Dimension scores are derived from public data and fields; weighted into the composite. Reference only.
InShelf Analytics is an AI-powered inventory and ordering optimization tool for grocery stores, developed and maintained by Taranuka AB, a software company based in Stockholm, Sweden. Its goal is to reduce food waste through more accurate demand forecasting, real-time inventory management, and automated ordering, while also preventing out-of-stock shelves.
The AI capabilities described on the website mainly focus on sales forecasting and inventory optimization. The system analyzes historical sales data, order records, seasonal patterns, and weather data, and further incorporates external signals such as local weather, holidays, and major nearby events to generate future sales forecasts and SKU-level ordering recommendations. Features include real-time inventory views, automatic ordering, replenishment reminders, sales spike alerts, delivery issue alerts, and a simple analytics dashboard. Its implementation process is also fairly clear: upload sales and order data, bring in external information, run analysis and forecasting, and finally produce actionable insights. The website says onboarding takes no more than 2 weeks.
In terms of pricing, the official website only offers a “book a demo” option. It does not publish plans, per-store pricing, SKU/store-based billing details, or any information about a free trial or free tier. For integrations, InShelf clearly emphasizes that it can connect to existing POS and inventory systems so stores do not need to replace their current tools. However, it does not list specific supported POS/ERP systems, nor does it provide API documentation or technical interface details.
Its main strength is its highly focused use case: solving inventory, out-of-stock, and food waste problems specifically for grocery stores. By incorporating external variables such as weather, holidays, and events, it is better aligned with fresh food and FMCG retail scenarios than forecasts based solely on historical sales. Automated ordering and real-time alerts can also reduce the burden of manual decision-making. The limitations are that the website does not disclose details about its models, forecast accuracy, case-study data, privacy and compliance measures, or pricing. Before procurement, buyers should carefully validate its actual effectiveness and data security.
It is better suited to grocery stores, convenience stores, or small retail chains with stable sales data, a relatively large number of SKUs, and high sensitivity to shrinkage and stockouts. For Chinese users, the website’s accessibility cannot be determined from the page content, and payment methods are not disclosed. There is also no visible Chinese interface or information about integrations with local Chinese POS systems. If deploying it in China, users should focus on confirming network accessibility, cross-border data compliance, payment options, and whether local inventory management, intelligent replenishment, or retail BI solutions could serve as alternatives.
⚠ This review is compiled from public sources and does not constitute a purchase recommendation. Verify all facts on the vendor's official site. Verify on inshelf.com official site.
inshelf.com is an Sweden SaaS provider. TG4G tracks its product information, an overall rating of 7.0/10, and a China-accessibility score of Workable. Click "Visit Official Site" to reach inshelf.com directly.