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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.

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.

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.

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.