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Nowadays, effective business performance depends on digital competitive factors and its ability to transform corporate capabilities in the light of digitalization. In this study, the evolving role of automated systems for global business operations in the era of Industry 4.0 has been assessed from a theoretical perspective with a practical approach. It is aimed to demonstrate the role of high technologies in an effective supply chain management system with new technological components and cyber-security issues of automated vehicles in SCM. This study highlights that developments in Artificial Neural Networks, Image Processing, Multi-Purpose Decision-Making, Blurred Linguistic Variables, and automated systems increase the performance of the supply chain management system and boost business performance. It is also shown that the evolving role of the automated system requires capital investments on Research and Development efforts

This study advances research and practice related to adopting artificial intelligence (AI) in the context of South Africa (SA). The study evaluated AI adoption by South African manufacturing Small and Medium Enterprises (SMEs); established the challenges faced by manufacturing SMEs in adopting AI; and developed a framework for adopting AI for manufacturing SMEs’ growth and survival. The study adopted a systematic literature review approach. Articles from Scopus and Google scholar databases, ranging from the years 2018 to 2024, were used. Of the 206 articles found, 54 were shortlisted. The systematic review analysis was performed using the PRISMA framework. The results identified AI adoption by South African manufacturing SMEs is low, limiting their innovation and productivity. The results also show, despite the numerous benefits AI adoption can offer manufacturing SMEs in the country, a major constraint is the lack of a framework to enhance adoption and implementation. Hence, this study was conducted to develop a framework to improve AI adoption by South African manufacturing SMEs. The findings contribute to the body of knowledge and provide new insights to manufacturing SME owners/managers, policymakers and practitioners into AI adoption to enhance manufacturing SMEs’ ability to compete on the global stage.

This paper examines the shift from Industry 4.0 to Industry 5.0 in relation to sustainable supply chain management (SCM), highlighting the incorporation of human-centric technologies. As industries progress, there is an increasing necessity to integrate technologies that enhance human-machine collaboration, optimise operational efficiency, and foster sustainability. This work aims to do a systematic review of the evolutionary process, emphasising the transformation of supply chain management by these technologies. A systematic review technique, adhering to the PRISMA framework, was utilised to collect and assess pertinent material published between 2010 to 2024. The review encompassed an exhaustive database search, stringent eligibility screening, and thematic analysis via Atlas-ti software to discern main themes and patterns concerning the incorporation of human-centric technology in supply chain management. The results indicate that the change to Industry 5.0 entails a substantial movement towards human-automation collaboration, with AI and machine learning as essential components. Digital transformation is redefining supply chain management by utilising big data, the Internet of Things, and blockchain technology to enhance transparency, traceability, and decision-making. Bionic supply chains, integrating human intelligence with machine efficiency, are developing as a vital foundation for operational resilience and sustainability. The study suggests that the incorporation of human-centric technology in supply chain management enhances efficiency and sustainability while fostering a more resilient supply network adept at reacting to interruptions. This paper offers essential recommendations for academics and practitioners seeking to enhance supply chain operations in the context of Industry 5.0.

Small and Medium Enterprises (SMEs) in South Africa previously faced challenges due to limited resources, restricted access to technology, and the need to constantly adapt to a dynamic business environment. The introduction of Generative Artificial Intelligence (AI) emerged as a potential solution to these issues, promising to enhance operational efficiency and strategic decision-making. As a representative of developing economies, South Africa experienced a growing interest in AI technologies. This study was conducted to explore the impact of generative AI on SME productivity in South Africa, an area which had been underexplored. Employing a qualitative methodology, the study evaluated the current state and implications of generative AI in South African SMEs. It involved in-depth interviews to gather perceptions, experiences, challenges, and benefits from SME owners and managers regarding the adoption of generative AI technologies. The findings analysed via R Statistical Software revealed significant insights into the specific areas where generative AI substantially impacted SME productivity. It also identified the challenges and opportunities associated with the adoption of generative AI by SMEs, as well as the potential long-term implications. Key findings included notable improvements in data-driven decision-making, operational efficiencies, and market expansion strategies. However, the study also highlighted barriers such as the lack of technical expertise, initial setup costs, and concerns over data security. Overall, the impact of generative AI on SMEs in South Africa was found to be predominantly positive, paving the way for further technological advancements and adoption in the sector.

