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Case-Based Reasoning: A Methodology to Build Hybrid Models in Forecasts for Stochastic Environments
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Author(s): Carlos Hernán Hernán Fajardo-Toro (Universidad EAN, Colombia), Andrés Lopez Astudillo (Universidad Icesi, Colombia), Paloma María Teresa Martínez Sánchez (Universidad El Bosque, Colombia), Paola Andrea Sánchez Sánchez (Universidad Simón Bolivar, Colombia)and Alvaro José Fajardo-Toro (Universidad Icesi, Colombia)
Copyright: 2020
Pages: 22
Source title:
Handbook of Research on Industrial Applications for Improved Supply Chain Performance
Source Author(s)/Editor(s): Jorge Luis García-Alcaraz (Universidad Autónoma de Ciudad Juárez, Mexico), George Leal Jamil (Informações em Rede Consultoria e Treinamento Ltda., Brazil), Liliana Avelar-Sosa (Universidad Autónoma de Ciudad Juárez, Mexico)and Antonio Juan Briones Peñalver (Polytechnic University of Cartagena, Spain)
DOI: 10.4018/978-1-7998-0202-0.ch015
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Abstract
Companies must deal with a high uncertainty caused by the characteristics of the markets and the economic, political, and social environment in which they offer their products and services. These characteristics are defined by the preferences of the consumers, which have a high variety coupled with the digital era. On the other hand, there is the necessity to implement measures that align the companies with the sustainability concepts, because of both legislations as well as the image that the customer could have of them. Due to this context, the organizations must find a way to optimize process and structures that require high flexibility given the need of combining perfect innovation, customization, standardization, and sustainability. Part of this planning process is the construction of forecast models that allows predicting with high precisión. In this chapter, a theoretical exposition is done and a literature revision of machine learning techniques is applied to try to solve the forecasting problem with special emphasis in neural networks and Case-Based Reasoning - CBR.
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