Este artículo analiza las políticas públicas y las redes para el desarrollo de las tecnologías 4.0 en Chile, a partir del estudio de la cartera de proyectos FONDEF-IDEA, desde 2012 a 2017. Mediante un análisis sintáctico de los títulos, objetivos y resúmenes se seleccionaron las iniciativas específicas que tienen directa o indirecta aplicación de las tecnologías 4.0, sobre una base inicial de 530 iniciativas públicas. Se analizaron las diferentes especializaciones sectoriales y la red social. Nuestra conclusión es que todavía no observamos al nivel de políticas públicas en Chile una estrategia que estimule el desarrollo descentralizado de estas nuevas tecnologías. Se comprueba una alta centralidad en la red de las aplicaciones de sensores en la minería del cobre, del monitoreo y la conversión del dato a la información, existiendo todavía una importante brecha a ser cubierta por las políticas públicas. A nuestro juicio es urgente contar con una estrategia de desarrollo tecnológico que acorte la brecha con los países que se encuentran en una etapa más avanzada.

The production planning problem in the Pinus radiata sawmill industry revolves around determining how to cut a set of logs of different diameters to obtain pieces with a rectangular base, typically of the same length as the original log. The primary objective is often to maximize the volumetric yield or the ratio between the total volume of the produced pieces and the available volume of the logs. Given the scarcity of timber forests, small-and medium-sized (SME) sawmills must optimize their operations to maximize the volumetric yield of logs, usually procured from third parties and may vary in quality and dimensions. In this context, the absence of decision support tools directly contributes to inefficient raw material utilization, consequently impacting the business’s profit. This study introduces a multi-objective mixed-integer linear programming model incorporating log availability and product demand as input parameters. The objective functions aim to minimize the total volumetric loss of utilized logs and the surplus quantity associated with products exceeding demand. The model integrates cutting patterns pre-determined for each log diameter as an additional input. The Cutlog© software was used to identify all the optimal cutting patterns. The ε-constraint method, implemented in the CPLEX© solver, was employed to solve the model. The model was validated using representative instances designed to emulate the challenges faced by SME sawmills. Real industry data, surveys, and commercial records from SME sawmills in southern Chile were utilized. The results confirm the effectiveness of the proposed model in addressing the multi-objective challenges encountered by these businesses. The model successfully identifies multiple solutions on the Pareto frontier, offering valuable insights for decision-making.

This paper focuses on adopting cybersecurity procedures in Chilean manufacturing companies in the context of the Fourth Industrial Revolution’s data-driven demands, which have exposed vulnerabilities in cybersecurity. This analysis is based on data from the Fifth Longitudinal Survey of Companies - ELE 5 - conducted by the National Institute of Statistics. Using the TOE adoption model, we employ binary and ordered Logit and Probit models with data from 574 companies and 17 explanatory variables. The objective is to gain insight into the factors influencing the adoption of cybersecurity processes, complementing the existing literature, which often focuses on developing specific technologies or conducting comprehensive analyses of digital transformation. The study highlights the significance of company size in explaining the adoption of cybersecurity procedures and reveals the relevance of explanatory variables as the depth of adoption increases. The findings underscore the need for public policies that facilitate the implementation of existing regulations, such as ISO 27.001, particularly for small companies. Additionally, the study emphasizes the importance of fostering a "culture of cybersecurity" across different sectors of society.

