QU4LITY mapped on Significant innovations and lessons learned, training aspects

One the one hand, some pilot owners expect that no particular skills will be requested after the QU4LITY project development implementations.  For example:

• all systems should remain accessible by the majority of the workers without specific expertise or knowledge (where for instance each correlation system has to remain within a blackbox and only provide rules outputs for production lines).  
• The AR app and the first training on the machine will be enough for start the production with new operators on the line.  
• In essence the job profile will remain the same, however, the operators need to understand & be able to work with these new technologies. This requires some basic knowledge on the (digitalized) systems, for the operators a lot can be captured in SOP’s (standard operation procedures), but the technical support staff should also have some basic knowledge on the workings and the hardware/software side of the systems in order to be able to support the shopfloor where needed

New job profiles and associated skills are:  Digital Business Processes Analyst, Expert in Machine Learning Algorithms, DevOps Development knowledge, Data scientist (programming and statistical knowledge), Artificial Inteligence knowledge, Cybersecurity expert, Ontology architects and modellers in MBSE, Digitalized systems Shopfloor worker, Digital and connectivity engineering, New systems integration Manufacturing Engineer, Cloud -Data Formats - Data analytics Engineer, Product, manufacturing and quality global knowledge.

Re- and upskilling needs were identified in the following areas: AI and Data analytics; Agile development, Multi disciplinary project management (IT, mechanical, electrical engineering); Design Thinking; Standardization; Data Analysis and Data Space technology for Manufacturing; IT Skills : Docker environment and languages like phyton of json; Data Analytics : basic skills , BI softwares
Programming languages such as C#, C ++, HTML, Java, Microsoft .NET and SQL Server ; data tools for data cleaning and preprocessing, data parsing, data feature engineering; machine to machine (M2M) data and protocols; Machine Learning Skilling for all languages/ ML Systems; Data analysis skills

The following knowledge delivery mechanism where identified as relevant:  AR/VR, gamification, on-the-job training, vocational training, MooCs (Massive Open Online Courses)

• For newcomers to the field of Zero Defect Manufacturing, MOOCs are the way to go, since they can cover more aspects of the Industry4.0 and ZDM, not just the data science part. For the work force already in place, vocational training or on the job training would be recommended, herewith quickly adapting to the new working situations.  On the job training would be enough to transmit knowledge of the technology. The solution develop has to be as user friendly as possible and be quickly understandable either on the HMI aspect and on the hardware side.