ShelfMind: Demand Forecasting and Stock Control for Retail SMEs using Global XGBoost and Context-Injected AI Chatbot - Aulia Syafitri; Aryanti Aryanti; Sholihin Sholihin (Forum Kerjasama Pendidikan Tinggi, 2026)
SME retail stores in Indonesia face serious challenges in inventory management: stockouts of popular products, waste from perishable items that expire before being sold, and restocking decisions relying on owner intuition. This research develops ShelfMind, a web-based inventory management system integrating three main components: a demand prediction model using a global XGBoost algorithm, an AI chatbot based on real-time data context injection, and an active notification system for low-stock and expiry risk alerts. Dataset from Toko Tika Baru covered 47 products in 11 categories with 8,225 daily sales records over 205 days. The XGBoost model was trained with 29 time-engineered features and achieved an MAE of 0.4107 units/day and RMSE of 0.5081 units/day on a 40-day test set. The global XGBoost method and context injection approach were selected due to their computational efficiency, avoiding the high costs associated with LLM fine-tuning, thus making it an ideal solution for SMEs with limited budget and infrastructure data. The AI chatbot, using context injection from inventory, sales, and XGBoost forecast data, achieved an average relevance and data accuracy of 4.94/5 across 20 test scenarios with an average response time of 2.94 seconds. Black-box functional testing of 31 scenarios passed completely. Usability evaluation scored 3.77/5, with notes on improving the communication of prediction features to non-technical users. This system proves that integrating XGBoost and LLM in an SME inventory management platform is technically feasible and provides tangible value for retail operations.