Algorithmic Supply Chains in Agricultural Hubs: Evaluating AI-Driven Inventory Control and Logistics Dynamics in Erode’s Turmeric Market

Abstract

Agricultural commodity networks in regional trading hubs face structural operational frictions caused by seasonal yield volatility, localized storage degradation, and fragmented distribution channels. This study examines the operational impact of Artificial Intelligence (AI) implementations—specifically predictive demand modeling, automated stock replenishment, and dynamic transport routing—within the commercial turmeric trading cluster of Erode City, Tamil Nadu. Utilizing empirical survey data collected from key market stakeholders (), the paper evaluates how machine learning algorithms alter inventory precision and distribution performance. Standard statistical and analytical tools, including descriptive measures, Pearson correlation, parametric regression, and Chi-Square () tests of independence, are applied to assess hypotheses. The findings confirm that AI-driven analytics significantly mitigate stockout frequency and holding costs while enhancing delivery timelines. However, adoption barriers such as legacy data silos and organizational inertia remain critical constraints. Strategic recommendations are provided to enable scalable AI deployment in agricultural supply chains.