Industry 4.0 is a paradigm shift in manufacturing that integrates advanced technologies, such as the Internet of Things (IoT), cloud computing and artificial intelligence, to create smart factories. Collaboration is a critical element of Industry 4.0 projects, as it involves different organisations working together to achieve a common goal. In recent years, the common goal for such projects has been the development of technology and applications that generate added value for the participating companies and can then be used in the industry. Data interop- erability is essential in collaborative Industry 4.0 projects, allowing organisations to share and exchange data without errors. This article discusses the importance of data interoperability in European collaborative projects in the context of Indus- try 4.0, analysing its benefits, challenges, and recommendations and providing a methodology to follow.

Currently, collaborative SC planning driven by lean Industry 4.0 technologies is helping manufacturers to be more agile and efficient in their operations. This paper addresses the problem of information sharing among supply network partners in the footwear industry for the computational optimisation of SC planning. The methodology called C2NET is used for model input data through standardised tables (STables). The results show a simplified database relational diagram, which can be applied by different companies from this industrial sector, as well as researchers for their mathematical optimisation developments.

The use of information to make smart decisions is the main ambition of Industry 4.0. Within the framework offered by this new concept, some technologies such as the Internet of Things (IoT) and artificial intelligence (AI) are becoming crucial for the development and modernization of companies. The use of the Industrial Internet of Things (IIoT) enables the monitorization of the status of the physical variables that influence the manufacturing process, whereas the use of AI to process the data and technological advances in terms of communication protocols (e.g., OPC/ UA) enables the interaction with the machine to reconfigure the manufacturing line, maintaining the process stable through smart and automatic decisions. This paper presents an application that is being developed within the i4Q project to immediately detect deviations in the manufacturing’s parameters and to the online automatic reconfiguration of the process to reduce quality problems, waste, and breakdowns in the machine tools, using technologies based on the Industry 4.0. Additionally, the application is deployed in a real company as a steep before its full implementation.

The job shop is a production environment that is frequently analyzed and modeled as an isolated cell with little or no interaction with other areas of the production system or the supply chain of which it forms part. For decades, the abstraction on which this endogenous approach is based has provided a profound understanding of the job-shop scheduling problem and the static aspects characterizing it. Nowadays, it is worth highlighting the dynamic and interconnected nature of the contemporary job shop, a space where the design principles and enabling technologies of Industry 4.0 acquire a significant role. This paradigm provides the job shop with new opportunities to improve competitiveness through its digital transformation, but also poses a challenge with risks and barriers. From this interconnected digital job-shop perspective, the efficiency of its operations, including that of the job scheduling process, is critically conditioned by the interoperability between its own production resources and those of the entities in its intra- and supracompany environment that make up the value chain. This article studies the support that interoperability can specifically provide in the job-shop scheduling itinerary toward higher levels of automation, autonomy, and capacity for real-time action. This transformation process is known in the literature as smart manufacturing scheduling.

This paper presents a containerization strategy and workload distribution models useful to build and distribute algorithms as microservices in Zero-Defects Manufacturing solutions. The proposed strategy and workload distribution model can be used to build and deploy algorithms that solve specific problems related to defect prediction and detection and process and product optimization. The proposed containerization strategy is rooted in a layered model that decouples the algorithm instantiation and execution, the interfaces to access data services, and the manage- ment of the algorithm as a service. The paper also presents different models for building and distributing algorithms as microservices in cloud/edge architectures.

This article aims to bridge the gap between university studies that currently integrate marketing-related educational content and the world of science and research by proposing an innovative approach to integrating marketing principles into university curricula in a unique way, that focuses specifically on the particular context of EU research and development projects. These projects increasingly embrace technologies and methodologies as diverse and cutting-edge as artificial intelligence and big data; resilience, sustainability and circular economy; blockchain; ultra-fast connectivity; or augmented and virtual realities. However, all these projects, in addition to these technologies and methodologies, require other types of knowledge and skills for their development and effective deployment, which are embodied in specific work packages for communication and dissemination, community building and the development of exploitation plans. Remarkably, these activities are strongly related to knowledge rooted in marketing, albeit from a different perspective. The proposed educational initiative advocates the inclusion of practical sessions in university marketing subjects to explore a specialised form of marketing: exploitation in the field of EU research and development projects. Underpinning this pedagogical endeavour are two primary objectives. Firstly, to broaden students' perspectives on the scope of marketing, traditionally associated with the corporate sphere, by emphasizing its applicability in unconventional domains such as science and research, thereby fostering a holistic understanding of marketing's multifaceted role. Furthermore, this initiative pretends to introduce students to research as an engaging workspace, which goes beyond the scientific and technological realm and paves the way for interdisciplinary exploration, encompassing knowledge areas such as management, intellectual property, exploitation strategies and, prominently, digital marketing. Employing active learning methodologies, including case studies and collaborative projects, students will be immersed in practical experiences mirroring real-world scenarios. By simulating the diverse challenges faced by marketing professionals in the context of EU research and development projects, students will not only acquire theoretical knowledge but also cultivate essential skills in strategic thinking, communication, and adaptability. Through this innovative pedagogical approach, the methodology presented in this paper seeks to inspire students to recognise the integral role of marketing in diverse fields, thereby fostering a generation of professionals prepared to adapt to the complexities of unique and diverse work environments.

