The role of artificial intelligence in enhancing supply chain performance: Insights from Tanzania brewery companies
Alfred Winfrid Luoga
International Journal of Research in Business and Social Science · 2026 · Vol. 14 / Issue 10 · 70-84
Publication metrics
External links open provider records or DOI searches. Provider counts are not combined. Mendeley readership measures saves, not citations.
Dimensions
Live provider badge. If unavailable, use the Dimensions link above.
Observed 21 Oct 2026, 07:22 UTC
Observed 21 Oct 2026 07:23 UTC
Observed 22 Oct 2026 01:05 UTC
OpenAlex and Mendeley observations refresh on article visits, at most once per 24 hours. Unavailable data is not zero. Google Scholar opens a DOI search.
Citations received by this article, grouped by the year of the citing work. Crossref and OpenAlex lifetime totals remain separate in Publication metrics.
2017–2026 Annual history last updated:
Citations received
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
View data & coverage
2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Years beyond a saved observation’s window remain unknown (—). Older citations are excluded, so this chart’s sum may differ from the OpenAlex lifetime total. The date above belongs to this annual history; an unsuccessful refresh keeps the previous observation.
Abstract
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.
Authors & affiliations
Alfred Winfrid Luoga
Tanzania Institute of Accountancy
Deposited references
Supplied references, not a list of publications citing this article.
Adam Babu, Y. (2012). Information technology and success of selected multinational corporations in East Africa (Unpublished master’s thesis).
Adong, A. M. (2024). Influence of process digitization strategy on performance of home-grown multinational firms in East Africa: A case of Equity Bank, Kenya (Unpublished master’s thesis, University of Nairobi).
DOI: 10.1108/IJLMA-07-2024-0251
DOI: 10.30574/ijsra.2024.11.1.0156
Bashasha, E. A. (2017). An assessment of delivery performance of engineering spare parts by suppliers: A case study of Tanzania Breweries Limited—Mwanza Plant (Unpublished master’s thesis, The Open University of Tanzania).
DOI: 10.1191/1478088706qp063oa
DOI: 10.1016/j.compind.2018.02.010
Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Dayar, S. A., & Mwendapole, M. J. (2024). Assessment of the impact of artificial intelligence-driven supply chain software in reducing shipping costs in Tanzania. World Journal of Innovation and Modern Technology, 8(4), 61–95.
DOI: 10.2478/izajolp-2019-0004
Irario, G. O. (2023). Sustainable logistics management practices and competitive advantage among food and beverage manufacturing firms in Nairobi County, Kenya (Unpublished master’s thesis, University of Nairobi).
DOI: 10.1386/ajms_00178_1
DOI: 10.1108/XJM-11-2023-0214
DOI: 10.1080/00207543.2020.1798035
Jaldi, A. (2023). Artificial intelligence revolution in Africa: Economic opportunities and legal challenges. Policy Center for the New South.
DOI: 10.3390/su16031341
Merritt, H. (2017). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. Denarius, 33, 235–239.
DOI: 10.4337/9781800370395.00013
DOI: 10.54660/.IJMRGE.2022.3.1.1064-1074
Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). SAGE.
DOI: 10.1007/s00170-019-03754-7
DOI: 10.1063/5.0229828
DOI: 10.1007/s11135-017-0574-8
Shafii, A. S. (2020). Shift working in private organizations: A case of Tanzania Breweries Limited (TBL), Dar es Salaam (Unpublished master’s thesis).
DOI: 10.1108/FS-09-2020-0093
DOI: 10.1108/IJSSP-05-2020-0174
DOI: 10.1108/BPMJ-10-2019-0411
DOI: 10.3390/su12093760
DOI: 10.1109/JSAC.2018.2804223