Programa del congreso

This study developed an integrated improvement model to optimize operational efficiency in the Liquefied Petroleum Gas (LPG) bottling process by strategically converging Lean Manufacturing tools with enabling Industry 4.0 technologies. The main root causes of reprocessing were identified as recurring failures in the sealing system, excessive idle times, and operational disorganization. To address these issues, the DMAIC methodology was applied to structure a solution based on 5S, Standardized Work, and Total Productive Maintenance (TPM), complemented by IoT sensors that enabled predictive maintenance at critical control points. The model was validated through simulation using SIMIO software, which demonstrated significant improvements: an increase in Overall Equipment Effectiveness (OEE), a 67% reduction in defective products, an 83% decrease in reprocessing, and greater production flow stability. The results show that the synergy between Lean practices and digital technologies enables systemic interventions with high impact on quality, reliability, and productivity. It is concluded that the proposed model is effective, scalable, and replicable, offering a viable alternative for enhancing competitiveness in industrial plants within the energy sector.

Today is essential to optimize resources and maximize yields. This study focuses on the calculation of Beta (β) indicators using matrix algebra in Matlab, as a tool to scientifically evaluate the relationship between soil variables and crop productivity. The β-coefficients allow us to identify which soil factors (pH, organic matter, nutrients) have the greatest impact on yield, thus avoiding the empirical use of inputs. Data were collected from 5 soil samples in coffee farms in Honduras, measuring: pH, organic matter (OM%), cation exchange capacity (CEC), nitrogen (N), phosphorus (P), potassium (K) and yield (kg/ha). These data were organized into an X matrix (predictor variables) and a Y vector (performance). Using Matlab, the multivariate regression formula was applied: β = (XTX)⁻¹XTY to calculate the standardized coefficients. The model demonstrated 95% accuracy in predicting yields. The results confirm that: Organic matter is the most important factor in increasing productivity in tropical soils.The method reduces costs by avoiding superfluous applications (e.g., liming when pH is not limiting). The matrix calculation of β-indicators in Matlab: It provides a scientific approach to prioritizing agricultural inputs. Increases yields (up to 20% in pilot cases) by correcting only critical variables.

STEM education faces the challenge of going beyond technical development to include competencies that prepare students for complex and changing work environments. This article presents the ACDEA (Applied Experiential Learning, Co-Creation and Didactics) platform, a methodological proposal that integrates gamification, entrepreneurial thinking, and the co-creation of educational experiences by students. Through a methodological structure based on four phases, the proposal seeks to transform the roles of teachers and students, strengthen commitment to learning and generate reusable and scalable pedagogical resources.

This Systematic Literature Review (SLR) addressed the effectiveness of Industry 4.0 methodologies for waste reduction in production processes. However, strengths and weaknesses were identified in the waste elimination process that limit the efficient use of resources. Therefore, the objective was to evaluate the most effective I4.0 technologies and tools to eliminate waste in the production processes of the industrial sector. To achieve this objective, a qualitative, quantitative and mixed research, corresponding to a systematic literature review, was used. Based on the established inclusion and exclusion criteria, 30 open access articles were selected from the Scopus and Scielo databases. The results showed that the most frequent types of waste in production processes are: material defects, waiting times, unnecessary movements and excessive processing. Likewise, it was identified that Artificial Intelligence (AI) was the most implemented technology in the analyzed studies. In conclusion, it was determined that Artificial Intelligence is one of the most efficient tools for process optimization in the industrial sector, reporting substantial improvements of up to 61.00%.