Rodríguez Aguilar, Román
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Rodríguez Aguilar, Román
Official Name
Rodríguez Aguilar, Román
Alternative Name
rrodrigueza
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ORCID
Scopus Author ID
56717663000
Researcher ID
AAO-6158-2020

Research Output
Now showing 1 - 10 of 23
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A machine learning-based analytical intelligence system for forecasting demand of new products based on chlorophyll : a hybrid approach

2024 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Garcia-Llamas, Eduardo , Rodríguez-Aguilar, Miriam , Marmolejo-Saucedo, Liliana

This manuscript addresses the problem of forecasting the demand for innovative products with limited and inhomogeneous sales data over time. The main objective of the study is to use the information available from a group of innovative chlorophyll-based food products to build a coherent demand forecasting system. From a transactional database, time series were constructed for each group of products, analyzing the stationarity and seasonality of the time series through the Dickey–Fuller and Canova–Hansen tests. Likewise, an ARIMA model, a long short-term memory (LSTM) recurrent deep neural network, and a support vector machine (SVM) were trained to select the best model for each product based on a forecast performance metric. A comparison between classical forecasting techniques and machine learning models is shown. The LSTM neural network was the best model for most products because the internal architecture of the network allows not only to capture non-linear relationships between variables but is also capable of controlling the flow of information to preserve characteristics over time that are relevant for forecasts. The second-best model was the SVM, which allows capturing non-linear behaviors through kernel functions and uses a smaller amount of data for its estimation. Finally, the ARIMA model presented the lowest performance for all products. The objective of having various methodologies is that the system allows the best forecast to be selected according to the type of product, availability of information and methodology used, which will allow the company to integrate new products into the system over time. ©Springer

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Selecting the Distribution System using AHP and Fuzzy AHP Methods

2024 , Saucedo-Martínez, Jania Astrid , Salais-Fierro, Tomás Eloy , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio

In this research, we present a supporting tool for decision making by designing a distribution system for a trading company of supplies for the welding industry in Mexico. The case study encompasses a distribution system with shortage problems and poor fleet capacity. To address these problems, improvement options were grouped into three possible scenarios through a third-party logistics (3PL) service. Furthermore, for the evaluation and selection of one of the scenarios, the Analytic Hierarchy Process (AHP) methodology was proposed integrating fuzzy logic as a tool for decision making, including factors of uncertainty and subjectivity as well as a comparison with traditional AHP obtaining the best scenario, meeting the requirements of the company, and showing potential improvements in the desired service level for its distribution system. © 2024 Springer Nature

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Modeling the Optimal Supply Chain of Liquefied Natural Gas as Fuel in Fishing Vessels in Mexico

2022 , Hernández-Palomo, Giovanna , Rodríguez Aguilar, Román , Venegas-Martínez, Francisco

The global energy transition process has generated a set of modifications in the generation and consumption of energy. Environmental objectives have gained great relevance for regions, countries and companies. The fishing sector has been identified as having a broad environmental impact, which is why the transition to cleaner energy sources in this sector has been considered. One of the proposed strategies is based on the transition from the diesel engines of the ships to the use of liquefied natural gas (LNG), however, this transition requires guaranteeing the supply of fuel as well as the process of reconversion of units in operation and the impulse of LNG gas engines for new units. This work presents a proposal for the design of an LNG gas supply chain for the fishing industry in the State of Tampico in Mexico that allows evaluating the feasibility of the transition from the use of diesel to natural gas in fishing vessels. The main results show the feasibility of the transition in the fuel supply and economic and environmental benefits for the fishing industry. However, there is a significant challenge in converting units in operation to the use of natural gas due to the lack of public policies that promote and support its use in this sector. © Mobile Networks and Applications

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Proposal for a comprehensive environmental key performance index of the green supply chain

2020 , Rodríguez Aguilar, Román

The consideration of environmental objectives in the design of green supply chains creates the need to build a set of key performance indicators for monitoring and control. There is a set of generally accepted environmental indicators for monitoring environmental objectives in the supply chain. However, so far these indicators have been disconnected from the operational and economic indicators of the supply chain. It is important to consider these elements in the integral performance of a green supply chain. The present work is a proposal of a general index of environmental performance that includes operational, financial, and environmental aspects that allow monitoring the integral performance of the supply chain by applying Principal Component Analysis of mixed data. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

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Dynamics of prices and consumption of unhealthy foods as a monitoring tool of the strategy against obesity in Mexico

