Cited time in webofscience Cited time in scopus

Estimating Primary Demand for a Heterogeneous-Groups Product Category under Hierarchical Consumer Choice Model

Title
Estimating Primary Demand for a Heterogeneous-Groups Product Category under Hierarchical Consumer Choice Model
Author(s)
Lee, HaengjuEun, Yongsoon
Issued Date
2016-06
Citation
IIE Transactions, v.48, no.6, pp.541 - 554
Type
Article
Author Keywords
demand untruncationconsumer choice analysisnested multinomial logit modelEM algorithm
Keywords
AlgorithmsASSORTMENT OPTIMIZATIONBRAND CHOICECompetitionConsumer BehaviorConsumer ChoiceConsumer Choice AnalysisDemand Untruncationem Algorithmem AlgorithmsExpectation-Maximization AlgorithmsIncomplete ObservationMarket Share InformationMaximum PrincipleMultinomial Logit ModelNested Multinomial Logit ModelNon-Linear OptimizationNonlinear ProgrammingOptimizationPURCHASE INCIDENCESalesSTOCKOUTS
ISSN
0740-817X
Abstract
This article discusses the estimation of primary demand (i.e., the true demand before the stockout-based substitution effect occurs) for a heterogeneous-groups product category that is sold in department store settings, based on historical sales data, product availability, and market share information. For such products, a hierarchical consumer choice model can better represent purchasing behavior. This means that choice occurs on multiple levels: a consumermight choose a particular product group on the first level and purchase a product within that chosen group on the second level. Hence, in the present study, we used the nested multinomial logit choice model for the hierarchical choice and combined itwith non-homogeneous Poisson arrivals over multiple periods. The expectation-maximization algorithm was applied to estimate the primary demand while treating the observed sales data as an incomplete observation of that demand. We considered the estimation problem as an optimization problem in terms of the inter-product-group heterogeneity, and this approach relieves the revenue management system of the computational burden of using a nonlinear optimization package. We subsequently tested the procedurewith simulated data sets. The results confirmed that our algorithm estimates the demand parameters effectively for data sets with a high level of inter-product-group heterogeneity. Supplementary materials are available for this article. Go to the publisher’s online edition of IIE Transaction for further discussions and detailed proofs. © 2015, “IIE”.
URI
http://hdl.handle.net/20.500.11750/2274
DOI
10.1080/0740817X.2015.1078524
Publisher
Taylor and Francis Ltd.
Related Researcher
  • 은용순 Eun, Yongsoon 전기전자컴퓨터공학과
  • Research Interests Resilient control systems; Control systems with nonlinear sensors and actuators; Quasi-linear control systems; Intelligent transportation systems; Networked control systems
Files in This Item:

There are no files associated with this item.

Appears in Collections:
Department of Electrical Engineering and Computer Science DSC Lab(Dynamic Systems and Control Laboratory) 1. Journal Articles
Department of Electrical Engineering and Computer Science Information and Communication Engineering Research Center 1. Journal Articles

qrcode

  • twitter
  • facebook
  • mendeley

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE