CNN model optimization and intelligent balance model for material demand forecast

The purposes are to improve the accuracy of inventory demand forecast, balance the indexes of enterprises, and reduce the costs of human, material and financial resources of enterprises and suppliers, thus reducing the supply chain costs and meeting the actual needs of enterprises. In terms of training a large amount of data, deep learning is better than traditional machine learning. The sales demand time series data and the previous material demand time series data are input and trained by back propagation (BP) neural network, and then the material demand value is output. Therefore, the historical data of sales demand forecast and material information are input, and the model is established by BP neural network, which not only takes into account the decisive factor of sales demand forecast, but also considers the material consumption, achieving more accurate forecast. The material demand budget of enterprises is analyzed and a material forecast demand model based on deep learning algorithm is proposed. The model uses a neural network to input the sales demand forecast data, material inventory information and material attribute information into the model, and then the model is trained by the training set in accordance with the error back propagation algorithm. Finally, the training effect of the model is tested by the test set. The results show that when the independent variables include sales demand forecast, material consumption forecast and material attribute information, the forecast error of both models is lower and the effect is better, compared with the material consumption data only as an independent variable. The forecast method based on neural network proposed increases the lead time of the forecast, give the supplier a longer time to prepare goods, and reduce the shortage or surplus of supply caused by the short lead time. Therefore, the material demand forecast model based on convolution neural network (CNN) algorithm provides an important reference for the enterprises, helps them improve their work efficiency and promotes the development of enterprises. This model achieves a great improvement on the accuracy of material demand forecast, and has a certain guiding significance in relevant theory and practice.