Machine Learning for Shelf Auditing in CPG Brands

Ensuring that your products are consistently available, visible, and well-positioned on the retail shelves is crucial to driving store sales. However, CPG brands across the globe often struggle with impending sales execution strategy such as out-of-stock (OOS), misplaced products, and inefficient shelf utilization by their brand.These issues can result in missed sales opportunities, damaged customer loyalty, and struggle to stay ahead of the competition.

This is where machine learning (ML) comes in. Machine learning enhances shelf auditing processes using advanced algorithms and real-time analytics, allowing brands to gain deeper insights into stock levels, optimize product placement, and automate key tasks. 

In this article, we'll explore how machine learning combined with other technologies such as Computer Vision and IoT can solve these challenges, improve shelf auditing and optimization, and ultimately drive better sales outcomes and store execution.

Key Takeaways

• ML utilizes real-time data from cameras, point-of-sale (POS) systems, and inventory feeds to detect out-of-stock items, planogram violations, and misplaced products. This allows CPG brands to make data-driven decisions and optimize shelf space.
• Unlike traditional object detection, deep learning models use neural networks for more accurate and faster product identification. This ensures more precise shelf monitoring and quicker action in retail environments.
• ML provides real-time inventory insights, improved shelf space allocation, and data-driven decision-making. It helps reduce stockouts, prevent overstocking, optimize product placement, and enhance the customer shopping experience.
• ParallelDots' ShelfWatch integrates machine learning and AI to provide real-time shelf analytics, from detecting out-of-stock items to tracking promotional compliance. The platform offers features like advanced image recognition, real-time data access, and seamless integration with existing systems.

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What is Machine Learning in Shelf Auditing and Optimization?

Machine learning (ML) is a subset of artificial intelligence that enables systems to learn from data and improve their performance over time without explicit programming. 

ML is transforming shelf auditing and optimization in retail, especially for Consumer Packaged Goods (CPG) brands. It analyzes large datasets from in-store cameras, point-of-sale (POS) systems, and inventory feeds. ML tools can automatically detect out-of-stock items, planogram violations, and misplaced products in real-time. 

This allows CPG brands to make immediate, data-driven decisions to improve product availability and placement. ML can also optimize on-shelf inventory by predicting demand patterns and adjusting stock levels accordingly, minimizing stockouts and overstock situations. With ML systems, CPG brands can improve shelf audits, ensure compliance with promotional strategies, and enhance the shopping experience, all while driving operational efficiency and sales growth.

Also Read: AI-Powered Retail Shelf Monitoring Solution Why Should CPG Brands Choose ParallelDots

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What are the Challenges in Shelf Auditing?

Managing shelf conditions isn’t as simple as keeping track of inventory. CPG brands face several common challenges in optimizing shelf space and product availability.

• High Out-of-Stock (OOS) Rates: One of the most significant issues in shelf auditing is out-of-stock (OOS) products. Research suggests that around 8% of retail products are out of stock at any given time, with some promoted items facing up to 15% OOS rates. Missing products can lead to lost sales and dissatisfied customers.
• Overstock Issues: Overstocking is another issue. Excess inventory not only ties up valuable capital but also increases storage costs. Over time, unsold stock can lead to waste, markdowns, and reduced profit margins.
• Manual Audits: Traditional and manual shelf auditing relies on human effort, often leading to errors in stock counts, product placements, and restocking orders. These inaccuracies affect real-time decision-making and make the entire process more labor-intensive and prone to inefficiencies.
• Inconsistent Planogram Execution: Inconsistent execution can lead to poor visibility of high-demand products, missed sales opportunities, and a negative impact on overall store layout efficiency. When products are not displayed as intended, it can confuse customers, decrease brand presence, and disrupt the overall shopping experience.
• Inconsistent Price and Promotional Displays: Inconsistent pricing or expired promotional signs can create confusion for customers. If promotions are not properly executed or visible, customers may overlook key offers, resulting in lost revenue opportunities for CPG brands. Furthermore, misaligned displays can undermine the effectiveness of marketing campaigns and impact overall brand perception.

