## 5 Application Example: Guidelines for an SSPC
### Guidelines for the Implementation
The integration of the human factor with the CPS demands the establishment of precise and reliable data acquisition methods. In light of this, operators focus on monitoring to diagnose, advise, or prevent non-value-added activities. At the cyber level, the system should assist in decision-making and optimizations based on acquired knowledge, acting as a supervision system [57]. Developing human capacities for this system requires evaluating the competencies of the company's employees. Therefore, employing qualified human  
Figure 3: SSVC: proposition. resources is crucial to ensure the continuity of the new system, providing the company with multi-skilled employees [58]. Finally, the IT sector plays a crucial role in the transition from conventional SPC to Smart SPC. Two fundamental activities are necessary. Initially, a well-defined governance strategy, with employees trained to participate in the development of this concept. Next, an assessment of the existing infrastructure, such as hardware, software, and IoT, among others, ensures a secure transition [58]. Thus, the IT department must derive operational data and coordinate them with the managerial demands of production and with the workers involved in the process that uses these [4]. Investments can contribute to IT infrastructure (hardware and software), connecting the factory end to end and favoring autonomous data exchange, horizontal and vertical integration, and high-speed networks [9]. In addition, systems for Cybersecurity must be considered to guarantee the integrity and quality of company data that are being shared by networks connected to the internet and stored in the cloud [59]. This topic remains a data security challenge when adopting a powerful cloud-computing platform [18]. The findings were consolidated into a final conceptual model supported by the two original models that outline a general structure, ranging from assessing the company's readiness for adopting new technologies to the process of implementing Smart SPC. Therefore, the insights from this research have contributed to the development of the conceptual model illustrated in Figure 4.  
The presented model delineates the comprehensive cycle for implementing the Smart SPC concept. Initially, the absence of Industry 4.0-enabling technologies necessitates the monitoring of items in the process. Following this, we assess the software requirements for controlling process quality indicators. This assessment leads to the conceptual evaluation of the model and the identification of optimization needs. Practical application development becomes imperative, with validation occurring through tests derived from the implementation, thereby measuring its performance. These tests are important to prevent eventual bottlenecks and major source of errors in the process. If the model necessitates revisions to measurement targets, it reverts to the development phase; otherwise, it progresses to the \"automation\" phase, whether maintaining its status quo or requiring minor adjustments. This phase involves instilling intelligence to empower the model to detect process variations and automatically suggest/execute adjustments using Industry 4.0-enabling technologies. The coding of the Smart SPC model (or IntelliSPC--ISPC) involves the IT sector programming the control chart and integrating it with a CPS model from a data center. Figure 4: A conceptual model for Smart SPC implementation. The process of making it \"intelligent\" for variation assessment, scenario prediction, and automatic process adjustments based on monitored item analyses is accomplished through artificial intelligence systems. Simply put, the control chart learns to adapt according to the monitored scenario. As a result, the analysis and decision-making process follows a streamlined decision tree model or more robust models composed of CIM, EDSS, and IF-THEN systems.