## 7 Discussion  
### Implementation Gaps  
The reviewed literature reveals that academic institutions worldwide are pushing the transition towards the new paradigm by establishing dedicated Industry 4 learning factories and Testlabs. Despite the growing interest in academic contexts globally, there remain critical implementation gaps that limit their educational, research, and industrial impacts. As our descriptive analysis demonstrates that institutions in Europe contribute 60.8% of the reviewed publications, followed by Asia (12.3%), North America (10.8%), Africa (6.9%), Latin America (5.4%), and Oceania (3.8%), which made the least contribution in this regard. Figure 2 illustrates the contribution statistics from different regions.  
The gaps fall into a wide range of fundamental aspects, including technological, pedagogical, and policy-level constraints.  
Lack of infrastructural intelligence: A deeper analysis suggests that significant parts of the effort remain focused on establishing physical infrastructure, such as intelligent robots, IIoT platforms, and 3D printers, rather than achieving more advanced features,such as decentralised decision making, predictive intelligence, and bi-directional system integration. It is understandable that it is easy for academic participants to simply employ visible and mature technologies to begin the development projects from scratch. But this also highlights a bottleneck that must be addressed to make academic Industry 4 facilities real catalysts for the ongoing industrial revolution, not just a demonstration.  
Lack of system integration: Another common implementation shortcoming in the reviewed literature is the disjoints between sub-systems. In many reviewed cases, collaborative robots are manually programmed with minimal data-driven feedback loops. The development of system middleware and standardised architectures is far behind the needs for building the expected scalability and interoperability of the facilities. The majority of the developments are still at the sub-system level, which shows a promising research trend to develop holistic Industry 4 systems in academic contexts.  
Lack of supportive analytics for educational purposes: Although one of the primary goals of Industry 4 facilities in academia is to enhance educational and training activities, very few implementations have focused on the outcomes of supporting student learning activities. How to use real-time data streams, adaptive feedback systems, and DT-guided decision making to facilitate teaching activities in universities is underexplored.  
Lack of long-term engagement with local industries: While the establishment of Industry 4 facilities is often started and pushed forward by collaborating with local industries, the actual depth and breadth of the engagement vary. It is not rare to see that some partnerships are limited to one-off projects, rather than long-term collaboration in curriculum development, research design, and workforce training. This may need more commitment from the high-level management of the universities or even the local government.  
Lack of strategic perspective: There is only a small number of reviewed articles that have shown their long-term strategic roadmap in the facility development, such as the STEP Learning Factory [35; 41; 43; 51; 59; 68; 154; 166; 167]. The majority of the cases are developed with a short-term vision, such as a PhD degree programme or a pilot project with industries, which limits the maturity of these facilities. Staged development plans are desired for academic participants to align their efforts in line with the institutional or national digital transformation agenda.