Fast Food Meets Data Speed
EP Editorial Staff | February 1, 2022
Edge analytics and low-code software streamline data for a French-fry giant.
Did the French fry originate with English fish and chips, French steak-frites, or Belgian frieten with mayonnaise? Many have laid claim to the fried potato. What can’t be denied is the dish’s popularity. This global demand requires intense processing and data management from food manufacturers. When Clarebout Potatoes, Belgium, (clarebout.com), a leader in frozen potato products, sought to do more with its data, it turned to Crosser, Stockholm, Sweden, (crosser.io), a pioneer in edge analytics for IIoT.
For Clarebout, handling a constant flow of potatoes—which require de-stoning and washing, peeling, sorting, cutting, baking, and freezing—is a repetitive and time-intensive process. Streamlining is difficult. The constant activity also makes it difficult to sync operations, with manufacturing data kept in silos. The company wanted to manage data more efficiently. This proved especially challenging with manufacturing data siloed from multiple origins, along with data from customer relation management (CRM) systems, sales reports, and plant equipment.
“We needed visibility, to see exactly what’s happening on our production lines and how that activity relates to the entire business,” explained Frederik Beun, Clarebout’s leader of Digital Engagement and Innovation. “We knew what we wanted to do with our data, but we were lacking a glue to unite our systems and form a rich, seamless snapshot of our production.”
You don’t always need cloud providers or platforms to run an enterprise IIoT project. For Clarebout, the answer to its challenge would be found at the edge. The company sought a solution that would act as one central-data-management platform. It wanted to transform its systems from reactive to proactive, and accessing data in real time would be a key factor in making that change. No stranger to data management, the company had been using a widely available flow-based programming tool. To reach the next level, it needed access to a scalable platform that could bond its systems together.
Andrea Magnago, Director of International Sales at Crosser explained, “The team wanted a tool that could deliver and was keen to start testing. A major benefit of using Crosser is that no downtime is required to get the platform up and running, 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 wasted, and the time taken to complete certain plant processes. The 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.
Edge-analytics software allows data produced by sensor-rich assets, such as factory equipment, to be pre-processed in real time closer to where it’s created. When it’s deployed on-premise, typically the shop floor, Crosser’s software is installed on a server, or virtual machine, where it can process sensor data from multiple on-premise machines and data sources.
Clarebout and Crosser began the onboarding process right away with an initial trial period before Crosser’s platform was adapted to suit the facility’s needs. It took just a matter of weeks to fully integrate the technology.
Low code, low hassle
A major driver in the speed of the rollout was low code. Crosser’s system is a low-code platform that provides reusable actions that users can drag-and-drop into processes for rapid development. Low-code development platforms enable teams to quickly assemble new processes and build applications without having to research, write, and test new scripts.
“While low code makes the process much easier, our team still needed to adjust,” explained Beun. “Crosser’s platform unlocks a far more granular way of managing data, as nodes are handled in smaller blocks. 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, with Crosser’s drag-and-drop function, we are adapting to a more streamlined way of data management. But it’s still a learning process.”
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 and experiment with the technology as they connect new machines. 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, and now all our plant data forms a rich insight into exactly what happens inside our fast-paced, always-on facilities.” EP
For more information, visit crosser.io.