Three Real Cases Where Inventory Dashboards Cut Waste and Saved Money

A UK convenience retailer with 200-plus stores had a food waste problem it could not see clearly. Its markdown process was manual, fixed by time-of-day rules, and produced almost no actionable data. Managers could not tell which stores were over-ordering, which SKUs were hemorrhaging margin, or whether markdowns were happening early enough to actually sell the product. The food waste was real and measurable. The visibility was not.

That situation describes a pattern repeated across grocery, convenience, and food service every week. What makes the cases below worth studying is that each operator moved from that opacity to clear, dashboard-driven visibility — and then reported the results.

Southern Co-op: Turning Markdown Chaos Into a 200-Store Data Signal

Southern Co-op operates over 200 convenience and food stores across southern England. By 2021, the retailer had deployed Retail Insight’s WasteInsight platform, replacing a rigid, time-based markdown system with one that uses historical sales data, stock levels, and category-specific rules to generate dynamic pricing recommendations pushed directly to store handhelds.

The results were measured against 2019 baselines. Food waste as a percentage of sales dropped by 0.19 percentage points. Markdowns as a percentage of sales fell by 0.97 percentage points. Those numbers sound small in isolation, but across 200-plus stores running at retail scale, a nearly one-point swing in markdowns represents a material recovery of margin.

The more durable outcome was operational. Southern Co-op’s team described the old system as opaque — fixed prices at fixed times with no feedback loop. The new dashboard gave the buying and operations teams an accurate, quantifiable view of waste and markdown activity across the entire network. Compliance with pricing standards became visible and measurable for the first time. The markdown process, in their own words, became “far more transparent and proactive.”

That shift — from gut feel to a closed feedback loop — is what prevents waste at scale. When a store manager can see that a specific chilled category is consistently over-ordered on Thursdays, she can act on it next week rather than discovering the pattern in a quarterly review.

Dollar General and Shelf Engine: Automating Fresh Produce Ordering Across 3,000 Stores

Fresh produce is the hardest category for any convenience or discount retailer to manage. Short shelf life, variable demand, and multi-day lead times mean that even a modest forecasting error creates waste. Dollar General historically kept shrink in check partly by keeping fresh produce scarce — a strategy that limited the waste problem but also limited the growth opportunity.

In late 2022, Dollar General piloted Shelf Engine’s AI-driven automated ordering system for produce in more than 400 stores. The system ingests point-of-sale data, vendor delivery schedules, case-pack sizes, and promotional calendars to generate a per-SKU, per-store, per-day order recommendation — removing the manual guesswork that typically drives both over-ordering and stockouts.

After the pilot, Dollar General moved to a national rollout, reaching approximately 3,000 stores by the end of fiscal year 2023. The goal, stated publicly, was to improve in-stock levels of fresh produce while simultaneously cutting waste and shrink — two outcomes that normally trade off against each other, but which data-driven ordering is designed to achieve together.

The business logic here is worth unpacking for any SMB or multi-unit operator. Automated reorder dashboards do not simply reduce waste in isolation. They change the economics of stocking a high-spoilage category: when you can forecast accurately, you can carry more fresh product without proportionally more risk. Dollar General’s expansion into fresh at this scale would not be commercially rational without that visibility layer.

Chosen Foods: Eliminating Spoilage Risk in Distribution

The third case sits upstream in the supply chain. Chosen Foods, a consumer brand sold through natural and specialty grocery channels, had a familiar problem: inventory moving through UNFI’s distribution network was sometimes reaching store shelves too close to its expiration date to sell. The spoilage risk was real and the cost was carried by the brand.

Working through Crisp’s spoilage dashboard, Chosen Foods gained a view of weeks-on-hand versus weeks-to-expiration across UNFI distribution centers. The dashboard calculates how long a given SKU will last in each DC relative to its shelf life, flagging units at risk of becoming unsellable before they leave the warehouse. By acting proactively on those signals — rebalancing inventory between DCs, adjusting replenishment timing, or redirecting at-risk stock to faster-moving channels — Chosen Foods achieved zero expected waste within the module.

That outcome matters beyond the waste metric itself. For a CPG brand selling through a major distributor, spoilage at the DC level is doubly painful: you lose the inventory cost and you damage your fill-rate metrics with the retailer. A dashboard that makes expiration risk visible several weeks in advance converts what was a reactive write-off into a preventable routing decision.

The Pattern Across All Three

These cases span convenience retail, discount grocery, and CPG distribution — different sectors, different geographies, different operational contexts. But the structural pattern is identical:

• Opacity was the root cause. In each case, waste was occurring not because operators lacked the will to prevent it, but because they lacked the signal to act on in time.
• Visibility preceded action. The dashboard did not automatically eliminate waste; it surfaced the specific decisions — markdown timing, reorder quantity, DC rebalancing — that humans could then make better.
• The feedback loop compounded. Southern Co-op described year-on-year incremental improvements. Dollar General expanded after a successful pilot. Chosen Foods built a repeatable process. One-time fixes do not compound; data-driven processes do.

RELEX, whose forecasting platform serves major grocery chains globally, reported preventing 350 million kilograms of food waste across its customer base in 2024 alone. That aggregate figure reflects what happens when the feedback loop described above operates at scale and over time.

For a Shopify merchant managing perishable products, a WooCommerce store with slow-moving SKUs, or a multi-unit food operator trying to get control of shrink before it eats next quarter’s margin — the entry point is smaller than Dollar General’s deployment and the principles are the same. The question is not whether dashboards reduce waste. The documented evidence is clear. The question is which decisions in your specific operation are currently invisible, and what it costs you every month that they stay that way.

If you want to think through what that visibility layer would look like for your business, we are happy to have that conversation at no charge. There is no pitch — just a straight conversation about where the data gaps are and whether they are worth closing.

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Sources: Southern Co-op WasteInsight Case Study — Retail Insight; Shelf Engine and Dollar General National Expansion — PR Newswire; Chosen Foods: Accelerating Growth and Eliminating Waste — Crisp; Smarter Supply Chains Deliver Real-World Impact for RELEX Customers — RELEX Solutions. Figures current as of mid-2026; verify against primary sources before acting. These are third-party, publicly documented engagements cited as industry examples, not Teknologia Solutions clients.