A Two-Step Dynamic Inventory Forecasting Model for Large Manufacturing

Inventory forecasting aims to predict the demand of a specific item in the future and reserve the amount of item based on the forecasting results. An accurate and reliable inventory prediction can avoid product overstock and greatly reduce the maintenance cost. Inventory data is a kind of time series data, which has its own characteristics of large volume, long time span, wide covering range and poor regularity. The existing inventory forecasting methods usually only consider the contemporary data or similar goods historical data and achieve the prediction by calculating past average, which cannot capture the complex characteristics, such as long term trend, periodic, and special events. In this work, we treat inventory management as a data mining problem and propose a two-step dynamic prediction model, which first adopts six machine learning techniques and combines them with time series analysis methods to obtain a forecasting basis, then takes into account multiple factors of inventory to fulfill a dynamic inventory forecasting. Moreover, our proposed dynamic forecasting model, as one of core algorithms, is incorporated into an intelligent inventory management system. The experimental results and practical application demonstrate the effectiveness and efficiency of our proposed method.