A Prediction-Based Inventory Optimization Using Data Mining Models

As the core of the supply chain management, the inventory management deserves more of our attention, and in the complicated supply chain, especially under the circumstance of spending a long cycle, the inventory management becomes very difficult, which we need to balance the amount of circulating funds used by overmuch inventory and the loss of stock-out. The demand of marketing is viewed as the foundation of the inventory management, so in this paper, we are to adopt this idea and combine it with the information of searching on the web to conduct demand prediction for inventory optimization, and we will use Back propagation neural network to train the prediction model. Then on the basis of prediction result, we will establish one simple and concise inventory policy. As a comparison result, a traditional inventory policy will be figured out by estimating a normal distribution of demand using the history sales data, and calculate the inventory cost with (s, S) inventory strategy. The result shows that the established inventory policy based on demand prediction has obvious superiority on reducing the total cost of inventory.