## 5 Application Example: Guidelines for an SSPC
### Guidelines for the Implementation  
As an illustration of the idea, a framework with general guidelines for transitioning from conventional SPC to Smart SPC has been developed. The transition process adheres to a sequence of steps and activities, progressively automating, enhancing flexibility, and incorporating smart features into the production system at each step [6]. To initial advances in digitization technologies, exploring low-level steps is essential, ensuring that each step is carefully navigated for the company to assimilate the transition fully. Ref. [2] stresses the importance of understanding production processes, evaluating current management practices, continually monitoring and analyzing the process, and ensuring the integration of production management data. The model is built upon two fundamental components: enterprise resource planning (ERP) and cyber-physical system (CPS). Figure 3 depicts an adapted representation of the proposed model. The ERP system assumes a supportive role in decision-making within manufacturing operations, focusing on identifying part failures or machine wear. Decision-making relies on algorithms and a structure built on data from the production process, where the information is analyzed to formulate concepts for specific scenarios. AI and ML techniques contribute to improving the efficiency and automation of decision-making models derived from intelligent data [9]. The operator's interaction with digital interfaces is a crucial aspect of this process. Human-specific decisions must be supported by a centralized platform overseeing decentralized and autonomous assets. This structure enables controlled interventions that smart assets cannot independently execute [19]. Decisions are organized into tree-like structures that model and predict system behavior, establishing connections between causes and effects. This structure provides context instructions and supports the development of routine operational activities through continuous information sharing [8]. In the realm of business intelligence, data are systematically collected to measure key performance indicators and generate accessible dashboard views for operators and managers. Neural networks organize these data and report them to the ERP, seamlessly integrating them into corporate applications. The system effectively communicates results through spreadsheets and graphics [4]. The CPS encompasses sensors, machines, and operators. Data collected from sensors are systematically organized in a data center to fuel artificial intelligence and machine learning procedures. This step involves the interaction among manufacturing resources in physical space, the transfer and storage of data from machines and the shop floor in the cloud, and the physical communication with cybernetics and vice versa. Analytical technologies transform sensor data from critical components into valuable information used for equipment health management and process monitoring. Following this, operations technology introduces a series of decisions and actions based on information derived from collaborative machine-machine data, contributing to the learning system in three modes: (1) self-aware, (2) self-predict, (3) self-configure, and (4) self-compare [20]. Datasets are acquired through sensors integrated into the production line, tailored to the specific type and specifications of the process. Essential for converting these data into meaningful information is specific algorithms designed for equipment health management and forecasting applications, facilitating the machine's self-awareness. In the realm of Cyberspace, machines need to be interconnected to form a network that allows for self-comparison. Appropriate infographics are necessary to organize the received data and transfer acquired knowledge to users effectively. Feedback from Cyberspace should be shared with the physical aspect, serving as support for supervision and corrective and preventive decision-making by humans [56].