Hierarchical time series demands exist in many industries and are often associated with the product, time frame, or geographic aggregations. Traditionally, these hierarchies have been forecasted using top-down, bottom-up, or middle-out approaches. The question we aim to answer is how to utilize child-level forecasts to improve parent-level forecasts in a hierarchical supply chain. Improved forecasts can be used to considerably reduce logistics costs, especially in e-commerce. We propose a novel multi-phase hierarchical (MPH) approach. Our method involves forecasting each series in the hierarchy independently using machine learning models, then combining all forecasts to allow a second phase model estimation at the parent level. Sales data from MonarchFx Inc. (a logistics solutions provider) is used to evaluate our approach and compare it to bottom-up and top-down methods. Our results demonstrate an 82-90% improvement in forecast accuracy using the proposed approach. Using the proposed method, supply chain planners can derive more accurate forecasting models to exploit the benefit of multivariate data.
翻译:在许多行业存在等级时间序列要求,而且往往与产品、时间框架或地理汇总相关。传统上,这些等级体系是使用自上而下、自下而上或中下的办法预测的。我们的目的是要回答的问题是如何利用儿童层次的预测来改进上层供应链中家长层次的预测。改进的预测可以用来大大降低物流成本,特别是在电子商务中。我们提出了一种新的多级等级(MPH)办法。我们的方法包括利用机器学习模型独立预测等级体系中的每个序列,然后将所有预测结合起来,以便在父级一级进行第二阶段模型估计。MonarchFx Inc(一个物流解决方案供应商)的销售数据被用来评估我们的方法,并将其与自下而上和自上而下的方法进行比较。我们的结果表明,利用拟议的方法,供应链规划者可以获取更准确的预测模型,以利用多变量数据的好处。