The purpose of this research work is to evaluate the use of statistical tools, specifically Ordinary Least Squares (OLS) and Artificial Neural Networks (ANN) and with the help of these tools to be able to independently and effectively predict the costs. of production in the context of supply logistics in the Peruvian paper industry. Both models that turn out to be different in their analysis, however, turn out to be complementary for a more exact and precise result, highlighting the ANNs for their superior performance in the precision of the evaluated metrics, where they managed to achieve an RMSE of 0.0171 and a MAE of 0.0122 compared to the OLS statistical model that achieved an RMSE of 0.0181 and a MAE of 0.2070. Likewise, the analysis between the dimensions studied, purchasing management stands out with a negative coefficient of -0.4978, which shows that its optimization will generate a positive impact on production costs, contrary to the case with the other two dimensions, which are: storage management and inventory management, which resulted in positive coefficients (0.7457 and 0.4667), which shows that their optimization does not necessarily generate a positive impact on production costs, but quite the opposite, that their inadequate management On the contrary, it can harm production costs. These results highlight the inherent need that Peruvian paper companies must have in being able to implement updated logistics systems, capable of integrating advanced statistical tools such as the use of ANN and MCO, which can scientifically help better decision making, allowing thereby improving your supply processes and thus being able to reduce your production costs.

The focus of this research is to analyze how supply chains’ management affects production costs in the cardboard and Packaging sector in Peru, specifically through the creation of artificial neural networks (ANN) to improve the logistical activities. Non-experimental quantitative design was applied, collected the data from the Year 2020 to the Year 2024 and sought to assess variables such as supplier capacities, stocks held, bottom line costs incurred and stock out ratios. The study revealed that there exists a proportionate inverse relationship between the logistical costs and production costs, proving that as the cost of acquiring goods needed for production as well as the cost of keeping and managing stock decreases, the overall production cost also decreases significantly. The ANN model was able to perform cost predictions with a high degree of accuracy which points out the relevance of sophisticated instruments in the shift of the supply chain. Also, it is important to note the core contribution of the research – effective logistics management is emphasized as a way of increasing competition in industries where supply chains are of critical importance. This research reinforces the effectiveness of designing ANN in minimizing costs, while adding knowledge to the reporting practice of the companies aimed at bettering their costs. The results are a good contribution in terms of technological change in logistics aimed at helping the organizations remain flexible in a changing economy.

The present study analyzed the relationship between the average annual temperature and the area of Amazonian forest in the departments of Peru during the period 2013-2021, using a panel data model with random effects. The data used come from the National Institute of Statistics and Informatics (INEI) and include the average annual temperature in degrees Celsius and the area of Amazonian rainforest in thousands of hectares, both disaggregated by department. Additionally, CO2 emissions resulting from the loss of tree cover, measured in megatons (Mt) of carbon dioxide equivalent (CO₂e), were considered as a control variable. The results revealed a positive and statistically significant relationship between the area of Amazonian forest and the average annual temperature, denoting that an increase of one thousand hectares in the extension of the forest corresponds to an increase of 0.0004 °C in temperature. In this sense, the finding contradicts the climate-regulating role played by forests, however, this is attributed to the influence of unobserved confounding variables that are linked to both forest area and temperature.

Job satisfaction and digital talent are topics of growing interest in the context of digital transformation. Digitalization is changing the way organizations operate and how employees perceive their work. The state and its administrative staff is no exception, as these capabilities are essential to perform operational tasks that underpin the public institution's documentary processes. This study investigates the influence of digital talent (independent variable) on job satisfaction (dependent variable), employing structural equation modeling (SEM) using WarpPLS software. Digital Talent is broken down into three sub-variables: Digital Competencies of Employees (DCE), Capacity for Digital Innovation and Creativity (CIDC) and Adaptability and Continuous Learning (ACL), while Job Satisfaction is measured through two sub-variables: Work Environment (WE) and Professional Development Opportunities (PDO). The analyses revealed that Capacity for Innovation and Digital Creativity (CIDC) has a significant impact on Work Environment, with a path coefficient (β) of 0.13 (p = 0.01). Similarly, adaptability and continuous learning (ACL) positively influence the work environment, with a path coefficient (β) of 0.10 (p = 0.04). In addition, a strong relationship was found between professional development opportunities (PDO) and work environment, with a path coefficient (β) of 0.68 (p < 0.001). For the relationship between digital competencies (DCE) and career development opportunities, the path coefficient was 0.10 (p = 0.04). Digital talent is a key predictor of job satisfaction in administrative staff. The results suggest that investing in the development of digital capabilities, especially innovation and creativity, as well as adaptability, is essential to improve the work environment and career development opportunities.

