TY - JOUR

T1 - Demand forecasting in supply chains

T2 - a review of aggregation and hierarchical approaches

AU - Babai, M.Z.

AU - Boylan, J.E.

AU - Rostami-Tabar, B.

N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 06/12/2021, available online:  https://www.tandfonline.com/doi/abs/10.1080/00207543.2021.2005268

PY - 2022/1/31

Y1 - 2022/1/31

N2 - Demand forecasts are the basis of most decisions in supply chain management. The granularity of these decisions lead to different forecast requirements. For example, inventory replenishment decisions require forecasts at the individual SKU level over lead time, whereas forecasts at higher levels, over longer horizons, are required for supply chain strategic decisions. The most accurate forecasts are not always obtained from data at the 'natural' level of aggregation. In some cases, forecast accuracy may be improved by aggregating data or forecasts at lower levels, or disaggregating data or forecasts at higher levels, or by combining forecasts at multiple levels of aggregation. Temporal and cross-sectional aggregation approaches are well established in the literature. More recently, it has been argued that these two approaches do not make the fullest use of data available at the different hierarchical levels of the supply chain. Therefore, consideration of forecasting hierarchies (over time and other dimensions), and combinations of forecasts across hierarchical levels, have been recommended. This paper provides a comprehensive review of research dealing with aggregation and hierarchical forecasting in supply chains, based on a systematic search. The review enables the identification of major research gaps and the presentation of an agenda for further research.  

AB - Demand forecasts are the basis of most decisions in supply chain management. The granularity of these decisions lead to different forecast requirements. For example, inventory replenishment decisions require forecasts at the individual SKU level over lead time, whereas forecasts at higher levels, over longer horizons, are required for supply chain strategic decisions. The most accurate forecasts are not always obtained from data at the 'natural' level of aggregation. In some cases, forecast accuracy may be improved by aggregating data or forecasts at lower levels, or disaggregating data or forecasts at higher levels, or by combining forecasts at multiple levels of aggregation. Temporal and cross-sectional aggregation approaches are well established in the literature. More recently, it has been argued that these two approaches do not make the fullest use of data available at the different hierarchical levels of the supply chain. Therefore, consideration of forecasting hierarchies (over time and other dimensions), and combinations of forecasts across hierarchical levels, have been recommended. This paper provides a comprehensive review of research dealing with aggregation and hierarchical forecasting in supply chains, based on a systematic search. The review enables the identification of major research gaps and the presentation of an agenda for further research.  

KW - aggregation

KW - combination

KW - forecasting

KW - hierarchies

KW - Supply chain

KW - Supply chain management

KW - Combination

KW - Demand forecast

KW - Demand forecasting

KW - Forecast accuracy

KW - Hierarchical approach

KW - Hierarchical level

KW - Hierarchy

KW - Inventory replenishment

KW - Leadtime

KW - Strategic decisions

KW - Forecasting

U2 - 10.1080/00207543.2021.2005268

DO - 10.1080/00207543.2021.2005268

M3 - Journal article

VL - 60

SP - 324

EP - 348

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

IS - 1

ER -