03908ntm a22005417i 4500 000728103 CZ-PrVSE 20260315164443.0 m d cr n|||||||||| 260315s2025 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Marszalek, Vojtěch ISIS:188197 dis SEASONAL SALES PREDICTION FOR INVENTORY OPTIMIZATION IN OUTDOOR RETAIL eng SEASONAL SALES PREDICTION FOR INVENTORY OPTIMIZATION IN OUTDOOR RETAIL / Vojtěch Marszalek 2025 ?? stran : digital, PDF soubor Vedoucí práce: Tomáš Karel Závěrečná práce - Institut celoživotního vzdělávání (MBA)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2026 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2026 This thesis proposes a machine learning framework utilizing the Light Gradient Boosting Machine (LightGBM) algorithm to predict seasonal demand for specific product category and store combinations across 41 brick-and-mortar stores operated by a Czech outdoor apparel retailer. Addressing the challenge of extreme data sparsity identified at the daily brand level, the research establishes a robust weekly forecasting pipeline leveraging Quantile Regression to generate probabilistic demand scenarios (Conservative, Median, Optimistic) over 4, 8, and 12-week horizons. The model integrates almost three years of SAP ERP transactional data with novel feature engineering techniques, including effective inventory weighting and store-normalized Google Analytics 4 (GA4) traffic signals. Validated on a chronological hold-out set comprising the final 15% of the dataset, the model achieved a Weighted Absolute Percentage Error (WAPE) of 22.1% for the 12-week strategic horizon. This performance represents a dramatic improvement over the industry-standard Seasonal Naive baseline (50.5% WAPE), effectively cutting the forecast error by more than half and doubling the precision of supply chain planning. The thesis further translates these technical gains into a comprehensive business strategy. It outlines a "risk-adjusted" inventory policy that protects working capital on seasonal goods while maximizing availability for core assortments. Additionally, a predictive "Smart Recall" mechanism is proposed to optimize reverse logistics, supporting the company’s international e-commerce expansion without cannibalizing physical retail performance. The final solution is architected on Google Cloud Platform, utilizing a custom Power BI dashboard to translate complex probabilistic forecasts into actionable insights for daily inventory management. Způsob přístupu: Internet LightGBM quantile regression inventory optimization omni-channel logistics machine learning retail analytics Google Cloud Platform SAP ERP seasonal demand forecasting Karel, Tomáš vse2015858033 ths Fojtík, Jan ISIS:87865 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/94802/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/94802 Hlavní práce https://insis.vse.cz/zp/94802/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/94802/posudek/oponent/89603 Oponentura https://insis.vse.cz/zp/94802/podrobnosti dc:identifier NEPOSILAT VSKP vse94802 260312 94802