Person:
Guamán Guachichullca, Noé Rodrigo

Birth Date

1988-01-17

ORCID

0000-0002-9577-0264

Scopus Author ID

57195215842

Afiliación

Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Departamento de Química Aplicada y Sistemas de Producción, Cuenca, Ecuador
Universidad de Cuenca, Facultad de Ciencias Químicas, Cuenca, Ecuador

País

Ecuador

Organizational Units

Organizational Unit

Facultad de Ciencias Químicas

Fundada en 1955 como la Escuela de Química Industrial, la facultad ha sido un pilar fundamental en la formación de profesionales altamente capacitados, comprometidos con el desarrollo de la ciencia, la educación y el bienestar social. La Facultad de Ciencias Químicas pone a consideración su trabajo académico, investigativo y de vinculación con la sociedad, desarrollado a través de la práctica de una docencia de calidad, investigación e innovación en su área de estudio. Desde su oficio de conocimiento se permite contribuir a la sociedad con cuatro carreras: Bioquímica y Farmacia, Ingeniería Química, Ingeniería Ambiental e Ingeniería Industrial. Su carta de presentación en la Academia, la coloca como una dependencia dinámica, donde confluye la solidez de una trayectoria de más de sesenta años. Aquí se trabaja en una continua formación de pregrado y posgrado de la más alta calidad, mediante la mejora continua con la innovación y a la vanguardia de las ciencias químicas.

Job Title

Profesor (T)

