Last year, Chick-fil-A’s finance department embarked on a robotic process automation (RPA) pilot for multiple use cases. One of the biggest challenges for the quick service restaurant chain was that it was experiencing rapid growth, and finance was experiencing capacity constraints as a result.
“Even if we wanted to hire more people, we could not find that many people fast enough to get them on board and up-to-speed in order to do the work at the pace it was growing,” said Camille Felton, CTP, FP&A, Senior Lead Analyst, Financial Analytics and Solutions, who discussed Chick-fil-A’s RPA implementation at AFP 2019. “One of the things we really struggled with is that everyone at Chick-fil-A today, like other companies, is running at 110% capacity. So we really just said, ‘Let’s take some transactional work and see if we can reduce that effort to free up capacity in areas that need it most.’ And those use cases were so successful that we were quickly able to see the value that this could have in the business.”
For treasury, the pilot use case was related to their cash position, which Chick-fil-A had previously performed manually in Excel. Chick-fil-A’s cash management team had created multiple process efficiencies, but they were ultimately gathering copious amounts of data and then populating spreadsheets. While some of their banks had application programming interfaces (APIs) that could be leveraged to pull necessary inputs, others had not yet explored this capability. Additionally, APIs or even using a TMS required initial connection and ongoing support from an IT team that was equally strapped for time, so the treasury team stuck within technologies where they had direct expertise.
Enter RPA. “We said, ‘What if we used a robot to pull down all of our transactional and balance activity from every single bank that we have?’ And then we can use some tools to push that downstream so that at any given time, we could have the cash position readily available,” Felton said. “That initial value-add pilot began to show everyone what RPA could do. Ultimately, we created a new group to do specifically RPA in financial services.”
RPA was also used to resolve reconciliation issues in accounts payable (AP). “Chick-fil-A had an opportunity to improve the efficiency of matching what we ordered at our stores versus what we were invoiced. Initially, this was done more manually than we’d like to admit, again via Excel,” she said. “But with RPA, we were able to utilize a bot to identify variances and report the discrepancies to our teams instead of them spending valuable time on this research daily.”
Now with RPA, AP, treasury and all of financial services have begun to see process efficiencies that are freeing up teammates’ time to shift their focus towards data-driven decisions.
Again, these may sound like problems that would be easily solved with a treasury system. However, in Chick-fil-A’s case, it made more sense to go a different way. “Five years ago, we felt we were too small for a TMS,” Felton said. “We just didn't have many banks. Now we're seeing we have a need for that, but we've found other ways around it because of our IT’s capacity constraints. Due to our growth, IT’s time is focused, rightfully so, on keeping the wheels on the bus for existing systems and their changes. If we were to implement a TMS, we’d get it stable and then turn around and say, ‘We want to add another bank,’ or, ‘We want to change tools.’ Our business is evolving faster than the pace of current IT implementations.”
A NEW WAY TO CONNECT
RPA may be ideal for treasury departments that are in Chick-fil-A’s situation—looking to connect disparate systems but lacking the bandwidth to support a TMS or an API. “If we had a centralized data management platform that could help different systems talk to each other, as well as manage documents in a better way across departments, then we maybe we could use that instead,” Felton said. “But in absence of that, and in absence of a TMS, RPA pairs well with other things.”
For more insights on RPA, artificial intelligence and machine learning, download AFP’s two-part Executive Guide to Emerging Technologies, underwritten by Kyriba.