The influence of business intelligence on the effectiveness of supply chain management in the Nigerian consumer goods sector

Itiri Idam Okpara 1, *, Patrick Chigozie Moneme 2, Olaide Monsurat Akano 1, Ogbonnaya Abraham Onuaja 1 and Temple Akpa 1

1 Department of Business Education, Federal College of Education (Technical), Isu, Ebonyi, Nigeria.
2 Department of Business Administration, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 1884–1896.
Article DOI: 10.30574/ijsra.2024.13.1.1855
Publication history: 
Received on 22 August 2024; revised on 01 October 2024; accepted on 03 October 2024
 
Abstract: 
Business performance depends on supply chain management (SCM), which can be enhanced through digital transformation to enable quick decisions to increase market share. By enabling informed decision-making, business intelligence (BI) can facilitate this improvement in SCM effectiveness and competitive edge. Real-time production and inventory level monitoring is made possible by BI capabilities, which help organisations adapt to changing market trends and meet customer demands. Particularly, BI technology can increase the SCM of enterprises in the consumer products sector by providing value-added and financial gains, enabling speedy industry adoption and rapid industry growth. The study employed a quantitative methodology for gathering data, and a simple random sampling method was employed to prevent sample bias. The study considered linear curve estimation (LCE) as a method of data analysis. The results of the model estimations showed that BI recorded positive and significant influence on SC visibility, flexibility, and resilience in the Nigerian consumer goods sector. The study concludes that BI increased the effectiveness of supply chain management (SCM) by improving visibility, anticipating demand, increasing resilience, optimising processes, and boosting productivity. SCM effectiveness depends on the efficient handling of disruptions and uncertainty. The study highlights the practical policy implications following the findings of the study.
 
Keywords: 
Supply chain management; Business intelligence; Inventory management; Decision-making; Linear curve regression
 
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