A New Approach for Demand Forecasting and Inventory Optimization of the Rarely Used Spare Parts
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摘要: 不常用备件具有需求量变化剧烈、需求发生间隔期长且不确定的特点，从而引发了备件需求预测失准而无法做出合理库存决策的问题。为此，提出了一种新的需求预测和库存优化相结合的方法以提高决策的精准度。利用高斯过程回归法预测需求发生间隔，结合Bootstrap增广样本统计方法来预测备件的需求概率分布；基于该概率统计结果，建立库存总成本最小的随机库存优化模型，采用粒子群算法求解得到最优库存决策变量。两个实际工业备件的实验结果表明，本文方法具有更高的预测精度，同时，在满足服务水平约束下库存决策实现了更低的库存总费用，具有实用性。Abstract: The main characteristic of rarely used spare parts include the sharp change of demand, and long and uncertain demand interval, which will result in the inaccurate prediction on the spare part demand such that it is 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 results, the stochastic inventory model on the total inventory cost is established and the particle swarm algorithm is further utilized to search the optimal inventory decision variable. Finally, the experimental results from two sets of practical industrial spare parts show that the proposed method has the higher 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 partsmethod.
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图 3 基于GPR的备件需求间隔预测结果
Figure 3. Spare parts demand interval prediction results based on GPR
图 5 提前期内需求量累积分布直方图
Figure 5. Histogram of cumulative distribution of demand in lead period
表 1 本文方法与其他算法的比较
Table 1. Comparison of the proposed method and other algorithms
Method Data 1 Data 2 MAE RMSE MAE RMSE This paper 0.398 0.208 0.4980 0.4480 ES 0.757 0.787 0.7980 0.8204 SVM 0.900 1.157 0.6630 0.5890 
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表 2 两种方法的评估结果
Table 2. Evaluation results of two methods
Lead period Actual demamd This paper Literature[10] Confidence interval Safety stock Confidence interval Safety stock 5 2 [0,4] 3 [0,6] 4 8 4 [1,6] 4 [0,8] 6 11 5 [1,7] 6 [1,10] 8
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表 3 历史需求量统计信息
Table 3. Historical demand statistics
Demand quantity Occurrence number Frequency/% 0 25 19.53 1 21 16.41 2 33 25.78 3 25 19.53 4 13 10.16 5 3 2.34 6 4 3.13 7 2 1.56 8 1 0.78 9 1 0.78
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