Customer order analysis for efficient financial prediction - Hořanová Štenglová, Monika; Zimmermann, Pavel, 1980-; Pour, Jan; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2025)
This thesis develops a predictive machine learning model to estimate the financial margin based on customer orders. Using ERP data exported through Power BI, Light Gradient Boosting Machine (LightGBM) model was trained in Python to predict actual sales and material consumption. Feature engineering included financial ratios, unit-based measures, and time variables. The model achieved strong predictive performance (R² ≈ 0.95), outperforming linear regression and enabling earlier identification of deviations between planned and realized margin. The prototype supports proactive pricing and sourcing decisions and is prepared for future deployment in Palantir Foundry as part of an automated predictive financial process.
Vedoucí práce: Pavel Zimmermann
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
Způsob přístupu: Internet