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Cloud-based analytics solution targets high-volume packaging lines

A lightweight, root-cause IoT solution leans heavily on OPC technology and allows enterprises to simplify control architectures for food and beverage packaging lines.

The system provides enterprises the ability to create and see multiple types of conditional elements, such as state, local reason, logic and root cause link. Source: Sage Clarity.
The system provides enterprises the ability to create and see multiple types of conditional elements, such as state, local reason, logic and root cause link. Source: Sage Clarity.

High-volume, food manufacturing lines thrive on standardization to pull through operational efficiencies, be it control architectures, processing systems or supply chains. Managing multiple plant standardization within an enterprise has been a great challenge for the last twenty years in the food and beverage industry, often due to integrating acquired companies.

For the food and beverage industry, identifying the source of downtime on fast-moving packaging or production lines can be difficult. However, advances in industrial networking technologies and cloud solutions are now enabling companies to be nimble and targeted with improvements for operational efficiencies.

In 2016, Chicago-based Sage Clarity introduced its Auto-tagging Business Logic Engine (ABLE) application that performs root-cause analysis via cloud-based modeling of machine centers for many types of manufacturing applications, including food and beverage. The solution, coupled with OPC-UA technology via Kepware ServerEX and its custom interface drivers, forms an industrial IoT solution for high-volume production lines that can automatically identify the source of downtime.

β€œIf a company has 30 plants, they can essentially look at all their plants’ configurations and modify them from a centralized repositoryβ€”the cloud,” says John Oskin, president and CEO of Sage Clarity.

In a recent presentation, Sage Clarity demonstrated how the cloud-based modeling works for packaging lines, with either simple or complex line flows. The modeling targets individual machine centers on production lines and creates a series of β€œcondition” elements that each represent a state and reason; optionally linking to a root cause.

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