A New Approach for Demand Forecasting and Inventory Optimization of the Rarely Used Spare Parts

The main characteristic of rarely used spare parts include the sharp change of demand, and long and uncertain demand interval, which will lead to inaccurate prediction of the spare part demand and difficult to make a reasonable inventory decision. Aiming at the above issues, this work proposes a novel demand forecasting and inventory optimization method to improve the accuracy of decision-making. In the proposed method, the Gaussian process regression is used to forecast the demand interval, and then, the Bootstrap augmented sample statistical method is combined to predict the probability distribution of spare parts demand. Based on the obtained demand probability statistics, a stochastic inventory model of total inventory cost is established, and the particle swarm algorithm is further utilized to search the optimal inventory decision variable. The experimental results of two groups of practical industrial spare parts show that the proposed method has high prediction accuracy. Meanwhile, the obtained inventory decision can achieve lower total inventory cost on the premise of satisfying service level, which illustrates the practicality of the proposed prediction and optimization method of infrequent spare parts method.