Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24342
Title: Fulfillment of retailer demand by using the mdl-optimal neural network prediction and decision policy
Authors: Ning, A
Lau, HCW
Zhao, Y
Wong, TT
Keywords: Decision rules
Demand prediction
Minimum description length
Neural network
Issue Date: 2009
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on industrial informatics, 2009, v. 5, no. 4, 5256154, p. 495-506 How to cite?
Journal: IEEE transactions on industrial informatics 
Abstract: Prediction of demand plays a critical role in replenishment, in supply chain management. Accurate prediction of demand is a fundamental requirement and is also a great challenge to demand prediction models. This has motivated the research team to develop the minimum description length (MDL)-optimal neural network (NN) which can accurately predict retailer demands with various time lags. Moreover, a surrogate data method is proposed prior to the prediction to investigate the dynamical property (i.e., predictability) of various demand time series so as to avoid predicting random demands. In this paper, we validate the proposed ideas by a full factorial study combining its own decision rules. We describe improvements to prediction accuracy and propose a replenishment policy for a Hong Kong food wholesaler. This leads to a significant reduction in its operation costs and to an improvement in the level of retailer satisfaction.
URI: http://hdl.handle.net/10397/24342
ISSN: 1551-3203
EISSN: 1941-0050
DOI: 10.1109/TII.2009.2031433
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