This paper analyses the possible determinants that induced Silicon Valley and First Republic Bank to Bankruptcy. We employ financial statements for a sample of Banks in line with the business core of Silicon Valley Bank. The period under assessment ranges from 2006-2022. We estimate an indicator of Bank Efficiency using the technique Data Envelopment Analysis (DEA). The latter indicator is used as the primary step to analyze failure within sample banks. According to the CAMEL model, macroeconomic variables are non-significant but relevant variables that drive failure were: Bank Efficiency, Capital adequacy, Earning ability, and Liquidity position ratio. Our study is relevant for any policy making to prevent any future bank failure.

In his research, he has established an analysis of the consumption of renewable energy and its impact on CO2 emissions in Peru, 1990-2020. The research employs a quantitative approach and longitudinal non-experimental design, with a multiple linear regression model. It uses time series drawn from the World Bank on renewable energy consumption and energy consumption. A progressive increase was reflected mainly driven by industrial growth, fossil fuel consumption and changes in consumption and production patterns.

Forest masses participate in the hydrological cycle and precipitation patterns. Therefore, the loss of these forest masses has significant implications for atmosphere-surface dynamics. The objective of this article is to determine the influence of forest cover loss on annual rainfall in the departments of Peru during the period 2013-2022. The methodology was quantitative, longitudinal non-experimental design, with panel data and a random-effects model was estimated. The results reveal a positive and statistically significant relationship between tree cover loss and total annual precipitation, specifically, a 1% increase in deforestation is related to an average increase of 0.186% in annual rainfall. The findings contrast with most previous evidence documenting reductions in precipitation due to deforestation, however, they are consistent with some studies. The research concluded that there is a positive relationship between the loss of forest cover and annual rainfall in the departments of Peru during the period studied.

Our research used a SVAR to analyze the underlying copper price shocks, that is, a commodity shock for the Junín department in Peru. The results of a short- and long-run SVAR were based on traditional matrix constraints that capture the fact that domestic shocks do not affect international prices. The main conclusion is that before the pandemic a shock in the international price of copper decreases economic growth and inflation in the department of Junín in Peru, after the pandemic the opposite happens. As a result of the model, the short and long-run effects of the international copper price on the main macroeconomic variables of Junín in Peru are statistically significant. Before the pandemic, the dynamics of the international copper price reflected the existence of the curse of mining resources in copper institutions. Before the pandemic, a percentage increase in the international price of copper decreased economic growth, reflecting the existence of the mining resource curse in copper institutions. After the pandemic, a percentage increase in the price of copper increases economic growth by up to 0.0488%, then decreases over time, noting the transitory effect of economic recovery and poor management of mineral resources.

Rainfall and temperature are essential inputs for agricultural production, especially climate change-related ones. In turn, it is necessary for the development and survival of both men and animals at all biotic levels. In a reserve of great importance for the country, such as the Pacaya Samiria, these two factors play a preponderant role in the survival of animals and inhabitants of it. In this paper, VAR analysis with Granger's causality was used to determine the relationship between both variables. It was shown that there was a short-term positive relationship between climate change and rainfall in Pacaya Samiria.

The effect of air injection into a coffee roasting chamber of a 2 kg rotary drum roaster is investigated on the temperature profiles and organoleptic properties of a Catuai coffee variety. In the experimental study, 1 kg of coffee beans were used and the air injection velocities were varied for the cases of 0 (standard roasting design), 1, 2 and 3 m/s; testing in triplicate and the two most representative were taken. In the coffee roaster, the temperature was monitored simultaneously in the air inlet duct, roasting chamber and gas outlet duct. The organoleptic properties were examined by a group of professionals from Negociaciones Agroindustrial Arévalo S.A. NARSA in the province of Chanchamayo - Peru, which has a G4 laboratory certified by the Specialty Coffee Association (SCA). In the organoleptic ratings, a maximum score of 83.21/100 was found for the 2 m/s test and a minimum of 80.63/100 for the 1 m/s test. For the numerical characterization, the principle of fluid mechanics such as turbulent regime flow and energy transport were used by the finite volume (FV) method using SolidWorks 2021 Flow Simulation software. The numerical results showed that for the 2 m/s test, the temperature of the bean reaches 195 oC at 500 s, indicating that the whole coffee bean reaches the cooking temperature required for the development time prior to eviction; while for the 1 m/s test, it did not exceed 190 oC. It is shown here that the effect of air injection in the roasting chamber, caused organoleptic changes with respect to the conventional design without air injection, due to a sustained cooking at different times and an adequate development time. However, there is a speed threshold where when exceeded, temperatures inside the chamber remain low, development time is extended (machine energy consumption is extended), and organoleptic properties should not improve. However, as the test speeds are exceeded, temperatures inside the chamber remain low, causing the development time to be extended with higher energy consumption and under these conditions the organoleptic properties are not improved.