Conference Agenda

The present research work aims to develop a predictive system for demand planning using machine learning techniques in M ​​& M Bookstores - Bazar in Metropolitan Lima. Currently, the company faces difficulties in accurately predicting the demand for its products, resulting in inefficient inventory management, unnecessary stock accumulation, and loss of sales due to lack of products. Various predictive models were evaluated, with the random forest model having the best performance, with an accuracy of 92% in predicting demand. Thanks to the implementation of the system, the company was able to reduce excess stock by 15% and optimize the product replenishment process, improving operational efficiency. These results demonstrate that the use of machine learning can significantly contribute to decision making in inventory management, with the possibility of being replicated in other retail stores to obtain similar benefits.

Earthquakes are natural events that can cause severe damage and loss of life, especially in seismic zones. Education on preparedness and response to these events is crucial, particularly for children and youth. The “Tremor Kid” project proposes an augmented reality mobile application aimed at educating children and young people about earthquake preparedness and response. The application, created with augmented reality technologies such as Unity, Vuforia and ARCore, offers a series of interactive and educational activities guided by a virtual assistant who dynamically explains safety protocols. Users interacted with the images provided by the application that were placed in their environment, learned how to identify safe areas, prepare emergency backpacks and first aid kits, and participated in activities to identify safe locations during an earthquake. To evaluate the effectiveness of the application, a pilot study was conducted with nine students from the “Jesus Reparador” school, applying questionnaires before and after the use of the application. The results of the study showed that users obtained an improvement in earthquake preparedness and response.

The onset of the fifth industrial revolution has brought an accelerated evolution in the automotive industry, thanks to emerging technologies that have enabled new trends that influence the value contribution of consumers. Therefore, in a globalized and competitive market, it is essential to understand the factors that influence consumer perceptions to guide sustainable innovation in the automotive industry. The above gives room for factors that can contribute positively to the consumer expectations of the new millennium. Therefore, the objective is to identify factors that positively impact consumer perception, establish sustainable trends and strategies for competitiveness in the global automotive industry, and contribute to the understanding of consumer-industry dynamics within the framework of sustainability. The qualitative approach, supported by interviews and documentary review, allowed for capturing detailed perceptions and verifying consistency in the findings. In the conclusions, advantages such as operational sustainability and cost reduction were highlighted as key factors in positive consumer perceptions. Finally, the conclusions underline the need for continued innovation in energy efficiency in battery systems, which, unlike previous research, this paper focuses on consumer perception of recharging technology, a little explored but crucial factor in competitiveness.

The adoption of IoT devices in smart homes represents a key opportunity to optimize energy efficiency and promote more sustainable consumption. However, the lack of common standards and interoperability issues among manufacturers constitute significant obstacles to large-scale implementation. The main objective of this review was to evaluate the impact of IoT devices on energy efficiency in smart homes, highlighting the most commonly used devices, as well as their benefits and limitations. To achieve this, the PICO technique was applied, identifying the components according to the intervention question and establishing the corresponding keywords. A total of 221 articles were found in the SCOPUS database, which were filtered and selected according to the established eligibility criteria using the PRISMA flow diagram. This resulted in a final sample of 72 studies. The results show that IoT significantly improves the monitoring and control of energy consumption, although it faces technical limitations that hinder its widespread adoption. A key aspect identified is the need to establish standards that promote interoperability, which would allow for a more efficient and effective use of these technologies in sustainable homes. The findings of this review provide a solid foundation to guide future research and practical applications, aiming to overcome current technological challenges and seize opportunities for optimization in domestic energy management.

The purpose of this study is to evaluate the effects of the implementation of Industry 4.0 in process management in industries in the productive, health and business sectors, as well as to identify the technological tools applied in companies. Therefore, a systematic review of scientific literature was carried out between 2017 and 2024 based on a collection of Scopus data. 22 investigations that met the eligibility criteria were identified. The results show the effectiveness of Industry 4.0 tools when implemented in a company's processes. These allow for greater versatility in decision-making, the empowerment of processes, and increased quality. The most representative tools are the MES (Manufacturing Execution System), the HORSE system, and the BPM (Good Manufacturing Practices). In conclusion, this study shows the importance of implementing I4.0 to achieve optimal integration of processes in business management, which impacts the performance and productivity of corporations

This article describes the implementation of an automation module with an IoT platform to control the water supply in the elevated tank of an educational institution, which operates manually. To do this, the procedure was carried out in three stages, the first was executing the Home Assistant platform on a Raspberry Pi 4 model B. Then, the data collection module was designed and programmed, which was implemented inside the elevated tank to measure the variable water level. And, as a last stage, an IoT relay or switch was used to remotely and automatically control the on-off of the educational institution's submersible pump, managing to dispense with manual control. The partial results obtained established the optimal filling levels of the elevated tank, determining that with a level greater than 1.48 m of water the tank overflows, and with a level less than 0.66 m the water pressure in the taps of the educational institution was deficient. Therefore, such parameters determined the implementation of an on-off control with hysteresis with a dead zone of +/- 0.32 m, allowing the level variable to be automatically established within the established limits, guaranteeing the water supply without intervention manual and filled to 93.34% or 9.25 m3 of its capacity, in order to serve an educational community of more than 700 students.