Operations Management under Consumer Choice Models with Multiple Purchases

2020 ◽  
Author(s):  
Shujie Luan ◽  
Ruxian Wang ◽  
Xiaolin Xu ◽  
Weili Xue
Keyword(s):  
Operations Management ◽  
Consumer Choice ◽  
Choice Models

Data pruning in consumer choice models

2006 ◽  
Vol 4 (3) ◽  
pp. 267-287 ◽  
Author(s):  
Elaine L. Zanutto ◽  
Eric T. Bradlow
Keyword(s):  
Consumer Choice ◽  
Choice Models ◽  
Data Pruning

Consumer Choice and Market Expansion: Modeling, Optimization, and Estimation

2021 ◽  
Author(s):  
Ruxian Wang
Keyword(s):  
Operations Management ◽  
Expectation Maximization ◽  
Consumer Choice ◽  
Choice Behavior ◽  
Real Data ◽  
Market Size ◽  
Solution Approach ◽  
Pricing Decisions ◽  
Market Expansion ◽  
Consumer Choice Behavior

The growth of market size is crucially important to firms, although researchers often assume that market size is constant in assortment and pricing management. I develop a model that incorporates the market expansion effects into discrete consumer choice models and investigate various operations problems. Market size, measured by the number of people who are interested in the products from the same category, is largely influenced by firms’ operations strategy, and it also affects assortment planning and pricing decisions. Failure to account for market expansion effects may lead to substantial losses in demand estimation and operations management. Based on real data, this paper uses an alternating-optimization expectation-maximization method that separates the estimation of consumer choice behavior and market expansion effects to calibrate the new model. The end-to-end solution approach on modeling, operations, and estimation is readily applicable in real business.

Consumer Choice Models and Estimation: A Review and Extension

2021 ◽  
Author(s):  
Qi Feng ◽  
J. George Shanthikumar ◽  
Mengying Xue
Keyword(s):  
Consumer Choice ◽  
Choice Models

A Comparative Empirical Study of Discrete Choice Models in Retail Operations

2021 ◽  
Author(s):  
Gerardo Berbeglia ◽  
Agustín Garassino ◽  
Gustavo Vulcano
Keyword(s):  
Empirical Study ◽  
Discrete Choice ◽  
Operations Management ◽  
Latent Class ◽  
Markov Chain Model ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Chain Model ◽  
Retail Operations ◽  
Demand Models

Choice-based demand estimation is a fundamental task in retail operations and revenue management, providing necessary input data for inventory control, assortment, and price-optimization models. The task is particularly difficult in operational contexts where product availability varies over time and customers may substitute into the available options. In addition to the classical multinomial logit (MNL) model and extensions (e.g., nested logit, mixed logit, and latent-class MNL), new demand models have been proposed (e.g., the Markov chain model), and others have been recently revisited (e.g., the rank list-based and exponomial models). At the same time, new computational approaches were developed to ease the estimation function (e.g., column-generation and expectation-maximization (EM) algorithms). In this paper, we conduct a systematic, empirical study of different choice-based demand models and estimation algorithms, including both maximum-likelihood and least-squares criteria. Through an exhaustive set of numerical experiments on synthetic, semisynthetic, and real data, we provide comparative statistics of the predictive power and derived revenue performance of an ample collection of choice models and characterize operational environments suitable for different model/estimation implementations. We also provide a survey of all the discrete choice models evaluated and share all our estimation codes and data sets as part of the online appendix. This paper was accepted by Vishal Gaur, operations management.

Consumer Choice Models with Endogenous Network Effects

2017 ◽  
Vol 63 (11) ◽  
pp. 3944-3960 ◽  
Author(s):  
Ruxian Wang ◽  
Zizhuo Wang
Keyword(s):  
Consumer Choice ◽  
Network Effects ◽  
Choice Models
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