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Advanced OSA Tracking: Revolutionizing Retail with Computer Vision
Advanced OSA Tracking: Revolutionizing Retail with Computer Vision
Khuldoon Bukhari
June 22, 2023

The Future of Retail: Harnessing Computer Vision for Advanced On-Shelf Availability (OSA) Tracking

In our last blog, we underscored the significance of On-Shelf Availability (OSA) in the retail industry's landscape. This time, we dive deeper into the OSA tracking methodologies, the technology that powers them, and how to identify the right solution tailored to your business requirements.

A Closer Look at Current OSA Tracking Methods in Retail

Several OSA tracking strategies are presently in use, ranging from traditional manual inspections to more sophisticated, automated systems. These methodologies can broadly be categorized into manual (involving human-operated processes) and automatic (utilizing advanced processes with minimal human intervention).

Manual Methods:

  1. Pen and Paper: This rudimentary method involves retail staff physically inspecting shelves and recording out-of-stock or low-stock items. The data might then be reported to a supervisor and potentially integrated into an inventory management system.
  2. Barcode Scanning: A more advanced technique where staff use handheld devices to scan product barcodes on the shelves. The collected data is cross-referenced with the inventory system to identify discrepancies. While it surpasses pen-and-paper checks, this method is still largely dependent on human involvement.
  3. Smartphone-based Computer Vision: Here, technology takes a more significant role as staff capture shelf images or videos using smartphones. Computer vision models analyze these visuals, and the deduced data is integrated into inventory management systems.

Automatic Methods:

  1. RFID Tagging and IoT Sensors: This approach uses technology to provide real-time data on each item's location and availability. RFID-tagged products are tracked by shelf sensors, making the inventory tracking process smoother and more precise.
  2. Static Cameras and Computer Vision: This method involves machine learning algorithms that analyze images/videos from static cameras to spot out-of-stock or low-stock items. It offers continuous monitoring with minimal human intervention.
  3. Robots and Computer Vision: This state-of-the-art technique involves mounting cameras on mobile robots that navigate the store, monitoring inventory and documenting OSA and other critical data.

Usage Trends in OSA Tracking Technology

While we lack exact data on the distribution of these OSA tracking methods, we can infer broad trends from industry reports and studies.

MarketsandMarkets research suggests that barcode scanning, with its low cost and ease of implementation, was the dominant OSA tracking technology in 2020. However, IoT sensors are gaining popularity due to their real-time inventory data provision and potential labor cost reduction.

Advanced OSA tracking technologies like RFID tagging, static camera-based computer vision, and mobile robot patrols are generally favored by larger retailers managing intricate supply chains.

Geographically, North America and Europe lead in adopting these OSA tracking techniques, thanks to better access to technology and resources. However, emerging markets like Asia-Pacific and Latin America show an increasing trend of investing in advanced OSA tracking technologies, underlining a global recognition of OSA's importance in enhancing efficiency and customer satisfaction.

Static Cameras and Computer Vision: Pioneering the Future of OSA Tracking

As retailers continue to prioritize OSA tracking, the fusion of static cameras and computer vision is emerging as a game-changer. This technology not only automates the process of OSA tracking but also offers several advantages that put it ahead of the curve.

Primarily, static cameras combined with computer vision offer real-time and continuous data. This feature distinguishes them from manual methods, which necessitate human operators for data collection and action. Contrasted with other automatic solutions such as RFID and IoT sensors, static cameras don't require additional labor for applying RFID tags on all products or installing a network of sensors on shelves. While mobile robots sidestep this constraint, they could struggle with maneuvering during periods of high store traffic, require maintenance and charging, and may find navigating crowded or complex store layouts challenging.

Moreover, cameras installed for OSA tracking can serve multiple purposes. They can monitor compliance with planograms, assist online order systems if the stores double as distribution centers, and be repurposed for security applications. Their continuous feed can be utilized by other innovative applications to detect theft and other security concerns, maximizing the return on investment.

As technology advances, the cost of cameras continues to decrease, making it more affordable for retailers to adopt computer vision technology for OSA tracking. Concurrently, breakthroughs in AI and computer vision technologies are reducing the cost of running these models. These can be hosted on cloud platforms, allowing retailers to pay only for what they use.

Elevating OSA Tracking with enRetail

With an acute understanding of the rising need for real-time and precise OSA data, enRetail has designed an efficient solution to streamline this process. Our platform integrates seamlessly with existing security cameras to provide comprehensive, real-time OSA data. By automating shelf management with enRetail, retailers can gain valuable insights, increase operational efficiency, and ultimately elevate the customer experience. As we continually refine our technology and services, enRetail remains committed to empowering retailers on their journey towards an optimized retail ecosystem.

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