THE EFFECT OF PREDICTIVE DATA ANALYTICS ON FRESH FOOD SUPPLY CHAIN PERFORMANCE FOR AGRITECH COMPANIES IN KENYA

Abstract

This study assessed the effect of predictive data analytics on the supply chain performance of Agritech Companies in Kenya. The study was grounded in Organizational Information Processing Theory. A descriptive research design was adopted. The target population consisted of 315 supply chain and information technology officers, from which a sample of 172 respondents was selected using Yamane’s formula (1967). Data were collected using structured questionnaires and interviews. Analysis was conducted using correlation analysis, and multiple regression analysis. The findings revealed that predictive data analytics had a positive and significant effect on food supply chain performance. The study findings indicated that predictive analytics improved demand forecasting and planning accuracy. The study concluded that predictive data analytics significantly enhances food supply chain performance, particularly when supported by strong organizational capabilities. It recommended that agritech firms invest in predictive data analytics systems to improve effective utilization of analytics tools in supply chain operations. JEL: M11, L23, L14, R41