## 5 Application Example: Guidelines for an SSPC
### Expected Results of the Implementation  
The smart SPC model underwent validation through an online interview with operational managers and questionnaires distributed to implementation specialists. The online interview served as a means to identify needs and clarify managers' queries regarding the use of Smart SPC. Managers emphasized the importance of a systematic procedure in the transition process to Smart SPC. Regarding the questionnaire for specialists, it evaluated the processes and technologies of Industry 4.0 applied in Smart SPC. Shared via email and LinkedIn, the questionnaire included key questions to determine the respondent's experience in a specific subject. It focused on the impact of Smart SPC on the IT department's production process. The questions were:  
* How can operators (production and maintenance) interpret data from Smart SPC? Are they responsible for defining actions, or is this interpretation carried out by the analysis and diagnosis machine/program? * What additional tasks can be integrated into the interaction between the physical (operators) and the digital? How does this activity relate to product quality control? * How can managers (production and maintenance) analyze data provided by the ERP System? What is the optimal metric for quantification? * Can another visualization and integration system (one or more systems connected with the company's ERP) of the data be considered? * Regarding data security, how can a Cloud Security system be incorporated? This study involved thirteen respondents, with six providing responses for the first part of the questionnaire (Table 1) and seven for the second part (Table 2). As it is a qualitative focus group, not a survey, the number of respondents is satisfactory. All respondents completed higher education in engineering or computer science bachelor. All the sampled participants are experienced practitioners in production and manufacturing management, working as managers in largely automated plants located at a metalworking industrial cluster in Brazil. \begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline \# & \multicolumn{1}{c}{**Summary of Answers**} \\ \hline
1 & Operators should monitor the system-generated data, adhering to algorithmic recommendations to identify anomalies in the process. However, there are instances where the interpretation of intervention lies with the operator. \\ \hline
2 & Develop a comprehensive and integrated end-to-end automation and tracking system to reduce variability, enhance manufacturing performance, and ensure process effectiveness and stability. \\ \hline
3 & Keep data analysis and interpretation systems straightforward. Utilize graphs and dashboards displaying the percentage of various measurement units, including performance indicators for process stability, productivity, production capacity adequacy, and non-quality costs. Additionally, indicators for equipment maintenance are necessary to gauge if organizational performance should be included. \\ \hline
4 & Implement Power BI as an alternative data visualization system. Emphasize KPI analysis and centralize data presentation in a unified system, ensuring compatibility with other data collection systems. \\ \hline
5 & Prioritize data security by keeping them within the intranet. If Cloud Computing systems require data sharing, encrypt the data with complex keys for added protection. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Clippings of answers from specialists in production processes. In the first group of responses, excerpts were extracted regarding suggestions on a specific issue. Regarding the first item, it emphasizes that the operator should follow the algorithm-provided data and perform controlled interventions. The second item stresses that the integration between the system and simulation enhances the quality of CPS environments. For the third item, the provided data must be related to the characteristics and performance of the process, ensuring easy interpretation.