ABSTRACT. Ongoing trends like the digitization of engineering processes and smart products result in a large amount of data about products, their development process, and their usage phase. Using this data is not without its challenges. Especially in Small and Medium-sized Enterprises (SMEs), the data is typically stored in heterogeneous data formats and IT systems across several departments. Integrating data for specific application scenarios, like the development of digital twins, typically requires a large amount of manual effort. This contribution describes a process for the collection of data from heterogeneous sources across the system life cycle. The process is based on the phases of the Analytics Solutions Unified Method for Data Mining/Predictive Analytics (ASUM-DM) known from data mining projects. Each step of the process allows for different methods. However, this contribution describes a set of methods that are especially suitable for an envisioned scenario in SMEs with heterogeneous IT systems. In this set of methods, the mapping of corresponding data objects across different sources is assisted by an Artificial Intelligence (AI-)based tool support. The method prescribes several steps to ensure the correctness of the AI’s suggestions and leaves the final decision to a human expert.

ABSTRACT. This research analyzes the effect of internal and external financing on the labor productivity of Ecuadorian companies from 2009 to 2014 using data from the National Institute of Statistics and Censuses (INEC). In addition, ordinary least squares (MCO) regression for panel data is applied as an estimation strategy. The study focuses on explanatory variables (external and internal funding) and independent variables (number of employees, source of foreign capital, innovation of new services, and purchasing of machinery and equipment). The results demonstrate that external financing, the number of employees, foreign capital sources, and machinery/equipment purchases have a positive impact on labor productivity. In contrast, internal financing has no effect, while the introduction of a new service reduces labor productivity. Identifying the factors affecting Ecuadorian companies' labor productivity is relevant for business economic development because with this information, companies could develop capabilities and strategies to improve competitiveness in the market, and consequently survival.

ABSTRACT. The digitization of production environments has become a key priority in the industrial sector due to its substantial benefits, including significant reduc-tions in production costs and response times. The emergence of digital twins (DTs) stems from the industry's continuous pursuit of advanced digitalization technologies. However, their implementation remains a considerable chal-lenge due to the complexity of many systems, resulting in prolonged de-ployment times and high financial costs for companies. This study aims to define an architecture that demonstrates the capabilities of Product Lifecycle Management (PLM) systems in facilitating the implementation of DTs in in-dustrial manufacturing environments. The proposed approach leverages cost effective systems with interoperability capabilities, thereby making DT tech-nology more accessible to small and medium sized enterprises (SMEs).

ABSTRACT. Manufacturing custom parts in several industry sectors has become challenging, especially in the aerospace industry. This factor is due to the complexity of the product, low manufacturing levels, and a competitive market in which it seeks to reduce the consumption of resources and optimise processes. Traditionally, the process of generating and analysing manufacturing plans is manual, in which a team of engineers analyses piece by piece and generates manufacturing plans for each customised part. In a complex product such as an aircraft, this number can exceed 500,000 parts. Disruptions due to project changes can lead to increased expenses, time investments, and environmental impacts. Based on this context, this paper discusses Ontology-Based Engineering (OBE) Systems for Knowledge-Driven Manufacturing using concepts such as Semantic Interoperability (SI), Automated Feature Recognition (AFR), Models for Manufacturing (MfM) and Large Language Model (LLM) to solve the problem of integration of data, information and knowledge at the stages of the production process. The proposed framework enables the seamless integration of digital and physical technologies, facilitating communication and transparency across the complex manufacturing process. Implementing the OBE system enhances manufacturing efficiency, reduces costs, and improves product quality in the aerospace industry. An experimental case showed the ability to store data and knowledge in a structured way and thus generate different manufacturing plans, supporting process decision-making. Future work will focus on validating the manufacturing plans generated by the system and comparing them with traditionally generated ones, with the objective of application in product development.

ABSTRACT. The development of smart cities and smart campuses is changing as a result of the combination of Artificial Intelligence (AI), Digital Twins (DT), and the Internet of Things (IoT), which allows for improved automation, lossless interoperability, and intelligent decision-making. In this paper, we propose a framework to support the application of DT technology in the context of smart cities. Node-RED is the main hub for managing IoT sensor data, real-time monitoring, and actuator control in the smart city-oriented system presented in this study. As a hypervision dashboard, ThingBoard is incorporated into the suggested design to guarantee the smooth integration of diverse IoT devices. Furthermore, natural language-based interactions are made possible by Large Language Models (LLMs), such ChatGPT-4, which allow AI-driven automation through text and voice instructions via a Telegram bot. Additionally, a Digital Twin built with Unity 3D is created to simulate and visualize urban infrastructure in real-time. The paper also discusses future enhancements, including predictive analytics, machine learning-driven optimization, and edge AI deployment, to further enhance the scalability and adaptability of smart city solutions.