International Journal of Science, Engineering and TechnologyAuthors: Mohana Dharsan, Momin Ameer Basha, Nallagatla Venkata Ramanaiah, Mr. K. Senthilkumararaja
Abstract: This paper presents a production-grade AI-powered multi-item sales forecasting framework designed for corporate business intelligence (BI) environments. The system employs the Extreme Gradient Boosting (XGBoost) ensemble algorithm to generate accurate daily sales predictions across ten distinct product lines. A two-year historical dataset (2024–2025) comprising 7,300 records is processed through a temporal feature engineering pipeline that constructs eight predictive features: two lag variables (lag_1, lag_7), two rolling averages (7-day, 14-day), day-of-week, week-of-year, month, and a binary weekend indicator. Two complementary Streamlit-based interactive dashboards are implemented: (i) a Corporate BI Dashboard delivering a full-year 2026 annual forecast with five KPI metrics, trend visualization, monthly distribution charts, and CSV export; and (ii) an Advanced Forecasting Dashboard offering configurable 7–60 day horizons, model MAE transparency, actual-vs-predicted validation charts, and 95% confidence interval bands. A vectorized O(1)-append rolling-buffer forecasting loop enables 365-day prediction in under two seconds on commodity hardware. Empirical evaluation demonstrates distinct item-level demand patterns: item_1 exhibits a sharp January peak with −25.22% year-on-year decline, while item_2 shows stable +1.19% growth with an October demand surge. These insights enable data-driven inventory pre-positioning, promotional timing, and safety-stock calibration across enterprise planning horizons.
