Supply chain management depends heavily on uncertain point forecasts of product sales. In order to deal with such uncertainty and optimize safety stock levels, methods that can estimate the right part of the sales distribution are required. Given the limited work that has been done in the field of probabilistic product sales forecasting, we propose and test some novel methods to estimate uncertainty, utilizing empirical computations and simulations to determine quantiles. To do so, we use the M5 competition data to empirically evaluate the forecasting and inventory performance of these methods by making comparisons both with established statistical approaches and advanced machine learning methods. Our results indicate that different methods should be employed based on the quantile of interest and the characteristics of the series being forecast, concluding that methods that employ relatively simple and faster to compute empirical estimations result in better inventory performance than more sophisticated and computer intensive approaches.

供应链管理严重依赖于产品销售的不确定点预测。为了处理这种不确定性并优化安全库存水平，需要能够估计销售分布的正确部分的方法。鉴于在概率产品销售预测领域所做的工作有限，我们提出并测试了一些估计不确定性的新方法，利用经验计算和模拟来确定分位数。为此，我们使用 M5 竞争数据，通过与既定统计方法和先进机器学习方法进行比较，凭经验评估这些方法的预测和库存绩效。我们的结果表明，应该根据感兴趣的分位数和被预测序列的特征采用不同的方法，