A Scalable Learning Factory Concept for Interdisciplinary Engineering Education: Insights from a Case Implementation

Abstract

This paper presents a concept for a Learning Factory (LF) designed for interdisciplinary engineering education. Learning factories are experiential learning environments that bridge the gap between theory and practice while supporting the demands of digital transformation. The proposed LF concept was developed using an integrated approach that assessed stakeholder needs and reviewed institutional infrastructure and capacity. These inputs were triangulated into a concept consisting of five core thematic components: Lean processes as an educational anchor, Enterprise Resource Planning (ERP) systems, Internet of Things (IoT)-based integration, simulation, and physical prototyping. Validation workshops with Small- and Medium-sized Enterprise (SME) managers, academic experts, and students confirmed the perceived relevance of this structure and its potential. The resulting concept focuses on practice-orientated, team-based learning methods that are in line with the principles of Education 4.0. The design sets goals in four key dimensions: educational integration, technological readiness, industrial relevance with SME orientation and flexibility and scalability. These design principles and practical insights can be utilized for future academic implementations of learning factories.

1. Introduction

The continuous evolution of manufacturing technologies and the increasing demand for digital integration have led to significant changes in both industry and education. In response, Learning Factories (LFs) have emerged as a concept that combines theoretical knowledge with practical, real-world production challenges. Learning factories are physical-digital environments designed for experimental learning and applied research, often modeled after real industrial processes (Abele et al., 2017). They are increasingly recognized as tools for teaching students and professionals the skills required in modern manufacturing systems.

With the advent of Industry 4.0, there is a growing realization that formal education alone is not enough to meet the demands of rapidly changing industrial environments. Education 4.0 focuses on learner-centered approaches, digital integration, and active, collaborative learning to meet the evolving needs of both students and industry. Lifelong learning—understood as the continuous development of skills over the course of a working life—is now a critical success factor for organizations, especially Small- and Medium-sized Enterprises (SMEs) (Kagermann et al., 2013; Schumacher et al., 2016).

While many companies today are investing in automation, Artificial Intelligence (AI) and cyber-physical systems, the Lean philosophy remains a cornerstone of efficient production. Lean production focuses on minimizing waste, improving processes and creating customer value. Rather than being replaced by digitalization, Lean principles are increasingly being enhanced by digital technologies—a phenomenon often referred to as Lean 4.0 (Kolberg & Zühlke, 2015; Tortorella & Fettermann, 2017). For example, real-time monitoring, digital work instructions and predictive analytics are being integrated into Lean workflows, leading to improved transparency, faster response times and smarter resource allocation. This convergence of Lean and digital transformation reinforces the need for educational environments where both paradigms can be explored simultaneously. Learning factories, which include real production facilities, IoT-enabled systems and structured Lean scenarios, provide a uniquely effective platform for this purpose (Bauer et al., 2018).

In addition, educational institutions play an important role in supporting regional industrial ecosystems, especially in regions where SMEs dominate the economic landscape. Many SMEs face challenges such as limited capital, a lack of digital maturity and a shortage of skilled labor (European Commission (EC), 2020). Academic institutions can address these gaps by tailoring their learning factory infrastructures to the specific needs of regional SMEs and offering co-developed training, prototyping and innovation services (Sorensen et al., 2022).

This paper presents a case-based LF concept tailored to academic environments that combine education with SME collaboration. The concept emphasizes digital technologies, modular infrastructure, and adaptability in education to support interdisciplinary curricula, staff training, and applied innovation projects. It is based on the principles of social constructivism, and emphasizes collaborative, student-centered learning as the foundation for skills development. Accordingly, the study examines how a compact learning factory can be configured for an SME-oriented academic context and considers which design features—spanning pedagogy, digital technologies, and organizational arrangements—most effectively balance educational integration with the practical requirements of regional industry.

Section 2 presents selected European learning factories, focusing on those that are embedded in university curricula and geared towards collaboration with SMEs. Section 3 presents a methodological framework adapted from established LF design approaches and tailored to the academic and SME context. Section 4 applies this framework to the Faculty of Engineering (RITEH) at the University of Rijeka, and describes the design phases, stakeholder involvement, spatial implementation, and the resulting LF concept. Finally, Section 5 provides concluding reflections and highlights the wider implications of this approach for interdisciplinary engineering education and collaboration with SMEs.

2. Learning Factory Practices in the European Context

In response to the need for practical, applied learning in modern production environments, learning factories have sprung up across Europe. These facilities simulate real production systems and allow both students and professionals to experience the complexity of today’s industrial processes. Thematic trends include digital transformation, which plays a central role in adapting academic learning to changing industrial requirements. Learning factories vary greatly in terms of scale, infrastructure and educational integration. To situate the proposed model within the diversity of existing practices, this section first presents prominent learning factories operated by academic institutions. These facilities often combine physical infrastructure with digital technologies and serve both education and applied research.

The examples presented in this section are not an exhaustive list, but rather a curated selection of prominent, academically run learning factories across Europe. The selection was based on their visibility in the literature, the availability of descriptive documentation and a focus on topics such as digitalization and interdisciplinary learning.

