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AI in manufacturing processes [52, 53] follows the production in different forms, including defect classification, defect prediction, sales prediction, assisted production, RE optimization, automated processing, layout and warehouse optimization, DB security predicting network attacks (cybersecurity risk prediction), logistic optimization, and in general supply chain improvements estimating Key Performance Indicators (KPIs). Important KPIs are:

 Count (the amount of product produced in the factory by the last change of machine or the sum of production of the whole shift or week).

 Rejection ratio (production of waste affects profitability goals).

 Production rate (different speed of machines and processes producing goods).

 Production goal (target values for production outputs).

 Cycle time (the amount of time for the completion of a task).

 Overall equipment effectiveness indicating the efficient utilization of available personnel and machinery.

 Idle time (period in which machines are not operating).

 Life cycle of a product.

AI can predict the KPI values thus suggesting decision in a BI key and achieving the main goals indicated in Table 1.8.

Table 1.8 Goals achievable in manufacturing industry.

KPI goal	Description
Safety and environment	Number of accidents at work, number of alarms, consumption, waste, recycling of material, etc.
Efficiency	Saved materials and resources, saved energy of production lines, improvement of services, maintenance optimization, less production shutdowns, decrease of production time
Quality	Percentage of finished product that does not meet the quality criteria, percentage of semi‐finished and raw products that do not meet the quality criteria, size of production losses and waste, internal and external services, etc.
Production plan tracking	Production time traceability, logistic layout optimization, improvement of warehouse management, programming efficiently picking processes, etc.
Employee's satisfaction	Completed works on time, lost workdays, innovation proposals, product/process innovation proposals, etc.

In order to improve the KPI, the real issue is to integrate AI in the whole supply chain by following the main scheme of Figure 1.11. The supply chain is characterized by a large number of different variables. A solution to optimize the supply chain is a dynamic monitoring controlling in real time the whole production system. In this way, AI and IoT solutions, by processing digitized data, improve dynamically real‐time production processes: if there is a prediction of sales of a particular product, the production line is addressed by the AI directly for the new strategic product to process. AI potentially supports industries to switch production according to needs and the possible adaptions of the available production machines in new requested products. Furthermore, if correctly implemented, AI provides improvements in the production time response and in the machine efficiency of the whole production line, where all the machines of a layout not only work to create a finished product, but also are set for the best efficiency. It is observed that, in an advanced local communication system, the single machine can communicate the identified setting solution to all the other machines involved in the production. By applying AI, it is possible to achieve the optimum of the economic and operating criteria, by setting the production level of all the individual units that work together. The production setting is managed by means of a supervision and coordination system, where the AI system is suitable to adapt the processing to each reconfiguration of the production programs and of the product mixing to meet customer requirements in the best way.

Figure 1.11 Artificial intelligence integrated into the supply chain.

In manufacturing industries, the AI system is integrated in a more complex multilayer structure, as illustrated in Figure 1.12, with the following layers:

 Layer 1 (direct control of the single units of the plant). Information about the unit production and about raw materials are executed and transmitted to the highest levels. The first layer is the operational layer enabling production and actuation. The production parameters are optimized by layer 3 related to the AI level.

 Layer 2. This layer represents the response to any emergency condition arising in its area of competence of the plant, the optimization and the coordination node keeping communications active with the upper and lower levels of the system. The layer is commanded by layer 3 and provides outputs to layer 1.

 Layer 3 (AI level). This level is suitable for verification and optimization of key production processes in real time. The management of this level is assigned to AI algorithms. Improvement solutions can be implemented in all phases of the production process by automatizing the whole supply chain. This layer is the core of the Industry 5.0 system and includes process mining (process decision making by data mining) [53], process optimization, process prediction and process control. IoT data contribute in this level to enrich the dataset processed by the AI algorithms for better control of processes upgrading the production. Graphical dashboards facilitate the decision‐making supported especially by supervised algorithms. The AI allows not only to improve the production process, but also to predict possible failures in a self‐adaptive manner.

 Layer 4 (definition of the activities to coordinate the production resources and to achieve the desired final products). This layer is specific for the production area involving RE facilities, production engineering, quality improvement, data processing, and production switching enabled by the AI level. This layer represents principally the production flexibility, the production quality, and the reliability.

Figure 1.12 Multilevel structure of manufacturing industry processes integrating artificial intelligence tools.

The multilevel model of Figure 1.12 is summarized by the block diagram in Figure 1.13, where AI is the feedback of the whole system, layers 1, 2, and 3 represent the single production station, and layer 4 indicates the whole production line processes performing machine diagnostics.

Figure 1.13 Artificial intelligence feedback system in manufacturing processes.

The proposed methodology is adaptable to different industry sectors and represents a valuable tool for the ISO 9001:2015 check control system.