In response to escalating global environmental challenges, mainly the urgent issue of climate change delineated by the United Nations Sustainable Development Goals (UN SDGs), large corporations are increasingly pressured to mitigate their carbon emissions and ecological footprint. The integration of artificial intelligence (AI) technologies has emerged as a pivotal strategy in this endeavour, promising to revolutionise how businesses approach sustainability while maintaining economic competitiveness. AI offers capabilities such as optimising operational efficiencies, enhancing energy management systems, and facilitating data-driven decision-making processes—all of which are instrumental in achieving sustainability objectives. However, the effective application of AI in environmental sustainability initiatives faces several challenges. These include addressing data scarcity, ensuring ethical deployment of AI technologies, and complying with evolving regulatory frameworks. This study investigates how large corporations are leveraging AI to reduce their environmental impact through AI-driven strategies for carbon footprint reduction. The study follows a qualitative approach, six medium-large corporates in Gauteng were interviewed to gain insights on the subject matter. The findings highlight that AI adoption enhances operational efficiency and environmental stewardship while highlighting ongoing challenges and the need for robust ethical and regulatory frameworks. Moreover, emerging trends such as AI-driven autonomous vehicles for logistics and advanced climate modelling illustrate AI's transformative potential in reshaping corporate ethos and addressing environmental concerns. The study contributes insights grounded in the Resource-Based View (RBV) theory, offering practical recommendations for corporations to harness AI effectively for sustainable growth within the framework of the UN SDGs and global environmental norms.

The Fast-Moving Consumer Goods (FMCG) business is under pressure to improve operational efficiency and solve sustainability issues in a digital world. Human capital and digitalisation drive productivity, responsiveness, and long-term competitiveness in sustainable operations. However, present research frequently isolate these elements, limiting knowledge of their combined impact. This PRISMA-guided Systematic Literature Review (SLR) examines human capital and digitisation in FMCG supply chains. Current research trends, theme insights, and knowledge gaps are reviewed. Data shows that AI, automation, predictive analytics, and the IoT boost efficiency, supply chain visibility, and decision-making. Adoption and use of these technologies require human capital, including digital skills, organisational learning, and human-machine collaboration. The analysis shows that FMCG contexts in emerging nations have underexplored human capital and digitalisation integration. The findings improve awareness of how human capital and digital capabilities support sustainable operations, and they show FMCG companies must integrate staff development with digital technology adoption. A detailed overview of their interplay and sustainable supply chain management research and practice directions are presented in this operations management paper

How are artificial intelligence (AI) and automating things changing how much we get done, how many new ideas happen, what jobs look like, and what is morally right? Specifically, this study looks at all of that…but in manufacturing. It gives you ideas for achieving an industrial revolution that’s fair to everyone and addresses the main challenges companies face as they start using AI at scale. The research is based on a thorough review of information from industry reports, official plans, and publications by other experts. And what it shows is that AI makes things run more smoothly by helping with fact-based decisions, creating designs, and figuring out when equipment needs fixing. But using AI is expensive to get started, the data it needs isn't always high-quality, and there are not enough people with the skills to use it, all of which make many companies hold back. As for automation in general, it raises tricky ethical questions: algorithms showing prejudice, people losing their jobs, and the impact on the planet. The study says that for AI to really work well, you need good oversight, help employees learn new skills, and lots of different types of organisations working together.

