An AI tool that generates optimized planograms (visual diagrams of product placement on shelves). It analyzes sales data, customer behavior, and product dimensions to recommend layouts that maximize sales and profit margins.
Planograms decide where every product sits on a shelf, and those decisions affect what shoppers see, what sells out and what gathers dust. Building them is laborious: category managers balance sales history, margin, supplier agreements, product sizes, shelf dimensions and store differences, often with spreadsheets and drag-and-drop software. Independent grocers and mid-sized chains rarely have time to tailor layouts by store, so one design gets rolled out everywhere regardless of local demand.
An AI planogram generator does the heavy calculation. It combines point-of-sale history, margin data, product dimensions and fixture measurements, then uses optimisation algorithms to propose layouts that meet the constraints you set, such as minimum facings, brand blocking and supplier commitments. Category managers compare options, adjust them and approve the final version. The model suggests; merchandising experts, who understand shopper behaviour and supplier relationships, decide what actually goes on the shelf.
Sales and margin history by SKU and store is pulled from the POS or ERP, along with product dimensions, case sizes and current shelf positions.
Category managers set rules, including minimum facings, adjacency groups, brand blocks, eye-level priorities, supplier agreements and stock replenishment limits.
An optimisation algorithm allocates shelf space to maximise the chosen goal, such as gross margin or sales, while respecting every rule and fixture measurement.
Optional computer vision compares store shelf photos with the approved planogram to spot compliance gaps and gaps caused by out-of-stocks.
Approved layouts export to planogram software or print-ready diagrams for store teams, and performance after the reset is tracked for the next review.
A supermarket chain uses an AI planogram tool to design its cereal aisle. The AI suggests placing high-margin, kid-focused brands at children's eye level, which the category manager tests in selected stores.
Optimization Algorithms, Sales Data Analytics, Computer Vision (optional), Planogram Software integration
Supplier agreements in Australian grocery often include shelf placement commitments, and the Food and Grocery Code of Conduct governs some dealings between large retailers and suppliers, so optimisation rules should reflect contractual obligations rather than override them. Shelf photos taken in store may capture customers or staff, so limit images to fixtures, avoid storing faces and check state workplace surveillance rules if cameras are fixed. Product placement for items such as alcohol, tobacco and certain medicines may be restricted by state laws or TGA scheduling, so encode those restrictions as hard rules.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Usually not. Most retailers keep their existing planogram tool for drawing and distributing layouts, and use the AI engine to generate optimised starting points. Exports and integrations move proposals into the familiar software, where merchandisers make final adjustments before sending them to stores.
It can be, if there is clean SKU-level sales data and product dimensions. Smaller retailers often start with a single high-value category, such as confectionery or health and beauty, to test the process before expanding. Simpler optimisation without computer vision keeps the project within the one-to-two-month timeframe.
Compare category performance before and after the reset against similar stores that did not change, and check for side effects such as increased out-of-stocks. Seasonal effects and promotions complicate the comparison, so run the analysis over a meaningful period rather than judging it in the first week.
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