# A Multi-Phase Approach for Product Hierarchy Forecasting in Supply Chain Management: Application to MonarchFx Inc

arXiv: https://arxiv.org/abs/2006.08931 · HTML: https://mlsanity.com/paper/2006.08931/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making](https://mlsanity.com/paper/2301.08877/) (2023)

## Abstract (arXiv)

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.