I spent more than four decades at the intersection of retail technology and supply chain execution. I was part of the team at Procter & Gamble that helped Walmart develop what would become Retail Link. I later led Walmart's RFID and on-shelf availability programs. I have watched a lot of technology cycles play out in retail. RFID in apparel is one of the most instructive.
 

The apparel sector has been among the earliest and most enthusiastic adopters of item-level RFID, with good reason. The technology is purpose-built for the problem: dozens of size and color variants per style, high SKU counts, and inventory that moves through fitting rooms, backrooms, and sales floors in patterns that are nearly impossible to track manually. Zara began deploying RFID across its global operation in 2014 and has since described the technology as integral to its ability to replenish stores faster, reduce overstock, and protect full-price sell-through. Lululemon paid for its entire RFID implementation in a single season, with its RFID program director citing the technology as a direct driver of holiday sales performance by getting product off backroom shelves and onto the floor. Uniqlo, through a combination of RFID and warehouse automation, reduced warehouse labor by 90 percent at its Tokyo facility while achieving near-100 percent inventory read accuracy.
 

The results when RFID is implemented well are not marginal. Inventory accuracy jumps from the 60 to 65 percent range to 95 to 99 percent consistently, according to multiple documented deployments. That level of accuracy fundamentally changes what is possible in ordering, replenishment, and markdown avoidance.
 

But RFID alone has a critical limitation that is rarely discussed plainly: it tells you what is in the store. It does not continuously tell you what is on the floor versus sitting in the backroom, or whether the right sizes are in the right locations at the right time of day. Periodic handheld scans help. They are not continuous. And the gap between a morning scan and a mid-afternoon rush is exactly where the inventory visibility breakdown happens. RFID Journal estimates fewer than 10 percent of the world's 500,000 apparel stores have implemented item-level RFID at all, meaning the majority of the industry is still operating without even the baseline accuracy that makes continuous visibility possible.

In grocery, IHL Group has documented for more than a decade that the single largest category of preventable out-of-stocks is product that is in the building but not on the shelf. The causes are the same in apparel, with additional complexity layered on top: fitting rooms that create temporary inventory displacement, mishangs that move items to wrong racks, and size-specific gaps that are invisible unless someone physically scans every position.

Manual processes cannot keep up with this. An associate scanning a rack with a handheld reader does it once, maybe twice a day. The floor changes continuously. A run on medium sizes during a weekend afternoon creates gaps that will not be captured until Monday morning's cycle count, by which time the customer who needed that medium has already bought from a competitor or online.

The solution requires continuous visibility at the item level, not periodic snapshots. It requires knowing not just that a unit is somewhere in the store, but that it is on the floor, in the right location, in the right size run, available to be purchased. That is the gap between RFID as an inventory system and RFID as a real-time execution tool.

This is where autonomous in-store scanning changes the equation. Badger Technologies' digital teammates traverse store aisles multiple times per day, capturing item-level data continuously and feeding those findings directly into replenishment and task management workflows. In apparel, combined with RFID infrastructure, autonomous scanning delivers what periodic handheld counts cannot: a continuously updated picture of what is on the floor, what has moved to a fitting room, what needs to be restocked from the backroom, and where size gaps are opening before they become lost sales.

The practical impact on markdowns is direct and measurable. When an associate knows in real time that the medium rack is running low and the backroom has six units waiting, they can act before the gap becomes a sell-through problem. When inventory data is continuously accurate rather than periodically approximate, buyers make better decisions about what to reorder and what to pull back. The cycle that leads from invisible inventory to compensatory over-buying to markdown pressure is broken at its source.

Retailers working with Badger Technologies have documented reductions in out-of-stocks in the range of 40 to 50 percent and 70 to 80 labor hours per week per store redirected from manual counting toward customer-facing activity. In an apparel context, that out-of-stock reduction translates directly into units that sell at full price instead of accumulating toward a markdown. These results come from operating stores with active deployments, across multiple banners and geographies. They are not pilot projections.

Apparel executives spend enormous energy on the downstream consequences of inventory distortion: end-of-season markdown cadences, promotional calendar timing, clearance strategies, and the gross margin math of moving excess units. These are real problems that require real solutions. But they are symptoms. The disease is the invisible inventory that accumulates before anyone knows it exists.

The industry has known this for years. IHL Group's inventory distortion research, now in its eighteenth year of tracking, consistently identifies in-store execution failures as among the largest controllable sources of lost margin. The global cost of inventory distortion across all retail now runs at $1.7 trillion annually. Apparel represents a disproportionate share of that figure, given the SKU complexity, the size and color variant problem, and the fitting room dynamics that no other retail category deals with at scale.

The technology to address it exists today. Every season an apparel retailer operates without continuous shelf-level visibility is another season in which the markdown decision is being made not in the buying office, but on the floor, silently, one misplaced size at a time.

Badger Technologies works with apparel retailers to scope a focused assessment: where is inventory distortion costing the most, which stores would benefit first from continuous floor-level visibility, and what does a structured 90-day pilot look like?

Our No-Risk Evaluation is designed to answer those questions in your own stores, with your own teams, using your own data. Five stores minimum. Twelve months. Three phases: proof of technology, proof of operation, proof of economics. At the end, you have the data to make a scale decision with confidence.

The markdown problem in apparel is not a mystery. It is a visibility problem. The retailers who understand that earliest will compound the advantage longest.

Consider what already exists. Proven technology. A deployment model that requires no capital expenditure. Documented, replicable results from early adopters. Competitive dynamics visible in earnings reports, IHL research, and the daily experience of shoppers who walk between a store with continuous shelf intelligence and one without it.

Grocery executives who are still evaluating whether autonomous in-store technology belongs in their operational roadmap should ask themselves a direct question: what specific condition are they waiting for that does not already exist? The ROI data is available. The labor economics are favorable. The RaaS model eliminates the traditional barriers of upfront cost and technology obsolescence. Every quarter spent deliberating is a quarter in which competitors with active deployments are compounding the advantages described throughout this article.

The right next step is not a multi-year planning exercise. It is a focused assessment: where is inventory distortion costing the most, which stores would benefit first from continuous shelf visibility, and what does a 90-day pilot look like? Badger Technologies works with grocery retailers to scope exactly that kind of evaluation, connecting autonomous in-store intelligence to the specific operational and financial metrics that matter to each organization. The conversation takes less time than most retailers spend on a single cycle count. The results from that conversation could reshape how the entire operation performs for the next decade.

Mike Graen is Principal of Collaboration LLC and Advisory Board Member for Badger Technologies. With more than four decades of experience at Procter & Gamble, Walmart, and CROSSMARK, Mike has made a career out of using technology to solve retail business problems. As P&G's Director of Information Technology for the Walmart account, he helped develop what would become Retail Link. He later led Walmart's RFID and on-shelf availability programs as Director of Innovations for Supplier Collaboration. He hosts the Supply Chain LEAD Podcast and the Conversations On Retail platform.

Badger Technologies, a product division of Jabil, is a leader in retail automation and artificial intelligence solutions. Its autonomous robots and digital teammates help retailers improve on-shelf availability, pricing accuracy, planogram compliance, and store safety.

With deployments across grocery, building supply, and other high-SKU retail environments, Badger Technologies provides retailers with real-time data and actionable insights that drive measurable results. Headquartered in Nicholasville, Kentucky, the company is committed to helping retailers build smarter, safer, and more efficient stores.