2.1. Mid- to Large-Scale Learning Factories in Academic Institutions

Germany is still the most active center for the development of LF. Among the most prominent is the Process Learning Factory CiP at TU Darmstadt (Technical University of Darmstadt), which is widely recognized for its long-standing contribution to Lean manufacturing training. Participants take part in machining, assembly and quality control activities that simulate an SME environment and focus on the production of pneumatic cylinders to apply Lean tooling, value stream mapping and continuous improvement techniques. TU Darmstadt also hosts several other learning environments that contribute to manufacturing-oriented education and research, including the Additive Manufacturing Center (AMC), which supports project-based learning in advanced manufacturing processes, the ETA Factory, which focuses on the energy efficiency of manufacturing systems, and the Flow Factory, which is used to research flexible and decentralized production control systems. Together, these specialized platforms form a comprehensive ecosystem that supports both formal engineering curricula and collaborative research on the future of manufacturing (Abele et al., 2024a).

At RWTH Aachen University, several learning environments support practice-oriented engineering education and digital transformation. The Textile Learning Factory 4.0 operated by the Institute of Textile Technology simulates an end-to-end intelligent manufacturing process with integrated Lean and Industry 4.0 functions. Although the Demonstration Factory Aachen (DFA) is operated as an independent company, it is a close partner of the university and is regularly used for student theses, project work and research collaborations—in particular by the Laboratory for Machine Tools and Production Engineering (WZL). These facilities represent RWTH Aachen University’s commitment to combining academic learning with industrial application and offer a variety of platforms for integration into the curriculum and training in SMEs (Küsters et al., 2017).

The learning factory at the Karlsruhe Institute of Technology (KIT) offers a unique and interdisciplinary environment in which students learn using a real-life scenario of a multi-stage assembly process for an electric motor and gearbox. The assembly stations can be equipped with different levels of automation. The LF is integrated into the curricula of the Master programs in Mechanical and Industrial Engineering at KIT. During the courses, the participants of the LF are confronted with and overcome typical real-life challenges, such as material defects, machine failures or irregular customer requirements (Karlsruhe Institute of Technology, 2016).

At the TU Munich, the Smart Production Lab (SPL) offers an interdisciplinary learning factory that reflects the challenges of modern production. The SPL is operated by the Institute for Machine Tools and Industrial Management (iwb) and includes practical modules in the areas of Lean manufacturing, digitalization, robotics and the circular economy. These areas are not treated in isolation, but as interconnected fields of learning, with Lean serving as the central pillar around which the other areas are grouped. Participants deal with real industrial products and technologies such as industrial robots, cost-effective automation and digital assistance systems. Topics range from value stream design and ergonomics in the workplace to collaboration between humans and robots and data-driven disassembly planning. Thanks to the flexible layout and scenario-based learning format, students and practitioners can customize their experience according to their learning needs and technology focus (Smart Production Lab, 2025).

At Reutlingen University, the Werk150 learning factory functions as an advanced educational, research and demonstration environment that comes very close to real industrial conditions. Housed in a fully functional production hall, Werk150 is embedded in the university’s NXT School of Sustainability and Technology and supports hands-on learning in the areas of discrete manufacturing, value stream-oriented production and digitally supported logistics systems. A notable component of the curriculum is a graduate training module on probabilistic prediction of turbulence in manufacturing and intralogistics, in which students work with real-world manufacturing execution system (MES), Enterprise Resource Planning (ERP) and machine learning tools to simulate, detect and mitigate disruptions in processes. The course focuses on a full analysis cycle—descriptive, diagnostic, predictive and prescriptive analysis—in a realistic factory environment that promotes both technical and decision-making skills (Schuhmacher & Hummel, 2022).

At Dresden University of Technology (TU Dresden), the Process-to-Order Lab (P2O-Lab) is a learning factory for the process industry with a focus on digitalization and modularization. In contrast to traditional manufacturing-oriented LFs, the P2O-Lab focuses on production flexibility, fast market introduction and individualized small batch production. It is jointly operated by the Chair of Process Control Systems and the Process Systems Engineering Group and integrates research and teaching on modular automation technologies, such as Module Type Package (MTP), and cyber-physical production systems. Digital twins, AI-based soft sensors and edge computing are central components of the learning environment. The laboratory offers structured training through lectures, self-study and practical laboratory experience. In addition, it facilitates technology transfer through direct collaboration with industry— and allows students to engage in the real world of hydrogen production and AI-powered modular automation systems. This makes the P2O-Lab a unique platform for interdisciplinary learning that bridges the gap between academic research, industrial application and next-generation process engineering (Vogt et al., 2023).

Outside Germany, at TU Wien (Vienna University of Technology, Austria), the Learning and Innovation Factory serves as a research and teaching platform that integrates product design, prototyping, production and logistics. This facility is part of the “Integrative Product Emergence Process (i-PEP)” in the bachelor’s and master’s degree programs, in which students design and manufacture slot cars in interdisciplinary teams. They go through various phases such as project planning, CAD modeling, CNC machining, assembly and final testing. The factory supports learning objectives in the areas of Lean manufacturing, process planning and cyber-physical systems (Kemény et al., 2016). In addition, TU Wien’s Pilot Factory Industry 4.0 focuses on discrete, variant-rich series production through to the production of very small quantities (high-mix and low-volume). All aspects of product creation, from design to assembly, are considered in an integrative approach (Pilot Factory at TU Wien, 2025).

