  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.
