The spare parts demand forecasting is very much essential for the organizations to minimize the cost and prevent the stock outs. The demand of spare parts/ car sales distribution is an important factor in inventory control. The valuation of the demand is challenging as the automobile spare parts/car sales demand are often recurrent. The renowned empirical method adopts historical demand data to create the distribution of lead time demand. Although it works reasonably well when service requirements are relatively low, it has difficulty reaching high target service levels. In this paper, we proposed Recurrent Neural Networks/ Long-Short Term Memory (RNN / LSTM) with modified Adam optimizer to predict the demand for spare parts. In this LSTM, weight vectors are generated respectively. These weights are optimized using the Modified-Adam algorithm. The accuracy of the forecast and the performance of the inventory are considered in the experimental result. Experimental results confirm that RNN / LSTM with a Modified-Adam works well with minimal error compared to other existing methods. We conclude that the proposed RNN/LSTM with Modified-Adam algorithm is well suited for the prediction of automobile spare parts.

备件需求预测对于组织最大限度地降低成本并防止缺货至关重要。零配件/汽车销售分销的需求是库存控制中的重要因素。由于汽车备件/汽车销售需求经常是经常性的，因此需求评估具有挑战性。著名的经验方法采用历史需求数据来创建提前期需求的分布。尽管在服务需求相对较低时它可以很好地工作，但是很难达到较高的目标服务水平。在本文中，我们提出了递归神经网络/长期记忆（RNN / LSTM）和改进的Adam优化器，以预测对备件的需求。在此LSTM中，分别生成权重向量。使用Modified-Adam算法优化这些权重。实验结果考虑了预测的准确性和库存的性能。实验结果证实，与其他现有方法相比，带有改进型Adam的RNN / LSTM可以很好地工作并且误差最小。我们得出的结论是，提出的带有Modified-Adam算法的RNN / LSTM非常适合用于汽车零部件的预测。