03214ntm a22005417i 4500 000729289 CZ-PrVSE 20260315164443.0 m d cr n|||||||||| 260315s2025 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Linhová, Klára ISIS:19804 dis Efficiency of direct labor eng Efficiency of Direct Labor / Klára Linhová 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 examines discrepancies between planned and actual direct labor hours in the production of automotive seating systems at Lear Corporation and proposes a data-driven forecasting framework to improve labor planning accuracy. The current Excel-based estimation approach relies on static average values and does not reflect changes in production mix, production calendar constraints, or operational conditions. To overcome these limitations, a predictive model integrating actual production mix, time-series dynamics, and seasonality effects is developed. Using cycle-time data from Palantir, production records from the LJS system, and HR attendance data, three machine learning algorithms—Linear Regression, Random Forest, and XGBoost—were evaluated using an expanding-window time-series validation approach in accordance with the CRISP-DM methodology. The results show that the Random Forest model achieves the highest predictive accuracy (R² = 0.809) and demonstrates strong robustness across all evaluated periods. The thesis concludes with the design of an analytical dashboard that supports Controlling in identifying inefficiencies and increasing transparency in direct labor planning within the context of Industry 4.0. Způsob přístupu: Internet Direct labor efficiency machine learning Linear Regression Random Forest automotive manufacturing labor variance cycle times XGBoost CRISP-DM Zimmermann, Pavel, 1980- jo20251268570 ths Pour, Jan jn20001103448 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/94936/podrobnosti VŠKP v InSIS https://aleph.vse.cz/neverejny_soubor_prace.pdf Hlavní práce https://insis.vse.cz/zp/94936/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/94936/posudek/oponent/89587 Oponentura https://insis.vse.cz/zp/94936/podrobnosti dc:identifier NEPOSILAT VSKP vse94936 260312 94936