2023 journal article

A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management

Supply Chain Analytics.

By: S. Taghiyeh  n, D. Lengacher, A. Sadeghi  n, A. Sahebi-Fakhrabad  n & R. Handfield n 

TL;DR: This work proposes a novel multi-phase hierarchical (MPH) approach that 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. (via Semantic Scholar)
Source: ORCID
Added: January 28, 2024

Hierarchical time series demands are often associated with products, time frames, or geographic aggregations. Traditionally, these hierarchies have been forecasted using “top-down,” “bottom-up,” or “middle-out” approaches. This study advocates using child-level forecasts in a hierarchical supply chain to improve parent-level forecasts. Improved forecasts can considerably reduce logistics costs, especially in e-commerce. We propose a novel multi-phase hierarchical approach for independently forecasting each series in a hierarchy using machine learning. We then combine all forecasts to allow a second-phase model estimation at the parent level. Sales data from a logistics solutions provider is used to compare our approach to “bottom-up” and “top-down” methods. Our results demonstrate an 82–90% improvement in forecast accuracy. Using the proposed method, supply chain planners can derive more accurate forecasting results by exploiting the benefit of multivariate data.