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What are the Technological Solutions for Shelf Auditing?

To tackle the challenges of shelf auditing, utilizing advanced technologies like AI, Computer Vision and Machine Learning can help improve efficiency and accuracy of data collection at Point of Sale. These tools help CPG brands monitor shelf conditions in real time, making it easier to execute product placement, inventory, and promotional displays. Here are some key technological solutions. 

1. Deep Learning for Object Detection

Advanced Machine learning models provide real-time, deep learning-powered object detection. These models can accurately identify products, detect out-of-stock situations, and differentiate between items on the shelf and those in the backroom, ensuring accurate stock management and visibility.

2. AI Cameras and Computer Vision

 AI-powered cameras equipped with computer vision technology can scan shelves and deliver real-time data on shelf conditions, including product placement and stock availability. This technology allows for continuous monitoring, ensuring products are in the right place at the right time.

3. Integration with Inventory Management Systems

Machine learning systems seamlessly integrate with inventory management tools, ensuring that the data captured during shelf audits matches inventory records. This alignment helps create a comprehensive view of stock levels, making it easier for CPG brands to make data-driven decisions and avoid stockouts or overstock situations.

4. Automated Shelf Auditing

Automated shelf auditing uses AI-driven tools to reduce the need for manual checks, offering more accurate and timely shelf data. This automation helps CPG brands improve their shelf auditing process, improve operational efficiency, and reduce the chances of human error.

5. Predictive Analytics for On-Shelf Inventory Optimization

CPG brands can use AI-powered predictive analytics to forecast seasonal demand and optimize on-shelf inventory levels. By analyzing historical data and market trends, brands can proactively adjust stock levels and product placements, minimizing stockouts and overstocking, while ensuring shelves are always stocked with the right products.

With these technological advancements, CPG brands can elevate their retail execution, enhance operational efficiency, and boost customer satisfaction through optimized shelf auditing processes.

ParallelDots offers cutting-edge AI-powered tools like ShelfWatch to help CPG brands streamline their shelf auditing processes. With advanced image recognition and real-time data analysis, ShelfWatch provides actionable insights into shelf conditions, product availability, and planogram compliance. This enables brands to make timely, data-driven decisions that optimize retail execution, reduce stockouts, and improve overall in-store performance.

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What are the Benefits of Machine Learning in Shelf Management?‍

Implementing ML and Image Recognition in shelf management offers CPG brands several benefits that can significantly improve operational efficiency, product availability, and the customer experience. These benefits not only streamline processes but also enhance brand loyalty and sales performance. Here are some of the key advantages:

• Enhanced On-Shelf Inventory: Machine learning along with Computer Vision enables real-time shelf monitoring, enabling brands to see how their products are placed on the shelf which in turn helps in maintaining optimal stock levels, correct positioning and more. This ensures that the right products are available at the right time, leading to smoother inventory flow and more efficient restocking.
• Improved Shoppers’ Experience: When products are consistently available and correctly placed on the shelf, customers have a better shopping experience. With the help of machine learning tools, retailers can ensure that the shelves are organized and that in-demand products are easy to find. 
• Data-Driven Decision Making: Machine learning enables CPG brands to leverage data to make smarter decisions regarding product placement, promotions, and restocking. Insights derived from ML models can guide both short-term actions and long-term strategies. By continuously analyzing data from the shelves, brands can stay ahead of consumer demand, improve operational efficiency, and enhance sales performance.
• Optimized Shelf Space Allocation: ML can also help optimize how shelf space is used by analyzing product performance. By understanding which products are frequently purchased, machine learning algorithms can suggest optimal placement strategies. This ensures the most profitable products are positioned in high-visibility areas, maximizing their sales potential.
• Real-Time Performance Monitoring: Machine learning provides real-time insights, allowing brands to monitor their shelves continually. This instant feedback loop allows businesses to make quick decisions about stock levels, promotional adjustments, and inventory replenishment, ensuring that they are always meeting consumer demand and maximizing sales opportunities.

