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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.

The advent of Industry 4.0 (I4.0), characterised by rapid technological advancement, presents a transformative opportunity for companies to adapt and innovate in all aspects of life. However, I4.0 also ushers in challenges related to resource scarcity, waste generation, pollution and sustainability concerns, particularly in operations and supply chain management (OSCM). Despite the growing importance of I4.0 for supply chain sustainability, more research must be conducted on the processes required to foster sustainable innovation through these technologies. This study aims to bridge this gap by exploring the role of multi-criteria decision-making (MCDM) methods in evaluating the factors that affect sustainable digitalisation within OSCM. The research analyses how MCDM methods can facilitate sustainable digitalisation in OSCM, the primary MCDM methods used for sustainable digitalisation in OSCM and the key indicators for measuring sustainable digitalisation in OSCM. Therefore, this study offers a unique contribution by exploring the uncharted territory of MCDM methods in the context of I4.0 and sustainability within OSCM, shedding light on essential indicators for this critical transformation, and equipping managers with the knowledge needed to steer their organisations towards a sustainable digital future.

Supply chain management (SCM) has evolved to fulfill the demands of the dynamic global business environment. The development of the Internet of Things (IoT), which offers unmatched connectivity and real-time data insights, has further transformed SCM. This chapter provides an overview of SCM development and its integration with IoTs. This integration led to improved inventory control, supply chain optimization (SCO), and visibility which further enhances the conventional SCM and provides benefits, such as more accurate real-time tracking and monitoring, improved data analytics, more efficient logistics and transportation management, and reduced costs and wastages. However, despite these benefits, there are various associated challenges and concerns, like privacy and data security, compatibility and interoperability, implementation costs, returns on investment, trained workforce, and training requirements, which are required to be addressed. Additionally, the outcomes of this study and managerial implications are provided along with the future research scope. Overall, this chapter provides valuable insight into the transformative potential of IoT in SCM and practical suggestions on how managers can successfully navigate difficulties and get benefits from the IoT-SCM integration. Organizations can enhance their supply chain operations, efficiency, and innovation by actively confronting challenges and taking advantage of the opportunities provided by IoT technologies. This will ultimately result in the delivery of greater value to both stakeholders and customers.

Aligning industrial operations with sustainable development has become a pressing need for organizations, as they recognize the mounting environmental challenges, social inequalities and resources scarcity. The transformations brought by the industry 4.0 have revolutionized supply chain operations to achieve sustainability and circularity. The aim of this chapter is to explore and map the supply chain 4.0 opportunities for sustainability. An extensive literature review of theoretical and empirical studies linking the supply chain 4.0 technologies and sustainability is conducted. Descriptive and content analysis are employed to scrutinize and categorize the knowledge extracted from the literature. Then, a conceptual framework is developed to highlight the role that each technology plays in promoting sustainability and circularity in the context of supply chain. This study provides new perspectives for theoretical researches and guide decision-makers to implement sustainability-based technologies in the supply chain 4.0. Future research can investigate the opportunities of supply chain 5.0 for social sustainability and circular business model.

This chapter examines the way in which Industry 4.0 will revolutionize achieving the Sustainable Development Goals (SDGs). This study provides insights on the relationship between global sustainability goals and cutting-edge technologies like automation, artificial intelligence and the internet of things; it looks at how Industry 4.0 may spur innovation, job creation and economic growth while tackling environmental issues. Also, this study analyses how Industry 4.0 can be used as a catalyst for positive change, in line with the larger vision of a sustainable and inclusive future, from navigating obstacles like job displacement and cybersecurity risks to presenting opportunities through policy frameworks and stakeholder collaboration. The results of this chapter shed light on the usage of technology in addressing global issues like poverty, inequality, climate change and sustainable economic growth and in turn the achievement of SDGs. The study discusses in detail about the implications for policymakers on the impact of Industry 4.0 on SDG 8 and SDG 12 and the risks associated with using Industry 4.0 to achieve the SDGs like job displacement, cybersecurity risks and ethical and legal challenges. The managerial implications of this study are numerous including increasing the skilled workforce and enhancing transparency, traceability and environmental performance across the whole supply chain. The study finally concludes by examining the potential prospects and future trends of Industry 4.0 and its integration with the SDGs.

This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance.

In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement.

All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce.

The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts.

The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment.

Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance.

This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.

The purpose of the present study is to synthesize and organize the existing literature on sustainable supply chain management (SSCM) challenges and potential solutions to overcome these challenges. The four-step research method has been used to collect and analyze pertinent literature, define the unit of analysis, select the classification context, collect publications, and evaluate the material. The study found 10 prevalent SSCM challenges and 18 potential solutions to overcome these challenges. By implementing these solutions, organizations can implement SSCM practices and contribute to achieving various Sustainable Development Goals (SDGs). The study significantly contributes to stakeholder theory, triple bottom-line theory, and resource-based view theory. The current study provides insights to managers working in supply chain management on SSCM implementation. Furthermore, the study also has practical implications for academicians and policymakers. This study is the first of its kind to amalgamate the SSCM challenges and solutions to overcome these challenges in a single framework by reviewing the literature.

This chapter explores the symbiotic relationship between digitalization and sustainability in the context of Industry 4.0. Examining key technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT), this chapter unveils their transformative impact on industries, emphasizing the role of data-driven decision-making, supply chain transparency, and circular economy principles. Real-world case studies illustrate successful implementations, showcasing how organizations leverage digital twins, blockchain for supply chain transparency, and extended reality for sustainable training. The regulatory landscape emerges as a crucial factor, shaping the adoption of digital technologies for sustainability, while emerging trends like 5G, edge computing, and AI promise to redefine the future. As a conclusion, policymakers are urged to strike a balance between innovation and regulation, fostering an environment conducive to responsible digital practices. Industries are encouraged to embrace emerging trends, and researchers are invited to explore the synergies between 5G, edge computing, and AI for holistic sustainability solutions. Together, these efforts aim to propel Industry 4.0 toward a resilient and sustainable future.

Recently, sustainability has emerged as a critical issue leading to substantial pressures on organizations. The integration of advanced technologies in the supply chain is supposed to enhance economic, environmental and social performance. However, several challenges are hindering the achievement of global sustainability. The purpose of this chapter is to analyze the challenges of supply chain 4.0 for sustainability and propose alternative solutions. A total of 14 challenges are identified and organized into clusters through extensive literature review and Delphi approach. With experts’ help, Best–Worst Method is used to provide the global and the local ranking of these challenges. Decision-Making Trial and Evaluation Laboratory method is employed to analyze the interactions between the most 10 relevant barriers. Next, a framework of alternative solutions is proposed to overcome these challenges. Sensitivity analysis is performed to validate the robustness of the proposed framework. This study provides new insights regarding the sustainability of supply chain 4.0 and the important solutions to overcome the associated challenges. The outcomes can assist decisions-makers to improve sustainability through adopting advanced technologies. Future research can integrate the benefits of industry 5.0 to the proposed framework.