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Customer Story

Under Armour Improves Performance of SAP Master Data Loading with Automate Studio

Founded in 1996 by former University of Maryland football player Kevin Plank, Under Armour is a major innovator in performance apparel. The company made its start by developing a moisture-wicking compression T-shirt for athletes to wear underneath uniforms or pads. Under Armour now designs, markets, and distributes a full line of apparel and footwear for active men and women of all ages, and the company is the official outfitter for college and professional teams across North America and Europe.

Business Challenge

Under Armour went live with SAP in 2006, when the company’s line of apparel was still relatively small. “The organization has grown significantly since then, but our IT staff has remained about the same size,” explains Steve Walker, Manager of Supply Chain Systems. “Anybody familiar with SAP knows that it’s data-intensive. It can drive your business, but it requires a lot of data to make those processes work. Our growth meant that we were dealing with more data than ever.” Twice a year, the company undergoes a seasonal product creation process that requires a massive data transfer from the company’s product development system into SAP. While a single team member oversaw this master material creation process, an additional 10 production planners were required to leave their day-to-day jobs to help create materials over a grueling two-week period.

“This seasonal ritual was a major bottleneck in terms of productivity, so we started looking at ways that we could automate MM01 transactions,” says Walker. “We could write a custom program internally, but that would be difficult and expensive to design and maintain. We could automate scripts using the SCAT transaction in SAP, but that’s not a transaction that most end-users can run. We realized that we needed an automated solution that would take some of the burden off of our IT group and put the power back in end-users’ hands”

Read more about how Under Armour was able to speed seasonal data transfer by 80%.