At the Czech Institute of Informatics, Robotics and Cybernetics (CIIRC) at the Czech Technical University in Prague (CTU), the Testbed for Industry 4.0 serves as a fully digitized environment for research, education and demonstration of Industry 4.0 technologies. The facility focuses on areas such as automated assembly, production planning, virtualization and digital twins and integrates advanced technologies such as additive manufacturing, robotic manipulation and intelligent conveyor systems. The Testbed for Industry 4.0 is used for research, development, teaching and collaboration with industry partners and enables the testing of solutions for advanced and integrated industrial production processes. The Testbed for Industry 4.0 supports educational activities, including doctoral education and student participation in research teams focused on flexible manufacturing, robotics and production technologies. Students gain hands-on experience with real-world digital manufacturing scenarios and simulate challenges relevant to industry partners (RICAIP Testbed, n.d.).

At Arts et Métiers in France, the Evolutive Learning Factory (ELF) is a cross-campus initiative that provides digitized learning environments that combine physical production lines and virtual systems. These facilities are designed for Industry 4.0 education, with a focus on cyber-physical production systems, digital twins and human-centered design. ELF is integrated into the university’s engineering education and lifelong learning formats and supports collaborative research, technology transfer and industrial education at multiple locations (Arts et Métiers, 2025).

Collectively, these learning factories are an example of the advanced integration of Lean principles, digital transformation, and curriculum embedding across Europe. They demonstrate the potential of large-scale infrastructures to support interdisciplinary education, industrial collaboration, and technological innovation.

2.2. Compact and Scalable Learning Factories in Academic Institutions

In contrast to large-scale environments, compact learning factories offer more accessible, adaptable models that are particularly suitable for smaller facilities or those with limited space. Despite their smaller footprint, these learning factories achieve high educational value by emphasizing modularity, cost-effective digital integration and interdisciplinary learning. The following examples show prominent, academically operated compact learning factories.

The LEAD Factory at Graz University of Technology, operated by the Institute of Innovation and Industrial Management (IIM), focuses on Lean management, energy efficiency, agile processes and digitalization. It features a modular scooter assembly line that serves as both research and learning environment. Participants take part in hands-on training to transform inefficient production processes into leaner, more energy-efficient and agile operations. The facility is equipped with state-of-the-art technologies, including digitally supported workstations and sensor integration, which facilitate hands-on research and interdisciplinary projects. In addition, the LEAD Factory serves as a testing environment for advanced manufacturing technologies and supports both academic teaching and applied research (Institute of Innovation and Industrial Management, n.d.).

At University of Applied Sciences Darmstadt, the AutFab (Automated Factory) is a compact but fully automated Industry 4.0 learning factory that is deeply embedded in the university’s curriculum. Unlike more flexible environments, the AutFab focuses on simulating fully integrated, intelligent production systems, making it ideal for demonstrating advanced automation concepts in a space-efficient format. It is used in a variety of academic programs, including the bachelor’s degree programs in Mechatronics, Electrical Engineering, Optoelectronics and Image Processing and Economic Engineering, as well as the international master’s degree program in Electrical Engineering. AutFab supports both laboratory and project-based education, where students develop human–machine interface (HMI) systems, simulate production processes and tackle interdisciplinary Industry 4.0 challenges. In addition to technical skills, the curriculum emphasizes project management and cross-functional teamwork—key competencies for navigating modern digital manufacturing contexts (Simons et al., 2017).

The Smart Mini Factory at the Free University of Bozen-Bolzano is a teaching and research platform dedicated to intelligent and flexible manufacturing. It focuses on modularity, batch size one production and the integration of Industry 4.0 technologies. The facility incorporates Radio-Frequency Identification (RFID) systems, MES and industrial automation components in a learning environment. It actively supports engineering curricula, including bachelor’s and master’s theses, and facilitates interdisciplinary student research projects. With a focus on real-time process monitoring, Lean methods and cost-effective intelligent integration, the Smart Mini Factory serves as a reproducible model for hands-on engineering education (Smart Mini Factory, n.d.).

The Lego Factory initiative was developed at the Politecnico di Milano to teach the integration of manufacturing systems through a hands-on, playful approach with LEGO^® MINDSTORMS^®. The program is primarily aimed at students in the Mechanical Engineering Master’s program but is also open to participants from other disciplines. Students will engage in building, modifying and optimizing miniaturized production systems modeled after real production environments. The course, which spans several sessions, covers topics such as prototyping, process control, system design and performance analysis. Participants often augment their systems with Python programming, Arduino or Raspberry Pi modules to implement features such as part traceability, robotic handling, image recognition and IoT capabilities. This initiative is an example of how low-cost, reconfigurable platforms can effectively facilitate experiential learning in the field of manufacturing integration, especially in contexts where there is no access to industrial-scale infrastructure (Lugaresi et al., 2020).

A Lean Learning Factory has been developed at the Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB) at the University of Split as part of a wider effort to modernize engineering education and promote applied research in cooperation with local SMEs. This facility focuses on smart manufacturing, automation and digital technologies and includes a range of equipment for mechatronics, robotics and production systems integration. The LF supports interdisciplinary student projects and is used in practical courses. The Lean Learning Factory at FESB is an important platform for practical experiments and serves as a place of collaboration for academic and industry stakeholders in the region (Veza et al., 2015). In addition, the institution participates in initiatives such as the Digital Learning Factory, which provides a virtualized environment where students can engage with digital manufacturing processes to improve their skills in line with Industry 4.0 standards (Celar et al., 2016).

At the Royal Institute of Technology (KTH) in Stockholm, the KTH Lean Center serves as a leading competence center for sustainable organizational development through Lean principles. The center offers a range of educational programs, coaching sessions and seminars aimed at promoting systematic improvement and leadership based on Lean thinking. It actively promotes methods such as Lean, Agile methods, Kaizen/Green Kaizen, the Green Performance Map and Leadership development. These approaches are embedded throughout the Center’s offerings—including courses that support professionals and organizations in implementing sustainable, agile and efficient practices (Royal Institute of Technology, 2025).

