Artificial intelligence is revolutionizing grocery retail. From predictive demand forecasting to real-time inventory tracking, AI-powered solutions are helping retailers optimize stock levels, reduce operational waste, and maximize profitability.
But even the most advanced AI models are only as good as the inventory data that feeds them. If stock records are inaccurate due to phantom or shadow stock, AI-driven replenishment and demand efforts won’t be effective.
To truly thrive grocery retailers must prioritize inventory accuracy. In doing so, you drastically improve the return on investment of AI investments through precise stock predictions.
Given the historical rise of AI, it is no surprise that retailers and solution providers alike are moving at pace to embrace the technology to improve productivity and revenue. But AI is not yet the panacea. It’s crucial to understand that AI-based solutions are built on data sets, and these need to be accurate to bring the best out of any investment. After all, good decisions are only as good as the information they’re based on!
To understand how to fix bad inventory data, it’s vital to understand the challenges facing a retailer’s inventory record.
The first challenge is the data itself. Many retailers still rely on manual audits conducted once or twice a year. As expected, this opens the door to error given the chase of day-to-day store operations. From mis-scans and manual errors to theft and waste, there are numerous reasons as to why inventory accuracy can drift, and significantly.
This often leads to two common presentations within the inventory record:
Phantom Inventory: Your system shows stock on hand, but shelves are empty, leading to lost sales and poor customer satisfaction.
Shadow Inventory: Your system indicates zero stock, but inventory is physically in-store. This leads to incorrect replenishment orders, unnecessary shipments, and excess inventory costs.
Retailers relying on inaccurate stock data will experience excess inventory or frequent stockouts, making retail effectiveness even more challenging. In fact, it costs retailers over $1.7 trillion every year from out-of-stocks and overstocks. We previously wrote about exactly how much inaccurate inventory is really costing retailers in a white paper on the financial impact of poor stock data.
Grocery retailers looking to recover lost sales and improve inventory efficiency need a proactive approach to inventory data accuracy. Here’s how leading grocers are leveraging innovative solutions to avoid stockouts, optimize stock levels, and boost inventory accuracy.
1. Implement real-time inventory auditing
Major retailers are shifting from manual stock counts to real-time inventory tracking, which allows for automatic error detection and correction. Automated inventory auditing software can then continuously analyze stock movements, helping to reduce excess inventory while ensuring optimal availability on store shelves.
By applying machine learning-based predictive analytics, retailers can identify patterns in inventory distortion and adjust stock replenishment proactively to avoid errors affecting store execution. With accurate inventory data retailers can more precisely prevent overstocking, minimize waste, and boost profitability.
For AI-powered replenishment to work effectively, clean and accurate inventory data is essential. By addressing stock inaccuracies first, smart stock control solutions can help prevent overstocking and stockouts, streamlining the process. Syncing this with automated demand planning then allows for adjustments based on real-time shelf movement data, leading to a more efficient and responsive inventory system overall.
In summary, retailers looking to get their inventory levels just right are stepping away from outdated tracking systems and a reliance on manual audits. They are prioritizing modern inventory accuracy solutions which monitor real-time stock levels and automatically trigger replenishments when necessary. This provides valuable insights into stock trends, sales patterns, and optimal reorder timings. Additionally, they enhance operational efficiency by enabling accurate inventory forecasts.
There are many solutions out there for retailers to help with replenishment, forecasting, and inventory accuracy. Whether you choose a point solution or a mixture of technologies, the right approach will depend on your challenges.
Retail Insight’s InventoryInsight offers a pragmatic solution for retailers looking to improve inventory data accuracy. It addresses inaccuracies in existing systems by proactively identifying and resolving stock discrepancies to minimize revenue impacts.
By leveraging a machine learning approach, retailers can improve their inventory forecasting and refine demand planning efforts. InventoryInsight is designed to assist in avoiding both stockouts and overstock situations, contributing to better inventory management. This focused approach not only helps prevent losses but also supports retailers in optimizing their replenishment strategies and recovering potential sales. With InventoryInsight, maintaining inventory accuracy becomes a reality.
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