Slotting Paid, Nothing Scanning: The Authorization Voids Costing Specialty Brands Six Figures a Year

A synthetic 50-SKU specialty food brand authorized across 640 retail doors runs a void scan. The result: 145 authorization voids, store-item combinations where the authorization matrix says the product should be on the shelf and POS data says it is not scanning. Forty-nine have never scanned a single unit — slotting was paid, the product never made it to the shelf — and 37 of those sit in one retailer's Southeast region, a single botched shelf reset worth $165,526 on its own. The other 96 were scanning and stopped. Dollarized against the median velocity of comparable stores that are scanning, the voids account for $305,294 over the trailing 26 weeks, against a forward run-rate of $731,202 a year. The two do not double into each other: the trailing figure counts only the weeks a void has already been open, while the run-rate projects every currently open void forward a full year. Those numbers were invisible the day before the scan ran.

The numbers are from Cinderhaven Provisions, a synthetic ~$25M wholesale brand (~$32.3M at retail, CY2025) with 50 SKUs across six retailers, built to demonstrate the methodology. Every figure above is the tool's default view — a 26-week window at a six-week void threshold, no filters — so a reader who clicks through sees the same numbers. The brand is invented; the mechanics are not. The voids, the dollar gaps, and the broker work list are the tool's actual outputs, and the pattern applies to any brand whose authorization footprint is wider than its scanning footprint, which is most brands above $5M in revenue.

Authorization voids are not out-of-stocks

The distinction matters because it changes who owns the problem. A traditional out-of-stock (the kind tracked by NielsenIQ's on-shelf availability benchmarks) means the product was on the shelf, sold through, and the store or DC failed to replenish. That is a supply chain problem. The retailer's perpetual inventory system should catch it. The fix is operational: better forecasting, faster replenishment triggers, tighter DC-to-store allocation.

An authorization void is different. The retailer's buying team authorized the product. The slotting fee was paid. The item is in the planogram. But POS data shows zero scans. The product either never arrived or arrived and was never set. This is not a replenishment failure. It is a distribution execution failure, and it sits in the gap between what the buyer approved and what the store-level team implemented.

Traditional OOS metrics miss authorization voids entirely because they measure replenishment against what is already scanning. If the item never started scanning, there is no replenishment signal to trigger. The void exists only when you compare the authorization matrix (which items are approved at which doors) against actual scan data. Most brands do not make that comparison because the two data sets live in different systems and nobody has joined them.

The join is nobody's routine, and that is the actual failure. Reconcile authorization against scan on a cadence — process hygiene — and a botched reset surfaces in weeks, not after 26 of them.

Never-scanned versus went-dark: two voids, two root causes

Not all voids are the same problem. The classification determines the fix.

Never-scanned means the store has been authorized for the item and zero units have ever scanned. In Cinderhaven's data, 49 of the 145 voids fall here. The most common cause: a botched mod reset. The category reset happened, the new planogram went live, and the product was never pulled from the back room or never shipped in the first place. These voids tend to cluster geographically: a single region where the distributor or broker dropped the handoff. The cluster is the tell. When 26 stores in the same region are all never-scanned for the same item, that is not 26 independent failures. It is one failure repeated 26 times.

A Gartner supply chain survey found that 60% of consumer goods companies cite store-level execution as a top-three challenge for new item introductions. Never-scanned voids are what that statistic looks like in POS data.

Went-dark means the item was scanning and stopped. In Cinderhaven's data, 96 voids fall here — the larger of the two groups. This piece gives never-scanned the longer treatment anyway, and the reason is fixability rather than volume: a missed shelf set usually yields to one visit, and it clusters, so a single call can clear many stores at once. Went-dark is more common and harder to close. The causes are more varied: discontinued by the store manager, lost shelf space in a competitive reset, chronic out-of-stock that the store stopped reordering, or a planogram change that removed the item without updating the authorization matrix. Went-dark voids require a different investigation than never-scanned voids. The product was there. Something changed. The fix starts with finding out what.

Dollarizing the gap from comparable-store velocity

A void is a fact. A dollarized void is a priority. The difference is what moves a broker to act.

The dollarization method: for each void, identify comparable stores that are scanning the item, matched by volume tier and region, and take the median weekly velocity. Median, not mean, because a single high-performing store should not inflate the opportunity estimate for a store that might sell half as much. Multiply the median velocity by the number of void weeks, and the result is the estimated revenue the void has cost.

This is conservative by design. The median of comparable stores is a reasonable expectation for what a store would sell if the product were on the shelf. It is not a ceiling. It does not account for promotional lifts that the void store missed. It does not compound the lost velocity against the category review timeline: a void that persists through a reset cycle may cost the brand the slot permanently, a loss that dwarfs the immediate revenue gap.

The output is a ranked list: every void sorted by dollar opportunity, with the store name, the item, the void type, the duration, and the estimated gap. The broker does not need to guess where to send the rep. The list tells them.

The work list is the deliverable

The analytical distinction between never-scanned and went-dark matters to the brand team. The broker does not care about the taxonomy. The broker needs a list: which stores, which items, what to do when they walk in.

The work list from Void Finder exports as a two-tab Excel file. Tab one: the summary — total opportunity, open voids, stores affected, and the never-scanned versus went-dark split, the figures the VP of sales puts in front of the broker. Tab two: every void, ranked by dollar opportunity, with store numbers and addresses. The summary is computed from the same rows as the work list, so the two always reconcile. Void trend over time, showing whether the problem is growing or stabilizing, is a view in the tool rather than a tab in the export.

This structure exists because the person who runs the analysis is not the person who fixes the void. The analyst needs the classification logic and the dollarization methodology. The broker needs a route list and a dollar number that justifies the drive. The VP needs the rollup that sizes the total gap for a quarterly business review. One scan, three audiences, two tabs.

Where this sits in the penetration picture

Void detection is the enforcement layer for distribution penetration. Penetration means three different things in CPG (distribution, velocity, and household), and confusing them funds the wrong strategy. Door Math shows how many doors carry the product. Spin Rate shows how fast it moves in the doors that have it. Void Finder answers the question that sits between them: of the doors that are supposed to have it, which ones don't?

A brand can report 72% ACV distribution and believe the number is solid. If 15% of those authorized doors are not scanning, the effective distribution is lower, the lost revenue is real, and the brand is paying slotting fees for shelf space it is not using. The void scan names the gap. The dollarization sizes it. The work list closes it.

Get the void scan on your data

Void Finder runs on Cinderhaven Provisions: synthetic data, real methodology. See the never-scanned clusters, the went-dark patterns, and the dollarized work list that a broker can act on Monday morning.

Lailara runs authorization void scans on client POS data and authorization matrices, with the full dollarization, classification, and broker-ready export. Send me your authorization list and scan data and I'll write back with how big the gap is and what it's costing you a quarter. No call.