Automating Planogram Compliance in Retail Stores

Smart retail stores are becoming increasingly important in our daily lives. These stores use various technologies to make shopping easier and more efficient. One crucial aspect of running a retail store is to ensure that products are displayed correctly on the shelves. This is where planograms come into play. A planogram is a visual guide that shows how products should be placed on the shelves. Having the right products in the right place is essential for sales and customer satisfaction. When products are not in their correct spots, it can lead to confusion for shoppers and lost sales for the store.

Traditionally, employees were responsible for checking if the shelves matched the planogram. However, this method has many issues. It can be time-consuming and prone to mistakes. To address these problems, technology is being introduced into the retail sector to automate this process.

#The Challenge of Planogram Compliance Control

Maintaining planogram compliance is critical for retailers. When shelves are not set up according to the planogram, it can negatively affect sales and customer experience. Studies have shown that stores typically achieve around 70% compliance, and a proper reset can boost sales significantly in a short time. To solve this problem effectively, advanced technologies like computer vision and machine learning can be utilized.

Using cameras and sensors to monitor shelves can help retailers ensure that products are in the right place. However, many existing methods either require expensive equipment or involve labor-intensive processes. This is where an embedded planogram compliance control system can provide a solution.

#The Proposed Embedded System

In this article, we discuss a new system designed to automatically check and maintain planogram compliance. Our system consists of several key components that work together to achieve this goal.

#Image Acquisition and Transfer

The first part of our system captures images of the shelves. We use a small camera that can take pictures and send them to a computer for processing. This camera is low-cost, low-power, and efficient, making it suitable for retail environments.

The camera captures the shelf images at set intervals and only sends new images if it detects any significant change compared to the previous image. This smart capturing method helps save energy and ensures that only important data is processed.

#Object Detection

Once the images are captured, the next step is to identify the products on the shelf. For this task, we utilize advanced techniques called object detection and deep learning. In simple terms, this means that our system can "see" what products are on the shelf and determine if they are in the right place.

To accomplish this, we use a powerful computer that processes the images. This computer analyzes the captured images and identifies the products based on their shapes and colors. This step is crucial, as accurate detection of products is essential for ensuring compliance with the planogram.

#Planogram Compliance Control

After detecting the products, the system checks whether they align with the planogram. If any products are missing or misplaced, the system will identify these discrepancies. This process helps store employees quickly understand what needs to be fixed.

The planogram compliance control works by sorting the detected products and comparing them with the reference planogram. If some products are not in the correct position, the system generates a report indicating what needs to be adjusted.

#Energy Management

One challenge with using electronic systems is ensuring they have enough power. Our embedded planogram compliance control system is designed to run on battery power for long periods. To extend battery life, we have integrated Energy Harvesting methods, including solar power and radio frequency energy harvesting.

The solar energy harvesting module uses sunlight or indoor lighting to charge the batteries, while the RF energy harvesting system captures energy from wireless signals. This dual energy approach allows our system to operate for extended periods, even in environments where traditional power sources may not be available.

#Testing the System

For our embedded planogram compliance control system to be effective, we tested it with two different sets of data. The goal was to evaluate how well the system performs in real-world conditions. We looked at two main aspects during testing: object detection performance and planogram compliance results.

#Object Detection Performance

In our tests, the object detection component of our system showed impressive results. The system was able to identify products accurately, achieving high scores for precision and recall. This means that our system not only found most of the products on the shelves but also avoided falsely detecting items that were not there.

The performance varied slightly between different datasets, but overall, the results demonstrated the effectiveness of our approach. The use of modern deep learning techniques for object detection significantly improved accuracy compared to traditional methods.

#Planogram Compliance Results

Once the object detection was complete, we evaluated the planogram compliance control. This step involved comparing the detected products with the expected placements defined in the planogram. Our system was able to highlight discrepancies, such as missing products or items that were in the wrong position.

The results from the planogram compliance tests were also encouraging. The system successfully identified compliance levels, allowing for quick adjustments to be made in the store. This capability can significantly impact the efficiency of retail operations and help maintain proper product placement.

#Timing and Power Consumption

An essential aspect of our embedded system is its timing performance. We measured how long it takes for various components to complete their tasks. The speed of image acquisition and processing affects how soon the store can respond to any detected issues.

In our tests, the image acquisition process took a few seconds, while the processing of the images for object detection was also completed relatively quickly. Overall, the system was designed to operate efficiently, minimizing delays and streamlining the workflow for store employees.

Power consumption is another critical factor in our system’s design. Using low-power components and energy harvesting methods allowed our system to maintain a long operational life. We estimated that the complete system could run for several months on battery alone, and this time could be extended even further with our energy harvesting modules.

#Conclusion

The development of an embedded planogram compliance control system represents a significant step forward for the retail sector. By utilizing modern technologies like computer vision and deep learning, we can automate the process of monitoring product placements on store shelves.

Our system not only improves the accuracy and efficiency of planogram compliance checks but also reduces the labor-intensive nature of the task. This allows store employees to focus on other essential areas of their work while knowing that the system is continuously monitoring for compliance.

With the integration of energy harvesting methods, our system demonstrates its potential for long-term deployment in retail environments, ensuring that it can operate independently without regular power supply interruptions.

As retailers seek ways to enhance customer experiences and drive sales, solutions like our embedded planogram compliance control system provide valuable tools to help them achieve their goals. Future development could include utilizing machine learning to further improve the accuracy of product placement suggestions, making smart retail even more effective.