Palantir Reinvents Wendy’s Supply Chain Network, Walgreens’ Operations

Artificial intelligence and data analytics firm Palantir is using its military intelligence background to deliver AI-powered solutions to major retailers to reinvent their supply chain network or make operations more efficient.

“We took a lot of the learnings from data integration analytics and putting data in the hands of operators that we learned in the government space, and we reapplied it to large commercial enterprises,” said Anita Beveridge-Raffo, lead deployment strategist at Palantir, in an interview with PYMNTS.

The company’s approach centers on building digital twins, or virtual models of a client’s entire operation — using a semantic layer that sits atop disparate systems and presents unified data on a dashboard.

Palantir’s retail clients seek two AI paths, Beveridge-Raffo said. The first is personalization to improve customer experience and drive top-line growth. The second is automation to optimize processes and improve the bottom line.

“AI has unlocked not only personalization capabilities but also automation capabilities,” she said. “You can look at processes that are cumbersome, that cross multiple different sources of data and systems, and you can use AI agents or agentic workflows within our platform to streamline those processes.”

The PYMNTS Intelligence report “Getting to Know You: How AI Is Shaping the Future of Shopping” found that consumers are increasingly demanding smart shopping experiences, and 51% want at least one kind of AI-assisted shopping feature.

Moreover, highest-value consumers are those who want AI-enabled retail journeys the most. Among those earning more than $100,000 annually, 59% want at least one type of AI-assisted shopping. That compares with 40% of those earning less than $50,000 a year.

Read also: Digital Doppelgangers: H&M Explores AI Digital Twins for Fashion Retail

Syrup Shortage for Wendy’s Frostys

Fast-food giant Wendy’s was seeking to meet customer expectations while becoming more efficient. One target area was the supply chain network.

Historically, the food service industry relied on stockpiling inventory as a buffer against disruption, often holding 30 days’ worth of supplies across the network, said Pete Suerken, CEO of Wendy’s Quality Supply Chain Coop (QSCC), during Palantir’s customer conference.

For Wendy’s, this translated into carrying $370 million in inventory across its restaurants in North America just so it could offer its $9 combo meal, an unsustainable model due to rising labor and storage costs.

Wendy’s had been using a fragmented, Excel-based system that was manually intensive and slow to react to new changes.

“Where [big retail customers] often find a challenge is in the last hour before a delivery goes out or a production schedule is made,” Beveridge-Raffo said.

These include truck delays, a delivery taking the wrong route, or a customer canceling an order at the last minute.

Palantir created a digital twin of Wendy’s to monitor the supply chain network in real time across 3,500 trucks, rail cars, ships and barges, connecting them to 60 core partners, more than 250 shipping points, 34 distribution centers and its 6,450 restaurants in the United States and Canada.

The digital twin automatically alerts Wendy’s to events such as supplies likely to go out of stock, raw materials the company should be ordering to prevent shortages today, and swaps they might have to do downstream in a week, month or quarter, Beveridge-Raffo said.

One use case at Wendy’s revolved around its Thin Mints syrup used in Frosty desserts. The system first identified a problem at the Oregon distribution center, where syrup demand exceeded immediate supply.

While there were four days’ worth of syrup in the overall network, it was not in the right place. The platform recommended reducing or canceling shipments to certain stores receiving deliveries from Oregon, particularly those with more than three days of inventory on hand.

The platform then flagged a larger issue: a network-wide shortage of 10,200 cases of the syrup. The system instantly analyzed supplier data and found 8,300 cases available across other distribution points. It calculated the exact number that had to be ordered immediately to avoid a crisis — 3,500 cases — and pinpointed which locations could absorb the demand.

It took Wendy’s five minutes to manage the syrup supply issue. A year ago, it would have taken 15 people a full day — calling stores, talking to distribution centers — to do the same thing, Suerken said.

See also: Virtual Replicas of Real-World Assets Help Companies Design for Efficiency and Innovation

Using AI for Walgreens Operations

Jeff Hoffman, vice president of product management for Walgreens’ pharmacy and healthcare businesses, said during Palantir’s customer conference that the drugstore chain was seeking to modernize its customer and employee experiences.

That’s no small feat. Walgreens has 8,500 stores in the U.S. and Puerto Rico and employs 220,000 workers serving 9 million customers daily. It runs 11 pharmacies that fill prescriptions using robotics and automation. With multiple customer journeys and engagement channels, Walgreens is collecting billions of data points each day, Hoffman said.

“At its core, Walgreens is a platform business with clinical and commercial offerings that service a wide range of patients and B2B partners,” he said. “What that means is that we participate in a large, complicated, integrated and interrelated pharmacy and healthcare ecosystem.”

Over time, Walgreens’ data has resided in “disparate and disconnected systems” that make it “challenging to manage, let alone continually assess and adjust our decision rubrics,” Hoffman said. The company ends up deploying rigid, rules-based decisions that target one part of operations, often not based on real-time insights.

“In essence, these limitations have prevented us from maximizing the full network effects of our pharmacy business end-to-end across the enterprise,” he said.

Palantir built a digital twin of Walgreens’ operations and integrated its data, models and transactional systems in fewer than 45 days. The AI-powered system enabled Walgreens to produce real-time insights and enable intelligent routing and “dynamic” decision making, Hoffman said.

“We always wanted to make sure that the right work was being completed in the right location by the right individual at the right time,” he said, adding that the system has to distinguish between the work done by pharmacists and pharmacy technicians.

Walgreens began a 10-store pilot program over six months, which expanded to 4,000 stores in eight months.

Hoffman shared the use case of tracking the inflow and outflow of pharmacy tasks in 15-minute intervals. With this visibility, Walgreens was able to route work to centralized staff members to do pharmacy tasks as a supplement to work being done in the stores.

Overall, workload balancing at Walgreens has cut down time spent on tasks by 30%, Hoffman said.

“We are still very early in the journey, but this has proven to be impactful relatively quickly and truly allows us to understand all the working components of our business simultaneously with greater intelligence,” he said.

For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.