The fourth industrial revolution is a concept that upsets the conventional vision of factory automation and digitalization. The objective of Industry 4.0, in fact, is the creation of a Smart-Factory, where the keystone is represented by CPS in production. In the last section we saw how this technology is able to reproduce the factory in virtual space in order to control and optimize processes according to the system's autonomy rate. The Internet of Things is considered the basis for the accumulation and transmission of information and knowledge between all parties within and outside the factory boundaries. Finally, with all the capabilities of Big-Data, data storage and processing, CPS in production is able to identify and predict the optimal way of functioning of each module or part of the factory. In this section we will study a smart-factory incorporated with CPS in the production carried out according to the three subjects that we will see in the course of this paragraph. The revolution is that each physical element within a Smart-Factory can be considered as an entity in its own right, with thinking capabilities through an embedded computational power that allows it to be autonomous in its operation. Therefore, during the working day it is monitored through widespread control operations. Therefore, each element in physical space has autonomous intelligence and goes in the direction of continuous improvement, to quickly react to possible uncertainties and problems. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay We will analyze the architecture of a smart manufacturing plant based on a CPS in production, the pillar is a modular system based on different layers that communicate with each other. Starting from physical modules composed of equipment, AGVs and product parts, the system creates a cyber-display to collect information on them in terms of movements, work progress, tasks and skills during production operations. The possibilities within cyber space are endless, but most importantly it connects all parts of the physical space and allows them to communicate whatever module is placed. This feature allows a complete and correct view of the production plant mentioned above, as well as transforming it into a flexible system capable of responding to problems. In this section we want to focus on the composition of a smart manufacturing plant and the different layers that compose it. There are mainly three material, logical and interaction layers. All these layers communicate with each other, exchanging enormous amounts of information and knowledge. Starting from the first layer, it can be considered as the set of elements that conduct the production processes and all the movements of resources necessary to carry out various tasks. It is easy to understand that the material layer is made up of different components, first of all the equipment and all the machines that physically create the product and the parts of the product during the production process. Secondly there are automated robots with the aim of managing and carrying out the movement of material resources once acquired and transported to the first production phase and finally storing the finished product in the warehouse. Thirdly, the AGV technology which will be explored in depth in the next paragraph, which moves material resources and semi-finished products throughout the entire production process by creating a connection between the components mentioned above. Given a general overview of the physical layer assembly workshop, we can say that the operations (production, transportation, movement,storage and distribution) are regulated by receiving and sending information and data in real time between entities in order to create a reactive system. In summary, the performances of physical entities are mainly two, realization of what was designed and acquisition of information. The second layer consists of a virtual representation of all the above-mentioned components of the material layer. In fact, in this level the presence of an LU supervisor is foreseen for each physical component, LU has different roles but as we can imagine the first role is of supervisor with the aim of improving and connecting different elements of the physical space. In this space, we must consider the logical unit as a single entity that has a strong computing capacity and therefore an intrinsic thinking capacity that is exploited through communication and networking between each LU. In fact, they extrapolate information from MCs and use this data to generate control interfaces. For example, LU organizes production planning for each piece of equipment and transportation planning for all automated vehicles to move parts between machines. Therefore, the LU composed of integrated thinking skills and time planning skills improves and organizes tasks using the information and knowledge exchanged through the communication link. The interaction layer is made to allow interaction between the other two layers. It is in fact based on two ports, the first takes and exchanges information in the material layer, while the second port is used to connect the logical layer. The technology relies on WIFI connection and commuting information to make information accessible from one layer to another. For example, let's consider the case when the material layer updates the logical layer regarding the new production conditions, in this case it is the task of the CL to communicate the notice and apply the translation process. Subsequently, the logical unit processes the information, creates a new production model and communicates it via CL to the material layer. The liaison role of the CL is therefore of primary importance. In the previous paragraph we explained the architecture of an intelligent production plant, i.e. one made up of physical elements that carry out vital activities for the production process. Subsequently, there are logical units equipped with computational power, i.e. distributed control interfaces that interact with each other and regulate the behavior of MC and AGV. The characteristics mentioned above are the basis for the development of a Smart-Factory, in an unpredictable context. In fact, it must be a flexible system, based on self-regulation and self-adaptation capabilities so as not to suffer uncertainties and difficulties. The final objective is to create an intelligent production plant, built on autonomy and thinking skills, which can be summarized within a production system called NEIMS based on the functioning of a biological system. Inspired by the biological system mentioned above, we will propose a production paradigm based on the functioning of the human nervous and hormonal system to react to the problems and uncertainties of the production context. The system based the regulation and productivity of the entities present within the plant through implicit commands typical of biological regulation. As explained above, this system is also based on widespread control interfaces, which contain thinking capabilities and computational power. The monitoring of physical elements includes disturbances in the context in which they operate and self-regulate the system autonomously. Now we will explain how the neurocontrol and hormonal regulation system works within a production plantbased on the architecture explained above. During the regular carrying out of the production process the system uses a normal biological monitor, so in case of an unexpected and critical situation to react to it, the NEIMS uses hormonal regulation bringing thinking capacity to each UBA, which communicates, cooperates and makes the decision to react autonomously to disturbances. Think about the customer order cycle. It all starts with an order on the site via smartphone, this reaches the production floor and must be processed. LU analyzes the order and finds all the necessary functions and production processes to fulfill it. Based on the date on which the product must be delivered to the customer, LU organizes the plan model for the production of the product with its built-in data processor. Taking into consideration the case of an order without a critical delivery date. The system lists the order and, through the paradigm of ordinary biological control, organizes and establishes the perfect production schedule. It may therefore happen that the company is not able to manage the customer's order, and this translates into an urgent order, because there is little time left to produce and deliver on the established date. In this case the logical units disrupt the normal working order and regulate the new way of working through hormonal regulation. In this way, the RUs store data in real time and process them, in order to reorganize and reprogram the production model and be able to cope with urgent orders or any difficulty. The following figure represents a possible case of unexpected events and difficulties in the operation of a machine. Starting from this figure, we want to analyze how the NEIMS system addresses problems while maintaining a huge level of productivity and efficiency. From the image above, it is easy to understand that there are problems with the operation of machine number three, those data related to the malfunction are transferred to the respective logical unit. At this stage, a reprogramming of the normal way of working is essential, in order to complete and restore the affected product efficiently. The production program is reprogrammed and the production process is left to the first machine and consequently, the movements of the pieces are reprogrammed by the logic unit of AGV number one which transmits the task to AGV1 which performs the transport. In conclusion, the product was created by machine one, making production possible without wasting time and respecting the delivery date. Through the examples shown previously, we can affirm that systems based on biological control and regulation improve the capabilities of the production plant in terms of reasoning, intelligence and reactivity. Therefore, NEIMS is not limited to managing urgent orders or machine breakdowns, but can be used to explore and develop future manufacturing solutions that can react promptly to unexpected challenges and disruptions in the market environment. In this section we will analyze a practical example approach of the Cyber-Physical System in production developed previously. The objective is to understand if the system can be considered feasible and if it leads to the incredible results mentioned above. The system architecture remains the same, divided into three layers, respectively physical, logical and communication. To delve deeper into this experiment we will take into consideration a series of assumptions and rules that cannot be overcome. Starting from the first: Transport robots move one or more pieces of product for each delivery; The automated guided vehicle remains at the same point, after which it concludes the movements of the parts. Looking forward to future orders; The equipment within the production plant performs an activity for each scheduled time; There.