This study was deliberately designed to empirically examine how Artificial Intelligence (AI) enhances supply chain performance within Tanzanian brewery companies, addressing a critical gap between global theoretical potential and local practical realities. Using a mixed-methods, embedded single-case study approach, data were collected from 86 participants at Tanzania Breweries Limited (TBL) through questionnaires, semi-structured interviews, and focus group discussions. The findings revealed a fragmented yet influential adoption landscape, where tools like predictive analytics and machine learning significantly boosted service quality and certain efficiency measures; however, these benefits were notably limited by organizational resistance, weak data governance, and major external infrastructural barriers. As an original contribution, this study offers fresh, context-specific insights into the socio-technical dynamics of AI adoption in a lesser-studied Tanzanian industrial setting. Practically, managers are urged to prioritize core investments in change management and data integrity, while socially, effective integration can promote market stability and skills growth, contributing to wider socioeconomic progress. In conclusion, the study emphasizes that AI’s transformative promise is not assured by technology alone but depends on strong internal capacity and a supportive external environment. A noted limitation is the single-case focus, which may restrict generalization across other industries. Therefore, it is recommended that firms adopt a phased implementation strategy grounded in solid data governance and pilot projects, while fostering public-private partnerships to address the broader external challenges slowing digital transformation.

State-owned enterprises (SOEs) have significant opportunities to enhance operational efficiency, decision-making, and service delivery through artificial intelligence (AI). However, several barriers hinder widespread AI adoption in these organizations. This article measures the impact of AI adoption on performance indicators like production output and customer satisfaction in State-Owned Enterprises. It analyses financial sustainability, resource management, and efficiency metrics while identifying key barriers such as high implementation costs, lack of technical expertise, and resistance to change. The barriers to AI adoption in the SOEs are studied quantitatively. Data was gathered from 240 carefully chosen SOE personnel in South Africa (SA) using a survey approach, which had a 93.28% response rate. The population of this study comprised employees of South African SOEs. The focus of the article is on examining the perspectives of individuals in SOEs in SA regarding AI implementation. Further complicating AI integration are data security and privacy concerns, as well as legal and compliance issues. A lack of strategic vision and financial limitations are also major obstacles to advancement. Developing successful plans to encourage AI adoption in SOEs, which will eventually spur innovation and enhance public sector performance, requires an understanding of these obstacles.  The article concludes that despite significant barriers to AI adoption in South African SOEs—such as lack of senior management support, inadequate infrastructure, resistance to change, data quality issues, and a shortage of skilled staff—there are clear opportunities for improvement.  This is one of the articles that focuses on identifying barriers of adopting AI in South African SOEs and it lays a foundation for future research on AI in SOEs.

In response to escalating global environmental challenges, mainly the urgent issue of climate change delineated by the United Nations Sustainable Development Goals (UN SDGs), large corporations are increasingly pressured to mitigate their carbon emissions and ecological footprint. The integration of artificial intelligence (AI) technologies has emerged as a pivotal strategy in this endeavour, promising to revolutionise how businesses approach sustainability while maintaining economic competitiveness. AI offers capabilities such as optimising operational efficiencies, enhancing energy management systems, and facilitating data-driven decision-making processes—all of which are instrumental in achieving sustainability objectives. However, the effective application of AI in environmental sustainability initiatives faces several challenges. These include addressing data scarcity, ensuring ethical deployment of AI technologies, and complying with evolving regulatory frameworks. This study investigates how large corporations are leveraging AI to reduce their environmental impact through AI-driven strategies for carbon footprint reduction. The study follows a qualitative approach, six medium-large corporates in Gauteng were interviewed to gain insights on the subject matter. The findings highlight that AI adoption enhances operational efficiency and environmental stewardship while highlighting ongoing challenges and the need for robust ethical and regulatory frameworks. Moreover, emerging trends such as AI-driven autonomous vehicles for logistics and advanced climate modelling illustrate AI's transformative potential in reshaping corporate ethos and addressing environmental concerns. The study contributes insights grounded in the Resource-Based View (RBV) theory, offering practical recommendations for corporations to harness AI effectively for sustainable growth within the framework of the UN SDGs and global environmental norms.