Collaboration plays a key role in the success attained to date by networks of innovation ecosystems generated around entities known as Digital Innovation Hubs (DIHs), recently created following European Commission initiatives to boost the digitisation of the European economic fabric. This article proposes a conceptual framework that brings together, defines, structures and relates the concepts involved in the collaborative interaction processes within and between these innovation ecosystems to allow comprehensive conceptualisation. The developed framework also provides an approach that helps to tangibilise collaboration as a management process. Here the goal is to ultimately move towards not only qualitative, but also quantitative modelling to bridge the research gap in the state of the art in this respect. The data-driven business-ecosystem-skills-technology (D-BEST) model, devised to configure DIHs service portfolios in a collaborative context, provides the reference basis for the interorganisational asset transfer methodology (IOATM). This is the keystone that structures the framework and constitutes its main contribution. Through the IOATM, this conceptual framework points out collaboration quantification, and serves as a lever for its modelling to deal with collaboration accounting by: turning it into a more controllable management element; guiding practitioners' efforts to improve collaborative processes efficiency with an approach that pursues objectivity and maximises synergies.

Smart manufacturing scheduling (SMS) requires a high degree of flexibility to successfully cope with changes in operational decision level planning processes in today's production environments, which are usually subject to high uncertainty. In such a unique and complex scenario as the real job shop, the modelling of SMS as a Markov decision process (MDP), and its approach by deep reinforcement learning (DRL), is a research field of growing interest given its characteristics. It allows us to consider achieving high flexibility levels by promoting process automation, autonomy in decision making, and the ability to act in real time when faced with disturbances and disruptions in a highly dynamic environment. This paper addresses the problem of scheduling a quasi-realistic job shop environment characterised by machines receiving jobs from buffers that accumulate numerous jobs using a wide variety of parts and multimachine routes with a diverse number of operation phases by developing a digital twin of the job shop based on a MDP with the DRL methodology. This is approached by: modelling the job shop scheduling environment with OpenAI Gym; designing an observation space with 18 job features; designing an action space composed of three priority heuristic rules; shaping a single reward function with a multi-objective characteristic; using the implementation of the proximal policy optimisation (PPO) algorithm from the Stable Baselines 3 library. This modelling approach, dubbed as job shop smart manufacturing scheduling (JS-SMS), is characterised by deterministic formulation and implementation. The model is subjected to validation by comparing it to several of the best-known heuristic priority rules. The main findings of this methodology allow to replicate, to a great extent, the positive aspects of heuristic rules and to mitigate the negative ones, which achieves more balanced behaviour in most of the measures established as performance indicators and outperforms heuristic rules from this multi-objective perspective. Finally, further research is oriented to dynamic and stochastic approaches to address the job shop reality in an Industry 4.0 context.

Validated quantitative models for lean supply chain planning (LSCP) are still scarce in the literature, particularly because conventional push systems have not been widely integrated and tested with pull systems in sustainable and resilient environments in the Industry 4.0 context. Hence the main contribution of this paper is to develop an optimisation model that is able to contribute to the LSCP with the combination of push and pull strategies. Here we present an integrated just-in-time (JIT) production system with material requirement planning (MRP) for a SC that takes a traditional five-level structure based on a mixed-integer linear programming model (MILP) dubbed as LSCP 4.0. The model is able to simultaneously plan the production and inventory of materials and finished goods to satisfy demand from forecasts and firm orders. The selection of alternative suppliers as a proactive measure to face disruptive events is also considered. Furthermore, sustainable practices are included in the objective function for profit maximisation by considering CO2 emissions. This proposal is tested in the footwear sector. The results demonstrate that the combined use of JIT and MRP through a quantitative approach improve performance in leanness, sustainability and resilience by decreasing the bullwhip effect at different SC levels.

Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome.

Smart manufacturing has emerged as a transformative force in the manufacturing industry, optimizing manufacturing processes through advanced technologies such as artificial intelligence, the Internet of Things, cloud computing, and big data
analytics. However, in order to reach Zero-Defect manufacturing it is crucial to utilize all data acquired during production.
In this paper, a novel approach is proposed that integrates quality assessment techniques with artificial intelligence to detect
defective parts and identify their root causes, leading to a more efficient and cost-effective manufacturing process. The approach
is validated by applying it to industrial injected plastic parts, demonstrating that it is possible to effectively detect faulty production causes and optimize the manufacturing process, resulting in reduced costs and waste. The results highlight the potential of this approach for use in a wide range of industries and its ability to facilitate the widespread adoption of this techniques.

Industry 5.0 emphasises social sustainability and highlights the critical need for personnel upskilling and reskilling to achieve the seamless integration of human expertise and advanced technology. This paper presents a methodological framework for designing personalised training programs that support personnel upskilling, with the goal of fostering flexibility and resilience amid rapid changes in the industrial landscape. The proposed framework encompasses seven stages: (1) Integration with Existing Systems, (2) Data Collection, (3) Data Preparation, (4) Skills-Models Extraction, (5) Assessment of Skills and Qualifications, (6) Recommendations for Training Program, (7) Evaluation and Continuous Improvement. By leveraging Large Language Models (LLMs) and human-centric principles, our methodology enables the creation of tailored training programs to help organisations promote a culture of proactive learning. This work thus contributes to the sustainable development of the human workforce, facilitating access to high-quality training and fostering personnel well-being and satisfaction. Through a food-processing use case, this paper demonstrates how this methodology can help organisations identify skill gaps and upskilling opportunities and use these insights to drive personnel upskilling in Industry 5.0.