La calidad es innegociable en las empresas de clase mundial y ahora puedes llevar este concepto a nuevos niveles de eficiencia y rentabilidad explotando tus datos.
OEE measurement and display is extremely important in order to know the status and performance of machines and production lines, regardless of the industry.
To know more about OEE of your machines is relevant too because allows you to get more insight about availability, downtimes and stoppages, number of defects, production times, among others that result critics along the entire operation. On average, OEE reachs 70% in plastic injection molding companies.
Injection molding industry must keep constant production and efficiency levels to meet customer requirements in a timely manner. For example, automotive companies demand high quality levels from all their suppliers.
Plastic injection molding companies must monitor key factors such as scrap, parts produced and those to be remanufactured, in order to calculate loses of raw material and implement best practices along quality process. At this part, every minute means an additional cost of energy, human resources and asset impairment, so it is crucial to measure performance using OEE to improve and optimize processes, as well as monitor the status of assets and health of the machines.
To get real-time visibility into the production rate allows you to discover and take action on problems that may arise such as material shortages, injection nozzle jams or decreased labor productivity. Therefore, it becomes necessary to identify, reveal and associate machine problems with their root cause and notify interested users.
The solution
Industrial IoT allows you to extract data and connect machines with information systems; it also facilitates to obtain variables that are used in the calculation and real-time monitoring of OEE, regardless of type or brands of control systems and IT infrastructures. It is also possible creating dashboards to visualize the processes and monitor them, as well as generate historical data for optimization and continuous improvement.
Newer machines already have protocols offered by the same manufacturers for data extraction. This is usually accompanied by a platform or software offered by the manufacturer for the visualization and exploitation of the data. However, even on slightly older machines, we can address standard and simple ways of connecting with machines and deriving all the data mentioned above.
At this point, for example, you can implement a very practical IoT solution that gives you a status of the equipment and the cycle time by detecting the mold closure. From these two data it is possible to derive more data with very good precision, such as equipment availability and performance.
This kind of solution can be complemented with a quality-defect report system, in order to get a comprehensive and functional OEE, which offers enough insight of the entire operation. From this point you are able to perform improves, reinvest in the technological solution to have more data and greater visibility of the machine and enter a virtuous cycle of operational improvement.
Conclusion: OEE monitoring using IIoT technologies is a key component that cannot be lacking in your operation and represents one of the best ways to enter the digitalization of processes with a focus on a short-term ROI. With this tool you will know the status of all processes and you will take corrective measures in a timely manner for a rapid reduction of costs and inefficiencies.
El cálculo y visualización del OEE (Overall Equipment Effectiveness o Efectividad total de los equipos) es de suma relevancia para conocer el estado y desempeño de las máquinas y las líneas de producción sin importar la industria.
La historia de la industria manufacturera está llena de ejemplos de mejora continua. Y también de costosísimos errores de calidad. Por ejemplo, la industria automotriz es una de las que pone más énfasis y cuidado de la calidad, y aun así las empresas pueden hacer rosarios con los recalls que constantemente deben hacer, ya sea por errores propios o de sus proveedores.
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