02886ntm a22004697i 4500 000729284 CZ-PrVSE 20260315164443.0 m d cr n|||||||||| 260315s2025 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Hakl, Tomáš ISIS:93745 dis Applying machine learning to optimize standard gross margin in medtech eng Applying machine learning to optimize standard gross margin in medtech / Tomáš Hakl 2025 ?? stran : digital, PDF soubor 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 This thesis explores how transaction-level machine learning can strengthen margin management in a global medical device company facing pressure on its standard gross margin (SGM). Using several years of invoice-line data, it develops an XGBoost model that predicts SGM% based on commercially intuitive factors such as product characteristics, sales channel, and consignment usage, complemented by simple time variables. The model outperforms a traditional rolling-average benchmark and is used to flag transactions where realized margins appear weak relative to model expectations. These outliers are then linked to a realistic set of pricing and portfolio levers to estimate their potential financial impact. The results show that focusing corrective actions on a relatively small subset of underperforming business can improve overall SGM with only a limited effect on revenue, illustrating how interpretable machine learning can be embedded into existing BI and pricing processes as a practical tool for margin governance. Způsob přístupu: Internet medical device standard gross margin machine learning Zimmermann, Pavel, 1980- jo20251268570 ths Fázik, Peter opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/94915/podrobnosti VŠKP v InSIS https://aleph.vse.cz/neverejny_soubor_prace.pdf Hlavní práce https://insis.vse.cz/zp/94915/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/94915/posudek/oponent/89600 Oponentura https://insis.vse.cz/zp/94915/podrobnosti dc:identifier NEPOSILAT VSKP vse94915 260312 94915