Abstract
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.
Data availability statement (DAS)
The data related to the final list of papers (results of the systematic literature review) can be freely available by the authors upon request.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
M. Zied Babai
M. Zied Babai is Senior Professor in Operations Management at Kedge Business School. He holds a Ph.D. in Industrial Engineering from Ecole Centrale Paris (France) where he also worked as a Teaching and Research Assistant for four years. From October 2006 to September 2008, he joined the Centre for Operational Research and Applied Statistics at the University of Salford (UK), working on a project funded by the Engineering and Physical Sciences Research Council (EPSRC, UK). His research interests relate primarily to demand forecasting and inventory management with a special emphasis on the development of quantitative models. He is the Editor-In-Chief of Supply Chain Forum: An International Journal (Francis & Taylor), Area Editor of IMA Journal of Management Mathematics (Oxford Press) and Associate Editor of International Journal of Production Research (Francis & Taylor).
John E. Boylan
John E. Boylan is Professor of Business Analytics at Lancaster University and Director of Lancaster's Centre for Marketing Analytics and Forecasting. He is currently serving as Director of the National Taught Course in Operational Research, an Editor-in-Chief of the Journal of the Operational Research Society and President of the International Society of Inventory Research. John's research interests are focussed on the inter-connection between forecasting and inventory management. He has advised software companies and other organisations on supply chain forecasting and is currently working with Jaguar Land Rover on addressing issues of stock obsolescence. He co-authored a book on the topic of intermittent demand forecasting, published in June 2021 by Wiley.
Bahman Rostami-Tabar
Bahman Rostami-Tabar is Associate Professor in Management Science at Cardiff Business School, Cardiff University, UK. Bahman holds a Ph.D. in Industrial Engineering from the University of Bordeaux, France. Bahman is the founder of Forecasting for Social good and Democratising Forecasting initiatives sponsored by the International Institute of Forecasters. You can check out these initiatives in https://www.f4sg.org/. In his research, he has been developing and using models to improve decision making in healthcare and humanitarian operations and supply chain sectors. He has been working with many organisations including the National Health Service (NHS), Welsh Ambulance Service Trusts and United States Agency for International Developments.