Bayesian Inference for Dynamic Demand Forecasting and Inventory Optimization

This paper constructs a dynamic model suitable for demand forecasting and inventory optimization based on Bayesian theory. The model uses Bayesian inference to achieve real-time updates of demand data and combines cost minimization methods to control inventory. The study looks at seasonal changes and sudden market problems. It uses probability distributions to give detailed forecast results. Tests show this method works better to lower inventory costs and improve service levels. The model keeps updating demand predictions. This helps companies act fast when markets change. It also helps them manage their inventory better. The model adapts to changing demand patterns, making inventory management more flexible. It also reduces the risk of stock shortages or excess inventory. In addition, the paper discusses in detail the model design principles, data processing procedures, and actual application effects and suggests further optimizing the model. The results show that dynamic adjustment strategies can effectively cope with the uncertainty of market demand, thereby promoting the improvement of enterprise operational efficiency.