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Flores Sigüenza, Pablo Andrés

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1987-12-29

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0000-0002-8038-2912

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57219597377

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Universidad de Cuenca, Departamento de Química Aplicada y Sistemas de Producción, Cuenca, Ecuador
Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Facultad de Ciencias Químicas, Cuenca, Ecuador
Universidad de Cuenca, Grupo de Investigación: Industrial Management and Innovation Research (IMAGINE), Cuenca, Ecuador

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Ecuador

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Flores Sigüenza

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Pablo Andrés

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2022 - 20233
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Now showing 1 - 3 of 3
  • Thumbnail Image
    Publication
    A systematic literature review of facility fayout problems and resilience factors in the industry
    (Springer International Publishing, 2022) Flores Sigüenza, Pablo Andrés; Avilés González, Jonnatan Fernando; Vanegas Peña, Paúl Fernando; Sigüenza Guzmán, Lorena Catalina; Lema Chicaiza, Freddy Roberto; Tigre Ortega, Franklin Geovanny
    Flexibility, quick responses to changes, and capacity to respond to external factors are strategies that companies must develop to ensure their continuity and face adverse situations. The flexibility that can be acquired by optimizing plant layout and resilience ensures continuity of production by reducing adverse external shocks. This article synthesizes critical elements used in models to solve facility layout problems, followed by analyzing the resilience variables and factors most frequently applied in the industry. A systematic literature review to achieve this objective was developed, including 170 articles published in 2010 and 2021. Its study was performed through the Atlas.ti software, an analysis of the 4W's (i.e., When, Who, What, and Where) was applied, and finally, answers to three research questions posed were given. Growth in the scientific interest of facility layout problems can be observed in the last five years, especially for dynamic problems and unequal areas, development of multi-objective models, and approach to metaheuristic solutions. Eventually, research gaps were also identified, highlighting the lack of inclusion of resilience factors in plant distribution models.
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    Publication
    Indicators to evaluate elements of industry 5.0 in the textile production of MSMEs
    (Springer Science and Business Media Deutschland GmbH, 2022) Vásquez Salinas, Bernarda Michelle; Flores Sigüenza, Pablo Andrés; Sigüenza Guzmán, Lorena Catalina; Sucozhañay Calle, Dolores Catalina; Arcentales Carrión, Rodrigo Nicanor
    Textile MSMEs are going through a period of instability and greater difficulty in executing their operations due to factors derived from the pandemic, globalization, policies, and environmental and social needs. This is driving companies to abandon classic methods and turn to the use of innovative concepts as manners to promote sustainability and resilience. One of these concepts is Industry 5.0, which, according to the European Commission, focuses on sustainable manufacturing and operator well-being and complements Industry 4.0 as it seeks to improve factory efficiency through technology by placing the human being at the center of development. At the same time, it minimizes environmental and social impacts and enhances resilience. Aware that implementing these new trends is a challenge for MSMEs, this study contributes to the generation of indicators to evaluate elements of Industry 5.0 in the textile production of MSMEs, supporting the development and implementation of strategies focused on this area. The construction of the set of indicators is based on a 3-phase framework that consists of doing a systematic literature review, selecting the indicators by a process of analysis and comparison, and expanding their characteristics through elaborating data sheets. As part of the results, 172 indicators completed a rigorous selection and validation process. These will serve as the basis for developing sustainable, resilient, and human-centered production models that can be carried out in future research
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    Publication
    Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data
    (Springer Science and Business Media Deutschland GmbH, 2023) Sigüenza Guzmán, Lorena Catalina; Flores Sigüenza, Pablo Andrés; Sigüenza Guzmán, Lorena Catalina
    Data analysis and processing allow for acquiring competitive advantages both in the business and academic and research worlds. One of the sciences that carries out this analysis is machine learning, which has evolved with greater emphasis in recent years due to its advantages and applicability in different areas. Aware of the importance and current relevance of data management for industries, especially in the banking sector, this study applies supervised learning techniques to generate classification and prediction models by treating a set of data from an Ecuadorian financial institution. Different algorithms are compared, and each of the steps to follow in constructing the models is explained in detail. This allows the financial entity to classify its clients as VIPs or not with greater certainty, as well as to predict the investment amounts of the potential clients based on variables such as age, occupation, and among others. The main results show that the K-nearest neighbor algorithm with k = 5 is optimal for classification, while for prediction, the multilayer perceptron algorithm is the most favorable.