Artificial Intelligence Applications and Supply Chain Performance among Large Supermarkets in Nairobi City County, Kenya

Abstract

Supermarket supply chains are facing problems like maintaining an efficient management of stock (avoiding stockouts or overstocking), unstable demand and shifting expectations of customers, interruptions by external factors like labor unrest, rising operational costs, and the need for greater technology integration. The objective of this research was to assess the effect of Artificial Intelligence Applications on Supply Chain performance Of Large Supermarkets in Nairobi City County, Kenya. The research specific objectives were to evaluate the effect of AI-Demand forecasting on supply chain performance of large supermarkets, to determine the effect of AI-inventory management on supply chain performance of large supermarkets, to investigate the effect of AI-Route optimization on supply chain performance of large supermarkets in Nairobi City County, Kenya, and to determine the moderating role of technological infrastructure on the relationship between Artificial Intelligence and supply chain performance of large supermarkets in Nairobi City County, Kenya. The study relied on Hybrid intelligence Model as conceptual frameworks for the Artificial Intelligence applications. Triple Triangle Constraint theory of Supply Chain performance and Technology Acceptance Theory as conceptual frameworks for bridging both Artificial Intelligence and Supply Chain performance. Descriptive research design was used in this research. The study population was employees who were working in the supply chain departments of 10 large supermarkets in Nairobi City County, Kenya. The target population was all the employees who were working in the supply chain departments. The researcher sampled 70 employees from the target population to participate in the study. The pretest was carried out among 7 respondents who were randomly selected from two Naivas supermarkets in Kiambu County, Kenya. The initial data was collected by conducting structured questionnaires. The data collected was tabulated using percentages, means. Inferential statistics such as correlation, regression analysis were utilized for identifying relationships between the variables. Data gathered for this study was analyzed using SPSS version 30. Results of the research revealed that AI-demand forecasting contributed greatly to supply chain performance (M=3.85, SD=0.81), (R2=0.982) (F = 3280.725, ρ<0.01). AI-inventory management contributed heavily to supply chain performance (M=3.76, SD=0.46), (R2=0.913), (F = 618.938, ρ<0.01). AI-Route optimization was statistically significant on supply chain performance (M=3.57, SD=0.43), R2=0.79), (F = 222.015, ρ<0.01). The conclusion of the study is that AI-demand forecasting positively influenced supply chain performance. AI-inventory management positively influenced supply chain performance. AI-Route optimization positively influenced supply chain performance. The study recommended that, to obtain the greatest advantages of AI-application tools, the supply chain managers should integrate various sources of information, invest in robust data infrastructure and human capabilities, align AI with business goals, and bridge departments. The study further recommends that a similar study be conducted in other countries especially developed country to identify how Artificial Intelligence applications are redefining supply chain performance in various industries