Application of Artificial Intelligence in Predictive Analytics Among Brewing Firms in Southeast, Nigeria

This study investigates the effect of artificial intelligence (AI) application on predictive analytics and operational performance among brewing firms in Southeast Nigeria. Specifically, it examines how the types of AI techniques used, extent of AI integration, specific AI tools or systems adopted, level of AI sophistication, and scope of AI deployment influence enhanced operational performance within these firms. A structured questionnaire was administered to 117 respondents across selected brewing companies, and data were analyzed using the Ordinary Least Squares (OLS) regression technique to ascertain the relationships among variables. Findings reveal that all independent variables significantly influence operational performance. The types of AI techniques used (β = 0.245, p < 0.01) positively influence performance, indicating that diverse AI methods improve operational outcomes. The extent of AI integration (β = 0.198, p < 0.05) also shows a significant positive relationship, suggesting that greater AI adoption correlates with better operational results. Specific AI tools or systems adopted (β = 0.312, p < 0.01) have a substantial positive effect, emphasizing the importance of deploying advanced AI systems. The level of AI sophistication (β = 0.227, p < 0.01) significantly enhances operational efficiency, and the scope of AI deployment (β = 0.183, p < 0.05) contributes positively to operational performance. The model explains approximately 62% of the variance in operational performance (R² = 0.62). Based on these findings, it is recommended that brewing firms in Southeast Nigeria invest in diverse and advanced AI techniques, expand AI integration across production processes, and adopt sophisticated AI tools to optimize operational efficiency. Policymakers and managers should prioritize capacity building in AI technologies to leverage predictive analytics for sustainable growth and competitive advantage in the brewing industry.

Publication Details

Journal
Umsida Repository (Universitas Muhammadiyah Sidoarjo)
Published
2025-05-01
Primary Topic
Internet of Things and AI
Type
article
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article

Application of Artificial Intelligence in Predictive Analytics Among Brewing Firms in Southeast, Nigeria

Umsida Repository (Universitas Muhammadiyah Sidoarjo)
Internet of Things and AI
article

Application of Artificial Intelligence in Predictive Analytics Among Brewing Firms in Southeast, Nigeria

article en

Abstract

This study investigates the effect of artificial intelligence (AI) application on predictive analytics and operational performance among brewing firms in Southeast Nigeria. Specifically, it examines how the types of AI techniques used, extent of AI integration, specific AI tools or systems adopted, level of AI sophistication, and scope of AI deployment influence enhanced operational performance within these firms. A structured questionnaire was administered to 117 respondents across selected brewing companies, and data were analyzed using the Ordinary Least Squares (OLS) regression technique to ascertain the relationships among variables. Findings reveal that all independent variables significantly influence operational performance. The types of AI techniques used (β = 0.245, p < 0.01) positively influence performance, indicating that diverse AI methods improve operational outcomes. The extent of AI integration (β = 0.198, p < 0.05) also shows a significant positive relationship, suggesting that greater AI adoption correlates with better operational results. Specific AI tools or systems adopted (β = 0.312, p < 0.01) have a substantial positive effect, emphasizing the importance of deploying advanced AI systems. The level of AI sophistication (β = 0.227, p < 0.01) significantly enhances operational efficiency, and the scope of AI deployment (β = 0.183, p < 0.05) contributes positively to operational performance. The model explains approximately 62% of the variance in operational performance (R² = 0.62). Based on these findings, it is recommended that brewing firms in Southeast Nigeria invest in diverse and advanced AI techniques, expand AI integration across production processes, and adopt sophisticated AI tools to optimize operational efficiency. Policymakers and managers should prioritize capacity building in AI technologies to leverage predictive analytics for sustainable growth and competitive advantage in the brewing industry.

Umsida Repository (Universitas Muhammadiyah Sidoarjo)
Industry, innovation and infrastructure
Openalex Percentile: Top 36%
Internet of Things and AI
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