2022
DOI: 10.1111/poms.13598
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Beyond Retail Stores: Managing Product Proliferation along the Supply Chain

Abstract: All rights reserved. This document may be distributed for free -electronically or in print -in the same formats as it is available on the website of the ESMT (www.esmt.org) for non-commercial purposes. It is not allowed to produce any derivates of it without the written permission of ESMT.

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Cited by 6 publications
(4 citation statements)
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References 36 publications
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“…To quantify the impact of operational flexibility on excess inventory, we model the evolutionary dynamics of demand forecasts. There are two types of demand models that can be used for such a purpose: (1) the additive demand model and ( 2) the multiplicative demand model [12,24]. The difference between the successive demand forecasts follows a normal distribution in the additive demand model, whereas the ratio of the successive demand forecasts follows a normal distribution in the multiplicative demand model.…”
Section: Model Preliminariesmentioning
confidence: 99%
See 3 more Smart Citations
“…To quantify the impact of operational flexibility on excess inventory, we model the evolutionary dynamics of demand forecasts. There are two types of demand models that can be used for such a purpose: (1) the additive demand model and ( 2) the multiplicative demand model [12,24]. The difference between the successive demand forecasts follows a normal distribution in the additive demand model, whereas the ratio of the successive demand forecasts follows a normal distribution in the multiplicative demand model.…”
Section: Model Preliminariesmentioning
confidence: 99%
“…[12] gives an example of a forecast-updating process such that demand planners use only the advance demand information to update the demand forecasts, which is modeled by a multiplicative demand model with a positive drift rate. When the forecasts are updated based on an unbiased judgmental demand process, the drift rate should be set equal to zero [12]. The multiplicative demand model, given by Equation (1), yields a lognormal distribution for the end demand, which is conditional on the demand forecast at t i :…”
Section: Model Preliminariesmentioning
confidence: 99%
See 2 more Smart Citations