This study constructs an analytical framework of "operating activities—categorized costs—unit costs—operating profit margin" to assess the verifiability of categorized cost budgeting and profit analysis for large enterprises. Using publicly available official data from the United States, this study generates quarterly observations for 702 enterprises from 14 large scheduled passenger transport companies from 2012 to 2025. Two-way fixed effects, multi-driver models, a complete hierarchical asymmetric model, and fixed-time out-of-sample tests are employed to identify cost drivers. The results show that fuel consumption and actual fuel prices are the most stable project drivers. Compared to the ASM model, the multi-driver model reduces the RMSE of operating costs and fuel costs by 10.0% and 18.8% respectively during the test period, but personnel, maintenance, and depreciation costs do not show stable predictive gains. The complete hierarchical model does not support the notion that fixed asset density generally enhances cost stickiness. The contemporaneous coefficients for categorized unit costs and operating profit margin are both negative, but the joint test is insignificant, and the lag relationship is weaker. These results indicate that profit and loss management for large enterprises should configure categorized drivers according to resource consumption mechanisms and distinguish between contemporaneous accounting correlations, conditional forecasts, and long-term causal effects.

Siyao Li. Research on Identification of Cost Drivers and Profitability Analysis from the Perspective of Large Enterprise P&L Management. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 3: 35-46. https://doi.org/10.38007/IJBMET.2026.070304.

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