Sajjad Taghiyeh , David C. Lengacher , Amir Hossein Sadeghi , Amirreza Sahebi-Fakhrabad , Robert B. Handfield
{"title":"A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management","authors":"Sajjad Taghiyeh , David C. Lengacher , Amir Hossein Sadeghi , Amirreza Sahebi-Fakhrabad , Robert B. Handfield","doi":"10.1016/j.sca.2023.100032","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Supply Chain Analytics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949863523000316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
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.