Federico Walas Mateo

In the context of industrial digitalization, the Industry 5.0 paradigm introduces digital twins as a cutting-edge solution. This study explores the concept of digital twins and their integration with the Industrial Internet of Things (IIoT), offering insights into how these technologies bring intelligence to industrial settings to drive both process optimization and sustainability. Industrial digitalization connects products and processes, boosting the productivity and efficiency of people, facilities, and equipment. These advancements are expected to yield broad economic and environmental benefits. As connected systems continuously generate data, this information becomes a vital asset, but also introduces new challenges for industrial operations. The work presented in this article aims to demonstrate the possibility of generating advanced tools for process optimization. This, which ultimately impacts the environment and empowers people in the processes, is achieved through data integration and the development of a digital twin using open tools such as NodeRed v4.0.9 and Python 3.13.5 frameworks, among others. The article begins with a conceptual analysis of IIoT and digital twin integration and then presents a case study to demonstrate how these technologies support the principles of the Industry 5.0 framework. Specifically, it examines the requirements for applying the golden batch concept within a biological production environment. The goal is to illustrate how digital twins can facilitate the achievement of quality standards while fostering a more sustainable production process. The results from the case study show that biomaterial concentration was optimized by approximately 10%, reducing excess in an initially overdesigned process. In doing so, this paper highlights the potential of digital twins as key enablers of Industry 5.0—enhancing sustainability, empowering operators, and building resilience throughout the value chain.

Considering the novel concept of Industry 5.0 model, where sustainability is aimed together with integration in the value chain and centrality of people in the production environment, this article focuses on a case where energy efficiency is achieved. The work presents a food industry case where a low-code AI platform was adopted to improve the efficiency and lower environmental footprint impact of its operations. The paper describes the adoption process of the solution integrated with an IIoT architecture that generates data to achieve process optimization. The case shows how a low-code AI platform can ease energy efficiency, considering people in the process, empowering them, and giving a central role in the improvement opportunity. The paper includes a conceptual framework on issues related to Industry 5.0 model, the food industry, IIoT, and machine learning. The adoption case’s relevancy is marked by how the business model looks to democratize artificial intelligence in industrial firms. The proposed model delivers value to ease traditional industries to obtain better operational results and contribute to a better use of resources. Finally, the work intends to go through opportunities that arise around artificial intelligence as a driver for new business and operating models considering the role of people in the process. By empowering industrial engineers with data driven solutions, organizations can ensure that their domain expertise can be applied to data insights to achieve better outcomes.

Nowadays industries like mining are focused in the need of improving processes towards net zero emissions and accomplishing with united nations' sustainable development goals. This article presents a case at a copper mine where an artificial intelligence solution is adopted to optimize industrial processes. The paper illustrates the way a software solution using a low code platform framework can democratize the use of advanced analytical tools in the industrial sector to improve production processes. The low code approach is complemented by lean startup methodology to adapt the solution to the industrial domain and establish a co-creation environment among software engineers and industrial processes experts. This study pretends to highlight the use of industrial data and the way traditional industries are migrating towards the industry 5.0 paradigm, empowering people at the plant and achieving more environmentally friendly processes by the use of digital solutions.

Connected products generate data that are being seen as a key source of competitive advantage, and the management and processing of that data are generating new challenges in the industrial environment. This paper proposes a conceptual framework and preliminary findings to go deeper into a bibliometric analysis around the idea of artificial intelligence and machine learning (AI/ML) as a tool for the optimization of processes within the Industry 4.0 model. Methodologically, a technological mapping was carried out through an exercise on Scopus indexed database, the results of which were analyzed using bibliometric indicators. The bibliometric study is completed with a screening of relevant papers searching for linking among the industrial Internet of Things (IIoT) or Internet of Things (IoT), IA/ML and process optimization. Finally, this paper gives information about the state-of-the-art AI/ML applied to the optimization of industrial processes and presents a novel Canadian startup that has a business model that aims to make AI/ML easy to use in the industrial world towards a lean processes strategy.

Traditional industries like mining are in the spotlight for the need of improving processes towards net zero emissions. This article presents a case on a new business model to ease the adoption of Machine Learning (ML) to optimize industrial processes, applied to a copper mine. The focus of the paper is to present an agile methodology to ease the adoption of ML in industrial environments. The authors intend to illustrate the way a ML platform with a Low code solution approach can give results in two months to optimize a traditional production process. The methodology used in the case allows to obtain a data model to be validated in less than two months. This work pretends to give more light to the use of industrial data and the way traditional industries can evolve towards the Industry 5.0 paradigm which emphasizes the need to achieve sustainability in the production environment. The featured process is copper milling and the adoption of the low code solution is based on lean startup methodology. The cycle to obtain validated results includes the involvement of people from the process as well as analytics experts. At the end, the contributions of the solution to improve Operational Equipment Effectiveness (OEE) can be seen, and the process operators became empowered with the predictions that give the platform.

This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of AI/ML adoption in steel manufacturing industries to optimize processes. The work aimed to highlight the opportunities that bring new business models based on AI/ML to improve processes in traditional industries. Methodologically, bibliographic research in the Scopus database was performed to establish the conceptual framework and the state of the art in the steel industry, then the case was presented and analyzed, to finally evaluate the impact of the new business model on the operation of the steel mill. The results of the case highlighted the way the innovative business model, based on a No-Code/Low-Code solution, achieved results in less time than conventional approaches of analytics solutions, and the way it is possible to democratize artificial intelligence and machine learning in traditional industrial environments. This work was focused on opportunities that arise around new business models linked to AI. In addition, the study looked into the framework of the adoption of AI/ML in a traditional industrial environment toward a smart manufacturing approach. The contribution of this article was the proposal of an innovative methodology to put AI/ML in the hands of process operators. It aimed to show how it was possible to achieve better results in a less complex and time-consuming adoption process. The work also highlighted the need for an important quantity of data from the process to approach this kind of solution.

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment.Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment.The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT or IoT under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization.The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm.The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment.

El paradigma de la Sociedad de la Información se fundamenta en la aplicación del conocimiento como variable clave en la generación de valor, y tiene como principal elemento difusor a la tecnología. Esta mancomunción entre información y tecnología se traduce en la mayor importancia que suman dia a dia en nuestra vida cotidiana las Tecnologías de Información y Comunicaciones (TICs). El impacto del Uso de la Información como herramienta de mejora se manifiesta asimismo en la capacidad de innovación de las empresas, convirtiéndose de esta forma en un elemento central para lograr niveles de competitividad superiores. En la nueva economía, la empresa y la tecnología pensadas en forma racional no pueden estar aisladas, se encuentran inseparablemente vinculadas en el permanente transito hacia nuevos desafíos Este trabajo se focalizará en la problemática de la incorporación de TICs en el medio productivo, y la necesidad de reforzar la cultura empresarial en lo que se refiere a consideración de la tecnología y la innovación como medio de apalancamiento para la competitividad de las PyMEs. El objetivo general es el análisis y desarrollo de conceptos asociados a Tecnologías de Información y Comunicaciones (TICs) de aplicación en el ámbito de la producción, y a la promoción de su implementación en distintos sectores tanto para lograr una mayor integración en las cadenas de valor, como así también facilitar el agregado de valor en productos o servicios. La metodología de trabajo incluye el desarrollo de un marco conceptual que describa el estado del arte de las TICs en ámbitos de la producción de clase mundial, y posteriormente el desarrollo de actividades para promover e incentivar el uso de estas herramientas tecnológicas en el ámbito empresario. Las actividades comprenden tareas de sensibilización, entrevistas con actores clave, y talleres de trabajo para discutir la temática.