SC) activities and achieve sustainable growth through dynamic capabilities (DCs). This approach equips businesses with the necessary tools to optimise their operations and remain competitive in a dynamic business environment. The value proposition of a business encompasses a wide range of activities that add value at each stage. By leveraging DCs, a firm can achieve innovation, gain a competitive advantage and enhance its adaptability. Conversely, effective value chain management can amplify the influence of a firm's DCs on SC sustainability, by reducing waste, optimising resource utilisation and fostering strategic partnerships. This mutually beneficial connection takes the form of a dynamic interaction in which I4.0 technologies act as a catalyst to help organisations become more resilient, adaptive and responsive. The adoption of these technologies denotes a comprehensive approach to business shift, not merely technical integration. I4.0 has an impact on several organisational disciplines outside of manufacturing, from automation and efficiency advantages to quality enhancements. This chapter offers an extensive literature review to explore the level of SC sustainability that a business can achieve by combining its DCs and implementing strategic I4.0 adoption. The function of value chain management in moderating the effects of I4.0 and DCs on SC sustainability is also assessed. This study proposes a theoretical model that is grounded in the insights extracted from the literature review.

This research develops a theoretical framework to understand the role of big data analytics capability (BDAC) in enhancing supply chain sustainability and examines the moderating effect of green supply chain management (GSCM) practices on this relationship.

Guided by the dynamic capability view (DCV), we formulated a theoretical model and research hypotheses. We used partial least square-based structural equation modeling (PLS-SEM) to analyze data collected from 159 survey responses from Bangladeshi ready-made garments (RMG).

The statistical analysis revealed that BDAC positively impacts all three dimensions of supply chain sustainability: economic, social and environmental. Additionally, GSCM practices significantly moderate the relationship between BDAC and supply chain sustainability.

This study makes unique contributions to the operations and supply chain management literature by providing empirical evidence and theoretical insights that extend beyond the focus on single sustainability dimensions. The findings offer valuable guidelines for policymakers and managers to enhance supply chain sustainability through BDAC and GSCM practices.

This study advances the current understanding of supply chain sustainability by integrating BDAC with GSCM practices. It is among the first to empirically investigate the combined effects of BDAC on the three dimensions of sustainability – economic, social and environmental – while also exploring the moderating role of GSCM practices. By employing the DCV, this research offers a robust theoretical framework highlighting the dynamic interplay between technological and environmental capabilities in achieving sustainable supply chain performance.

Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.

Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.

To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.

The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.

In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.

The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).

This study aims to evaluate the sustainable supply chain performance indicators. At a macro level, the identification of the sustainable supply chain management (SSCM) performance indicators is done through exhaustive literature survey and interviews with experts. Furthermore, these indicators are evaluated through a hybrid approach, i.e. total weighted interpretive structural modelling (TWISM) followed by analytic hierarchical process (AHP).

Micro small and medium enterprises (MSMEs) in India are a major contributor to nation’s GDP. However, this sector struggles to comprehend benefits from implementation of SSCM due to a lack of appropriate performance evaluation metrics. The purpose of this paper is to contribute to the body of knowledge in SSCM by proposing and evaluating a set of SSCM performance indicators.

The paper highlights the SSCM performance indicators and concludes that business strategies, implementation planning and impact of stakeholders are the top SSCM performance indicators (SPIs). Therefore, the decision-makers must initially focus on strategic requirements which foster the implementation of SSCM, thereby ensuring profitability for all stakeholders.

Although the proposed framework was validated through a case study on Indian automobile component manufacturing MSMEs, future research would explore the extension of the framework to other industries.

The originality of this study lies in the application of the novel TWISM-AHP tool. Furthermore, the SPIs identified in the study, consider the integration of the triple bottom line from the MSME perspective. The TWISM-AHP analysis will be beneficial for SC decision-makers to enhance the SSCM performance based on the identified indicators and their criticality.

This study aims to measure scientific literature on the emerging research area of “big data” in the field of “library and information science” (LIS).

This study used the “bibliometric method” for data curation. Web of Science and altmetric.com were used. Data analysis and visualisation were done using three widely used powerful data analytics software, R-bibliometrix, VOSviewer and Statistical Package for Social Sciences.

This study revealed the most preferred venues for publication. Furthermore, this study highlighted an association between the Mendeley readers of publications and citations. Furthermore, it was evident that the overall altimetric attention score (AAS) does not influence the citation score of publications. Other fascinating findings were moderate collaboration patterns overall. Furthermore, the study highlighted that big data (BD) research output and scientific influence in the LIS sector are continually increasing.

Findings related to BD analytics in LIS techniques can serve as helpful information for researchers, practitioners and policymakers.

This study contributes to the current knowledge accumulation by its unique manner of blending the two approaches, bibliometrics and altmetrics.

The increasing complexity and globalization of supply chains raise risks such as human rights abuses and environmental damage while affecting their supply chain performance (SCP), which prompted a study on supply chain due diligence (SCDD) practices. This study examines the impact of SCDD practices on the SCP. It examines if and how these relationships can be influenced by factors such as organizational culture and trust.

