Inventory Optimisation: Closing the Loop Between Shelf and Supply Chain

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For all the sophistication that sits in our forecasting tools, Enterprise Resource Planning (ERP) systems and planning models, there’s still one place where even the best-run supply chains can lose their footing: the store.

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What’s on the shelf, in the backroom or on POS displays doesn’t always match what our systems think is available. That disconnect fuels phantom inventory, over- and under-ordering and ultimately, out-of-stocks (OOS) which erodes both sales and shopper trust.

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At Neurolabs, we see this challenge every day. Brands spend millions fine-tuning their demand plans and optimising logistics, only to be blindsided by what’s actually happening at the shelf.

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That’s where Image Recognition (IR) steps in, connecting what’s planned and shipped with what’s truly available to the shopper. It’s about giving your supply chain real eyes in-store.

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The Business Problem

Even with all the right systems in place, teams still lack a true sense of on-hand reality.

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Here’s what that looks like in practice:

• Phantom inventory: Systems show product on-hand, but the shelf is empty or stock is hidden somewhere in the backroom.
• E-commerce pick pressure: When a picker can’t find a product in-store, the order gets shorted or substituted. That’s a direct hit to both shopper experience and retailer margin.
• Backroom blind spots: Cases are sitting in the wrong bay or simply unaccounted for, delaying replenishment and skewing forecasts.
• Over/under-ordering: Store orders are based on outdated data, not what’s actually on the shelf today.

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The outcome is familiar: lost sales, missed orders, wasted deliveries and increasingly, dissatisfied shoppers.

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And as hybrid retail models expand, blending brick-and-mortar, direct-store-delivery (DSD), franchise and e-commerce fulfilment, the challenge is multiplying. More nodes, more partners, more chances for data to drift from reality.

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The Quick Wins

What’s powerful about IR is how quickly it delivers value. With in-store photos captured through apps or field teams, you suddenly gain a real-time view of what’s on-shelf, off-shelf or out-of-stock.

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Some of the fastest ROI cases we’ve seen come from:

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• OOS & phantom inventory detection: Spot missing SKUs or facings across shelves and displays in near real time, backed by visual proof.

• Planogram-aware replenishment: Ensure store orders reflect the intended capacity, especially when promotional or seasonal displays shift shelf space.

• Backroom visibility: Understand what’s sitting in the backroom but not yet replenished, a classic “hidden OOS.”

• Overstock detection & rebalance: Identify where you’re heavy on inventory (or misplaced stock) and act before shrink sets in.

• Automated Image-to-Order (PicToOrder): When IR detects missing SKUs from shelf photos, it can trigger the next order automatically.

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In practice: A beverage manufacturer now processes ~10,000 additional cases every week through Image-to-Order. The system identifies missing SKUs from shelf photos and injects them into the next delivery cycle. The result? A +1ppt lift in first-time-pick accuracy for online orders and a much smoother e-commerce fulfilment experience.

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Building the Full Inventory Picture: Backroom, Shelf and Displays

The real step-change comes when you combine front-of-house and backroom visibility into a single source of truth.

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• Backroom: The in-store buffer that often goes uncounted, creating hidden OOS.
• Shelf: What shoppers (and pickers) can actually see and buy, the ultimate test of availability.
• Displays & POS: Temporary but high-impact inventory that can dramatically skew velocity if not tracked.

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Bringing these together creates a real-time, image-verified view of store-level inventory that’s more accurate than any system projection.

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In practice: A global bottler added backroom visibility to its automated replenishment model. Combining shelf, display and backroom signals boosted replenishment accuracy from 89% → 91%. That 2-point gain translated into millions in recovered sales and a measurable lift in working-capital efficiency.

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Distribution Visibility: The Missing Link

One of the more overlooked challenges sits in the distribution model itself.

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CPG distribution is rarely linear anymore. Between direct-store-delivery (DSD), franchise and hybrid models and regional distributor networks, visibility tends to fracture the moment stock leaves the depot.

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That’s where we see OOS issues spike. Product might be sitting in a distributor’s warehouse or on a delivery truck but at shelf level, it’s as good as gone.

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Image Recognition brings this back into focus. Because it captures what’s actually happening in-store, it provides a neutral source of truth across every route-to-market, no matter how complex:

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• For indirect models, IR validates that what’s been delivered downstream is truly reaching shelves.
• For hybrid networks, it enables consistent visibility across territories and partners.
• For DSD routes, it triggers on-the-spot replenishment or redistribution before shoppers ever notice a gap.

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Distribution complexity no longer has to mean distribution blindness.

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E-Commerce & Picker Guidance: The Modern Visibility Test

Click-and-collect and home delivery have redefined what “in-stock” really means. When a picker can’t find an item, it’s an OOS from the shopper’s perspective, even if stock exists in the backroom.

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With IR integrated into store systems, pickers can:

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• Be directed to exact backroom bays (e.g., “C8: 12 cases available”) to fulfil online orders.
• Avoid false OOS by confirming the product is present but misplaced.
• Improve “ready-for-pickup” reliability reducing substitutions, refunds and churn.

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For omnichannel retailers, that’s no longer a nice-to-have. It’s a competitive necessity.

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From Reactive to Predictive

Once image-verified inventory data starts flowing into ERP, Warehouse Management System (WMS) or order management systems, the game changes from reactive to predictive.

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• AI-driven reorder triggers combine shelf and backroom visibility with promo, weather and velocity data to predict OOS before they happen.
• Root-cause analytics finally isolate whether a stockout was caused by poor planning, late delivery or execution issues in-store.
• Same-day ordering becomes a reality, powered by verified SKU-level accuracy at scale (>97%).

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The result is a more agile, responsive supply chain. One that learns from the shelf up, not the system down.

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Why This Matters

Inventory optimisation sits at the crossroads of growth, cost and trust.

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• Growth, because every avoided OOS is a captured sale.
• Cost, because every corrected overstock or misdelivery prevents waste.
• Trust, because every image-verified replenishment strengthens retailer partnerships.

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We talk a lot about “data-driven collaboration,” but this is where it really happens: when both supplier and retailer can see the same truth, in real time and act on it together.

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That’s how the shelf finally connects to the supply chain and how the supply chain starts learning from the shelf.

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Next in the Series

This article is part of our ongoing series exploring the Four Strategic Levers of Retail Execution in 2025.

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If you missed the earlier instalments, start with From Compliance to Growth: Why Smarter Audits Drive Retail Execution, where I explored how intelligent audit automation drives visibility and speed across the shelf.

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Then check out Field Force Optimisation: From Data Collectors to Sales Drivers, which looked at how Image Recognition empowers field teams to focus on action, not admin.

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In this piece, we’ve shifted focus to Inventory Optimisation, where connecting shelf visibility to the supply chain helps close the loop between what’s planned, shipped and sold.

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Next, I’ll explore Commercial Strategy, where retail execution meets trade ROI, category growth and innovation tracking.

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— Remus Pop, CRO & Co-Founder

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