Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands
AbstractA 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.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Production Economics.
Volume (Year): 128 (2010)
Issue (Month): 2 (December)
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Web page: http://www.elsevier.com/locate/ijpe
Top-down forecasting Demand aggregation Disaggregation Bivariate VAR(1) time series;
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- Zotteri, Giulio & Kalchschmidt, Matteo & Caniato, Federico, 2005. "The impact of aggregation level on forecasting performance," International Journal of Production Economics, Elsevier, vol. 93(1), pages 479-491, January.
- Gfrerer, Helmut & Zapfel, Gunther, 1995. "Hierarchical model for production planning in the case of uncertain demand," European Journal of Operational Research, Elsevier, vol. 86(1), pages 142-161, October.
- Nesim Erkip & Warren H. Hausman & Steven Nahmias, 1990. "Optimal Centralized Ordering Policies in Multi-Echelon Inventory Systems with Correlated Demands," Management Science, INFORMS, vol. 36(3), pages 381-392, March.
- Dekker, Mark & van Donselaar, Karel & Ouwehand, Pim, 2004. "How to use aggregation and combined forecasting to improve seasonal demand forecasts," International Journal of Production Economics, Elsevier, vol. 90(2), pages 151-167, July.
- Tiao, George C & Tsay, Ruey S, 1983. "Multiple Time Series Modeling and Extended Sample Cross-Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(1), pages 43-56, January.
- Dangerfield, Byron J. & Morris, John S., 1992. "Top-down or bottom-up: Aggregate versus disaggregate extrapolations," International Journal of Forecasting, Elsevier, vol. 8(2), pages 233-241, October.
- Aigner, Dennis J & Goldfeld, Stephen M, 1974. "Estimation and Prediction from Aggregate Data when Aggregates are Measured More Accurately than Their Components," Econometrica, Econometric Society, vol. 42(1), pages 113-34, January.
- Shin-Lian Lo & Fu-Kwun Wang & James T. Lin, 2008. "Forecasting for the LCD monitor market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 341-356.
- Sbrana, Giacomo & Silvestrini, Andrea, 2013.
"Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework,"
International Journal of Production Economics,
Elsevier, vol. 146(1), pages 185-198.
- Giacomo Sbrana & Andrea Silvestrini, 2013. "Forecasting aggregate demand: analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," Temi di discussione (Economic working papers) 929, Bank of Italy, Economic Research and International Relations Area.
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