Rights statement: 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
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -