Target Builds Digital Twin Platform 'Proxima' to Revolutionize Inventory Management

Why Target Built Its Own Digital Twin

In a move that underscores how central inventory accuracy has become to retail competitiveness, Target has unveiled Proxima, an internally developed digital twin of its inventory positioning system. The platform creates a virtual replica of Target's entire supply chain — from regional distribution centers to individual store shelves — allowing teams to simulate decisions before implementing them in the real world.

The decision to build in-house was driven by necessity. According to Jake Krings, Target's vice president of technology for global supply chain and logistics, the company evaluated external vendors but found that none could support the breadth of Target's product categories. The company needed a system capable of modeling everything from fresh groceries to seasonal apparel to bulky home goods — all within a single simulation environment.

"We have patterns in place and an architecture in place that allows us to rapidly scale up capabilities like this," Krings told Chain Store Age, explaining that the company's existing data infrastructure made it possible to iterate quickly, test simulations, and verify accuracy before going live.

How Proxima Works

Proxima uses the same data and logic as Target's live inventory platform. That means the simulations aren't approximations — they're running on production-grade information about how products move through the supply chain. Teams can test how changes to distribution patterns, reorder timing, or seasonal allocation would play out before those changes ever touch a physical warehouse.

The platform was developed jointly by Target's supply chain, product, and engineering teams, reflecting the cross-functional nature of modern retail technology initiatives. In one pilot involving 63 fresh food items, Proxima identified inventory flow adjustments that improved on-shelf availability by 2.5% — a meaningful gain in a category where even small improvements translate directly to reduced waste and increased sales.

Proxima also played a critical role in Target's launch of a new "receive center" in Houston, a facility designed to handle seasonal, bulky, and hard-to-forecast items. By emulating how inventory would move in and out of the building before it opened, Target was able to validate the operational model and catch potential bottlenecks in advance.

The Bigger Picture: Digital Twins in Retail

Target isn't alone in adopting digital twin technology. Walmart has used digital replicas of stores and warehouses to predict operational issues and optimize layouts. Lowe's has built digital twins of physical spaces for both planning and customer-facing marketing applications. The technology is part of a broader trend where retailers use simulation and AI to reduce the cost of trial-and-error in physical operations.

What makes Target's approach distinctive is the emphasis on inventory positioning rather than physical space modeling. While other retailers have focused on warehouse layout or store design, Proxima is specifically designed to answer the question: Is the right product on the right shelf at the right time?

This question has become existential for Target. CEO Michael Fiddelke acknowledged last fall that the company had been operating with legacy technology that doesn't meet modern needs. Since then, Target has hired a new supply chain leader, opened experimental distribution facilities, and reevaluated which stores it uses to fulfill online orders. Proxima represents the technology layer tying all these initiatives together.

What This Means for AI in Ecommerce

Target has explicitly stated that Proxima will generate insights that inform future AI capabilities, helping teams "evaluate options, prioritize actions, and automate operational responses." In other words, the digital twin is a stepping stone toward AI-driven supply chain automation — a system that doesn't just simulate decisions but makes them.

For the broader ecommerce industry, the implications are significant. Inventory availability is one of the most important factors in customer experience and conversion rates. When a product isn't on the shelf — or shows as out-of-stock online — the customer doesn't just defer the purchase; they often go to a competitor. AI-powered inventory management, enabled by digital twin simulation, gives retailers a tool to prevent those losses before they happen.

Siobhán McFeeny, Target's senior vice president of technology, framed the vision: "At Target, we're focusing on connecting signals, systems, and decisions so our teams can move faster and solve increasingly complex problems. Proxima is a powerful example of that vision in action."

What Merchants Can Learn

Most Shopify merchants won't build their own digital twins. But the principles behind Proxima are applicable at any scale:

1. Simulate before you commit. Before changing reorder points, warehouse layouts, or fulfillment strategies, model the impact. Even spreadsheet-based simulations can catch costly errors.
2. Invest in data quality. Proxima works because it uses production-grade data. If your inventory counts are wrong, your simulations will be too.
3. Think cross-functionally. Proxima was built by supply chain, product, and engineering teams working together. Siloed teams can't solve inventory problems that span the entire operation.
4. Prepare for AI automation. Clean data and well-modeled processes today become the training inputs for AI-driven decisions tomorrow.

The era of AI-powered retail operations isn't coming — it's already here. Target's Proxima is proof that the companies investing in simulation infrastructure now will be the ones best positioned to deploy AI agents across their supply chains in the years ahead.