El Aprendizaje automático o Machine learning es una herramienta que está siendo utilizada para optimizar procesos industriales de alta complejidad. En la industria de producción de paneles contrachapados, el secado de chapas es uno de los procesos más importantes ya que permite obtener productos con alta calidad. La naturaleza biológica y alta variabilidad estructural de la madera hace que su procesamiento industrial sea multivariado y difícil de controlar. La gran cantidad de variables presentes y la posibilidad cada vez más frecuente de medirlas en tiempo real están permitiendo la disponibilidad de una gran cantidad de datos. En la actualidad, el enfoque basado en datos y las técnicas de Inteligencia Artificial, específicamente Machine learning, pueden permitir enfoques robustos de predicción y de control. En la industria de procesos con altos niveles de automatización es posible viabilizar la toma de decisiones para predecir la calidad del producto, monitoreando las variables de control explicativas. El objetivo de este trabajo fue evaluar el comportamiento de tres algoritmos de Machine learning para predecir la calidad del proceso de secado de chapas a partir de un número considerable de variables de entrada capturadas de un proceso industrial real. Se utilizó la plataforma Weka y código Python. Se evaluaron los algoritmos: K-Nearest-Neighbor, eXtreme Gradient Boosting y Support Vector Machine. Se realizó reducción de variables y dimensionalidad de análisis de correlación y de componentes principales. Los resultados demostraron que eXtreme Gradient Boosting logró una precisión del 76 % en la predicción de resultados de calidad. Finalmente, se concluye que la metodología de ingeniería de datos y los algoritmos fueron eficientes para predecir los datos industriales.

Wood industry is key for sustainability and an important economic activity in many countries. In manufacturing plants, wood variability turns operation management more complex. In a competitive scenario, assets availability is critical to achieve higher productivity. In a new fourth industrial revolution, Industry 4.0, data engineering permits efficient decisions making. Phenomena difficult to model with conventional techniques are turned possible with algorithms based on artificial intelligence. Sensors and machine learning techniques allow intelligent analysis of data. However, algorithms are highly sensitive of the problem and his study to decide on which work is critical. For the manufacturing wood processes, Industry 4.0 is a great opportunity. Wood is a material of biological origin and generates variabilities over the manufacturing processes. For example, in the veneer drying, density and anatomical structure impact the product quality. Scanners have been developed to measure variables and outcomes, but decisions are made yet by humans. Today, robust sensors, computing capacity, communications and intelligent algorithms permit to manage wood variability. Real-time actions can be achieved by learning from data. This paper presents trends and opportunities provided by Industry 4.0 components. Sensors, decision support systems and intelligent algorithms use are reviewed. Some applications are presented.

The plywood industry is one of the most significant sub-sectors of the forestry industry and serves as a cornerstone of sustainable construction within a bioeconomy framework. Plywood is a panel composed of multiple layers of wood sheets bonded together. While automation and process monitoring have played a crucial role in improving efficiency, data-driven decision-making remains underutilized in the industrial sector. Many industrial processes continue to rely heavily on the expertise of operators rather than on data analytics. However, advancements in data storage capabilities and the availability of high-speed computing have paved the way for data-driven algorithms that can support real-time decision-making. Due to the biological nature of wood and the numerous variables involved, managing manufacturing operations is inherently complex. The multitude of process variables, and the presence of non-linear physical phenomena make it challenging to develop accurate and robust analytical predictive models. As a result, data-driven approaches—particularly Artificial Intelligence (AI)—have emerged as highly promising modeling techniques. Leveraging industrial data and exploring the application of AI algorithms, particularly Machine Learning (ML), to predict key performance indicators (KPIs) in process plants represent a novel and expansive field of study. The processing of industrial data and the evaluation of AI algorithms best suited for plywood manufacturing remain key areas of research. This study explores the application of supervised Machine Learning (ML) algorithms in monitoring key process variables to enhance quality control in veneers and plywood production. The analysis included Random Forest, XGBoost, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Lasso, and Logistic Regression. An initial dataset comprising 49 variables related to the maceration, peeling, and drying processes was refined to 30 variables using correlation analysis and Lasso variable selection. The final dataset, encompassing 13,690 records, categorized into 9520 low-quality labels and 4170 high-quality labels. The evaluation of classification algorithms revealed significant performance differences; Random Forest reached the highest accuracy of 0.76, closely followed by XGBoost. K-Nearest Neighbors (KNN) demonstrated notable precision, while Support Vector Machine (SVM) exhibited high precision but low recall. Lasso and Logistic Regression showed comparatively lower performance metrics. These results highlight the importance of selecting algorithms tailored to the specific characteristics of the dataset to optimize model effectiveness. The study highlights the critical role of AI-driven insights in improving operational efficiency and product quality in veneer and plywood manufacturing, paving the way for enhanced industrial competitiveness.