Industrial systems have experienced four major revolutions from the past to the present. In light of these revolutions, mechanical systems have replaced human power for the first time. Over time, starting from the steam in mechanical systems, a mass production system has been started, which has reached the present day with electrical energy. Automatic studies have been carried out using electronic relays and control units in systems that have emerged as the concept of automation. Today, controllers have begun to collect the data they receive from the system and need to enable the system to run a process without the need for a human. Thanks to these developments, the importance of collecting and processing data in production facilities are increasing day by day. In line with these collected data, many improvements and reports can be made in production with the support of artificial intelligence. Within the scope of this study, it is aimed to transfer the data obtained from the vulcanization presses in the facility or on the site to the ERP (Enterprise Resource Planning) system. It is aimed to provide faster and ubiquitous access to the requested data by selecting cloud ERP, not physical ERP, as the source of the ERP system. The data are processed primarily in PLCs (Programmable Logic Controller) located over the presses. The processed data is recorded by connecting to a database through the KEPServerEX program based on the OPC (Open Communication Platform). The Node-RED development tool is used for the connection of the database and cloud ERP system, as well as for urgent field maintenance and process preparation stages. Thanks to this program, it is easy to connect multiple applications that are different from each other. Thanks to a node located in the system, the data determined can be sent to authorized persons via e-mail or SMS notification. In this way, the user is notified quickly to prepare the production line for the anomalies that occur or for the different product types in the queue. In line with the collected data, failures that may occur in the future can be prevented, maintenance periods can be deducted with predictive maintenance and other problems that may occur can be prevented. Out of this, production capacity can be determined, the working performances of the working operators can be deduced and planning works can be carried out healthily.