Summary
This paper explores the Influence of Predictive Analytics Utilization on Supply Chain Dcision-Making in Kenya’s Agribusiness Sector.
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Authors
Kennedy Toboso Alaly Intra Africa Research Centre tobosok@gmail.com
Vivian Cherono, Kenya Methodist University maiywa2000@yahoo.com
Abstract
Predictive analytics has revolutionized supply chain decision-making globally, yet its adoption in Kenya’s agribusiness sector remains limited, leading to inefficiencies, post-harvest losses, and fragmented value chains. Despite emerging digital platforms, poor infrastructure, weak data systems, and a lack of skilled personnel hinder widespread use. This study aimed to analyze the influence of predictive analytics utilization on supply chain decision-making in Kenya’s agribusiness sector. Specifically, the study sought to: assess the utilization of predictive analytics in forecasting, evaluate its influence on inventory management, determine its impact on procurement decisions, examine its role in risk mitigation, and (v) analyze its effect on decision-making efficiency. Anchored in the Technology Acceptance Model (TAM), which posits that perceived usefulness and ease of use determine technology adoption, the study employed a qualitative research design based on document analysis. The target population consisted of 33 secondary sources—peer-reviewed articles, reports, and case studies—published post-2020. Purposive sampling was used to identify relevant documents; sample size determination was guided by thematic saturation across selected literature. The sample included 33 documents, covering global (10), African (11), and Kenyan (12) perspectives. Data was collected through online academic libraries and search engines and analyzed thematically using deductive and inductive coding. Thematic categories were organized into summary tables capturing forecasting, inventory, procurement, risk, and decision efficiency. Findings revealed that predictive analytics improved demand forecasting in 85% of studies, optimized inventory in 79%, enhanced procurement planning in 79%, enabled proactive risk mitigation in 73%, and accelerated decision cycles in 88% of cases. The study recommends scaling analytics adoption, improving infrastructure and skills, promoting policy frameworks, and encouraging empirical research to guide data-driven agribusiness transformation in Kenya.