The Effect of Ai-Route Optimization on Supply Chain Performance among Large Supermarkets in Nairobi City County, Kenya

Eben Ezer Amani Kazadi and Reuben Musyoka Mwove
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Eben Ezer Amani Kazadi: School of Business and Economics, Department of Commerce, Daystar University, P O Box 44400- 00100 Nairobi
Reuben Musyoka Mwove: School of Business and Economics, Department of Commerce, Daystar University, P O Box 44400- 00100 Nairobi

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 11, 3751-3759

Abstract: Supermarkets increasingly face logistical challenges such as fluctuating demand patterns, rising distribution costs, and the need for timely and efficient product delivery. Artificial Intelligence (AI) has emerged as a critical tool for improving logistics decision-making, particularly through route optimization systems that enhance delivery speed, reduce transportation costs, and improve overall supply chain responsiveness. This study examined the effect of AI-Route Optimization on supply chain performance among large supermarkets in Nairobi City County, Kenya. The study was grounded in the Hybrid Intelligence Model, Triple Triangle Constraint Theory, and Technology Acceptance Theory to explain the integration and influence of AI technologies on supply chain outcomes. A descriptive research design was adopted, targeting employees working in the supply chain departments of 10 large supermarkets within Nairobi City County. A sample of 70 respondents was drawn from this population, and data were collected using structured questionnaires. Reliability was tested through a pretest involving seven respondents from selected Naivas branches in Kiambu County. Descriptive statistics, including means and standard deviations, were used to summarize the data, while inferential statistics such as correlation and regression analysis were applied to determine the relationship between AI-route optimization and supply chain performance using SPSS version 30. The findings indicated that AI-Route Optimization had a statistically significant effect on supply chain performance (M = 3.57, SD = 0.43), with the regression model showing strong explanatory power (R² = 0.79) and statistical significance (F = 222.015, Ï

Date: 2025
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