At Aalto University in Finland, the Aalto Factory of the Future (AFoF) serves as a dynamic learning and research environment focused on the next generation of industrial automation and smart manufacturing. The facility is located in the Department of Electrical Engineering and Automation and covers an area of 80 m². It integrates a physical and digital infrastructure for experiments with flexible production systems, robotics, digital twins and cyber-physical systems. Since its inception in a modest 20 m² space in 2017 and expansion in 2019, the AFoF has supported interdisciplinary, project-based learning and collaborative research.

Across the reviewed European learning factories, several consistent features emerge. Many combine Lean methods with Industry 4.0 capabilities such as data capture and visualization, MES/ERP integration, and cyber-physical systems. These factories are embedded in curricula and support lifelong learning for professionals and SMEs. Modularity and reconfigurability are common, enabling layout changes and staged capability additions. Several exemplars model end-to-end flows—from planning to assembly and logistics—to study coordination and variability. Advanced digital themes such as digital twins, soft sensors, and AI/analytics appear at multiple sites, alongside energy and sustainability emphases in some facilities. Despite scale diversity, ranging from compact labs to hall-scale lines, the predominant pedagogy is hands-on, team-based, scenario-driven learning with iterative improvement, often spanning disciplines such as automation, robotics, data/IT, and systems integration.

2.3. Review of Existing Learning Factory Design Methodologies

While the physical and educational diversity of learning factories in Europe is well documented, there are only a limited number of studies that provide structured models for their systematic planning and design. Existing contributions have outlined frameworks for the design of learning factories in terms of didactic objectives, physical infrastructure, and industrial relevance. One of the best-known approaches comes from Abele et al. (2024b) who propose a three-level planning model that encompasses the macro, meso and micro levels. This model combines competency-based learning objectives with physical configuration and emphasizes a modular and iterative planning process. In their work, they also present the Learning Factory Morphology, a framework for describing and evaluating learning factories across dimensions such as operator type, subject content, didactic approach, infrastructure complexity and business model.

Other researchers have proposed quantitative design tools, such as Kress and Metternich (Kreß & Metternich, 2020), who present an algorithm-based configuration system that translates organizational objectives and trainee competencies into optimized LF layouts using axiomatic design principles, the use of quality functions, and the logic of bin-packing. Although this approach is very powerful, it is best suited for environments with highly structured design constraints.

Meanwhile, Bauer et al. argue for the integration of Lean production and Industry 4.0 technologies in LF environments (Bauer et al., 2018). Their work emphasizes the synergy between physical and digital systems and advocates digitally supported Lean tools (e.g., data-based value stream mapping), feedback loops and immersive production scenarios. This research is of great importance to institutions seeking to train students in the operation of cyber-physical systems while laying a foundation for Lean manufacturing principles.

Taken together, these methodologies provide a structured basis for the development of learning factories, in particular by linking didactic objectives with infrastructural and industrial requirements. The frameworks generally emphasize adaptability, modularity, and the integration of digital tools as recurring design principles. At the same time, recent studies emphasize the importance of tailoring these principles to regional contexts, where institutional resources, educational objectives and collaboration with SMEs can vary widely. This contextualization remains an open challenge in the literature, and it is within this framework that the contribution of the present study should be seen. Accordingly, the following section describes the methodological steps taken to adapt existing frameworks for the design of LFs to the institutional and regional context.

3. Methodological Framework for Designing an Academic Learning Factory Concept

3.1. Methodological Foundations

The development of a concept for a learning factory requires a structured yet adaptable methodology that combines theoretical foundations with practical applicability. This paper follows a design science research approach (Hevner & Chatterjee, 2010) that views design as an iterative process that incorporates educational integration, technological readiness, industrial relevance and flexibility and scalability into a scientifically grounded outcome. In this study, these dimensions are organized to lead to the morphological dimensions of the LF concept developed by Abele et al. (2024c), which enables a systematic assessment of the learning factory business model, its main purposes, learning content and target groups.

3.2. General Phases in Developing a Learning Factory Concept

Based on the synthesis of existing LF models, practices, and the LF morphology framework, the following phases outline a generalized methodology for the development of learning factories in academic institutions:

• Exploratory analysis and design objectives: Identify best practices, configurations, and educational approaches from existing learning factories and define educational, technological, and operational objectives based on stakeholder needs and internal constraints.
• Assess stakeholder needs: Obtain input from students, academic staff, and SMEs, specifically to identify skills gaps and digital transformation needs.
• Infrastructure and capability audit: Map available institutional resources, including physical space, equipment, software, staff expertise, and the potential for flexible use and integration of technologies appropriate to the intended learning and objectives.
• Concept development and validation: Develop a concept for a learning factory and validate its structure and feasibility through stakeholder workshops, pilot sessions, or expert assessments.

3.3. Key Dimensions to Consider

In addition to following a phase-based development sequence, successful LF planning requires continuous assessment of the key dimensions that define educational and industrial alignment. The LF morphological framework highlights a set of critical dimensions that need to be assessed at all phases:

• Educational integration: Aligning the design of the learning factory with the desired learning outcomes and teaching methods, active learning approaches and realistic environments that simulate the manufacturing and service context.
• Technological readiness: Selection of technologies that reflect current industrial practice while being suitable for a flexible, educational environment.
• Industrial relevance and SME orientation: Ensuring that the learning factory responds to the industrial needs.
• Flexibility and scalability: Considering the degree to which the LF should be modular, reconfigurable, and expandable over time. This dimension emphasizes planning for both adaptability and sustainability.

