Conference Agenda

Abstract– This systematic literature review compiles and
analyzes the main research developments, emerging approaches,
and trend lines related to the use of Machine Learning
techniques aimed at improving energy efficiency in industrial
settings. Using the PRISMA and PICO methodologies, 42
selected studies are evaluated based on inclusion criteria such
as recency, thematic relevance, and practical application. The
results show that Machine Learning techniques—such as
neural networks, decision trees, and hybrid models—have been
primarily used for energy consumption prediction, anomaly
detection, and industrial process optimization. However, a
fragmentation of knowledge is evident, with a predominance of
studies conducted in simulated environments and a lack of
validation in real-world scenarios. Limitations in the
interoperability and scalability of the implemented models
are also identified. In this regard, this review proposes a unified
conceptual framework to guide future research and integrated
industrial applications, promoting intelligent and sustainable
energy management aligned with green manufacturing and
green information technologies. The findings support the
potential of Machine Learning as a key tool in the digital
transformation of the industrial sector toward greater energy
efficiency and emissions reduction.
Keywords — Machine Learning, Energy consumption, Data
analysis, Industrial sustainability, Energy optimization

This research was presented in response to the need to improve the operational condition of a 12-km stretch of road located on the Casagrande-Roma-Chiclín highway. This was due to a high degree of deterioration in the surface and structural layers of the pavement comprising the section. Therefore, various studies were conducted, highlighting the different requirements and procedures that must be followed to correctly implement the AASHTO 93 method. The overall objective was to design a flexible pavement using the AASHTO 93 method to improve the operational condition of the Casagrande-Roma-Chiclín section in the province of Ascope, La Libertad, thus contributing to road safety, transportation efficiency, and the economic development of the region.

A quantitative methodology was employed, requiring data collection from traffic studies, soil studies, precipitation studies, and the development of the pavement design for the roadway. The structural design proposed a structure composed of a 10 cm surface layer, a 25 cm granular base, and a 30 cm subbase. This design proposal was presented with the goal of improving the roadway's operational condition by using the AASHTO 93 method for design.

The research is framed within the eighth SDG, which promotes decent work and economic growth. On the other hand, the general objective was to identify the relationship between inventory management and customer loyalty in a warehouse located in Ventanilla during the year 2025. In turn, the thesis was applied, with a quantitative approach, non-experimental and cross-sectional design, in addition it used the hypothetical-deductive method and a correlational level of research. The population was made up of 250 warehouse customers, of which a sample of 152 was taken. Thus, among the main results, a Spearman correlation coefficient (ρ) of 0.395 was obtained, indicating a weak positive relationship between inventory management and customer loyalty. In turn, a bilateral significance of 0.000 was obtained, allowing the null hypothesis to be rejected and concluding that there is a relationship between both variables.

Inadequate pharmaceutical inventory management generates stockouts in 69% of establishments and economic losses of 3.5-8.3% of annual revenue, affecting the continuity of medical treatments. This systematic review analyzes the effectiveness of Machine Learning and Business Intelligence techniques to optimize demand forecasting in small and medium pharmacies with limited technology. PRISMA methodology was implemented to identify relevant studies in Scopus between 2020-2025, using structured PICOC criteria. From 1,327 initial records, 40 studies that met specific inclusion criteria for pharmaceutical predictive analysis were selected. Data was extracted through standardized protocol, evaluating predictive accuracy, operational impact, and implementation barriers. Results demonstrate significant improvements of 30.5% in predictive accuracy compared to traditional methods. Neural networks showed highest effectiveness with 85.3% average accuracy, followed by ensemble models with 83.7%. A 28.5% reduction in stockouts and 25.7% inventory optimization was achieved. Main barriers identified include technical training limitations in 58% of staff and inadequate infrastructure in 43% of establishments. Machine Learning and Business Intelligence techniques represent scalable solutions to optimize pharmaceutical inventories, being viable even in resource-limited contexts, significantly contributing to economic sustainability and public health improvement.

his study aimed to analyze the relationship between organizational climate and job satisfaction among workers in the Peruvian retail sector during 2025. A quantitative, non-experimental, cross-sectional research design was employed, and data were collected through validated surveys. The sample consisted of 43 workers, and the Spearman’s rho test was used to examine correlations between variables. Results revealed a significant and positive correlation between organizational climate and job satisfaction (ρ = 0.739; p < 0.001). Furthermore, most dimensions—such as reward, interpersonal relationships, and support—showed moderate to strong correlations with job satisfaction. These findings suggest that improving the organizational climate could lead to higher levels of employee satisfaction, reinforcing the importance of fostering supportive and well-structured work environments in the retail sector.

Keywords- Organizational climate, job satisfaction, motivation, work environment, retail sector.