Artificial intelligence capabilities and supplier risk management of manufacturing firms in Nigeria: Empirical Evidence from Nestle Nigeria PLC

Abstract

This study examined the effect of Artificial Intelligence (AI) capabilities on supplier risk management in Nestlé Nigeria Plc. Specifically, the study investigated the effects of predictive analytics, machine learning, and automated supplier monitoring on supplier risk management. The study adopted a quantitative survey research design. The population comprised 134 employees of Nestlé Nigeria Plc, from which a sample of 100 respondents was determined using the Yamane formula at a 5% level of precision. Data were collected through a structured questionnaire and analysed using descriptive statistics and multiple linear regression. The findings revealed that predictive analytics had a positive and significant effect on supplier risk management (β = 0.184, p = 0.023). Machine learning also had a positive and significant effect (β = 0.412, p < 0.001), while automated supplier monitoring had a positive and significant effect (β = 0.354, p < 0.001). The regression model was statistically significant (F = 26.363, p < 0.001) and explained 45.2% of the variation in supplier risk management (R² = 0.452). The study concludes that predictive analytics, machine learning, and automated supplier monitoring are significant AI capabilities associated with improved supplier risk management in Nestlé Nigeria Plc. The study recommends strengthening AI-enabled supplier risk assessment, investing in machine learning tools and enhancing automated monitoring systems to support timely identification and management of supplier-related risks.