| Popis: |
Demand forecasts are the basis of most decisions in supply chain management. The\ud granularity of these decisions, either at the time level or the product level, lead to different forecast requirements. For example, inventory replenishment decisions require\ud forecasts at the individual SKU level over lead time, whereas forecasts at higher levels,\ud over longer horizons, are required for supply chain strategic decisions, such as the location of new distribution or production centres. The most accurate forecasts are not\ud always obtained from data at the ’natural’ level of aggregation. In some cases, forecast\ud 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\ud aggregation. Temporal and cross-sectional aggregation approaches are well established\ud in the academic literature. More recently, it has been argued that these two approaches\ud do not make the fullest use of data available at the different hierarchical levels of the\ud supply chain. Therefore, consideration of forecasting hierarchies (over time and other\ud dimensions), and combinations of forecasts across hierarchical levels, have been recommended. This paper provides a comprehensive literature review of research dealing with\ud aggregation and hierarchical forecasting in supply chains, based on a systematic search\ud in the Scopus and Web of Science databases. The review enables the identification of\ud major research gaps and the presentation of an agenda for further research. |