2020 , Lozano-Díez, José Antonio , Rodríguez Aguilar, Román

Introduction: Mexico faces an epidemic of overweight and obesity, in 2018 75% of adults were overweight or obese. This condition as a risk factor generates a significant financial impact in the Health Sector. In response, the National Strategy for Prevention and Control of Overweight, Obesity, and Diabetes was implemented in 2013, which included as one of its pillars the implementation of fiscal policies. As part of fiscal policy, taxes were established on sugary drinks and foods with high-calorie content. Seven years after the implementation of the Strategy to control the epidemic of overweight and obesity, there have been some results. However, it is necessary to continue working and especially monitoring the performance of the different actions implemented. Objectives: Propose an analytical intelligence model for monitoring the fiscal policies implemented to control overweight and obesity in Mexico. Methods: The proposed analytical intelligence model considers three methodological bases, a) price index of healthy and unhealthy foods through Principal Component Analysis, b) volatility measurement of both baskets through a GARCH model and c) monitoring of consumption patterns through household income and expenditure surveys. Results: The main results identified a price differential between the baskets of products healthy and unhealthy, especially at the beginning of the fiscal policy. Healthy products have higher price volatility than unhealthy products and according to consumption patterns, on average Mexican households spend 30% of their food expenditure on unhealthy products. Conclusion: To strengthen fiscal actions to control overweight and obesity, it is recommended to have monitoring systems for the dynamic design and implementation of public policies. Although taxes have reduced in some grade the consumption of unhealthy products, it is necessary to promote the affordability of healthy products, helping to improve the diet of Mexican households. © 2019 José Antonio Lozano Díez & Roman Rodríguez Aguilar, licensed to European Alliance for Innovation.

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Structural Dynamics and disruption events in Supply Chains using Fat Tail Distributions

2019 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio

The analysis of structural dynamics in a supply chain requires robust methods for the modeling of disruption events that can be faced. Statistical modeling, the machine learning application and access to large amounts of data require much more realistic models to manage risk in the supply chain. This study proposes a statistical methodology for the modeling of disruption events in the supply chain with heavy tailed distributions, which will allow the construction of models more closely linked to reality for risk management in the supply chain. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

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Demand prediction using a soft-computing approach : a case study of automotive industry

2020 , Salais-Fierro, Tomás Eloy , Saucedo-Martínez, Jania , Rodríguez Aguilar, Román , Vela-Haro, Jose Manuel

According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data. © 2019 by the authors.

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An analytical intelligence model for the management of resources for the treatment of high-cost diseases: the case of HIV in Mexico

2020 , Rodríguez Aguilar, Román , Rivera-Peña, Gustavo

In the health sector, it is very important to have adequate control over the allocation of resources; this becomes much more relevant in the case of high-cost diseases, HIV is one example of this. The use of analytical intelligence allows the transformation of raw data into meaningful and useful information to make decisions. To support the management of resources in the health sector an analytical intelligence model based on survival analysis of patients under antiretroviral treatment in the Ministry of Health of Mexico is proposed. A survival model was carried out using a cohort of people with HIV under antiretroviral treatment attended by the Ministry of Health for the period 2007–2015. Sociodemographic variables, viral load, dates of treatment initiation and death were used. Kaplan–Meier method and the logarithmic rank test, as well as the Cox proportional-hazard model, were used. The proposed model can serve as a strategic information management tool for decision-making about the care and financing of high-cost diseases in the health sector. The results show that the probability of survival in people with HIV is higher for currently preferred treatments for treatment initiation and recently incorporated. Increasing the level of CD4 for the start of treatment generates greater probabilities of survival for patients. It is necessary to comprehensively evaluate the prescription and initiation of treatment policies according to CD4 levels to guarantee the financial sustainability of antiretroviral treatment in the Ministry of Health since these measures imply greater use of resources. It would be helpful to implement this type of analytical intelligence model for the monitoring and management of resources in the health sector. © Springer Nature

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Conceptual framework of Digital Health Public Emergency System: digital twins and multiparadigm simulation

2020 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio

Introduction: Two major technological paradigms have been developed in recent years, digital twins and the multiparadigm simulation. In the Health Sector, the enormous potential of both approaches for the management of public health emergencies is envisioned. Objectives: This study aims to develop the conceptual framework for the development of a Digital Public Health Emergency System. Methods: The integration of the digital twins in health with the multi-paradigm simulation for the design of a digital system of public health emergencies is proposed.

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Microdata analytics of out-of-pocket and catastrophic health spending in Mexico: an analysis by quantiles

2022 , Rodríguez Aguilar, Román

Out-of-pocket and catastrophic health spending are key indicators for assessing the financial coverage of a health system. Out-of-pocket spending represents expenditures related to the health care of a household member, while catastrophic spending represents expenditures that constitute more than 30 % of the household’s ability to pay. Measurements in Mexico of out-of-pocket household spending show that it is an item that has not decreased from 2016 to 2018, the out-of-pocket household spending increased by 4 % real representing in 2018, 109 billion of Mexican pesos. Analysis of out-of-pocket spending by quintile shows that average monthly household spending on health is in the range of Q1-$17 to Q5-$1,900 pesos with high dispersion in the data (SD=1,446). The quantile regression shows that there are significant differences between the factors associated to out-of-pocket spending among the quintiles, especially due to the presence of chronic diseases in the household, belonging to the rural environment, the age of the head of the household and the total number of household members. The incidence of catastrophic spending represented 2.19 % [2.18-2.19, N=760,3030] of total households. According to the results of the logistic model, the incidence of catastrophic spending is mainly influenced by households that had hospital spending (OR=20.13) and maternity spending (OR=20.77). Affiliation with a health institution decreases the probability of incurring catastrophic spending (OR=0.93), and when households are segmented by income quintile, the incidence is higher in Q2 and Q4. Mainly affected by spending on hospitalization and maternal care. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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