Advanced Search
MyIDEAS: Login to save this article or follow this journal

Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands

Contents:

Author Info

  • Chen, Argon
  • Blue, Jakey
Registered author(s):

    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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.sciencedirect.com/science/article/B6VF8-50J9GWF-1/2/755fb773b31c681cbdad3b2ab0f34181
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 128 (2010)
    Issue (Month): 2 (December)
    Pages: 586-602

    as in new window
    Handle: RePEc:eee:proeco:v:128:y:2010:i:2:p:586-602

    Contact details of provider:
    Web page: http://www.elsevier.com/locate/ijpe

    Related research

    Keywords: Top-down forecasting Demand aggregation Disaggregation Bivariate VAR(1) time series;

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    1. 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.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:128:y:2010:i:2:p:586-602. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.