Performance Analysis of Demand Planning Approaches for Aggregating, Forecasting and Disaggregating Interrelated Demands

Abstract : A synchronized and responsive flow of materials, information, funds, processes and services is the goal of supply chain planning. Demand planning, which is the very first step of supply chain planning, determines the effectiveness of manufacturing and logistic operations in the chain. Propagation and magnification of the uncertainty of demand signals through the supply chain, referred to as the bullwhip effect, is the major cause of ineffective operation plans. Therefore, a flexible and robust supply chain forecasting system is necessary for industrial planners to quickly respond to the volatile demand. Appropriate demand aggregation and statistical forecasting approaches are known to be effective in managing the demand variability. This paper uses the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating, forecasting and disaggregating two interrelated demands. Through theoretical development and systematic analysis, guidelines are provided to select proper demand planning approaches. A very important finding of this research is that disaggregation of a forecasted aggregated demand should be employed when the aggregated demand is very predictable through its positive autocorrelation. Moreover, the large positive correlation between demands can enhance the predictability and thus result in more accurate forecasts when statistical forecasting methods are used.
Type de document :
Article dans une revue
International Journal of Production Economics, Elsevier, 2010, 128 (2), pp.586-602
Liste complète des métadonnées

https://hal-emse.ccsd.cnrs.fr/emse-01098365
Contributeur : Jakey Blue <>
Soumis le : mercredi 24 décembre 2014 - 09:07:21
Dernière modification le : mardi 6 janvier 2015 - 01:01:05

Identifiants

  • HAL Id : emse-01098365, version 1

Collections

Citation

Argon Chen, Jakey Blue. Performance Analysis of Demand Planning Approaches for Aggregating, Forecasting and Disaggregating Interrelated Demands. International Journal of Production Economics, Elsevier, 2010, 128 (2), pp.586-602. 〈emse-01098365〉

Partager

Métriques

Consultations de la notice

94