These four dimensions act as stable guiding categories throughout the LF design process. In each development phase, objectives within these dimensions are defined, refined, and validated to progressively shape the final LF concept, Figure 1.

4. Concept Development: Translating Framework into a Learning Factory Concept

4.1. Institutional and Regional Context

The proposed Learning Factory is being developed within the Department of Industrial Engineering and Management at the Faculty of Engineering (RITEH), University of Rijeka, Croatia. The faculty offers accredited bachelor’s and master’s degree programs in Mechanical Engineering, Naval Architecture, Electrical Engineering, Computing, and Mechatronics and Robotics. These programs provide a valuable opportunity to integrate interdisciplinary learning into the LF environment. By designing the LF to support collaboration between the disciplines of mechanical engineering, electrical engineering, computing and naval architecture, the institution can facilitate cross-program learning experiences, joint coursework, laboratory activities, and final projects that include vertical (BSc–MSc–PhD) and horizontal (cross-disciplinary) pathways.

The ecosystem of the Croatian economy consists predominantly of SMEs, which account for 99.8% of all active companies, employ 70.4% of the workforce and generate 58.2% of value added in the non-financial business economy (European Commission (EC), 2024). A significant proportion of these SMEs operate in the manufacturing sector, particularly in areas such as metalworking, marine engineering and flexible production systems. The manufacturing industry is increasingly characterized by low-volume and customized production, especially in B2B markets, reflecting a shift towards personalized manufacturing models (Mladineo et al., 2024).

RITEH is well positioned to develop a central hub for applied interdisciplinary learning and innovation. Through the LF concept, the institution has the opportunity to promote an application-oriented education that serves different engineering profiles and at the same time is aligned with the needs of regional industry. The confluence of regional manufacturing needs, interdisciplinary educational offerings, and the growing demand for practical digital skills presents an opportune time for RITEH to establish an LF that directly addresses both academic and industrial change.

The development of LF is also in line with the broader objectives of RITEH, namely to strengthen university–industry partnerships, modernize engineering education, and support collaborative innovation. Over time, the LF can also develop into a regional demonstrator for SME-driven process innovation and digital technology applications. Through the LF concept, the institution can promote application-oriented education that serves different technical profiles while being aligned with the needs of regional industry.

4.2. Applying the Design Phases

In accordance with the methodology described in Section 3, the LF concept was developed in four successive phases:

4.2.1. Exploratory Analysis and Design Objectives

As described in Section 3, the first phase of the methodological framework involved establishing preliminary design objectives within the four guiding dimensions. The aim of this phase is to articulate broad targets that reflect institutional priorities and expected stakeholder needs. These objectives provided a foundation for aligning later survey findings, SME interviews, and infrastructure audits into a coherent concept. The exploratory analysis sets preliminary objectives within the four design dimensions:

• Educational integration—aimed at aligning the LF with the RITEH interdisciplinary engineering curricula and to support different teaching methods, such as project-based and collaborative learning. The priority at this dimension was to define the LF as an educational resource embedded in multiple engineering disciplines, while being adaptable to future pedagogical developments.
• Technological readiness—intended to guide selection of technologies that reflect current industrial practice while remaining appropriate for flexible educational use. This dimension also recognizes that regional SMEs operate at different stages of digital maturity (Krulčić et al., 2025), requiring the LF to accommodate both foundational and advanced technological practices.
• Industrial relevance and SME orientation—aimed at ensuring that the LF supports regional SMEs by equipping students with applicable engineering and management skills, while serving as a collaborative platform for industry training and applied research. The dimension emphasizes hands-on learning that reflects the realities of regional SMEs operations.
• Flexibility and scalability—intended to ensure adaptability and the potential for incremental development. The LF should be designed as a system that can evolve over time—both technologically and pedagogically—to remain relevant as institutional capacity and industry needs change.

Together, these four dimensions outlined the intended scope of the LF concept at RITEH and provided the reference points against which the subsequent stakeholder contributions and infrastructure audits were interpreted.

4.2.2. Stakeholder Needs Assessment

In order to align the LF concept with both educational relevance and the needs of regional industry, a combined stakeholder input process was carried out. This included a formative survey of students and graduates employed in SMEs, supplemented by semi-structured interviews with SME managers. The aim of this phase was not to obtain statistically representative results, but to gain exploratory insights that could be triangulated with curriculum objectives and infrastructural feasibility. In this way, learner expectations and SME priorities were treated as contextual inputs— that helped to identify perceived gaps, desirable learning formats, and applied technology areas.

The survey was voluntary and served as a formative needs assessment aimed at 120 people, including 105 students and graduates from all degree programs and 15 alumni currently employed in SMEs. A total of 41 valid responses were received (34.2%), which were adequate for scoping preferences but not for statistical generalization. Respondents’ study programs were Mechanical Engineering (61.9%), Computing (23.8%), and Electrical Engineering (14.3%). No personal identifiers or sensitive demographic data were collected, as participation was anonymous.

