Effect of AI-Inventory Management on Supply Chain Performance of Large Supermarkets in Nairobi City County, Kenya

Abstract

Supermarket supply chains face numerous challenges, including ineffective inventory management (resulting in stockouts or overstocking), unpredictable demand, shifting consumer expectations, disruptions from external factors such as labor issues, rising operational costs, and the increasing need for technology integration. This study sought to examine the effect of Artificial Intelligence (AI)-based inventory management on supply chain performance among large supermarkets in Nairobi City County, Kenya. The study was anchored on the Hybrid Intelligence Model as the guiding framework for artificial intelligence applications. The Triple Triangle Constraint Theory was used to explain supply chain performance, while the Technology Acceptance Model provided a framework for linking artificial intelligence adoption and supply chain performance. The study adopted a descriptive research design. The population comprised employees working in the supply chain departments of ten large supermarkets in Nairobi City County, Kenya. The target population included all employees within these departments. A sample size of 70 employees was selected to participate in the study. A pretest was conducted using seven respondents drawn from two Naivas supermarkets in Kiambu County, Kenya. Primary data were collected through structured questionnaires. The collected data were summarized using percentages and means. Inferential statistics, including correlation and regression analysis, were employed to determine the relationships between variables. Data were analyzed using SPSS version 30. The findings revealed that AI-based inventory management had a statistically significant effect on supply chain performance (M = 3.76, SD = 0.46), (R² = 0.913), (F = 618.938, p < 0.01). The study concluded that AI-based inventory management positively influences supply chain performance. The study recommends that, to fully realize the benefits of AI applications, supply chain managers should integrate diverse data sources, invest in robust data infrastructure and skilled personnel, align AI initiatives with strategic business goals, and foster cross-departmental collaboration. The study further recommends that similar research be conducted in other countries, particularly developed economies, to examine how artificial intelligence applications are transforming supply chain performance across different industries.