Elia, Belgium’s high-voltage infrastructure operator, keeps around 33,000 separate materials and components in its six warehouses. To ensure that its warehouse personnel and field technicians can identify, categorize and locate so many components in SAP, Elia worked with Flexso to create an innovative machine-learning tool with its own expert eye.
Elia relies on SAP for many of its core business processes and is constantly looking for new ways to enhance them. Illustrating its commitment to innovative SAP technologies, the firm rolled out Fiori Launchpad and several SAP Fiori apps used at Elia have already been fully mobile-enabled.
A Fiori app called ‘Materials Display’, was already in use to help Elia’s warehouse technicians search for, categorize and find materials. But so many material descriptions and images are tough to keep up to date, and some components might have substitutes or updates that aren’t in the system. Even more, it’s not always easy to figure out the right keywords to use to search for unfamiliar materials in the SAP database.
During a design thinking session, Elia invited Flexso to come up with new ways of improving the materials identification and documentation process. Flexso’s solution was an add-on to the Materials Display app that can accurately recognise components and materials from images taken by employees in the warehouse or the field.
Together, Elia and Flexso took an agile approach to development, creating a machine-learning proof of concept powered by SAP Leonardo. They trained it using images of Elia materials, enabling it to accurately recognise 187 materials in 3 groups. The proof of concept didn’t need to be capable of identifying a material perfectly on the first try, but it should be within the top five suggestions. The Flexso team delivered a solution that surpasses Elia’s requested inclusion rate.
“Our new, user-friendly proof of concept enables better operational efficiency and accuracy and makes us less reliant on data quality. It’s living proof that SAP offers powerful, innovative new ways to tackle complex challenges.”
Lorenz Sunt, Project Manager at Elia
In the future, the proof of concept will be trained and tested to recognise assembled parts as well, since it’s not often possible for technicians to disassemble a component and take pictures of its individual pieces in the field.
The possibilities of this image recognition solution extend beyond stock control processes. They can be applied in various ways to solve a wide range of business problems in production, logistics, operations and more.
While the applications of this technology are diverse, during the course of this project, we learned some concrete lessons regarding how to take pictures that machine-learning image recognition platforms can work with. By keeping the following photography tips and tricks in mind, it’s possible to maximize the accuracy of the solution.
We offer these functionalities bundled in a packaged solution, the ‘Flexso Material Wizard’ – find out more .
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