Enhancing Planogram Compliance in Retail Stores through an Image Processing System

This study introduces the design and implementation of an advanced image processing system developed to enhance planogram compliance within the retail sector. Among the various image processing algorithms examined the SIFT (Scale-Invariant Feature Transform) algorithm was selected due to its robust feature extraction capabilities and superior performance in matching tasks. The system was rigorously tested using reference planogram images provided by a prominent retail organization and shelf images captured from its store locations. The SIFT algorithm demonstrated high accuracy in matching products on shelves with the reference planogram, thereby ensuring precise product placement and alignment with strategic merchandising standards. To evaluate the economic viability of the proposed system, an engineering economics analysis was conducted, which indicated that the system would achieve a positive cash flow within a two-year timeframe. The system is designed to automate the traditionally manual process of planogram compliance checks, significantly reducing the workload for store personnel and enhancing overall operational efficiency. By enabling rapid and accurate detection of discrepancies, the system ensures optimal product placement, which is critical for improving customer satisfaction and driving sales performance. Furthermore, the adoption of this innovative solution positions the organization as a leader in retail technology, potentially increasing its competitive advantage and market share.