Store inventory intelligence becomes a core supply chain capability in omnichannel retail

While GreyOrange’s announced a partnership with Zebra Technologies to launch real-time store inventory intelligence as part of its gStore platform fits inside the store inventory ecosystem, the implications reach far beyond just the four walls of the store.

At a time when supply chains are under pressure to move faster, carry less excess inventory, and support omnichannel fulfillment models, real-time store inventory visibility is increasingly being treated not as a retail optimization tool, but as a core supply chain capability.

“Store and supply chain are very, very connected,” said Akash Gupta, CEO of GreyOrange, told Supply Chain Management Review in a recent conversation at the NRF retail show. “As we start bringing both of them together, there is a lot of intelligence that you can bring all across.”

From inventory accuracy to inventory intelligence

Retailers have long invested in inventory tracking technologies. What has changed is the expectation of what that data should enable. Near real-time updates such as refreshing inventory counts and locations every few minutes are now table stakes. The differentiator is what happens next.

“We are pretty much at near-real-time level,” Gupta said, describing how inventory count and location data are updated roughly every 10 minutes. But simply knowing how much inventory exists is no longer sufficient, particularly as stores increasingly serve as fulfillment nodes for e-commerce orders.

“Location of inventory matters a lot,” Gupta said. “It’s not just the count of the inventory. It’s also where exactly it is in the store.”

In a ship-from-store environment, the cost of not knowing that location adds up quickly. A few extra minutes spent searching for an item can erase any shipping savings and introduce labor inefficiencies that ripple upstream, he noted.

The joint solution combines Zebra SmartLens overhead RFID reader hardware and software with GreyOrange’s gStore execution platform to help retailers identify on-shelf gaps and immediately direct store teams to execute corrective action. According to Zebra’s latest global shopper study, inventory inaccuracy remains one of retail’s most persistent and costly challenges, leading to lost sales, inefficient labor, and cancelled omnichannel orders.

Closing the loop between store execution and supply chain planning

What distinguishes the current generation of store intelligence platforms is their focus on execution, not just insight, Gupta pointed out.

“For us, inventory is just the input,” he said. “We drive a lot of execution within the stores with that inventory.”

That execution layer—task prioritization, associate direction, replenishment triggers—creates a closed-loop system in which store activity feeds back into broader planning decisions.

GreyOrange refers to this as store orchestration, but the underlying principle is more general: events at the edge of the network should inform decisions upstream, and vice versa, Gupta said.

“If, in the store intelligence, you are suggesting to put something on the sales window,” Gupta explained, “the same AI agent also is figuring out what to do on the supply chain because you know the impact of something you put on the sales window.”

Promotions, visual merchandising changes, and sudden demand spikes no longer live solely in the retail domain. Each action has implications for replenishment, warehouse allocation, and transportation planning.

Why visibility gaps still matter in 2026

Despite years of investment, visibility gaps remain one of the most persistent challenges in supply chain execution, particularly in complex retail and wholesale environments. Retailers are increasingly asking for tighter feedback loops between stores and distribution centers. “They are looking for real-time feedback from store to warehouses,” Gupta said.

The challenge becomes even more pronounced in wholesale and multi-brand ecosystems, where inventory ownership and visibility are fragmented across partners.

“Connecting warehouses and connecting stores, especially in the wholesaler environment, is a pretty difficult piece,” Gupta noted.

For supply chain leaders, the implication is clear: without shared, timely visibility into inventory positions downstream, upstream planning assumptions degrade quickly, regardless of how sophisticated forecasting models may be.

Inventory intelligence as a resilience lever

While tariffs, geopolitical risk, and long lead times continue to shape supply chain strategy, Gupta argues that inventory visibility is not a reactionary investment, it is foundational.

“Either way you’ve got to have it,” he said. “People are more aware and have a level of agency to say, ‘We need to make sure we have the right visibility.’”

That mindset shift reflects a broader recognition that resilience is built on information flow, not just physical redundancy. When stores operate as blind spots, supply chain plans become increasingly brittle.

From intuition to data-driven store decisions

One of the more significant downstream impacts of store-level intelligence is its effect on labor and execution decisions inside the store, areas that have traditionally relied heavily on intuition.

“All the decisions that are taken in the store today are fairly intuitive,” Gupta said. “How do you change that to a data-driven kind of decision making?”

Gupta pointed to several use cases that emerging today including:

• Real-time replenishment from back-of-house to sales floor
• Prioritization of associate tasks based on demand signals
• Labor allocation between fulfillment and customer-facing activities
• Visual merchandising optimization tied to local demographics and trends

These decisions may appear operational in nature, but their cumulative effect shapes service levels, fulfillment performance, and ultimately demand patterns that flow back through the supply chain.

AI as an application, not a strategy

As with many areas of supply chain technology, AI is often discussed at a high level, detached from specific problems. Gupta feels strongly that AI isn’t the answer alone. “I don’t think we need to buy AI platforms,” he said. “You’ve got to buy a very specific application that is truly powered through AI.”

The distinction matters, he noted, as AI delivers value when it is embedded in workflows that already matter, such as inventory accuracy, execution speed, and decision prioritization, and not when it exists as a generalized capability in search of a use case.

“Forget about AI,” Gupta added. “That’s just a tool. What problems are you trying to solve?”

That framing aligns with a growing consensus among supply chain leaders: AI maturity follows data maturity.

Why these investments are sticking

Retail and supply chain technology trends have a history of cyclical enthusiasm. Gupta sees the current focus on inventory intelligence as different.

“These are all very long-term problems to be solved,” he said. “Getting more sales out of the square feet you have in physical retail is the core problem.”

As retailers stabilize post-pandemic and recalibrate their omnichannel strategies, the emphasis is shifting from experimentation to execution.

“Basic things need to be right—the data needs to be right, the backend needs to be right,” Gupta said. “Now they’re really looking forward to getting more intelligent use cases on top.”

The GreyOrange–Zebra announcement underscores a broader shift underway, one where store-level visibility is no longer a retail-side optimization solution, but rather a critical node in the supply chain intelligence network.

When inventory data is timely, location-aware, and executable, it enables better decisions across planning, fulfillment, and labor. When it is not, even the most advanced supply chain systems struggle to compensate.

As retailers continue to blur the line between store, warehouse, and fulfillment center, the ability to treat stores as fully integrated supply chain assets, not informational dead ends, may prove to be one of the defining capabilities of the next phase of supply chain evolution.

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