The evolution of technology from the most recent industrial age to the technology era better known as Industry 4.0 resulted in greater demand for horizontal, vertical and end-to-end digital integration. Prior studies show that Industry 4.0 adoption majorly influences the sustainability aspects in a supply chain network. The purpose of this paper is to identify the Industry 4.0 enablers of supply chain sustainability and further attempt to propose a research framework to bridge the theoretical gaps. In this research study, the authors have used a systematic literature review methodology in the field of Industry 4.0 and sustainable supply chain management. The list of papers was downloaded from Scopus (www.scopus.com) database. Through strict screening, only journal papers were selected for conducting the review of the literature. The review brings out some interesting findings which will be helpful for the research community. There have been limited research in the area of managing supply chain network sustainability through Industry 4.0 technologies. The authors found only 10 papers out of a total of 53 papers which emphasize on smart manufacturing, smart production system, smart warehouse management system, smart logistics and sustainability. Most of the previous research studies have ignored the social aspects of supply chain sustainability. Finally, the authors identified 13 key enablers of Industry 4.0 playing an important role in driving supply chain sustainability. The strategies for Industry 4.0 should be refined and detailed to develop economic and social systems that can act flexibly to sudden changes in the system. Top management must be convinced for prioritizing investment support and creating a system that can facilitate technology convergence. Managers must also act on new models of employment and frame plans to continuously improve the system. In addition, managers must focus on establishing a collaborative platform to facilitate high-tech research and developments. Finally, it is essential to develop a performance management system for monitoring all actions in the supply chain network. Integrating two independent subjects is the uniqueness of the current study. Here, Industry 4.0 and supply chain sustainability have been integrated to build the research framework, and in such a process, the authors have extended the existing knowledge base.

The rise of stakeholder interest globally in sustainable business practices has resulted in a rise in demands from stakeholders that companies report on the environmental and social impacts of their business activities. In certain cases, however, companies have resorted to the practice of providing inaccurate disclosures regarding sustainability as part of their corporate communications and sustainability reporting—commonly referred to as “greenwashing”. Concurrently, technological improvements in artificial intelligence have presented the means to rapidly and accurately analyze large volumes of text-based information, such as that contained in sustainability reports. Despite the possible impacts of artificial intelligence and machine learning on the fields of greenwashing and sustainability reporting, no literature to date has comprehensively and holistically addressed the interrelationship between these three important topics. This paper contributes to the body of knowledge by using bibliometric and thematic analyses to systematically analyze the interrelationship between those fields. The analysis is also used to conjecture a conceptual and thematic framework for the use of artificial intelligence with machine learning in relation to greenwashing and company sustainability reporting. This paper finds that the use of artificial intelligence in relation to greenwashing, and greenwashing within sustainability reporting, is an underexplored research field.

Small and medium-sized enterprises (SMEs) are key drivers of economic growth and development. Innovation through digital transformation has the capacity to enable sustainability, competitiveness and customisation in products and services. However, adoption of digital technologies by SMEs to underpin sustainability outcomes is poorly understood. A systematic literature review identified an initial dataset of 1300 articles, which after screening and application of exclusion criteria resulted in a dataset of 64 articles for synthesis. Analysis was carried out according to three main areas, namely the technology aspects of digital transformation, sustainable development according to the triple bottom line (i.e. economic, environmental and social aspects), and the business characteristics of SMEs. In the latter case, business strategy and management, organizational structure, organizational culture, skills and qualifications, and leadership factors are identified from the literature. Furthermore, literature expressing the triple bottom line dimensions and the type of Industry 4.0 technology areas adopted are synthesized. Correlation of the data through bibliographic analysis is provided on the type of technology enabling SMEs towards a pathway for sustainable development as well as synthesis of future research directions arising from the study.

The globe has been subjected to an unprecedented health challenge in the form of COVID-19, indiscriminately impacting the global economy, global supply chains, and nations. The resolution of this unprecedented challenge does not seem to be in the short-term horizon but rather something the globe has to live with. Initial data provides for some insights on responses, precautions, and sustainability protocols and processes. The Food and Beverages Manufacturing sector in South Africa (SA) and globally is an expeditious respondent to the COVID-19 challenge. Food is essential for human existence, but the food value chain is subjected to significant COVID-19 risks. The Food and Beverage Sector Education and Training Authority is responsible for skills development in the Food and Beverages (FoodBev) Manufacturing Sector in South Africa and seeks to quantify Foodbev sustainability. This research paper reviews global literature, performs a high-level knowledge classification, with the aim of expedited awareness, knowledge sharing, and most importantly, quantification of an expedited response, within the FoodBev Manufacturing sector in SA. The research is contextualized via a SA sector-based instrument deployment and data analysis. The paper provides insights into COVID-19 impact, adaptations, and responses in the SA Food and Beverages Manufacturing sector.

This systematic review focuses on digital transformation in vascular healthcare to identify key focus areas for innovation, benefits, challenges, best practices, sustainable approaches, and the impact on patient outcomes. This study includes mobile health technologies and artificial intelligence (AI), examining aspects such as data analytics and interoperability with patient-centered care. The finding reveals a growing utilization of telehealth, AI, and mobile health technologies in vascular (circulatory) healthcare. The use of telehealth services facilitates remote patient monitoring with an enhancement in patient access to care. AI supports predictive models and decision support, while mobile health technologies promote patient engagement and self-management. This review emphasizes that prioritizing digital transformation in vascular healthcare brings various advantages, including improved efficiency and enhanced patient outcomes with a reduction in cost. However, challenges such as limitations, interoperability issues, and data security concerns must be addressed. The best practice comprises stakeholders’ engagement and comprehensive training. The impact of digital transformation on patient outcomes in circulatory healthcare is positive, mainly in the management of life-threatening conditions. This review offers valuable insights for prioritizing the digital transformation and enhancement of patient outcomes in vascular healthcare.

In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of computational techniques in drug discovery in its early stages has significantly transformed the pharmaceutical industry, enabling further efficient screening and selection of possible drug candidates. Therefore, this study explores RO5 using algorithms of Machine Learning (ML), offering a comprehensive method to predict the druggability of pharmaceutical compounds. The study developed, evaluated, and validated the performance metrics of multiple supervised machine learning models. The best model was used to build an application that can predict and classify potential drug candidates. The findings revealed promising capabilities across all models for drug classification. Among all the explored models, Random Forest (RF), Extreme Gradient Boost (XGBoost), and Decision Tree (DT) classifiers demonstrated exceptional performance, achieving near-perfect accuracy of 99.94%, 99.81% and 99.87% respectively. This highlights the robustness of ensemble learning methods in classifying compounds based on RO5 adherence. The comparative analysis of these models underscores the importance of considering balanced accuracy, precision, F1-score, recall, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) score, interpretability, and computational efficiency when choosing between ML algorithms in drug discovery. The DrugCheckMaster application was subsequently developed using the most predictive model and is now available on Render (https://capstone-project-dc7w.onrender.com/).