Artificial Intelligence Demand Forecasting and Supply Chain Performance of Large Supermarkets in Nairobi City County, Kenya

Abstract

Artificial Intelligence (AI) has been a transformative power in contemporary supply chain management, with its offerings that optimize operational effectiveness, lower costs, and facilitate data-informed decisions. The general objective of this study is to examine the effect of artificial intelligence applications on supply chain performance among large supermarkets in Nairobi City County, Kenya. The study will be anchored on three theories, namely, the Hybrid Intelligence Model and the Technology Acceptance Theory. This study will adopt the descriptive research design. The population of this study is the employees working in the supply chain department in 10 large supermarkets in Nairobi County, Kenya, while the target population is 10 large supermarkets operating in Nairobi County. The sample size of the study will consist of 70 employees working in the supply chain departments of 10 large supermarkets based in Nairobi City County. The questionnaire will be pretested using 7 respondents who will be selected from two Naivas supermarkets in Kiambu County, Kenya. The primary data will be collected through administration of structured questionnaires. The data collected will be summarized using percentages, means, and standard deviations. Inferential statistics such as correlation and regression analysis will be utilized to identify the relationships between variables. Data obtained for this study will be analyzed using SPSS version 30. Values drawn from the sample will inform the findings, conclusion, and recommendations of this study. The regression findings revealed that AI-demand forecasting had a coefficient of estimate that was significant based on β1 = 0.714 (p-value = 0.000, which is less than α = 0.01). This study concluded that AI-demand forecasting has a statistically significant effect on supply chain performance among large supermarkets in Nairobi City County, Kenya