Explore the evidence.
Understand the reach.

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.

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

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.

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.