The instrument included Likert scale and multiple-choice questions on topics commonly emphasized in engineering education literature, such as hands-on learning, interdisciplinary collaboration, and exposure to digital technologies. Because the items were single-indicator prompts (not multi-item scales) intended to surface priorities rather than measure latent constructs, internal-consistency reliability was not applicable. Basic data-quality checks (completion screening) were applied. Findings are reported descriptively and triangulated with SME interviews and the infrastructure audit.

The results of the survey, shown in Figure 2, Figure 3 and Figure 4, indicate that respondents have a strong preference for experiential learning. All respondents either agreed or strongly agreed with the statement “What I hear—I forget, what I see—I remember, what I do—I understand”, Figure 2a. Nevertheless, 56.1% rated their practical engagement during their studies as low (score 1 or 2), indicating a clear need for greater integration of practical activities into engineering education, Figure 2b.

The perceived value of an interdisciplinary, hands-on laboratory was also high; 85.4% rated it as valuable or extremely valuable, Figure 3a. In terms of integration into the curriculum, most respondents (61.0%) preferred that the LF be part of compulsory courses, while the rest favored its inclusion in elective courses or elective student projects, Figure 3b.

When asked to select their preferred LF activities and technologies, respondents most frequently selected digital process monitoring (82.9%), followed by Lean process methods (63.4%), ERP system training (61.0%), IoT/automation practice (58.5%), project management tasks (53.7%) and process simulation (51.2%), Figure 4. The survey results served as input for shaping design directions, ensuring that subsequent concept development reflected both students’ learning interests and the skill requirements of SMEs.

In parallel, twelve leaders from regional SMEs were interviewed to gather industry perspectives. Participants were recruited through purposive sampling from the faculty’s industry collaboration registry, which includes past projects, internships, and training partnerships. Inclusion criteria were (i) active operations in the regional manufacturing ecosystem, (ii) engagement in or intent to pursue continuous improvement or digitalization, and (iii) willingness to discuss workforce development needs. The aim was to identify design requirements, not to produce statistically representative estimates. Interviews followed a semi-structured guide covering perceived workforce skills and gaps, experience with Lean and digital readiness, and expectations for university collaboration.

Participants included executives and operations managers from companies in metalworking, welding and fabrication, marine and shipbuilding supply, industrial equipment and automation, and IT solution providers. This mix reflects prominent sectors in the regional manufacturing ecosystem. The interviews were not prescriptive but served as contextual pointers to areas where educational provision could be better aligned with the needs of regional industry. A consistent focus was on the integration of hands-on, team-based learning, Lean process improvements, and management skills. SMEs also highlighted the importance of ERP platforms and adaptable technologies that enable incremental adoption.

By integrating these interviews with the student/graduate survey, the study applied complementary stakeholder perspectives as part of a triangulated design. This ensured that both educational and industry perspectives were incorporated into the development of the LF concept without over-reliance on a single source. Although the sample size of the survey did not allow for statistical generalization, the convergence of findings from students, SMEs, and the infrastructure audit (Section 4.2.3) provided a solid basis for deriving the five thematic components, which were later summarized in Section 4.2.4.

4.2.3. Infrastructure and Capability Audit

An audit of existing resources was conducted to determine the infrastructural basis for the LF concept and identify gaps that needed to be addressed. This audit combined (i) an inventory of available laboratory space, equipment, and digital tools, and (ii) a validation of these resources against SME priorities identified through interviews and learner expectations from the survey. In this way, the audit served as a practical feasibility check within the approach and triangulated the infrastructural possibilities with stakeholder needs.

A dedicated 43 m² space within RITEH’s Department of Industrial Engineering and Management was selected for conversion into a compact but fully functional LF. Although the available space is smaller than most learning factories described in the literature, it allowed the development of the intended platform and simulates the essential features of a realistic production and learning environment. Rather than being a limitation, this compactness enhances accessibility for students and enables efficient supervision.

The foundational component of the LF is a standardized manual assembly workstation with an integrated digital interface, Figure 5. Manual assembly was selected because it provides a highly adaptable platform: it can model a wide range of production processes, accommodate incremental integration of digital and automation layers, and remain accessible to students from different engineering backgrounds. The addition of the digital interface further extends this versatility by enabling real-time data capture, system interaction, and experimentation with IoT-based applications. Beyond manufacturing contexts, the combination of workstations and digital interfaces also allows the simulation of workflow organization, team coordination, and process improvement in domains such as logistics or service-oriented activities. In this way, the LF balances simplicity and versatility, supporting both introductory Lean training and advanced cyber-physical integration within a single platform.

Each workstation is equipped with a height-adjustable work surface (F), an ergonomic chair (G), its own lighting (A), a power supply (D) and a magnetic board for Lean visual management (B). A digital interface (C), based on a microcomputer and ERP learning software, serves as a central control node for data acquisition and system interaction. It communicates with microcontroller-based systems (E), enabling IoT applications. Unlike commercial industry interfaces, this open-source platform is modifiable and extensible—students can develop their own digital modules, test control logic, and implement IoT scenarios. This promotes a deeper understanding of digital manufacturing architecture and practical system integration. Prototypes of embedded systems developed by students can be assembled and tested directly at the workstations.

A separate mobile flow rack (H) with material containers (I) complements each station and enables the simulation of single piece flow and production scenarios in different batch sizes. This design allows flexible adaptation of the production layout to different process types and learning objectives, Figure 5.

