Prediction intervals for future demand of existing products with an observed demand of zero
A proposed technique for forming reliable prediction intervals for the future demand rate of existing products with observed demand of zero is illustrated using methodology adapted from software reliability. By using the demand information from a group of products which includes slow-moving products, prediction intervals for the future demand rate of the products with an observed demand of zero are constructed. A simulation study examined the reliability of these prediction intervals across experimental conditions that included product group size, mean time between demand, and Type I error levels. The proposed prediction intervals had empirical Type I errors closer to their nominal values when there were a sufficient number of products with no sales and also with some sales.
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.
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.
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.:
- Dolgui, Alexandre & Pashkevich, Maksim, 2008. "Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance," International Journal of Production Economics, Elsevier, vol. 112(2), pages 885-894, April.
- Willemain, Thomas R. & Smart, Charles N. & Shockor, Joseph H. & DeSautels, Philip A., 1994. "Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method," International Journal of Forecasting, Elsevier, vol. 10(4), pages 529-538, December.
- Harwell, Michael R., 1991. "Using randomization tests when errors are unequally correlated," Computational Statistics & Data Analysis, Elsevier, vol. 11(1), pages 75-85, January.
- 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.
- Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
- Leven, Erik & Segerstedt, Anders, 2004. "Inventory control with a modified Croston procedure and Erlang distribution," International Journal of Production Economics, Elsevier, vol. 90(3), pages 361-367, August.
- John A. Muckstadt & L. Joseph Thomas, 1980. "Are Multi-Echelon Inventory Methods Worth Implementing in Systems with Low-Demand-Rate Items?," Management Science, INFORMS, vol. 26(5), pages 483-494, May.
- Porras, Eric & Dekker, Rommert, 2008. "An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods," European Journal of Operational Research, Elsevier, vol. 184(1), pages 101-132, January.
- Vereecke, Ann & Verstraeten, Peter, 1994. "An inventory management model for an inventory consisting of lumpy items, slow movers and fast movers," International Journal of Production Economics, Elsevier, vol. 35(1-3), pages 379-389, June.
- S. Illeris & G. Akehurst, 2002. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 22(1), pages 1-3, January.
- Boylan, J.E. & Syntetos, A.A., 2007. "The accuracy of a Modified Croston procedure," International Journal of Production Economics, Elsevier, vol. 107(2), pages 511-517, June.
- Rob J. Hyndman & Lydia Shenstone, 2005.
"Stochastic models underlying Croston's method for intermittent demand forecasting,"
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 24(6), pages 389-402.
- Lydia Shenstone & Rob J. Hyndman, 2003. "Stochastic models underlying Croston's method for intermittent demand forecasting," Monash Econometrics and Business Statistics Working Papers 1/03, Monash University, Department of Econometrics and Business Statistics.
- Syntetos, A. A. & Boylan, J. E., 2001. "On the bias of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 457-466, May.
- Altay, Nezih & Rudisill, Frank & Litteral, Lewis A., 2008. "Adapting Wright's modification of Holt's method to forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 111(2), pages 389-408, February.
When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:119:y:2009:i:1:p:75-89. 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.