“…Sader et al (2019a) discussed the challenges and barriers that impede achieving full advantage of I4.0 to quality management and stated that these challenges could be "scientific, technological, economic, social and political". Some of the Aspect Opportunities QC -Obtain information in real-time (Szajna et al, 2020) -Faster handling of advanced tasks (Szajna et al, 2020) -Automatic, continuous and real-time monitoring and alerting (Szajna et al, 2020;Gray-Hawkins et al, 2019) -New types of data and metrics available in real-time (Foidl and Felderer, 2016) -Availability of new techniques and inspection tools (Sader et al, 2019b) -Early failure detection and self-adaptation techniques (Sader et al, 2019b) -Identify quality related causes and adjust processes without delay (Foidl and Felderer, 2016) -Increased adjustability (Gray-Hawkins et al, 2019) -Detect new types of defects that were not possible previously (Godina et al, 2019) -QC can be made instantly on the production line and no longer needs a distinct metrology section (Godina et al, 2019) -Recording 100% of the data and ensuring 100% control (Godina et al, 2019) -Possibility of "in situ" correction of the process (correct the quality shift during the production itself) (Petritoli et al, 2020) -Improved data-driven decision making using big data analytics and cloud solutions (Miladin et al, 2019) -AR technology can be used in inspection by showing graphical instructions and tutoring the user about the inspection procedure (Etonam et al, 2019) -AI techniques support visual inspection (Chouchene et al, 2020) -Training models on potential out of control situations using ML techniques (Amini and Chang, 2018) -Recommend corrective actions using AI and ML techniques (Ramezani and Jassbi, 2020) QP -Using prescriptive analytics algorithms in QP to determine the best solutions and provide recommendations (Sony et al, 2020) -Customer specifications can be collected due to the end-to-end integration facilitated by I4.0 (Chiarini, 2020) -Data can be automatically streamed from customers to the design department in terms of specifications and quality characteristics (Chiarini, 2020) -Get feedback about the customer experience and the performance of the product in field (Foidl and Felderer, 2016; (Sony et al, 2020) -Process improvement facilitated by data driven quality prediction…”