The spatial arrangement enables a step-by-step implementation. In its initial configuration (Figure 6a), the room offers space for:

• Four manual assembly workstations (1) with assigned mobile flow rack to facilitate Lean manufacturing principles, reconfiguration between serial and hybrid (serial-parallel) layouts, ergonomic material handling and simulation of process flows and inventory logistics
• A mobile flow rack (2) that serves as a simulation area for an input–output warehouse
• A centralized teamwork and digital interface area (3) with a conference table equipped with desktop workstations. This area supports planning, interaction with ERP systems and simulations
• Additional area for Kanban or Lean visual management (4)
• Additional area for static shelving for general storage (5)
• Dedicated areas for prototyping and 3D printing (6) to support physical validation of student design tasks and small part manufacturing.

A second layout scenario (Figure 6b) was also developed as a scalable expansion option, adding another manual assembly workstation and mobile flow racks with a two-bin Kanban system with an addition of:

• A collaborative robot station (7) for advanced learning in safe automation environments
• A digital warehouse interface with touch-sensitive terminals (8) to simulate real-time inventory management or picking lists.

The audit showed that, despite space constraints, the LF can accommodate a range of practical, digital, and collaborative learning activities if it is designed with adaptability and open-source extensibility in mind. This formed the infrastructural boundary conditions for concept development.

4.2.4. Concept Development and Validation

The development of the LF concept followed a triangulation approach, incorporating findings from the student and alumni survey, SME interviews, and the infrastructure and capability audit. Because the survey was formative, the rank order of preferences was used as a design cue. Cross-checking that order with SME themes and audit feasibility led to the following sequence: digital process monitoring first, a Lean–ERP spine next, and IoT/automation and simulation as phased extensions.

The interviews with SMEs reinforced these themes while providing an applied industry perspective. Executives emphasized the importance of training employees in Lean improvements, ERP-enabled workflows, and the practical use of IoT and automation technologies. Above all, they wanted to ensure that the skills learned in the LF could be directly applied in their organizations to improve operational performance. This emphasized the need for a hands-on environment where participants not only learn theoretical concepts but also how to implement them in realistic production environments.

The infrastructure audit provided a pragmatic foundation by identifying the available laboratory space and digital tools within the institution. These resources determined what could be implemented in an initial LF configuration while leaving room for expansion as needs evolved.

Manual assembly workstations with an integrated digital interface were prioritized as a foundational element. Although simple in form, they support flexible reconfiguration and provide a didactic platform for modeling different production process flows. Furthermore, the combination of workstations and digital interfaces can be adapted to represent organizational workflows beyond production (e.g., logistical or service-oriented activities), where the principles of flow, waste reduction, and process integration are equally relevant. This versatility makes them an educationally efficient starting point for an interdisciplinary LF.

By synthesizing the three input streams, the LF was structured into five thematic core components. In this context, “thematic components” refer to broad functional domains that capture both the educational and technological scope of the LF. They serve as conceptual anchors that link educational objectives with industrial practices, ensuring that the LF remains coherent and adaptable across different use cases. The decision to focus on five components reflects the consistent convergence of themes identified through student and alumni surveys, SME interviews, and the infrastructure audit. These recurring priorities provided a balanced framework that captures both foundational learning needs and forward-looking technological integration.

The five thematic core components are as follows:

• Lean processes—the educational anchor for hands-on improvement and flow-based learning
• ERP systems—enabling enterprise-level coordination and integration of processes with management
• Simulation environments—to support scenario exploration, decision-making, and process optimization
• IoT technologies—provide real-time monitoring, data-driven feedback, and cyber-physical connectivity
• Physical prototyping—linking product development and process design and reinforcing iterative learning.

Lean consistently emerged as the most relevant theme across the inputs and literature reviews, which positioned it as the central educational anchor. The other four areas offer complementary managerial and technological dimensions, which together provide a modular LF concept. This structure, illustrated in Figure 7, provides cross-functional, hands-on learning experiences that link production practice with digital integration.

An isometric view of the initial LF configuration is shown in Figure 8. This visualization illustrates how the five thematic components were translated into a spatial and infrastructural layout, with manual assembly stations serving as the foundation and digital interfaces enabling ERP, IoT, and simulation integration.

During the validation workshops, which took place in four separate sessions, the suitability of the proposed structure was discussed with various stakeholders. The process involved two complementary formats: sessions with SME managers and academic experts with experience in implementing learning factories and separate sessions with students to capture their perspective as end users. Feedback converged on three themes: transferability (short, repeatable Lean exercises with tangible outputs are most useful for workplace application); phased progression (moving from manual tasks with basic monitoring toward ERP, IoT, and simulation aligns with how capabilities are typically adopted); and data visibility and role clarity (simple, real-time feedback and clearly defined team responsibilities improve engagement). These themes support the chosen configuration and suggest practical directions for gradual refinement without major changes to the concept.

4.2.5. Morphological Characterization of the Learning Factory

To contextualize the proposed Learning Factory in the broader landscape of LF implementations,

Table 1. Morphological characterization of the proposed Learning Factory.

Dimension	Characteristic(s)
Main purpose	Education and training
Secondary purpose	Test environment/pilot projects
Product life cycle	Prototyping
Factory life cycle	Factory concept, process planning, assembly, logistics
Order life cycle	Configuration & order, order sequencing, planning, scheduling
Indirect functions	Procurement (simulation via teamwork/digital area)
Learning environment	Physical LF supported by digital tools (ERP, IoT, VR)
Environment scale	Life-size (miniature factory with full-process simulation)

characterizes the concept using a morphology-based framework adopted from Abele et al. (2024c).

