CASE STUDIES

AI-Powered Aisle Monitoring That Reduces Effort and Elevates Store Efficiency

AI in Retail
Customer

A global leader in retail digitalization and in-store automation solutions, Europe

Background
The customer accelerates digital transformation in physical retail through IoT, digital shelf systems, and cloud-connected technologies. Their solutions help retailers improve inventory accuracy, optimize store operations, and enable data-driven decision-making across aisles and product categories.

Problem Statement

  • The store’s multiple aisles each contain numerous SKUs with topping-stock situated above the visible shelf front; these stock items required periodic manual inspection.
  • Manual efforts involved staff walking each aisle, noting stock levels, comparing inventory lists, identifying low-inventory products, and planning restock runs.
  • Key challenges included:
  1. Lack of real-time visibility of which SKUs were low in stock or had moved location.
  2. Inefficient navigation to aisles needing restocking, resulting in staff time loss.
  3. High operational effort and labor cost tied to simple inventory tracking tasks, limiting focus on higher-value customer-facing activities.
  • The retailer needed a solution that automates inventory tracking, maps product location in aisles, and provides actionable alerts when stock is low or needs replenishment.
AI in Retail

Solutions

Logituit helped design and deploy an IoT-plus-Computer Vision tracking system across the store floor:

  • Cameras (ceiling-mounted) were placed above each aisle, oriented to monitor the top stock area of shelves.
  • A computer-vision algorithm processes the video feed to detect individual retail products, identify barcodes, and decode them.
  • Once decoded, product details (SKU, barcode, timestamp) are uploaded to the cloud, along with the exact aisle and location coordinates (via predefined zone mapping).
  • The cloud platform aggregates this data and provides dashboards and alerts: showing live top-stock inventory per aisle, highlighting low-stock SKUs, and signaling where restocking is required.
  • The system integrates into the retailer’s operational workflow so that restock staff receive guided navigation (which aisle, which shelf segment, which SKUs) rather than blind manual scanning.
AI in Retail

Business Impact

  • Remote and real-time visibility of aisle-level inventory enabled proactive decision-making.
  • Significant reduction in manual effort, eliminating the need for staff to walk each aisle for routine checks.
  • Faster restocking cycles, as the system highlighted which SKUs were low and where replenishment was required.
  • Operational efficiency improved, reducing labor time, enhancing stock availability, and supporting smoother store operations.
  • Ultimately, the retailer achieved a more accurate, automated, and scalable approach to inventory management.

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