## 4 Results and Discussion  
In this context, the overall ZigBee-based WSN performance within the workshop has been assessed in terms of packet error rate (PER). As a first test, each ZigBee node has been programmed to send 10,000 packets in a time interval of 2 h, providing automatic link control supported by the ZigBee protocol. This measurement campaign has been performed for two different scenarios: during a high activity period at the workstations and during the rest period. Table 4 presents the correctly received packets by the coordinator from each node. It can be seen that no packet has been lost during both the activity and  
Figure 13: The implemented nodes at different workstations: (**a**) knife cutting machine; (**b**) laser cutting machine; (**c**) thermal welding machine 1; (**d**) thermal welding machine 2; (**e**) automatic slat machine. rest periods, obtaining a PER of 0.00% for both scenarios. These results confirm the results expected from the preliminary radio planning tasks presented in the previous section. Then, the deployed WSN system has been tested for two complete working days (i.e., 48 h), during which the collected data has been stored and saved as an excel file. As an example, a macro of part of the information included in the stored packets is presented in Table 5, which shows the date, the workstation number, the worker ID, the fabrication order, and times (in seconds) related to specific tasks for each workstation. The data collected from the implemented WSN has a lot of valuable information that can be integrated into the Enterprise Resource Planning (ERP) system for optimizing the production process, fabrication time, and enhancing the cost estimation of the products. In order to make this information available and accessible for those making decisions, several types of graphics have been implemented. An example of the extracted information from the WSN data is presented in Figure 14, where the involvement of employees during the fabrication of different products can be seen. Additionally, the different workstations participating in the process can be also seen, giving a complete and easy manufacturing overview. Further data analysis is presented in Figure 15, which shows: the time (in percentage) that each product manufacturing process consumed during the mentioned two complete working days (Figure 15a); how much time (in percentage) has spent a product in each workstation (Figure 15b); and the employees that took part in the manufacturing of a product (Figure 15c). Finally, Figure 16 presents the time spent manufacturing different products in the heat-welding workstation by two different employees. \begin{table}
\begin{tabular}{c c c} \hline
**Node** & **Activity Period** & **Rest Period** \\ \hline Cutting table & 10,000 & 10,000 \\ Laser & 10,000 & 10,000 \\ Soldering & 10,000 & 10,000 \\ Automatic slats & 10,000 & 10,000 \\ \hline \end{tabular}
\end{table} 
Table 4: Correctly received packets by the coordinator from each WSN node (from 10,000 transmitted packets) during activity and rest periods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Time** & **Date** & **Workstation** & **Worker** & **Fab. Order** & **T1 ID** & **T1 Time** & **T2 ID** & **T2 Time** \\ \hline
14:01:27 & 7 October 2020 & \(1\) & 34 & 0 & \(1\) & & \\
14:01:44 & 7 October 2020 & \(1\) & 34 & 78,055 & 8 & 17 & \\
14:03:00 & 7 October 2020 & \(1\) & 34 & 78,055 & 9 & 0 & 9 & 19 \\
14:04:55 & 7 October 2020 & \(1\) & 34 & 78,055 & 10 & 39 & 15 & 24 \\
14:06:50 & 7 October 2020 & \(1\) & 34 & 78,055 & 11 & 52 & 9 & 23 \\ \hline \end{tabular}
\end{table} 
Table 5: Raw data with part of the information sent in the packets. Figure 14: Participation of employees and workstations in different product fabrications. Figure 15: _Cont._  
Figure 16: _Cont._  
Figure 15: Data analytics examples: (**a**) time that each product manufacturing process consumed during two complete working days; (**b**) time spent by a product at each workstation; (**c**) employees that took part in the manufacturing of a product.