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

TL;DR

This paper introduces a multi-phase hierarchical forecasting method that leverages machine learning to improve parent-level demand predictions in supply chains, significantly enhancing accuracy over traditional approaches.

Contribution

The paper presents a novel multi-phase hierarchical approach that combines independent machine learning forecasts with a second phase model to improve overall forecast accuracy in supply chain hierarchies.

Findings

Achieved 82-90% improvement in forecast accuracy.

Demonstrated effectiveness on real logistics data from MonarchFx.

Outperformed traditional top-down and bottom-up methods.

Abstract

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.