In the aircraft engine Maintenance, Repair, and Overhaul (MRO) process, effective task planning relies heavily on the expertise of lead engineers. However, when predictive models are used to assist decision-making, issues with incomplete, unbalanced, and inconsistent data can lead to errors in the planning task. Therefore, reliable predictions are crucial for optimising the operational efficiency. This paper proposes a methodology to improve the prediction of maintenance times for an aircraft engine MRO process by integrating K-means clustering with Cosine and Jaccard similarity methods to define a reliable prediction interval. This methodology was compared with the predictions of the Simple Linear Regression method, which resulted in the prediction interval approach significantly reducing prediction errors, increasing prediction accuracy, while optimising process management and maintenance task planning throughout the MRO process.

The emergence of Digital Twins (DT) in Industry 4.0 has enabled the decision support systems taking advantage of more effective recommendation systems (RS). Despite the RS's growing popularity and ability to support decision-makers, these face two significant challenges, cold-start and data sparsity, which limits the system's capability to provide effective and accurate decision support. This paper aims to address these issues by conducting a literature review, analysing the current research landscape, and identifying the main enabling methods, algorithms, and similarity measures to mitigate these challenges. The performed analysis enables the point out of future research directions for developing effective and accurate RS that empower decision-makers.

The research about the digital twin concept is growing worldwide, especially in the industrial sector, due to the increasing digitisation level associated to Industry 4.0. The application of the digital twin concept improves performance of a system by implementing monitoring, diagnosis, optimisation, and decision support actions. In particular, the decision-making process is very time consuming since the decision-maker is presented with hundreds of different scenarios that can be simulated and assessed in a what-if perspective. Bearing this in mind, this paper proposes to integrate a digital twin-based what-if simulation with a recommendation system to improve the decision-making cycle. The recommendation system is based on a reinforcement learning technique and takes user knowledge of the system into consideration and trust in the system recommendation. The applicability of the proposed approach is presented in an assembly line case study for recommending the best configurations for the system operation, in terms of the optimal number of AGVs (Autonomous Guided Vehicles) in various scenarios. The achieved results show its successful application and highlight the benefits of using AI-based recommendation systems for what-if simulation in digital twin systems.

The digital transformation that is on-going worldwide, and triggered by the Industry 4.0 initiative, has brought to the surface new concepts and emergent technologies. One of these new concepts is the Digital Twin, which recently started gaining momentum, and is related to creating a virtual copy of the physical system, providing a connection between the real and virtual systems to collect and analyze and simulate data in the virtual model to improve the performance of the real system. The benefits of using the digital twin approach is attracting significant attention and interest from research and industry communities in the last few years, and its importance will increase in the upcoming years. Having this in mind, this paper surveys and discusses the digital twin concept in the context of the 4th industrial revolution, particularly focusing the concept and functionalities, the associated technologies, the industrial applications and the research challenges. The applicability of the digital concept is illustrated by the virtualisation of an UR3 collaborative robot which used the V-REP simulation environment and the Modbus communication protocol.

The digitalization advent is characterized by the increasing availability of huge amounts of data that allows to generate new knowledge to support decision-making, problem solving and process optimization. The successful implementation of this digital transformation strongly depends on the skills and competences that professionals can have in the different dimensions of the multidisciplinary vision associated to the data scientist profile. This paper describes a study, conducted under the scope of the Da.Re Erasmus + project, that surveys the existing trainings paths dedicated to the academic education of data scientists and the required hard and soft skills of the job offers for these professionals. The survey covers three countries of the project consortium, namely Portugal, Italy and UK.

Presently, many industries are facing strong challenges related to the demand of customized and high-quality products. These pressures lead to internal company's conflicts where current production systems have a rigid structure, forcing the company into a organization stall when a fast product change is required. Therefore, the need to smoothly migrate traditional systems into more feature-rich and cost-effective systems, namely Cyber-Physical Production Systems (CPPS), became a highly discussed topic. PERFoRM project focuses the conceptual transformation of existing production systems towards plug&produce ones to achieve flexible and reconfigurable manufacturing environments. In particular, the smooth migration process is considered crucial to effectively transpose existing production systems into truly CPPS. This paper describes the use of Petri nets to design the migration process under the PERFoRM perspective, taking advantage of its inherent capabilities to design, analyze, simulate and validate such complex processes.

Nowadays, many organizations intend to convert their existing production systems towards ones that are terized by adaptability, openness, flexibility and modularity. This requires a redesign of existing information processing systems especially related to control, leading possibly to cyber-physical production systems (CPPS). However, the implementation of new control technologies will have a direct impact on the normal operational status of production while engineers will also face several challenges and obstacles in adopting intelligent automation systems. New step-wise migration strategies are required to holistically support industries in their journey towards CPPS taking into account technical, economic and social aspects. This paper discusses the migration state-of-the-art strategies, analyzing them and providing a first attempt to define a migration approach for innovative production systems.