Last Name

Guamán Guachichullca

First Name

Noé Rodrigo

Full item page

Search Results

Now showing 1 - 10 of 15

A model for implementing enterprise resource planning systems in small and medium-sized enterprises
(Science and Technology Publications, 2021) Tapia Cárdenas, Daniela Estefania; Vintimilla Álvarez, Paola Fernanda; Álvarez Palomeque, Lourdes Ximena; Llivisaca Villazhañay, Juan Carlos; Peña Ortega, Mario Patricio; Guamán Guachichullca, Noé Rodrigo; Sigüenza Guzmán, Lorena Catalina; Jadán Avilés, Diana Carolina; Vintimilla Álvarez, Paola Fernanda
Small and medium-sized enterprises (SMEs) are considered dynamic agents within the business environment. Currently, SMEs have great potential for strong growth and great profit. However, their growth is restricted by the lack of systems that would allow integrating their data and activities. One possible solution is the implementation of Enterprise Resource Planning (ERP) systems to increase the company’s level of efficiency, effectiveness, and productivity. However, implementation processes require investing resources and bring certain problems, e.g., the difficulty to fully adapt to the organization’s accounting and management procedures, and lack of experience of end-users in handling ERP systems. The aim of this study is focused on constructing a model for successfully implementing ERP systems into SMEs. This model used a group of critical success factors (CSF) to analyze empirical evidence in organizations. To its development, the interpretive structural modeling methodology was used, and it was validated in a focus group of experts in implementing and using ERP systems. The results show that the model is adequate for a successful implementation in SMEs engaged in sales, production, or service activities.
Feature engineering based on ANOVA, cluster validity assessment and KNN for fault diagnosis in bearings
(2018) Peña Ortega, Mario Patricio; Cerrada, Mariela; Álvarez Palomeque, Lourdes Ximena; Jadán Avilés, Diana Carolina; Lucero, Pablo M; Barragán Landy, Milton Francisco; Guamán Guachichullca, Noé Rodrigo; Sánchez, René Vinicio
The number of features for fault diagnosis in rotating machinery can be large due to the different available signals containing useful information. From an extensive set of available features, some of them are more adequate than other ones, to classify properly certain fault modes. The classic approach for feature selection aims at ranking the set of original features; nevertheless, in feature selection, it has been recognized that a set of best individually features does not necessarily lead to good classification. This paper proposes a framework for feature engineering to identify the set of features which can yield proper clusters of data. First, the framework uses ANOVA combined with Tukey's test for ranking the significant features individually; next, a further analysis based on inter-cluster and intra-cluster distances is accomplished to rank subsets of significant features previously identified. Our contribution aims at discovering the subset of features that discriminates better the clusters of data associated to several faulty conditions of the mechanical devices, to build more robust multi-fault classifiers. Fault severity classification in rolling bearings is studied to verify the proposed framework, with data collected from a test bed under real conditions of speed and load on the rotating device. © 2018 - IOS Press and the authors. All rights reserved.
Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery
(Institute of Electrical and Electronics Engineers, 2017) Peña Ortega, Mario Patricio; Álvarez Palomeque, Lourdes Ximena; Jadán Avilés, Diana Carolina; Lucero, Pablo; Barragán Landy, Milton Francisco; Guamán Guachichullca, Noé Rodrigo; Cerrada Lozada, Mariela; Peña Ortega, Mario Patricio
The number of extracted features for fault diagnosis in rotating machinery can grow considerably due to the large amount of available data collected from different monitored signals. Usually, feature selection or reduction are conducted through several techniques proposing a unique set of representative features for all available classes; nevertheless, in feature selection, it has been recognized that a set of individually good features do not necessarily lead to good classification. This paper proposes a general framework to analyse the feature selection oriented to identify the features that can produce clusters of data with a proper structure. In the first stage, the framework uses Analysis of Variance (ANOVA) combined with Tukey's test for ranking the significant features individually. In the second stage, a further analysis based on inter-cluster and intra-cluster distances, is accomplished to rank subsets of significant features previously identified. In this sense, our contribution aims at discovering the subset of features that are discriminating better the clusters of data associated to several faulty conditions of the mechanical devices, to build more robust fault multi-classifiers. Fault severity classification in rolling bearings is studied to test the proposed framework, with data collected from an experimental test bed under real conditions of speed and load on the rotating device. © 2017 IEEE.
An overview of optimization models and technological trends of logistics in the retail sector
(Springer Science and Business Media Deutschland GmbH, 2022) Peña Ortega, Mario Patricio; Jadán Avilés, Diana Carolina; Sigüenza Guzmán, Lorena Catalina; Guamán Guachichullca, Noé Rodrigo; Arcentales Carrión, Rodrigo Nicanor; Llivisaca Villazhañay, Juan Carlos
Recently, the e-commerce market has grown rapidly. For example, e-commerce generated sales of USD 504 billion in the US from January 1, 2020, to July 1, 2020, representing an increase of 11.58% over the same period in 2019. This growth has forced the retail industry has had to adopt strategies to become more efficient. About 40% of many companies ‘available time is devoted to logistics. Because these activities are consuming a disproportionate share of many companies’ time, logistics is a prime topic of interest. In this context, this study aims to present an overview of optimization models and technological trends in logistics in the retail sector. Findings show that retail logistics has focused on reducing costs, time usage, and inventories while increasing transport capacity. Optimization in logistics has focused on using mathematical algorithms such as genetic algorithms with different variants, and simulation has supported testing optimization proposals. Finally, big data, omnichannel, and e-commerce continue to grow, especially in the retail sector where it has grown considerably
Prediction of standard times in assembly lines using least squares in multivariable linear models
(Springer, 2020) Ramírez Vargas, Jhon Sleiter; Guamán Guachichullca, Noé Rodrigo; Colina Morles, Eliezer Null; Sigüenza Guzmán, Lorena Catalina
Currently, the highly competitive environment of the assembly industries has been an engine for them to seek differentiating factors for improving their efficiency. One of these factors is the study of times and methods (referred to the analysis and the critical and systematic examination of how a task is presently performed, facilitating to find more effective methods), which allows alleviating internal and external aspects that affect productivity and provides the basis for management decision-making. The present work has two primary objectives; firstly, the calculation of standard times within the enterprise operative area and, additionally, the development of a mathematical model for time prediction. For the fulfillment of these purposes, a referential conceptual framework was established about the study of time and the multiple linear regression model. This framework allowed elaborating a procedure for the development of the mathematical prediction model, together with its validation. The study concludes with a discussion on the importance of having models to estimate standard times in business decision making, and the establishment of relevant conclusions.
