Abstract
Accurate prediction of maintenance equipment logistics and distribution costs can help enhance the competitiveness of enterprises. In order to obtain higher accuracy prediction results, a neural network logistics and distribution cost prediction method of maintenance equipment was proposed, based on historical data, the learning sample of maintenance equipment logistics distribution cost prediction was established through transformation technology, and the BP neural network was introduced to train the learning sample. The change characteristics of maintenance equipment logistics and distribution cost were fitted, so as to realize the cost prediction. The results show that the prediction accuracy of the BP neural network is not only more than 5% higher than that of the gray model on average, but also the prediction stability is better.
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