## 5 Application Example: Guidelines for an SSPC
### Expected Results of the Implementation
In the fourth item, the centralization of information is underscored, meaning that results and actions should be consolidated in a single program. The fifth item emphasizes utilizing the intranet for data sharing. However, when shared with other companies, it suggests employing robust cryptography. The questions were:  
* Can operators contribute to coding and programming the control chart by explaining the process's functionality? * Is a decision tree system adequate for analyzing and interpreting process anomalies? What level of robustness is required for this decision-making system? * If the system needs to be more robust, the following questions arise: (a) Does \"IF-THEN\" logic programming replace the decision tree? (b) Does programming an expert decision support system (EDSS) using algorithms constitute a more robust system than the decision tree? (c) Regarding the system's structure, how can data be transferred to a graphical user interface (GUI)? How can operators in computer integrated manufacturing (CIM) analyze the data? * Are there other concepts that can be integrated into any of these data analysis and decision-making systems? How would the implementation occur? * Can AI or ML learn the control chart? Or can another learning system be developed? How would the implementation take place? In the second group of respondents, the main highlighted topics were extracted. The sixth item underscores the necessity of operators' involvement in constructing the Smart SPC model due to their profound understanding of the production process. The seventh item acknowledges the sufficiency of decision trees while recommending the development of more resilient systems. The eighth item underscores the importance of considering process characteristics. The ninth item aligns with the proposed Smart SPC model's concepts. The last item confirms the feasibility of the learning process with AI, ML, and NN concepts. Thus, based on the results of the questionnaire, the general Smart SPC model aligns with its concept and practical application guidelines. Measuring a process's maturity or its digitization stage lacks a one-size-fits-all approach. However, for companies venturing into this realm, it is important to assess whether initial signs are contributing to a more robust implementation or transformation of Industry 4.0 concepts. Certain classifications are indispensable in the evaluation, such as identifying equipment, technologies, processes, and relationships among key variables of the processes. Considering such a scenario, a comprehensive understanding of the processes is imperative. Initiating the definition of the project scope involves describing the existing \begin{table}
\begin{tabular}{c p{341.4 pt}} \hline \hline \# & **Summary of All Answers** \\ \hline
6 & Certainly, operators’ insights are invaluable, as they engage in day-to-day production activities. \\ \hline
7 & While a decision tree system offers simplicity, if utilized, it needs to be meticulously structured to assess primary issues within a process effectively. However, employing robust algorithms is advisable to ensure comprehensive anomaly identification. \\ \hline
8 & It is crucial to assess each company’s specific needs. In general, expert decision support systems (EDSSs) and AI systems are resilient decision-making solutions for process anomalies. \\ \hline
9 & Multi-criteria decision-making methods can collaborate with developed algorithms, aiding in prioritizing equipment that requires special attention compared to others. This collaboration can involve tasks such as data cleaning, data classification, and model training. \\ \hline
10 & AI techniques, particularly artificial neural networks, can play a significant role in training algorithms derived from the control chart. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Clippings of responses from experts in programming and Industry 4.0.  
equipment and related technologies, including connections, the level of automation, and potential interconnections.