Textile micro, small and medium enterprises (MSME) layout dynamics in the ecuadorian context
(Springer International Publishing, 2022) Jadán Avilés, Diana Carolina; Flores Sigüenza, Pablo Andrés; Guamán Guachichullca, Noé Rodrigo; Sigüenza Guzmán, Lorena Catalina; Rosero Mantilla, César Aníbal
Micro, small, and medium enterprises’ (MSME) role in developing national economies is critical. These enterprises constantly search for effective tools to improve their production processes. However, methodologies proposed by numerous authors are not explicitly adapted to the size of such companies. MSMEs have been developed according to their specific needs, using particular procedures and adapting to a lack of financial resources to invest in substantial improvements. This study seeks to identify the characteristics, needs, and general context that impact layout planning decisions in textile MSMEs. This work proposes an evaluation tool that has been developed within different stages. These stages included a literature review, validation of the information obtained, and the application of the resulting tool in three case studies of textile MSMEs that face different situations. There are marked differences and similarities in the three textile enterprises, making it possible to identify the most important decisions that should be made when planning the layout for their production processes.
Key performance indicators for the supply chain in small and medium sized enterprises based on balance score card
(2020) Llivisaca Villazhañay, Juan Carlos; Jadán Avilés, Diana Carolina; Guamán Guachichullca, Noé Rodrigo; Arcentales Carrión, Rodrigo Nicanor; Peña Ortega, Mario Patricio; Sigüenza Guzmán, Lorena Catalina
A supply chain is a network of interaction between different actors, and indi-cators govern its behavior. The current research deals with the analysis and ranking of critical indicators for the supply chain in small and medium-sized enterprises (SMEs). To this end, firstly, a systematic review of supply chain management indicators for SMEs was carried out. Using data sources such as Scopus, ProQuest, and Google Scholar, 189 metrics were selected. Then, through practical and methodological filters, this number was reduced to 149. To organize these indicators, both models, the Balanced Scorecard (BSC) and the Supply Chain Operations Reference (SCOR-model), were used to connect company strategies to their performance. Secondly, these measures formed part of a questionnaire answered by 30 SME experts. From their responses, critical indicators were evaluated through Principal Component Analysis (PCA), resulting in 50 key indicators. Finally, these indicators were ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). For SCM in SMEs, findings indicate that the primary key perfor-mance indicators (KPIs) are cash flow, satisfaction rate, inventory rotation, and exchange of information through the supply chain.
Towards a model for analyzing the circular economy in ecuadorian companies: a conceptual framework
(2022) Sucozhañay Idrovo, Gabriela Carolina; Vidal, Iván; Vanegas Peña, Paúl Fernando; Guamán Guachichullca, Noé Rodrigo
A successful implementation of a CE requires an appropriate comprehension of its conceptual framework and the identification of the elements that composed it. However, there is still no consensus on a unique CE definition, which makes it difficult for companies to adopt circular practices in their business models. In this context, this study proposes a CE framework based on four fundamental elements: principles, drivers, stakeholders and strategies, which was complemented by circular-organization-oriented metrics. Then, local case studies were used to assess the implementation of CE strategies in Ecuadorian companies. Among the identified elements, it was found that there are general action lines that facilitate the understanding of the CE. On the other hand, the majority of elements and metrics are oriented toward the production and end-of-life stages, while extraction, design, use, marketing and distribution are less considered. Furthermore, although environmental, economic and social aspects are considered, the latter are mostly limited to employment generation. Finally, for the local case studies it was observed that the current adoption of circular practices in companies derives from the incorporation of the sustainability approach instead of a structured and systematic implementation of CE strategies.
Toward a sustainability balanced scorecard for managing corporate social responsibility: a conceptual model
(Springer, 2021) Cabrera Barbecho, Fanny Narcisa; Sigüenza Guzmán, Lorena Catalina; Guamán Guachichullca, Noé Rodrigo; Sucozhañay Calle, Dolores Catalina; Vanegas Peña, Paúl Fernando; Sucozhañay Idrovo, Gabriela Carolina
The Sustainability Balanced Scorecard (SBSC) allows companies to track organizational operations and measure their impact on company objectives. To monitor the impact of Corporate Social Responsibility (CSR) activities and ensure its alignment with the company’s strategy, CSR elements need to be integrated into the organization’s dashboards. The main goals of this study are threefold: (1) identify the main CSR elements and the proposal of an analysis scheme for CSR’s strategies; (2) assess local and regional CSR implementations using the previously identified elements, and (3) incorporate the identified CSR metrics into a management dashboard based on the findings of the previous steps. Both a systematic literature review and in-depth analysis of case studies were used in this study. The results show that four elements: principles, dimensions, stakeholders, and means can be applied to analyze CSR strategies consistently. In addition, a structure for a management dashboard that incorporates subcategories and indicators for the assessment of sustainable CSR strategies aligned with the company goals is proposed.
Demand forecasting for textile products using machine learning methods
(Springer International Publishing, 2022) Medina Samaniego, Héctor Wilfrido; Sigüenza Guzmán, Lorena Catalina; Peña Ortega, Mario Patricio; Guamán Guachichullca, Noé Rodrigo
Due to its close relationship with various operational decisions, market demand forecasting has been considered one of the essential activities in all organizations. Unfortunately, the textile industry has been the most difficulty generating forecasts, mainly due to the volatility caused in the market by short product life cycles, special events, and competitions. From the beginning, forecasting has been using traditional statistical methods. However, the increasing use of artificial intelligence has opened a new catalog of prediction methods currently being studied for their high precision. This study explores machine learning (ML) as a tool to generate forecasts for the textile industry, applying regression-focused algorithms such as Linear Regression, Ridge, Lasso, K-nearest neighbor, Support Vector Regression (SVR), and Random Forest (RF). To this end, time series were used as inputs for the models, supported by external variables such as Google Trends and special events. The results show that ML as a prediction method has higher precision than purely statistical basic prediction models