This paper is research aimed at optimization of inventory decisions in the case of Chinese firms facing the uncertainties of demand by formulating a hybrid stochastic-robust optimization model. The model combines the stochastic programming and robust optimization methods to make improved decisions amid the dynamic demand and external market interference. The performance of the model was assessed using the data of 15 Chinese manufacturing and retail firms, which was put to the test using the simulation and sensitivity analyses. The findings show that the hybrid model is much more cost efficient, and service focused than traditional EOQ and purely stochastic models and lowers total costs by an average of 12 percent and stockout rates by 15-20 percent. The results indicate that combining probabilistic demand forecasting and robust optimization can significantly improve the resilience of operations and the performance of inventory in uncertain conditions. Practical investments are given to Chinese managers to implement optimization-based approaches and policy implications are given to show the government investment needs based on initiatives such as Made in China 2025 and digital supply chain reforms. Future research directions such as multi-echelon modeling and integration of sustainability are outlined in the conclusion of the paper.

Gaojie Zhao. Optimization of Firm Inventory Decisions under Demand Uncertainty. Academic Journal of Business & Management (2026), Vol. 8, Issue 1: 177-187. https://doi.org/10.25236/AJBM.2026.080124.

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