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As retailers increasingly embrace artificial intelligence (AI), robotics and autonomous vehicles, a new retail model is beginning to emerge: mobile stores that drive directly to customers instead of waiting for customers to come to them.
New research published in the INFORMS journal Manufacturing & Service Operations Management finds that retailers operating fleets of mobile “stores on wheels” can outperform traditional fixed-location stores by continuously relocating to areas of highest demand while simultaneously learning where future demand is likely to occur.
The study examined how retailers can solve the challenge of deciding where to position mobile stores when customer demand is uncertain, constantly changing and influenced by factors such as weather, population density and neighborhood activity.
Using a case study based on Toronto retail data, the researchers found that adaptive mobile stores increased profits by 2.56% compared with conventional location strategies simply by repositioning themselves in response to changing demand patterns.
The study, “Online Facility Location: Running Stores on Wheels with Spatial Demand Learning,” was authored by researchers from the University of Texas at Austin and McGill University.
Unlike traditional brick-and-mortar stores, mobile retail units can relocate quickly to high-demand areas, operate with lower labor and real estate costs, and allow retailers to test new markets without committing to permanent locations. Companies such as Robomart, Nuro and Neolix have already begun deploying variations of these mobile retail concepts.
“The future of retail isn’t simply autonomous stores — it’s autonomous stores that know where they should be,” said the researchers. “Retailers don’t have to choose between exploring new markets and maximizing today’s profits. With the right analytics, they can do both at the same time.”
The researchers developed a mathematical framework that enables retailers to determine where mobile stores should operate while simultaneously improving their understanding of consumer demand over time. Rather than relying on fixed operating plans, the model continuously updates store placement decisions as new information becomes available.
Mobile retail does face practical challenges. Customer demand is initially unknown and constantly evolving, while limited onboard inventory means stores must balance serving customers with making costly trips to replenish stock. The study demonstrates how retailers can account for both challenges simultaneously.
“A mobile store has an advantage only if it knows where demand is moving,” said the study authors. “Mobility creates the opportunity, but learning creates the value. The retailers that continuously adapt will have the greatest competitive advantage.”
The findings suggest retailers considering mobile stores should adopt adaptive, data-driven location strategies rather than static operating plans. By continuously balancing what they know with what they still need to learn about consumer demand, retailers can improve profitability while reducing the risks associated with expanding into new markets.
As autonomous technology continues to mature, mobile retail stores could become an increasingly common part of urban commerce. The research suggests their success will depend not only on advances in self-driving technology, but also on the analytics that determine where those stores should go next.
