In order to have a big picture regarding the training needs, broad training topics were used to present the compiled results. In table below, the training needs in the three countries (Portugal, Hungary and Czech Republic) were presented.
Training Needs by industrial sector and by country
| Training topics | Portugal | Hungary | Czechia |
|---|---|---|---|
| DATA MANAGEMENT Data gathered Advanced data analytics Real time production management | |||
| QUALITY CONTROL/ASSURANCE AI solutions Image recognition Augmented reality applications (marketing and real time data visualizing, and simulation) | |||
| SOFT SKILLS Intellectual curiosity Critical thinking | |||
| TECHNICAL SKILLS (related to the technologies they adopted/plan to adopt) | |||
| VIRTUALIZATION Virtual prototypes Digital twin | |||
| SECURITY (CYBERSECURITY) | |||
| INTEROPERABILITY System integration Data compatibility | |||
| PREDICTIVE MAINTENANCE Data gathering Algorithms for predictive maintenance | |||
| Legend: : Automotive industry : Textiles, clothing, leather and leather products : Food : Furniture : Machines | |||
The information presented in this table is essential and valuable for moving forward in project implementation and for launching and proposing the upcoming technical steps.
Thanks to the involvement of technical experts and companies from Portugal, the Czech Republic and Hungary, it was possible to define specific challenges / problems and needs in the field of digital transformation by companies by industry and by country.
In the next steps, a customization proposal will be created based on the needs analysis of WPT2, multiple configurations for demonstration, structured into different blocks in the PBN. The basic component of the methodology is the feedback loop build-time-learn. After that, demo operation, test installations and adjustments according to the requirements of the partners will be carried out at INESC TEC, INEGI (Portugal) and CTU (Czech Republic), where testing and fine-tuning of the concept will also be carried out by means of piloting.
In the Teaching and Learning Factory adaptation phase, data science will be used and operational research will be built into a highly automated smart factory, including Industry 4.0 technologies and advanced manufacturing concepts. As part of the integration of smart sensors into smart materials, it will also be applied to smart factories.