Also Read: How AI is driving Shelf Space Management and Optimization Strategies

With all these benefits, machine learning can clearly improve shelf management significantly. However, it’s important to recognize that implementing these technologies also comes with its own challenges, which must be carefully considered for a successful rollout. 

High-quality, labeled data is required to train models effectively, which can be resource-intensive to gather. The cost of deploying machine learning systems can also be significant, particularly when scaling across large networks of stores or extensive product ranges. 

Additionally, integrating these solutions with existing retail systems, such as POS and inventory management systems, requires careful planning to ensure seamless data flow and accurate decision-making. These challenges, though, are surmountable with the right planning, and the potential for improved operational efficiency makes the investment worthwhile.

One brand that has successfully navigated these challenges is a global confectionery manufacturer that used ShelfWatch, an AI-powered retail execution solution from ParallelDots. It used machine learning and image recognition technology. This enabled the brand to transition from monthly to weekly audits, ensuring improved product visibility and availability, planogram compliance, and overall in-store execution.

The results were remarkable:

• 84% accuracy in shelf audits compared to traditional manual methods.
• 80% improvement in cooler planogram compliance.
• 70% better cooler purity compliance.

Through ShelfWatch, the brand gained actionable, real-time insights that empowered their field sales teams to take corrective actions quickly. This resulted in improved product availability, enhanced brand visibility, and stronger relationships with retailers. For more details on how the confectionery brand achieved these improvements, click here.

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Conclusion

Machine learning is changing how CPG brands manage their shelves, offering unparalleled benefits such as improved product availability and visibility and optimized shelf space. By using ML, CPG brands can streamline their shelf auditing processes, improve decision-making, and enhance the overall customer experience. However, the success of these initiatives hinges on addressing key challenges, such as data quality, integration with existing systems, and the upfront cost of deployment.

ParallelDots provides the ideal solution to help CPG brands integrate machine learning into their retail operations. Our AI-powered platform, ShelfWatch, is designed to provide real-time, actionable insights, enabling you to optimize your shelf management and improve retail execution. Key features of ShelfWatch include:

• Advanced Image Recognition: Our technology uses deep learning algorithms to detect out-of-stock products, misplaced items, and planogram violations in real-time, ensuring efficient stock management.
  
  
• Real-Time Data: Gain immediate access to SKU-level data on shelf conditions, stock availability, and pricing, enabling quick decision-making and reducing delays in restocking.
  
  
• Actionable Insights & Compliance Tracking: ShelfWatch provides detailed insights on promotional displays, planogram compliance, and product visibility, allowing your team to take corrective actions instantly.
  
  
• Competitive Intelligence: Monitor competitor pricing, assortments, and promotional strategies to gain an edge in the market, optimizing your brand's presence on the shelf.
  
  
• Seamless Integration: Easily integrates with your existing POS and inventory management systems, ensuring accurate data flow and enhancing operational efficiency.
  
  
• Offline Capability: ShelfWatch operates in areas with limited or no internet connectivity, ensuring continuous auditing even in remote locations.
• Rapid AI Training: Unlike other Image Recognition platforms, ParallelDots allows rapid AI training with just one packshot or shelf images being captured by the field teams in the markets. The system can be trained to detect new or unknown SKUs within 80% accuracy within minutes and ramped up to >95% accuracy within hours with no manual intervention or tagging. 

ParallelDots helps you integrate these powerful tools into your retail strategy, enabling data-driven decisions that improve your bottom line, enhance the customer shopping experience, and keep you ahead of the competition.

Book a demo with ParallelDots today to explore how our tech-powered solutions can elevate your retail operations and drive growth.

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