## 8 Conclusions
### Future Research  
In order to address the limitations identified in the previous section, we propose a two-fold approach. On the one hand, the number of universities involved in this research could be increased by taking advantage of already existing communication channels, such as the Erasmus+ framework, which connects training centers all over Europe. On the other hand, setting up a virtual environment in which educational institutions would be encouraged to upload the details--maybe in standardized public repositories--of their training programs would ease the task of mapping academics and their skills.  
In this work, we have explored the current role of academics in transferring knowledge and skills on Artificial Intelligence, Internet of Things, and Edge Computing for Industry 4.0. In terms of hard skills, we have identified that IoT and EC are covered with less emphasis than AI (see Figures 4 and 6). In terms of soft skills, we have seen that the field of Innovation and Change Management (I&CM) (i.e., properly managing the adoption of innovative solutions to reshape industrial processes) is not broadly incorporated. This conclusion comes from the observation that topics related to AI, IoT, and EC are not related to I&CM topics in university training programs. Therefore, it might be interesting to develop a multidisciplinary training program in which students (from both academia and industry) gain the necessary skills to bring value to Industry 4.0. Specifically, this course could start with an Innovation and Change Management workshop in which groups of students would (1) propose innovative ideas to solve Industry 4.0 challenges, (2) learn to distinguish what is innovative, and (3) come up with the best way to implement--from a perspective of both management and technical--these innovations in the industrial process. Next, students would learn all the necessary skills to bring the latest achievements in AI, IoT, and EC to real-world Industry 4.0 use-cases. This part should be split into two sections: the first one aimed to train the technology fundamentals, and the second part aimed to develop hands-on experience with these technologies. Finally, the course could end up with a Final Project mentored by an industry expert that would bring her/his insights and experience to the student.  
To sum up, this work considers the challenge of Industry 4.0 training from the academics (partial) perspective. Conducting similar research from the industrial and students' points of view could contribute to obtaining (and complementary) a holistic view of the whole situation. This would enable practitioners to draw broad conclusions that would encompass the main stakeholders of training in Industry 4.0.  
Conceptualization, D.S.; methodology, D.S. and M.M.; validation, C.S., D.M., G.D. and L.P.; formal analysis, M.M. and M.T.; investigation, L.P., R.F., C.S. and X.S.-B.; resources, G.D.; and R.F.; writing--original draft preparation, D.S., R.F., M.T. and X.S.-B.; writing--review and editing, D.M., D.S., C.S. and J.N.; visualization, D.M., X.S.-B., D.S. and G.D. supervision, G.D., D.S. and J.N.; project administration, G.D.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by Programme Erasmus+, Knowledge Alliances, Application No 621639-EPP-1-2020-1-IT-EPPKA2-KA, PLANET4: Practical Learning of Artificial iNelligence on the Edge for indusTry 4.0.  
The authors declare no conflict of interest.