A data-driven newsvendor model with unknown demand and supply distribution.

In the new normal of the supply chain, shortage risk is increasingly becoming a primary concern for operations management. While increasing order quantities can help alleviate shortage costs, surplus order quantity concurrently raises the risk of overstocking. Shortage and holding risk are affected by both demand and supply uncertainty, and the distributions of the random variables remain unknown. Consequently, solving the stochastic optimization problem analytically proves to be challenging. This study aims to exploit the data-driven stochastic optimization method and to analyze the optimal ordering decisions under different demand and supply scenarios. Numerical results indicate that the linear machine learning (LML) method incorporating data features outperforms the sample average approximation (SAA) method in terms of optimization performance. Incorporating $ l_2 $ regularization into the LML method effectively mitigates overfitting and produces similar results under different scenarios. In addition, the optimal order quantity may decrease as the supply variance increases when the unit shortage cost is relatively low, while the optimal order quantity may increase as the supply variance increases when the unit shortage cost is high. The opposite is true in the context of demand research. Finally, smaller supply and demand variances, together with larger supply means, lead to lower firm costs. The effect of the demand mean on the firm's costs is negligible. At the same time, the effect of the supply mean on the firm's costs is more pronounced than that of the supply variance. [ABSTRACT FROM AUTHOR]