Lead Time Demand for Simple Exponential Smoothing

Ralph D. Snyder, Anne B. Koehler and J. Keith Ord

No 267484, Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: A new simple formula is found to correct the underestimation of the standard deviation for total lead time demand when using simple exponential smoothing. The traditional formula for the standard deviation of lead time demand is to multiply the standard deviation for the one-period-ahead forecast error (estimated by using the residuals) by the square root of the number of periods in the lead time. It has been shown by others that the traditional formula significantly underestimates variation in the lead time demand when the mean of the process is somewhat changing and simple exponential smoothing is appropriate. This new formula allows one to see readily the significant size of the underestimation of the traditional formula and can easily be implemented in practice. The formula is derived by using a state space model for simple exponential smoothing.

Keywords: Demand and Price Analysis; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 13
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/267484/files/monash-256.pdf (application/pdf)
https://ageconsearch.umn.edu/record/267484/files/monash-256.pdf?subformat=pdfa (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:monebs:267484

DOI: 10.22004/ag.econ.267484

Access Statistics for this paper

More papers in Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().