Conference Agenda

In the context of Industrial Digital Transformation (IDT), Overall Equipment Effectiveness (OEE) has become a key metric for evaluating production efficiency. This study presents the development of a prototype for generating OEE reports using Ignition and Sepasoft's OEE Downtime module. The objective is to automate OEE calculation in an industrial packaging process for rice production lines, minimizing manual data collection errors and improving real-time decision-making. The proposed system integrates real-time data acquisition, downtime tracking, and efficiency analysis, providing a comprehensive tool for monitoring equipment performance. The results demonstrate that automated OEE tracking enhances operational visibility, enabling companies to optimize production and reduce inefficiencies. This research highlights the relevance of MES (Manufacturing Execution Systems) and Industry 4.0 technologies in improving manufacturing productivity and data-driven decision-making.

This study presents the design and implementation of an autonomous, IoT-enabled weather station engineered for real-time climate monitoring and high-precision forecasting. Addressing the need for localized meteorological tools in agriculture, urban planning, and environmental risk management, the system integrates diverse hardware and software technologies into a cohesive, portable unit. Data acquisition of key variables—including temperature, humidity, atmospheric pressure, and wind dynamics—is managed by Arduino™-based embedded systems, while a Raspberry® Pi facilitates localized edge computing. Central to its predictive capability is a multivariate Long Short-Term Memory (LSTM) deep neural network, trained to identify non-linear temporal patterns within climatic datasets. Experimental validation confirmed high operational reliability in data transmission and storage. The LSTM model achieved exceptional predictive accuracy, maintaining a Mean Squared Error (MSE) below 5%, thereby demonstrating its capacity to anticipate complex environmental trends. By synthesizing LoRa™ connectivity with edge-deployed Deep Learning, this research provides a low-uncertainty solution for hyper-local climate prediction. This architecture represents a significant advancement for data-driven decision-making, offering a scalable and efficient tool for sustainable resource management and smart city initiatives in climate-sensitive applications.

This work presents the design, implementation, and experimental validation of a real-time monitoring module for electric vehicle chargers (EVSE), based on the ESP32 microcontroller and IoT technologies. The system acquires critical electrical variables (voltage, current, and power) through Modbus RTU over RS485 communication with an energy meter, and integrates a DS18B20 temperature sensor for thermal supervision. Data are displayed locally on a TFT screen and remotely through an embedded web server accessible via Wi-Fi. Tests performed on a commercial One-S charger demonstrate that the solution is stable, reliable, and capable of reflecting real-time changes between charging and no-load states without interfering with the equipment's operation. The proposed system offers an economical, scalable, and easily implementable alternative to increase operational transparency and monitoring capabilities in mid-to-low-end EVSE, thereby contributing to a safer, more efficient charging infrastructure prepared for integration into smart grids.

In industrial automation, PLCs such as the Siemens S7-1200 are essential components for process management; however, the need for remote access to these devices introduces vulnerabilities such as unauthorized access. Although VPNs are used to protect the communication channel, their authentication is often limited to password-based access or manually granted permissions. In addition, industrial monitoring systems lack simple and interactive remote query mechanisms, limiting the ability to verify the PLC status, access history, or the user who performed an operation.

This study proposes the implementation of a secure remote control system based on a Raspberry Pi 5 acting as a gateway running ZeroTier to establish secure connectivity and microsegmentation to a Siemens S7-1200 PLC. Access is restricted through facial biometric authentication implemented with an anti-spoofing module, MediaPipe, and Teachable Machine. A conversational monitoring workflow developed in n8n is also integrated to query the PLC status via WhatsApp.

Validation included 240 biometric attempts (120 authorized and 120 unauthorized), achieving an accuracy of 95.42%, FAR of 0.00%, and FRR of 9.17% under a confidence threshold of 0.95. For PLC control, 50 Put/Get operations were performed, achieving 100% success, with an average end-to-end latency of 192.40 ms (p95 = 238.68 ms). These results show that the proposed architecture enables secure remote PLC access with biometric authentication while maintaining communication performance compatible with industrial environments.

Unplanned downtime is a key cause of availability and OEE losses in industrial systems. This article develops and implements a Cyber-Physical Production System with distributed control based on Multi-Agent Systems, where Resource Agents (RA) interact with physical process variables and their functionalities. The development of the CPPS is structured through model-based engineering using SysML for system description and analysis, facilitating conceptual transfer and reusability. The cyber layer is integrated non-intrusively with an industrial controller through the OPC UA protocol, preserving control determinism. In the presence of induced sensor faults (physical layer), the RA detects inconsistencies, classifies criticality and, when the fault is compensable, executes resilience through soft-sensors and override flags. This enables temporary substitution of the physical signal, maintaining operational continuity and logging maintenance alerts, improving the availability variable of OEE. The proposal is validated in an emulated Pick & Place cell and subjected to tests with 20 cycles and random perturbations, compared against centralized industrial control and with the multi-agent system activated. With ideal quality and performance (≈90%), preliminary results show an availability of at least 70%. The MAS-based CPPS can increase OEE in traditional industrial systems.

Recent research highlights the growing interdependence between knowledge management, organizational culture, innovation, and performance, particularly in contexts shaped by digital transformation. However, the literature remains conceptually fragmented, often addressing leadership, innovation, efficiency, and performance as isolated constructs. This paper proposes the IDEA Model of Innovative-Digital Organizational Culture as integrative analytical framework grounded in a systematic review of per-reviewed studies published between 2020 and 2024. Following PRISMA guidelines, 87 articles were analyzed using bibliometric mapping and conceptual synthesis techniques supported by R software. The model organizes four interrelated analytical components –incremental and radical innovation, organizational performance, operational efficiency, and innovation agility– and examines their articulation through managerial roles and organizational configurations. Rather that establishing causal relationships or normative typologies, the IDEA Model provides a structured framework for interpreting recurrent patterns identified in the recent literature. The proposed model contributes to knowledge management research by offering a coherent structure to analyze how organizations integrate cultural, leadership, and knowledge-based practices to sustain innovation, efficiency, and adaptability in digital organizational contexts.