Unilever activated 35 brands and 180 limited-edition products for the FIFA World Cup across more than 120 countries and millions of retail locations. The campaign required the consumer goods giant to compress its supply chain timelines from months to hours, using AI, advanced analytics, and digital twin technology to plan production and respond to demand spikes in real time.
The company has reduced its concept-to-pilot timelines to 12 weeks and is building predictive customer operations and stronger digital capabilities across its teams, according to Vicky Cuthbert, chief product supply chain officer for Unilever's personal care business.
"We have created a tech ecosystem that's allowing us to identify potential disruption early, assess scenarios and make faster, better-informed decisions that help us stay future-fit," said Cuthbert.
Planning for a worldwide product launch
For the tournament, Unilever had to ensure material sourcing, manufacturing, and logistics capacity were in place to produce and deliver promotional products. That included hundreds of thousands of small "mom and pop" stores along with major retail customers.
Following a marketing push that activated more than 50,000 creators globally across social media, in-store displays, out-of-home advertising, and TV, the company expected a demand spike at any time. That meant keeping close watch on which winning teams' products it needed to produce quickly.
Unilever's factory in Raeford, N.C., played a central role in the FIFA campaign. Using digital twins - computer simulations of manufacturing processes - the plant optimized planning and production of personal care products, resulting in a 20% reduction in waste and a 10% increase in capacity, according to Unilever.
How AI shapes demand forecasts
The company pairs AI with advanced analytics in its Forecast Engine Utility, which combines machine learning and data science to generate a 104-week forecast every week across more than 5 million product-customer combinations in 40 operating markets.
"Data and AI enable us to process much more information than would be possible manually, significantly improving the accuracy of our forecasting and scenario planning," said Cuthbert, "as well as optimizing logistics routes."
Manufacturing will become increasingly predictive and scenario-based, Cuthbert said. Unilever plans to roll out more than 40 AI-enabled digital twins across its network over the next 18 months to help teams identify issues earlier, simulate scenarios faster, and make smarter decisions.
"The biggest opportunity isn't just deploying AI," she said. "It's fundamentally redesigning our underlying processes so people and AI can work together effectively."
Why this matters for product development
Unilever's FIFA campaign shows how product development and supply chain planning are becoming locked into a single loop. The company anticipated demand from a global event, built a plan across 5 million product-customer combinations, and adjusted in real time as outcomes shifted. For product developers, the takeaway is practical: the tools that predict demand also change which products get produced, in what quantity, and at what speed. The challenge is not just forecasting demand - it's integrating those forecasts with the physical constraints of manufacturing. For product developers, the takeaway is practical: the tools that predict demand also change which products get produced, in what quantity, and at what speed. The challenge is not just predicting demand - it's connecting that prediction to the physical reality of manufacturing. As Unilever's experience shows, the companies that compress that cycle - from 12 weeks to real time - will be the ones that stay ahead of demand without flooding the market with waste.
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