Exploring Marketing to Engineering Information Mapping in Mass Customization: A Presentation of Ideas, Challenges and Resulting Questions

2011 ◽  
Author(s):  
Scott Ferguson ◽  
Andrew Olewnik ◽  
Phil Cormier
Keyword(s):  
Discrete Choice ◽  
Mass Customization ◽  
Latent Class ◽  
Mixed Logit ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Hierarchical Bayes ◽  
Customer Preferences ◽  
Lack Of Information ◽  
Engineering Information

The paradigm of mass customization strives to minimize the tradeoffs between an ‘ideal’ product and products that are currently available. However, the lack of information relation mechanisms that connect the domains of marketing, engineering, and distribution have caused significant challenges when designing products for mass customization. For example, the bridge connecting the marketing and engineering domains is complicated by the lack of proven tools and methodologies that allow customer needs and preferences to be understood at a level discrete enough to support true mass customization. Discrete choice models have recently gained significant attention in engineering design literature as a way of expressing customer preferences. This paper explores how information from choice-based conjoint surveys might be used to assist the development of a mass customizable MP3 player, starting from 140 student surveys. The authors investigate the challenges of fielding discrete choice surveys for the purpose of mass customization, and explore how hierarchical Bayes mixed logit and latent class multinomial logit models might be used to understand the market for customizable attributes. The potential of using discrete choice models as a foundation for mathematically formulating mass customization problems is evaluated through an investigation of strengths and limitations.

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.

Discrete Choice Methods

2009 ◽  
Author(s):  
Garrett Glasgow ◽  
R. Michael Alvarez
Keyword(s):  
Political Science ◽  
Discrete Choice ◽  
Statistical Models ◽  
Mixed Logit ◽  
Science Research ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Choice Situation ◽  
Multinomial Probit ◽  
Political Agents

This article describes the statistical models commonly used to study discrete choices. It concentrates on the ‘basic’ discrete choice models, and the theoretical choice situations that lead to these models. Specifically the choice situation addressed include: the ordered choice situation and the unordered choice situation. In addition, the article discusses two extensions of the basic discrete choice models commonly seen in political science research — models allowing for heteroskedasticity in the choices made across political agents (such as the heteroskedastic probit), and models that estimate substitution patterns across choice alternatives (such as the multinomial probit and mixed logit). Suggestions for further reading are also given.

scholarly journals Modeling enterprise location choice decision behavior

2021 ◽  
Vol 14 (1) ◽  
pp. 669-691
Author(s):  
Nguyen Cao Y
Keyword(s):  
Spatial Correlation ◽  
Discrete Choice ◽  
Choice Model ◽  
Location Choice ◽  
Mixed Logit ◽  
Multinomial Logit ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Spatial Effects ◽  
Modeling Framework

This study presents a location choice model that incorporates urban spatial effects for enterprises. A modeling framework is developed to analyze decisions regarding location choice for enterprises using a series of discrete choice models including multinomial logit without any urban spatial effects, multinomial logit incorporating urban spatial effects, and mixed logit incorporating urban spatial effects. In this framework, urban spatial effects, such as the urban spatial correlation among enterprises in deterministic terms and the urban spatial correlation among zones in the error term, are captured by mixed logit models in particular and discrete choice models in general. The results indicate that the urban spatial effects and the land prices in a given zone strongly affect the decision-making process of all the enterprises in the Tokyo metropolitan area. Moreover, the important role of urban spatial effects in the proposed model will be clarification through comparing the three above models. This comparison will be implemented on the basis of three types of indicators such as the log likelihood ratio, Akaike information indicator, and hit ratio of each model.

Theoretical, methodological and practical issues of choice models

2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta
Keyword(s):  
Discrete Choice ◽  
Model Building ◽  
Estimation Method ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Decision Makers ◽  
Model Formulation ◽  
Random Utility Maximization ◽  
The Relationship ◽  
Method Model

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

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