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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 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.

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.

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.

The evolution of artificial intelligence and varying perspectives on its integration within the supply chain management landscape tend to influence organisations’ ability to adapt to changing market conditions and maintain relevance and competitiveness. Using a quantitative approach, this study explored the drivers of artificial intelligence adoption in Nigeria’s supply chain management landscape. A survey questionnaire was the primary means of collecting quantitative data from 80 local supply chain practitioners, which was analysed through statistical tests. Results from the study established support and leadership from senior management, availability of technological infrastructure, and regulatory framework and regulatory considerations as the foremost drivers of AI adoption in Nigeria’s supply chain landscape. The study's findings provide valuable insights for policymakers, industry practitioners, and academic researchers. The study posits that fostering a conducive environment for AI implementation, addressing regulatory ambiguities, and enhancing technological capabilities will be imperative for unlocking the full benefits of AI in Nigeria's supply chain management landscape.

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

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.

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.

The evolution of artificial intelligence and varying perspectives on its integration within the supply chain management landscape tend to influence organisations’ ability to adapt to changing market conditions and maintain relevance and competitiveness. Using a quantitative approach, this study explored the drivers of artificial intelligence adoption in Nigeria’s supply chain management landscape. A survey questionnaire was the primary means of collecting quantitative data from 80 local supply chain practitioners, which was analysed through statistical tests. Results from the study established support and leadership from senior management, availability of technological infrastructure, and regulatory framework and regulatory considerations as the foremost drivers of AI adoption in Nigeria’s supply chain landscape. The study's findings provide valuable insights for policymakers, industry practitioners, and academic researchers. The study posits that fostering a conducive environment for AI implementation, addressing regulatory ambiguities, and enhancing technological capabilities will be imperative for unlocking the full benefits of AI in Nigeria's supply chain management landscape.