## 5 Industry 4.0 Solutions  
Table 4 offers a summary of the results of the statistical analysis and provides a set of process variability management solutions that are empowered by the adoption of Industry 4.0 technologies. The first column of the table includes the main input parameters (_SEQ_, _PFCTV_, _FP_, _ALCTV_) and refers, in particular, to their variations. The second and third columns report the effects in terms of the impact level of the variability of a certain input parameter on all three main output parameters (_T_, \(W\), _B_). The negative impact level is represented with the aid of a set of black dots: the more dots, the worse the negative impact of the variability of the input on the output parameter. The fourth column indicates the Industry 4.0 technologies that can be adopted to address the process variability issue. The reported technologies were chosen from--and were consequently numbered according to--the classification proposed in [33]: this is one of the most widely adopted Industry 4.0 classifications, coming from one of the most cited scientific articles on the topic, and it provides a rather wide list of (up to 32) Industry 4.0 technologies. The fifth, and last, column shows how the 14.0 technologies can be combined in order to provide an effective solution to the process variability. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Variations.** & **Output** & **Negative Impact Level** &
\begin{tabular}{c} **Useful 14.0** \\ **Technologies** \\ \end{tabular} & **Possible 14.0 Solutions** \\ \hline T & \(\bullet\bullet\bullet\bullet\) & & 1-Introduce sensors \\ W & \(\bullet\bullet\bullet\bullet\) & & within assembly lines \\  & & & to monitor the real \\  & & & sequences of models \\  & & 1. Sensors & and parts at assembly \\  & & 7. Simulation of & stations. \\  & & processes & 2-Compare in real time \\  & & 10. & model sequences with \\  & & Machine-to-Machine & planned sequences. \\  & & communication & 3-Compare in real time \\ SEQ & & 13. Traceability of raw & parts sequences \\  & & materials & withplanned sequences. \\  & & 14. Traceability of final & 4-Send real-time \\  & & products & information to the parts \\  & & 22. Remote monitoring & feeder at the warehouse \\  & & of production & highlighting if \\  & & 23. Remote operation & real-time models and \\  & & of production & parts sequences in the \\  & & & assembly line are \\  & & & different from planned \\  & & & ones. \\  & & & 5-Act accordingly \\ \hline T & \(\bullet\bullet\bullet\) & 1. Sensors & 1-Introduce sensors on \\ W & \(\bullet\bullet\bullet\bullet\) & 7. Simulation of & tow trains to monitor \\  & & processes & the real-time positions \\  & & 10. & across the routes. \\  & & Machine-to-Machine & 2-Compare the \\  & & communication & real-time positions with \\ PFCV & & 13. Traceability of raw & the planned positions \\  & & materials & according to the parts \\  & & 22. Remote monitoring & feeding cycle time. \\  & & of production & 3- Send real-time \\  & & & information to tow \\  & & 23. Remote operation & trains operators4-Act \\  & & of production & accordingly \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the negative impact level of the variations of the input over the output parameters and possible solutions enabled by the adoption of Industry 4.0 technologies. For example, let's focus on the first input parameter: the models sequence variation _SEQ. SEQ_ has the worst negative impact level on all three output parameters, as indicated by \(\bullet\bullet\bullet\bullet\). An intuitive solution would be to freeze the sequence of models for long periods of time. However, the current market is characterized by large variability in demand, which forces productive systems to be as reactive as possible to changes, making the freezing solution impractical.