Of all the performance metrics, Overall Equipment efficiency or OEE stands out as the key metric for overall manufacturing process improvement. The metric pertains to the quality, performance and availability of the production process. Unscheduled downtime leads to a low OEE as the performance and availability of the equipment reduce, which suggests a high need for manufacturing optimization. Having a high OEE would result in overall high revenues for the plant.
To predict the potential reasons for downtime, the real-time status of the processes and equipment must be tracked. By doing so, unscheduled downtimes can be prevented through scheduled equipment maintenance. This prevents time wastage and minimizes costs. The real-time status of the equipment can be effectively tracked through edge analytics which allows more informed decisions for continuous improvement and manufacturing optimization.
Take Advantage of the Edge
Plants must implement edge computing and take benefit of edge analytics which forms a bridge between the data centre or cloud and the multiple devices transmitting data. By attaching edge devices to the equipment, local data can be collected, processed and stored locally. Based on business algorithms and rules, on-premise analytics can be used to gather, filter, store, and transmit crucial data to the data centre or cloud. This helps in saving costs of the cloud services and bandwidth.
Further, the real-time capability of the edge devices can be used to apply automatic changes to production based on a pre-determined algorithm. The capability is further enhanced through the facility managers’ or equipment operators’ informed decision-making.
Benefitting from Predicting
Numerous processes can benefit from the edge device’s capability of real-time integration and analytics. It helps in making use of a predictive maintenance strategy which prevents failures from happening. According to a report published by Deloitte, breakdowns in plants can be drastically reduced by 70% by taking up predictive maintenance, leading to a 20% increase in equipment uptime. For example, condition monitoring can be used to predict when equipment could fail. But through predictive maintenance, the failure is predicted even before it occurs, and the maintenance can run in parallel without disrupting the production processed. Downtimes can also be planned accordingly so that there are no sudden halts to production.
Data Yields Power
Predictive maintenance is considered to be a powerful strategy by many manufacturers as it allows the systems to be continuously optimized. Such strategies prepare the equipment for any impending failures, prevent them from happening and keep them running for longer without any halts. According to Markets and Markets, by the year 2025, the size of the global predictive maintenance market is expected to increase to more than 12 billion dollars from 4 billion dollars in the present years. This increase in predictive maintenance is driven by a huge need to reduce downtime through industry 4.0 platforms, leading to overall manufacturing growth.
Even though edge analytics is essential to implement a predictive maintenance strategy, it is even more essential to select the right digital manufacturing platform. The industry 4.0 platform should be such that it is easy to operate on and integrate. Brabo, a manufacturing connectivity and intelligence platform developed by Solulever, a Dutch technology startup, acts as a hybrid platform with a unique architecture that promotes smart manufacturing. It hides all the complexities in the platform, making it easy for the plant operators to use it with ease. Brabo is easy to integrate and promotes Edge computing through seamless OT-IT connectivity. It collects data from the PLCs or sensors linked to the devices, which is then morphed, analyzed and acted upon. Real-time decisions are promoted as the data is processed at the edge of the source itself. This helps in executing business rules in a rapid fashion. Brabo helps manufacturers achieve the dream of smart manufacturing at effective costs as it cuts down the downtime periods and its associated costs drastically. The instantaneous information produced from edge analytics is used by the predictive maintenance strategy to cut down on unscheduled downtime. This is possible as the real-time status of the processes and the equipment is captured. Overall, the productivity of the plant goes higher, enabling the operators to make more well-informed decisions.