Aramark's AI Forecasting Tools Cut Fan Wait Times at Golf Majors

Aramark Sports + Entertainment has built two in-house data platforms — Fairway Forecast and Homegrown — that pair point-of-sale (POS, the checkout data trail from every register) history with external signals like player pairings and weather, and it has already run them at two U.S. Opens and a Grand Prix.

The business problem is concrete: golf is the hardest demand-planning environment in sports foodservice. A stadium fixes where 60,000 fans sit. A golf course moves the crowd constantly, and the venue itself changes every year. Aramark's answer is forecasting built on three inputs — POS history, USGA player-grouping data and weather forecasts.

"A lot of the technology that we're looking at is how can we enable our front line to provide a more hospitality-like experience by reducing the friction points behind the scenes using data and AI and technology," says Scott McDade, VP of Data Science at Aramark Sports + Entertainment.

Where did Fairway Forecast prove out?

The platform piloted last year at Oakmont Country Club in Plum, Pennsylvania, during the 125th U.S. Open. It returned this year with additional functions at the 126th U.S. Open Championship at Shinnecock Hills Golf Club in Southampton, New York.

The output is operational, not analytical. Aramark pushes daily demand projections to team members stationed along the course, then refreshes them intraday as play shifts the crowd.

"We're giving the team on the ground projections every day of where we would anticipate demand and then refreshing those over the course of the day, so they were able to sort of react over the course of a long service day," McDade says.

The model generalized. Aramark deployed the same forecasting engine at the Freedom 250 Grand Prix of Washington, D.C. earlier this summer — a motorsport event with the same core variable: demand that moves across a large footprint.

"A lot of the same solutions were applicable in terms of forecasting when demand would take place across the locations we are servicing," McDade says.

The takeaway: demand forecasting that works at a roving golf major transfers to other distributed-venue formats.

What does Homegrown do for fixed venues?

For collegiate and major league sports venues, Aramark built a second platform, Homegrown, which McDade calls "a mobile command center." It compares historical POS projections against real-time POS data streaming from the venue.

The logic is inventory and labor control at the point of sale:

• Flags underperforming POS locations so staff can be redeployed
• Flags items outperforming projections so restocking happens before stockouts
• Sends those messages directly to operators on the ground during service

"We're sending teams messages to all of our operators on the ground, letting them know whether a point of sale is underperforming or if items are outperforming projections and, therefore, we need to restock," McDade says.

In distribution terms, Homegrown closes the loop between forecast and actuals in near-real time — the same discipline a wholesaler applies to inventory turns, compressed into a single service day.

What comes next?

The roadmap splits along the same back-of-house versus front-of-house line. Back of house, Aramark will keep extending platforms like Fairway Forecast and Homegrown. Front of house, the company is investing in checkout-free formats.

"We're really doubling down on things like autonomous stores and looking at facial recognition for the purposes of payments, which is prevalent in Major League Baseball and a little bit in the NFL, and so we're looking at ways that we can continue to optimize that," McDade says.

Notably, Aramark is following rather than leading on biometric payments — facial-recognition checkout is already established in MLB parks and appearing in NFL venues. The company's stated edge is optimization, not first deployment.

One caveat for the record: Aramark has not disclosed wait-time reductions, sales uplift or forecast-accuracy figures tied to either platform. The deployments are verified; the performance claims remain qualitative, framed by McDade around friction reduction rather than measured throughput.

The next test of the approach is scale: whether forecasting built for one-week golf majors and single-race weekends can hold up across a full season of collegiate and major league venues.