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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
Authors
Lee, H[Lee, Haengju]Eun, Y[Eun, Yongsoon]
DGIST Authors
Lee, H[Lee, Haengju]; Eun, Y[Eun, Yongsoon]
Issue Date
2016-06
Citation
IIE Transactions (Institute of Industrial Engineers), 48(6), 541-554
Type
Article
Article Type
Article
Keywords
AlgorithmsCompetitionConsumer BehaviorConsumer ChoiceConsumer Choice AnalysisDemand UntruncationEM AlgorithmEM AlgorithmsExpectation-Maximization AlgorithmsIncomplete ObservationMarket Share InformationMaximum PrincipleMultinomial Logit ModelNested Multinomial Logit ModelNon-Linear OptimizationNon-Linear ProgrammingOptimizationSales
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
  • Author Eun, Yong Soon DSC Lab(Dynamic Systems and Control Laboratory)
  • Research Interests Resilient control systems; Control systems with nonlinear sensors and actuators; Quasi-linear control systems; Intelligent transportation systems; Networked control systems
Files:
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Collection:
Information and Communication EngineeringDSC Lab(Dynamic Systems and Control Laboratory)1. Journal Articles


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