A semi-parametric approach for estimating critical fractiles under autocorrelated demand

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Forecasting critical fractiles of the lead time demand distribution is an important problem for operations managers making newsvendor-type inventory decisions. In this paper, we propose a semi-parametric approach to forecasting the critical fractile when demand is serially correlated. Starting from a userdefined but potentially misspecified forecasting model, we use historical demand data to generate empirical forecast errors of this model. These errors are then used to (1) parametrically correct for any bias in the point forecast conditional on the recent demand history and (2) non-parametrically estimate the critical fractile of the demand distribution without imposing distributional assumptions. We present conditions under which this semi-parametric approach provides a consistent estimate of the critical fractile and evaluate its finite sample properties using simulation and real data for retail inventory planning. (C) 2013 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2014-04
Language
English
Article Type
Article
Keywords

SUPPLY CHAIN; INVENTORY SYSTEMS; INFORMATION; POLICIES; FORECASTS; MODELS; IMPACT

Citation

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, v.234, no.1, pp.163 - 173

ISSN
0377-2217
DOI
10.1016/j.ejor.2013.10.055
URI
http://hdl.handle.net/10203/187414
Appears in Collection
MT-Journal Papers(저널논문)
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