Conference Agenda

The research evaluates the behavior of an expediting model that is being executed in the supply chain (SCM) in Peru, with the objective of validating that the application of the acceleration methodology allows increasing on-time and complete deliveries. (OTIF) from suppliers. The model is structured in 6 stages, from order tracking planning to obtaining the OTIF. The study has a quantitative approach, applied type, pre-experimental, correlational, and explanatory design. The population matches the sample that includes 24-month historical data (2023-2024) of acceleration categories. The technique used was the documentary review, using as an instrument a structured guide to collect, analyze and process reliable data from the model. The multiple linear regression method was used, exploring cause-effect relationships between the predictor variables and the response variable, using the SPSS statistical program. The result shows a significant relationship between the predictor variables and the OTIF, with an adjusted coefficient of determination R² of 0.96, which indicates that the model explains 96% of the variability of the OTIF. Furthermore, the ANOVA analysis and the individual significance tests of the coefficients confirm that all the predictor variables have a statistically significant impact on the OTIF (p<0.05). Likewise, the model does not present autocorrelation in the residuals (Durbin-Watson = 2.414) and shows a very low standard error of estimation (0.02909), guaranteeing precision in the predictions.

This study explores the combination of Lean Manufacturing with ergonomic practices to improve both productivity and the work environment in the industrial sector. A systematic literature review was conducted following the PRISMA methodology. The search was carried out in the Scopus database using a search equation that combined terms related to Lean and ergonomics. 50 articles were obtained for analysis, after using the inclusion and exclusion criteria carefully applied. The main findings indicate that 62% of the methodologies used are based on Lean Manufacturing and 24% on ergonomic practices. The most commonly used tools include Value Stream Mapping (VSM), REBA, RULA and Kaizen. These practices have been shown to reduce cycle times, improve operational efficiency, and decrease ergonomic risks. The studies highlight that the combination of these methodologies improves both productivity and worker well-being. Furthermore, the integration of both offers significant benefits, although it also poses challenges. The simultaneous implementation of both methodologies is crucial to eliminate waste and improve working conditions. However, a lack of representation of qualitative studies and research in certain industrial sectors was observed. Tools such as VSM and ergonomic assessments are effective in reducing waste and improving occupational health. Additional studies are recommended to validate these findings in various industrial contexts and to further explore the interaction between both methodologies.

The application of Lean methodologies is widely recognized for optimizing workflow, ensuring production efficiency, and minimizing costs. While methodologies such as Kanban have proven effective across various industries, their application in fragrance microenterprises remains largely unexplored. This study aims to assess the impact of the Kanban methodology on cost reduction in a fragrance-producing microenterprise by developing a replicable model. By applying realistic constraints to select the optimal alternative, followed by its design and implementation, the findings indicate a significant improvement in operational efficiency of 10.65%. Additionally, the reduction in opportunity costs resulted in a profit increase of $5,953.25. Furthermore, financial indicators, including a net present value (NPV) of $264.82 and an internal rate of return (IRR) of 16.20%, confirm the financial viability of the proposal. This study contributes to the adoption of Lean methodologies in resource-constrained microenterprises by providing a practical and effective approach to process improvement.

The main objective of this study was to design an ergonomic improvement for the workers of a heavy load transport company. For this purpose, two ergonomic tools were used, the REBA method and the Nordic questionnaire, both of which allowed us to know the current situation of the company, which had a score of 7 (medium risk), which represented an immediate intervention. On the other hand, the Nordic questionnaire showed us that there was discomfort in 4 out of 5 parts of the body and that it was shown as a pain level of 3. For this reason, 3 ergonomic strategies were proposed to improve the situation, these were active breaks, an improved driving procedure and the inclusion of a lumbar support. After estimating the results, the REBA score was reduced to 2 and the dysergonomic risks were reduced from 80% to 20%, and the discomfort was reduced from 52% to 32%, which demonstrates the importance of implementing strategies that allow improving the ergonomic condition of workers to avoid possible short- and long-term injuries.

Plastics recyclers face challenges in efficiently sorting materials due to manual processes that are slow and error-prone. The color sorting process represents a significant bottleneck in terms of time and accuracy. To address this problem, an automation model using an IoT-based system with Arduino microcontroller, color and proximity sensors, and actuators is proposed that optimizes this process by reducing the time required to sort materials. The model was validated by creating a working prototype, which showed a considerable improvement in efficiency: the manual sorting time of 2.75 seconds per material was reduced to 1.95 seconds in automated form, representing a 29% reduction in time per material. This breakthrough demonstrates the potential of automation to increase productivity, reduce errors and improve consistency in the plastics recycling process.

Advances in manufacturing have driven the development of automated systems that improve efficiency in industrial processes such as recycling. In this context, the implementation of automation and sensor technologies in recycling plants is essential for the accurate classification of recyclable materials. Capacitive and inductive sensors allow metals and other non-metallic materials to be detected, optimizing waste segregation, which reduces operating costs and improves workplace safety by reducing manual intervention. A key aspect of this modernization is the integration of the Internet of Things (IoT), which transforms the monitoring and control of these systems. Through IoT platforms such as Blynk, it is possible to view data such as the weight and quantity of recycled materials in real time, improving supervision and facilitating quick decisions. The scalability of the system allows efficient recycling management in the face of increasing demands. Key indicators include a reduction in cycle time by 58.3%, a decrease in the error rate between 15-20%, and a classification efficiency of 94.7%. In addition, productivity increased by 73.16%, allowing the classification of 32 pieces per minute, compared to 18.48 for the manual method, and processing capacity grew by 50.8%, reaching 60 kg/hour. Together, automation and IoT offer a comprehensive solution to recycling challenges, improving sustainability and reducing the environmental impact of industrial processes, while optimizing resources.