Good morning.
“People are keeping their cars longer, and that’s good for our business to continue to grow,” Matt Castonguay, SVP of finance, analytics, and supply chain at Team Car Care, tells me.
At Gartner's CFO and Finance Executive Conference in National Harbor, Md., on Thursday, I had a conversation with Castonguay about the company's digital transformation journey in financial planning and analysis (FP&A). When he joined Team Car Care three years ago during the pandemic's supply chain crunch, demand for car maintenance and repair began to skyrocket. FP&A and accounting were doing a lot of manual work and spreadsheets, he said. Team Car Care is the largest franchisee of Jiffy Lube stores. The teams support over 500 Jiffy Lube outlets in 26 states.
So, the company made the move to modernize how it handles financial workflows with automation and machine learning. “The first thing we try to figure out is how many guests are going to show up at a Jiffy Lube," Castonguay says. "For us to get to that point, it really requires precision around guest count, and not just for the month but for the day.”
Castonguay said the FP&A team uses Workday Adaptive Planning, which has a machine learning model with a predictive forecaster that’s built into the platform. (Workday is a CFO Daily sponsor.) “This allows us to come up with forecast suggestions based on several years of historical data, based on weather data that we bring in from NASA, and coordinating that together to be seasonal.”
Why? “Believe it or not, people have different behaviors around oil changes depending on whether it's snowing or raining,” he said.
Before using the platform, “coming up with that detail of a forecast was a four-month exercise, passing spreadsheets back and forth, you know, 100 times,” he said. “Now, we can read forecasts within minutes.”
But with progress, there’s always change. With A.I. and machine learning performing all of these functions, what then will be an FP&A professional’s core objective? They're increasingly playing a big role in the CFO’s office—predicted to have enterprise-wide data strategy as a key responsibility in the next few years.
“My expectation is that within FP&A, you need to be a data scientist,” Castonguay told me. However, that doesn't “require us to just say, ‘Alright, we need to completely change who we hire for our FP&A team,” he said. “It means when people come on board, they need to be trained a little differently, a little more technical than in the past.”
Matthew Mowrey, senior director, analyst at Gartner, said on Thursday during the conference that many companies are hitting roadblocks when it comes to implementing tech for FP&A. "I have a couple of clients that categorize their FP&A technology ambitions as a risk project," Mowrey said.
He shared research findings for a "strategic roadmap for FP&A technology,” which identifies three vital components of implementing a successful plan:
1. Building good data models: "We're currently letting these data models atrophy and die. And one of the reasons why that's happening is because we're taking our old legacy systems and lifting and shifting those data models into the new," Mowrey said.
2. Focusing on A.I.: Start experimenting with A.I., he said. You can do this with low risk because the existing technology already hasn't built it in. “Is it perfect, no,” Mowrey said. “Is it evolving? Yes.”
3. Paying attention to governance: “We need to think about—how feasible are these projects?” he said. “And if they’re not feasible, what would make them feasible in the future? And everyone's going to want to know, what value is this delivering to our business?”
Have a good weekend. See you on Monday.
Sheryl Estrada
sheryl.estrada@fortune.com
