Warehouse equipment maintenance strategy based on the prediction of total amount of stock in & out operations

Abstract

Warehouse is an important part of the supply chain. However, the frequent failure of warehouse equipment greatly restricts scheduling efficiency. To achieve efficient operation and reduce the frequency of sudden failures, a new strategy for warehouse center maintenance based on the prediction of total amount of stock in & out operations is proposed. The feasibility of this strategy has been proved by using the historical data of operation and maintenance for three auto parts warehouse centers in South China, North China and East China. The orthogonal experimental method, which can select the preferred combination of hyperparameters quickly, is used to implement hyperparameter estimation for the predictive models. Based on the performance of different predictive models on the warehouse operation datasets, we determined the best model to predict the future total amount of warehouse stock in & out operations. The results show that compared with other models, the bidirectional long-short-term memory (Bi-LSTM) model obtains the highest accuracy on the test set, which is used for subsequent prediction. According to the prediction results, there is no need to arrange an overhaul plan for the North China and East China warehouse centers in the short term. However, the South China warehouse center may have frequent equipment failures in two months. It is necessary to formulate corresponding maintenance plans in advance.