Overall Equipment Effectiveness (OEE) is one of the primary measures of process efficiency. This KPI is linked to the availability, performance and quality of the production process. A high OEE often means good earnings, a low OEE indicates the need for optimization and improvements. Unplanned downtime will significantly affect availability and performance, which will significantly reduce OEE.

Knowing the real-time status of all equipment and processes is key to predicting potential causes of downtime. This allows simple maintenance tasks to be performed to avoid downtime, or if downtime should occur, it can be planned in the best possible way to minimize costs and avoid wasted time. Manufacturers who implement advanced analytics will be equipped with the data needed to make informed equipment and production decisions to optimize maintenance processes and schedules.

Take advantage of the periphery

Edge computing is a computation performed on or near the devices that generate the data. Typically, data collected from machines or devices is sent directly to a local data center for storage and historical analysis. But today, businesses want to harness that data using big data analytics and expansive cloud services. This poses major issues regarding security, latency, bandwidth, cost, and reliability.

These concerns are amplified for installations with a high number of devices transmitting data at the same time. A large number of devices operating simultaneously generate a massive amount of data.

Instead, edge analytics can form a bridge between devices and the cloud or data center, providing a local source of processing and storage. On-premises analytics can collect and filter data, store it or send it to the right location based on anomalies, business rules and algorithms. Only necessary data is sent to the cloud or data center, resulting in significant savings in bandwidth and cloud service costs.

Additionally, real-time capacity at the edge can take advantage of automatic production changes according to a predetermined algorithm, or be assessed by equipment operators and facility managers to make informed decisions.

Predict to prevent

There are a number of processes that real-time data integration and analysis can benefit from. An example is condition monitoring, which involves monitoring a certain parameter of machine condition, such as vibration or temperature, in which a change could indicate the development of a fault.

Condition monitoring can be used as part of a predictive maintenance strategy by running condition data through predefined predictive algorithms that can estimate when equipment may fail. This means that maintenance work can be carried out in an organized manner before the failure occurs, either without the need to stop production or during planned downtimes, which avoids the sudden occurrence and costly unplanned downtime.

This contrasts with the traditional reactive maintenance strategy, where manufacturers waited for a machine to break down before applying maintenance work. According to predictive maintenance Deloitte reportadopting a predictive maintenance strategy can reduce breakdowns by 70% and increase equipment availability by 20%.

Dedicated to data

More and more manufacturers are adopting predictive maintenance strategies, as they allow systems to be maintained before failure occurs and run for as long as possible without interruption. Market research firm MarketsandMarkets estimates the global predictive maintenance market size to grow from $4 billion in 2020 to over $12 billion by 2025. The growing need to reduce downtime is highlighted as a major driver of the expected growth.

A advanced analysis supports a predictive maintenance strategy, but it’s important that manufacturers choose a platform that’s easy to integrate and use. Crosser Edge Node software acts as a real-time engine and its unique architecture hides the complexity from the user so it can be easily used by existing staff. The software allows manufacturers to collect data from any source, such as sensors or APIs, and create automated workflows to transform, analyze, and act on the data. Data can be processed at the edge to enable rapid rule-based actions or advanced business code.

Long and short downtime can be a costly reality, but can be minimized if manufacturers are informed of the real-time status of equipment and processes. Edge Analytics enables real-time data collection, integration and analysis, enabling automatic production changes while providing manufacturers with important data to make informed decisions. The instantaneous insights that result from edge scanning can be used as part of a predictive maintenance strategy to reduce unplanned downtime.

To learn more about Crosser and its industry-leading solutions, visit crosser.io.


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