There’s debate over the origins of the French fry — did it originate with English fish and chips, French steak-frites or Belgian frieten with mayonnaise? What cannot be denied, however, is the dish’s global popularity. But this demand requires intense processing, and data management, from food manufacturers. That’s why, when a world leader in frozen potato products, Clarebout Potatoes, sought to do more with its data, it turned to Crosser, a pioneer in edge analytics for the Industrial Internet of Things (IIoT).
Clarebout Potatoes is a producer of frozen potato products for private labels. Working closely with restaurant chains, retail giants and other major players in the food industry, Clarebout exports its products to more than 60 countries worldwide.
With manufacturing plants in Nieuwkerke and Waasten, both in Belgium, Clarebout works tirelessly to manufacture enough potato-based goods to meet the demand of its global clientele. In fact, its facilities never switch off.
Continuous manufacturing can be overwhelming for any plant. For Clarebout, handling a constant flow of potatoes with no chance for downtime made streamlining difficult.
“A lot happens at our facilities, all at once,” said Frederik Beun, leader of digital engagement and innovation at Clarebout. “Many of our processes are repetitive, and we found that this can increase the number of manual errors being made by workers.”
This constant stream of activity made it difficult to sync-up operations. As a highly-automated producer that’s always keen to upgrade its equipment, Clarebout wanted to solve this issue — particularly as its plants produce so much data. However, this would prove challenging with data from multiple origins being siloed.
“We needed to see exactly what’s happening on our production lines and how that activity relates to the entire business,” Beun explained.
“We wanted to do it with our data, but we were lacking a glue to unite our systems and form a rich, seamless snapshot of our production.”
Edge analytics: a manufacturer’s glue
Contrary to what many manufacturers think, you don’t always need cloud providers to run an enterprise IIoT project. For Clarebout, the answer to its challenge would be found at the edge.
Clarebout is no stranger to data management, and had been using a widely available flow-based programming tool for some time. To level up, Clarebout needed a scalable platform that could bond its systems together — a data management glue. That’s why the company reached out to Crosser at the beginning of 2021.
“Clarebout got stuck-in from the start,” said Andrea Magnago, director of international sales at Crosser. “A major boon of using Crosser is that no downtime is required when deploying new logic or data pipelines — so Clarebout could continue its operations without a hitch.”
The first requirement was to capture all the data running through its shop floor, including data on machine health and status, the amount of ingredients being used, and the time taken to complete certain plant processes. Its next goal was to link all this production data to a manufacturing execution system (MES) and enterprise resource planning (ERP) system. The MES and ERP would make the data available to the entire business, from shop floor to top floor.
How does edge analytics software benefit this? It allows data produced by sensor-rich assets to be pre-processed in real-time closer to its source. When it is deployed on-premise — typically the shop floor — Crosser’s edge analytics software is installed on a server, or virtual machine, where it can process data from multiple on-premise sources.
Low code, low hassle
Clarebout implemented the Crosser platform in a matter of weeks. A major driver in the speed of the roll-out was low code. Because Crosser’s system is a low code platform, it provides reusable actions that users can drag-and-drop for rapid development. Low code platforms enable teams to quickly assemble new processes and build applications without having to research, write and test new scripts.
“Low code guarantees a low learning curve,” explained Magnago. “This makes it possible to design modern enterprise software more intuitively, and doesn’t require extensive training and programming capability.”
However, implementing IIoT use cases can still be challenging — even for a company like Clarebout that’s already experienced in data analytics.
“While low code makes the process much easier, our team still needed to adjust,” revealed Beun. “Crosser’s platform unlocks a far more granular way of managing data,. What’s more, because we were so reliant on coding and reprogramming each time we wanted to install a new process, we were constantly reinventing the wheel. Now, we are adapting to a more streamlined way of data management. But it’s still a learning process.”
Since integrating Crosser’s platform, there has been no stopping Clarebout, according to Magnago: “When Crosser’s agility met Clarebout’s own dexterity, change happened fast.
“What’s great about this speed is, with Crosser, the team can continue to test out new processes as they connect new data sources. The simulation element of the platform allowed Clarebout to get things right from the start, but the value-adding journey never really ends.”
Beun agrees that the agility of edge analytics has been a key ingredient in the project’s success: “We are no longer daunted by data that is so vast, it cannot tell a story. Now, factory data is not only smaller and more meaningful, but also joined together. We have found our data glue.”
Although it takes many manufacturing processes to transform a humble potato into its frozen final product, managing this data needn’t be chaotic. By processing data close to the source, in ways that are agile and don’t require complex coding, Clarebout can ensure its high-tech production plants deliver on-time.