{"id":25219,"date":"2026-02-24T14:36:16","date_gmt":"2026-02-24T13:36:16","guid":{"rendered":"https:\/\/retail-optimiser.de\/guest-feature-how-edekas-netto-adds-transparency-to-loss-prevention\/"},"modified":"2026-02-25T11:49:42","modified_gmt":"2026-02-25T10:49:42","slug":"guest-feature-how-edekas-netto-adds-transparency-to-loss-prevention","status":"publish","type":"post","link":"https:\/\/retail-optimiser.de\/en\/guest-feature-how-edekas-netto-adds-transparency-to-loss-prevention\/","title":{"rendered":"Guest Feature: How Edeka\u2019s Netto adds transparency to loss prevention"},"content":{"rendered":"\t\t
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Netto Marken-Discount, part of Edeka Group, took an innovation-led step to make shrink measurable \u2013 without slowing the checkout experience. The discounter’s deployment of AI-powered computer vision from Trigo has just earned Netto the Reta Award 2026 in the Artificial Intelligence category, with Trigo recognised as Top Supplier Retail 2026. But the award matters less than what the technology revealed \u2013 data that is now adding transparency to Netto\u2019s loss prevention effort and delivering a proactive, real-time strategy.<\/p>\n

German grocery retail is caught in a familiar bind. Labor shortages \u2013 particularly among sales staff and cashiers \u2013 have made self-checkout not just convenient but necessary. Trigo\u2019s analysis indicates self-checkout concentrates a significant portion of avoidable loss \u2013 spanning friction-driven mis-scans and deliberate scan avoidance. Economic pressures have compounded the problem, driving both increased theft attempts and rising operating costs simultaneously.<\/p>\n

Most European grocers know shrinkage is a problem. Fewer have a full view on exactly where it happens, when, or how. Estimates from security, operations, and finance rarely align. \u201cThe data you don’t have is the data you can’t act on,\u201d says Thilo Freund at Trigo. \u201cMost loss prevention strategies are built on assumptions. The question is whether those assumptions are correct.\u201d<\/p>\n

The visibility gap<\/strong><\/h3>\n

European grocers see the opportunity to get ahead of the problem. AI-based technology can detect incorrect or non-scanned products in real time. Employees receive immediate alerts, customers see a discreet pop-up if an item needs to be rescanned.<\/p>\n

This allows staff to respond quickly before losses occur. Crucially, AI-based systems replace subjective assessments of in-store inventory discrepancies with a uniform, data-based foundation \u2013 creating the opportunity to identify recurring patterns early and deploy targeted prevention measures. What the data revealed challenged conventional assumptions: a significant share of losses originated not at checkout, but on the sales floor itself.<\/p>\n

Where checkout monitoring hits a wall<\/strong><\/h3>\n

This insight points to a fundamental limitation in how most systems approach the problem. It points to an architectural limitation in most SCO loss prevention solutions. If a customer conceals a product in aisle seven and walks calmly to self-checkout, a system watching only the till has no visibility into that item. You cannot generate an alert for something you never saw.<\/p>\n

Trigo developed its product-processing expertise while building an autonomous checkout, where accuracy requirements are unforgiving. Those learnings now underpin the company’s loss prevention approach \u2013 adapted to work with a retailer’s existing camera infrastructure, no new hardware or store renovations required.<\/p>\n

Three-layered approach<\/strong><\/h3>\n

The technology operates across three layers. The first validates the scan, catching operational errors, intentional fake scans, or mis-scans. For example, if a customer scans incorrectly and the barcode doesn’t register, a prompt nudges them to retry. The second verifies the basket by identifying items present at checkout but never scanned \u2013 products left in the trolley or sitting on the counter. The third tracks concealment on the sales floor itself, connecting what happened three aisles back to what appears \u2013 or doesn’t \u2013 at the till.<\/p>\n

\u201cThis is what separates transaction monitoring from genuine loss prevention,\u201d says Thilo Freund. \u201cIf you’re only watching checkout, you’re watching the end of a journey that started elsewhere.\u201d<\/p>\n

From alert fatigue to actionable intelligence<\/strong><\/h3>\n

Detection alone doesn’t solve the problem. When every alert carries equal weight, associates stop paying attention. Peak hour, queues building, multiple flags firing simultaneously \u2013 staff clear them without looking up. Trigo\u2019s approach differentiates by risk. A missed scan on a low-value item triggers a simple customer prompt. Concealment of premium spirits escalates to full associate intervention. The response scales with the actual risk \u2013 staff are only interrupted when it matters.<\/p>\n

A multi-retailer study validated this model. Analysing 1,000+ verified theft cases across major European grocers, Trigo found concealment happens early \u2013 80 per cent of commonly stolen items are hidden in clothing or bags before they ever reach self-checkout. Checkout-only monitoring is tuned for the visible 20 per cent \u2013 it misses where most loss actually occurs.<\/p>\n

What comes next<\/strong><\/h3>\n

When theft does surface at SCO, the patterns are subtle but repeatable: items left in the basket\/bagging area without scanning (31.7 per cent) and fake scans where products are waved over the scanner but don\u2019t register (27.3per cent). Category signals are concentrated too \u2013 beverages (22 per cent), fresh produce (19 per cent), and bakery (10 per cent) and incidents peak on weekday afternoons and evenings, especially Thursday afternoons. Data like this now informs how retailers respond including intervention rules, staffing, and placement decisions.<\/p>\n

Retail’s labour and margin pressures aren’t easing. But for the first time, retailers have a way to see exactly where and how losses occur \u2013 and act before they walk out the door. That’s not a promise. For Netto and other leading retailers, it’s already an operational reality.<\/p>\n

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Netto Marken-Discount, part of Edeka Group, took an innovation-led step to make shrink measurable \u2013 without slowing the checkout experience. The discounter’s deployment of AI-powered computer vision from Trigo has just earned Netto the Reta Award 2026 in the Artificial Intelligence category, with Trigo recognised as Top Supplier Retail 2026. But the award matters less than what the technology revealed \u2013 data that is now adding transparency to Netto\u2019s loss prevention effort and delivering a proactive, real-time strategy.<\/p>\n","protected":false},"author":67,"featured_media":25263,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[1786,35],"tags":[1694,878],"class_list":["post-25219","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-advertorial-en-3","category-reports-en","tag-edeka-group-en","tag-trigo-en"],"yoast_head":"\nGuest Feature: How Edeka\u2019s Netto adds transparency to loss prevention - Retail Optimiser<\/title>\n<meta name=\"description\" content=\"Netto Marken-Discount, part of Edeka Group, took an innovation-led step to make shrink measurable \u2013 without slowing the checkout experience. 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