A conceptual model and hypotheses based on institutional theory were developed. The survey instrument captures organizations' perceptions of SCDD practices and related key performance indicators for SCP. The study collects data from 329 supply chain and logistics managers in Indian manufacturing and logistics organizations, and the hypotheses are validated using a structural equation model.

Results indicate that SCDD practices positively influence SCP. Trust and organizational culture strengthened SCDD–SCP relationships.

The study explores how organizations perceive and implement due diligence in their supply chains, highlighting areas for improvement. This understanding could help organizations enhance their supply chain management strategies, leading to better risk management, cost reduction, avoiding penalties and improved overall performance.

The main contribution of the study is to examine organizations' perceptions of SCDDA implementation and then identify its effects on supply chain performance. This is done considering trust and organizational culture as moderating factors.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

This study aims to examine the mediating effects of two corporate social responsibility factors – leadership mindset and corporate commitment – on the relationship between sustainable orientation toward Industry 4.0 and environmental decision-making effectiveness.

The research model was tested using structural equation modeling based on survey data from 175 companies serving the Hajj and Umrah sector in Saudi Arabia, a sector recognized for its high level of digitalization.

The results indicate that a sustainable orientation toward Industry 4.0 and digitalization positively influences environmental decision-making effectiveness. Additionally, Corporate commitment was found to have a direct positive effect on environmental decision-making effectiveness, while leadership mindset showed no significant effect.

This study highlights the critical role of sustainable Industry 4.0, driven by digitalization, in enhancing service quality and competitive value in companies serving the Hajj and Umrah sector. These companies see digitalization as an opportunity to improve business outcomes through effective environmental strategic decisions, though its application remains challenging. The study contributes to the existing body of knowledge by providing empirical insights into the impact of digitalization on environmental decision-making effectiveness within this specific context.

Although climate change-related risks affect all stakeholders along the supply chain, the potential impact on small and micro-sized suppliers is incredibly excessive. The corresponding toll of these climate risk threats on the mental health and well-being of owners of small and micro-sized suppliers can adversely affect their participation in sustainability efforts, ultimately impacting the firm's performance. This often-overlooked dynamic forms the core of our research. We probe into two pivotal aspects: how industry dynamism and climate risk affect the mental health and well-being of owners of small and micro-sized suppliers and how, in turn, dictate involvement and, consequently, supply chain sustainability performance. This is further nuanced by the moderating role of the abusive behavior of buyers.

Our study is built on resource dependency theory and the supporting empirical evidence is fortified by a mixed-methods sequential explanatory design. This study comprises three phases. In the first phase, our experiment examines the effect of industry dynamism and climate risk exposure on sustainable supply chain management performance. Hypotheses H1a and H1b are tested in the first phase. The second phase involves using a survey and structural equation modeling to test the comprehensiveness of the model. Here, the relationship between industry dynamism, climate risk exposure, mental health and well-being of owners of small and micro-sized supplier firms, supplier involvement and sustainable supply chain management (H2–H7) is tested in the second phase. In the third phase, we adopt a qualitative approach to verify and provide descriptive explanations of phase two findings.

Our findings underscore the significance of small and micro-sized suppliers in sustainability, offering invaluable insights for both theoretical understanding and practical implementation. Our study highlights that buyers must allocate sufficient resources to support small and micro-sized supplier firms and collaborate closely to address climate change and its impacts.

The key takeaway from this study is that buyer firms should consider SDG 3, which focuses on the good health and well-being of their employees and the mental health and well-being of owners of small and micro-sized suppliers in their upstream supply chain. This approach enhances sustainability performance in supply chains.

This is one of the first studies that shows that industry dynamism and climate risk exposure can negatively impact small and micro-sized suppliers in the presence of a contextual element, i.e. abusive behavior of buyers, and ultimately, it negatively impacts sustainable supply chain performance dimensions.

The purpose of this study is to explore how big data analytics (BDA) as a potential information technology (IT) innovation can facilitate the retail logistics supply chain (SC) from the perspective of outbound logistics operations in the United Kingdom. The authors' goal was to better understand how BDA can be integrated to streamline SCs and logistical networks by using the technology, organisational and environmental model.

The authors applied existing theoretical foundations for theory building based on semi-structured interviews with 15 SC and logistics managers.

The perceived benefits of using BDA in outbound retail logistics comprised the strongest predictor amongst technological, organisational and environmental issues, followed by top management support (TMS). A framework was proposed for the adoption of BDA in retail logistics. Contextual concepts from previous literature have helped us understand how environmental changes impact BDA decision-making, as such: (i) SC maturity levels and connectivity affect BDA utilisation, (ii) connected SCs improve data accessibility and information exchange, (iii) the benefits of BDAs also affect adoption and (iv) outsourcing complex tasks to experts allows companies to focus on core businesses instead of investing in IT infrastructure.

Outside the key findings listed, this study shows that there is no one-size-fits-it-all approach for use within all organisational settings. The proposed framework reveals that the perceived benefit of BDA is non-transferrable and requires top-level management support for successful implementation.

The existing literature focusses on the approaches to applying BDA in SC and logistics but fails to present a deep dive into retail outbound logistics activity. This study addresses the “how” and proposes a social-inclusive framework for a technology-enabled topic.