African Supply Chain Management | 28 June 2008
AI-Powered Supply Chain Risk Mitigation in Darjeeling Tea Exports to Uganda: A Comparative Assessment Over One Year
S, s, e, r, u, n, k, u, m, i, r, a, T, u, r, y, o, k, k, o
Abstract
This study addresses a current research gap in Computer Science concerning ✅ "AI-Based Supply Chain Risk Management for Exporting Tea from Darjeeling Region: Market Performance Impact Over One Year" in Uganda. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. ✅ "AI-Based Supply Chain Risk Management for Exporting Tea from Darjeeling Region: Market Performance Impact Over One Year", Uganda, Africa, Computer Science, comparative study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin<em>{\theta}\sum</em>i\ell(y<em>i,f</em>\theta(x<em>i))+\lambda\lVert\theta\rVert</em>2^2$, with performance evaluated using out-of-sample error.