According to Arimo, manufacturers can handle up to 800 hours of downtime per year. As unplanned stays are much more expensive than planned, it is important for manufacturers to have real-time information about their equipment and processes to avoid unexpected errors and damage. Here is Johan Johnson, CMO and co-founder of peripheral analyzes The developer of the platform, Crosser, explains how peripheral analysis provides instant data to prevent unplanned outages.

One of the key indicators of process efficiency is the overall efficiency of the equipment (OEE). This KPI is related to the availability, productivity and quality of the production process. High OEE often means good revenue, low OEE indicates the need for optimization and improvement. An unplanned stay will drastically affect availability and productivity – drastically lowering OEE.

Being aware of the real-time status of all equipment and processes is crucial to predicting potential reasons for downtime. Manufacturers applying peripheral analysis will be equipped with the necessary data to make informed decisions about equipment and production to optimize processes and maintenance schedules.

Use the edge

Edge computing is a calculation that is performed on or near the devices that generate data. Typically, data collected from machines or devices is sent directly to a local data center for storage and historical analysis. But now businesses want to use that data using big data analysis and extensive cloud services. This raises some major concerns about security, latency, bandwidth, cost, and reliability.

These concerns are growing for facilities with a large number of devices transmitting data at the same time. Instead, peripheral analysis can bridge the gap between devices and the cloud or data center, providing a local source of processing and storage. On-site analysis can collect and filter data by storing or sending it to the right place based on anomalies, business rules and algorithms. Only the necessary data is sent to the cloud or data center, which allows great bandwidth savings and cloud service costs.

Moreover, real-time edge capability can use automatic production changes according to a predetermined algorithm or be evaluated by equipment operators and facility managers to make informed decisions.

Predict to prevent

There are several processes for which real-time data integration and analysis can be helpful. One example is condition monitoring, which involves monitoring a certain parameter of the machine’s condition, such as vibration or temperature, at which the change may indicate the development of a malfunction.

Condition monitoring can be used as part of a predictable maintenance strategy by running status data through pre-built prediction algorithms that can assess when a piece of equipment may fail. This means that maintenance work can be carried out in an organized manner before the damage occurs or without the need to stop production, or during a planned downtime, which avoids the sudden and costly start of unplanned downtime.

According to estimated support Deloitte reportAdopting an estimated maintenance strategy can reduce damage by 70 percent and increase equipment uptime by 20 percent.

Dedicated to data

An increasing number of manufacturers are adopting predictive maintenance strategies as they allow systems to be maintained before failure occurs and to operate for as long as possible without interruption.

An peripheral analyzes The strategy supports a predictive maintenance strategy, but it is important for manufacturers to choose a platform that is easy to integrate and operate. Crosser Edge Node software acts as a real-time engine and its unique architecture hides complexity from the user so that it can be easily used by existing staff. The software allows manufacturers to collect data from any source, such as sensors or PLCs, and build automated workflows to transform, analyze, and act on data. The data is then processed at the edge to enable fast actions based on advanced business rules or codes.

Long and short downtimes can be an expensive reality, but can be minimized if manufacturers are informed about the state of equipment and processes in real time. Edge Analytics enables real-time data collection, integration and analysis, allowing for automatic changes in production, while providing manufacturers with important data to make informed decisions and reduce unplanned downtime.

To learn more about Crosser and its ultimate solutions, visit

Embrace the edge to avoid unscheduled downtime @crossertech #Engineering #Manufacturers #Automation #Solutions

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