This multidimensional representation illustrates the educational intent, functional scope, and integration of the key life-cycle dimensions of LF. In particular, the concept is designed as a life-size educational LF, with a physical infrastructure supported by open-source digital platforms, focusing on Lean assembly, different learning paths and real-world process simulations tailored to the needs of SMEs. The morphological profile helps to position the LF within established design taxonomies and facilitates comparison with best-practice examples across Europe.

4.3. Resulting Learning Factory Concept

The resulting concept shares key features with compact models such as the Smart Mini Factory and LEGO^®-based factory initiatives, specifically modularity, rapid reconfiguration, and incremental integration of digital capabilities. Rather than organizing learning around a single, product-bound platform, the concept focuses on platform-agnostic manual assembly workstations with an integrated digital interface, allowing different artifacts and processes to be hosted at the same stations.

Within a 43 m² footprint, the concept emphasizes generic SME workflow patterns—order/material coherence, basic KPIs, traceability, and changeover—while maintaining modularity so scenarios can be reconfigured as needs evolve. A phased progression—from digital monitoring to ERP coordination, IoT connectivity, and simulation—was derived from triangulated inputs, distinguishing the concept’s sequencing from other compact exemplars. At this stage, practical challenges in the concept design included the relatively small laboratory footprint, simple and uniform data exchange between stations and teaching tools, and standardized reconfiguration procedures.

The design and validation process led to three overarching results:

• Validated thematic structure—The LF is organized around five thematic components. The validation workshops have confirmed that this structure is perceived as relevant by SMEs and engaging by students.
• Educational focus—The LF is based on experiential and hands-on learning methods. The focus of activities is on teamwork, process simulation, and iterative improvement, supported by digital integration. This positions the LF as a platform not only for the development of technical skills, but also for interdisciplinary collaboration, in line with the principles of Education 4.0.
• Physical layout and scalability—The resulting LF design is compact, occupying an area of 43 m² configured around manual assembly stations. Despite the limited footprint, the space supports a step-by-step evolution: starting with manual workflows, through ERP integration and IoT-based monitoring, robotics and digital warehousing.

Taken together, these results illustrate how a systematically derived LF concept can integrate educational objectives, industrial requirements and infrastructural constraints into a coherent design. In addition to its structural and technological dimensions, the LF concept is framed by educational principles that emphasize active, hands-on learning. The use of manual assembly stations provides a versatile didactic platform, that enables the simulation of different production processes and offers students the opportunity to combine theoretical knowledge with applied practice. By embedding Lean improvements, digital integration, and teamwork into realistic workflows, the LF creates a learning environment that reflects interdisciplinary collaboration and problem-solving tasks encountered in practice. This educational framework ensures that the LF is not just a technical infrastructure, but also a vehicle for developing transferable competencies demanded by SMEs.

5. Conclusions

Learning factories provide a better model for engineering education because they restore authenticity to learning: participants work in a miniaturized socio-technical system where materials, information, and people must be coordinated under real constraints. In this setting, they not only learn about processes but also become part of the process, exercising judgment, teamwork, and continuous improvement. These qualities—authentic tasks, visible data and consequences, multi-role collaboration, and reflection on outcomes—are difficult to achieve in traditional classrooms or isolated labs.

This study presents a concept for a compact Learning Factory (LF) tailored to interdisciplinary engineering education and collaboration with Small and Medium-sized Enterprises (SME). By applying a structured, phase-based methodology—encompassing exploratory objectives, stakeholder needs, infrastructure auditing, and validation—the concept was focused on five thematic components: Lean processes, Enterprise Resource Planning (ERP) systems, Industry of Things (IoT) integration, simulation, and physical prototyping. Lean was positioned as an educational anchor, supported by digital and managerial layers that provide a balanced framework for experiential learning.

The resulting LF concept demonstrates how manual assembly stations with integrated digital interfaces can serve as a versatile foundation. Despite spatial limitations, this configuration enables gradual expansion while supporting interdisciplinary teaching, applied research, and SME-oriented training. Validation workshops confirmed the relevance of the concept from the perspective of students, academics and industry.

As implementation proceeds, impact will be monitored through three lenses: (i) student learning (pre- and post-performance on Lean exercises, short knowledge checks on ERP/IoT data use, teamwork and communication rubrics); (ii) SME collaboration (participant counts, training hours, short post-session satisfaction and intent-to-adopt surveys, and brief follow-ups on pilot adoptions); and (iii) LF operations (session utilization, reconfiguration time between scenarios, and cross-disciplinary participation). These measures will provide formative evidence to iteratively refine the LF.

The main contribution of this work is to show how existing frameworks for LF design can be adapted to a regional academic and SME context, where resources and industrial priorities may differ from large-scale initiatives. The case highlights four guiding dimensions—educational integration, technological readiness, SME orientation, and flexibility and scalability—as useful reference points for future implementations.

At the same time, some limitations should be acknowledged. The findings are context-specific, as the SME sample was purposive and reflected prevalent regional sectors. The study was based on exploratory surveys, interviews, and a single institutional context; broader generalization will require comparative studies in other settings. Furthermore, the LF is in the early stages of implementation, and its long-term impact on student learning outcomes and collaboration with SMEs has yet to be assessed.

Future research should therefore address the question of how compact LF models can evolve through incremental implementation, evaluate their educational effectiveness in different disciplines, and investigate sustainable funding and governance structures. In this way, learning factories can become an even more robust bridge between engineering education and SME-driven innovation.