## 7 Conclusions and Future Work  
This work proposed that Connectivity as a design function allows the introduction of Industry 4.0 principles into the architecture of production equipment. Connectivity allows powerful computer resources to interact with the machine control to improve the capacity to monitor and adapt to changes in the environment, in essence improving the intelligence of the process. The article also explained the importance of in-line metrology for the new generation of smart factories. The advantages and opportunities that these concepts offer were explored through the development of an in-line measuring machine. This work also showed how functional decomposition techniques could be used to clarify the role of Connectivity. A connectivity block was presented for use in IDEF0 type diagrams. Diagrams that used these representations of Connectivity were presented. Their goal was to show how Connectivity was implemented in the particular architecture of the machine. They are particularly useful for documentation. The design principles and methodology presented in this work were applied to the design, construction, and testing of an in-line measuring machine that accomplishes the features of flexibility, accuracy, robustness, and speed. In addition to those features, the machine is ready for a smart factory (Industry 4.0) environment as a result of the added dimension of intelligence through Connectivity. This case demonstrated that Connectivity offers the potential to improve the efficiency of the operation. It should be noted that while implementation added little cost, there were costs associated with managing and maintaining the hardware and software. There were also issues of reliability and robustness. For example, missing data from a faulty sensor can cause the system to crash. Therefore, it is important to address questions about the data that will be collected, analyzed, processed, and stored as early as possible in the design stage in such a way that risks can be assessed. At this stage of the development of the prototype, all of the analysis has been done by humans. The dashboard presents information that administrators can use to understand the process in more depth and develop practices that can lead to a more efficient operation. A few examples of the types of improvements that can be made to the control to reflect the improved intelligence have already been described. Specifically, adding a variable to monitor peak acceleration and generate a warning when a specific value is exceeded represents one case. Future work includes the implementation of these features or the control of the machine, specifically, the design of a dashboard for engineering that includes warnings. Making the best use of new technologies is not a straightforward process. [PERSON] argues that the skill sets for \"innovation capabilities\" are different from those associated with \"production capabilities\". Design, engineering, and testing are key competences of innovation capability [23]. In this regard, the application case presented here provides valuable experience for the user of the in-line measuring station. There are a number of opportunities for further work. The most important area of interest that was not addressed in this exercise was cybersecurity. In the current design, no automatic feedback was programmed to make modifications in the measurement cycle. Consequently, no provisions were made to safeguard the data other than what was afforded by the normal vendor services or what is built into the data transfer protocols and what the LAN provided. Without a doubt, this approach will have to be revisited during the development of a new version of the machine. The second direction to take should be the implementation of automatic responses or warning to changes in the environment. Conceptualization, H.A.-G., T.R.K. and E.G.d.C. ; methodology, A.P.C.-M., D.G.-L. and M.F.M.-A. ; validation, H.A.-G., P.D.U.C., P.A.O.C., T.R.K. and E.G.d.C. ; formal analysis, A.P.C.-M., D.G.-L. and M.F.M.-A.