Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System

In this study, we investigate inventory allocation and pricing strategies for retailers by incorporating demand information into the issue of inventory allocation during the presale period. In a presale system, retailers offer presale goods at a price lower than the retail price. By offering product...

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Autores principales:	Jinxian Quan, Sung-Won Cho
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	e-commerce presale system ordering policy for retailers bayesian information update two-period inventory allocation model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/99ca9ac9bdd14319976de1bdad3ab6ea
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id	oai:doaj.org-article:99ca9ac9bdd14319976de1bdad3ab6ea
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spelling	oai:doaj.org-article:99ca9ac9bdd14319976de1bdad3ab6ea2021-11-25T19:01:46ZOptimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System10.3390/su1322125252071-1050https://doaj.org/article/99ca9ac9bdd14319976de1bdad3ab6ea2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12525https://doaj.org/toc/2071-1050In this study, we investigate inventory allocation and pricing strategies for retailers by incorporating demand information into the issue of inventory allocation during the presale period. In a presale system, retailers offer presale goods at a price lower than the retail price. By offering products at a discount, retailers may attract additional demand. In addition, this system enables retailers to reduce the uncertainty of market demand and establish a strategy for inventory allocation based on the results of presales. A Bayesian approach was employed to analyze and update demand information, and inventory allocation was formulated as a newsvendor problem to determine the optimal policy that maximizes retailer profit. A numerical analysis was conducted to validate the effectiveness of the proposed strategy. Results suggest that the proposed strategies can support retailers by more accurately predicting demand and achieving higher profits with less inventory. Furthermore, retailers can experience greater benefits from risk-averse customers than from risk-neutral customers.Jinxian QuanSung-Won ChoMDPI AGarticlee-commercepresale systemordering policy for retailersbayesian information updatetwo-period inventory allocation modelEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12525, p 12525 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	e-commerce presale system ordering policy for retailers bayesian information update two-period inventory allocation model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	e-commerce presale system ordering policy for retailers bayesian information update two-period inventory allocation model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Jinxian Quan Sung-Won Cho Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System
description	In this study, we investigate inventory allocation and pricing strategies for retailers by incorporating demand information into the issue of inventory allocation during the presale period. In a presale system, retailers offer presale goods at a price lower than the retail price. By offering products at a discount, retailers may attract additional demand. In addition, this system enables retailers to reduce the uncertainty of market demand and establish a strategy for inventory allocation based on the results of presales. A Bayesian approach was employed to analyze and update demand information, and inventory allocation was formulated as a newsvendor problem to determine the optimal policy that maximizes retailer profit. A numerical analysis was conducted to validate the effectiveness of the proposed strategy. Results suggest that the proposed strategies can support retailers by more accurately predicting demand and achieving higher profits with less inventory. Furthermore, retailers can experience greater benefits from risk-averse customers than from risk-neutral customers.
format	article
author	Jinxian Quan Sung-Won Cho
author_facet	Jinxian Quan Sung-Won Cho
author_sort	Jinxian Quan
title	Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System
title_short	Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System
title_full	Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System
title_fullStr	Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System
title_full_unstemmed	Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System
title_sort	optimal ordering policy for retailers with bayesian information updating in a presale system
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/99ca9ac9bdd14319976de1bdad3ab6ea
work_keys_str_mv	AT jinxianquan optimalorderingpolicyforretailerswithbayesianinformationupdatinginapresalesystem AT sungwoncho optimalorderingpolicyforretailerswithbayesianinformationupdatinginapresalesystem
_version_	1718410419740082176

Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System

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