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3 Ways to Apply AI for Real-Time Retail: Part 1

AI has become a well-discussed term in recent years, finally moving from sci-fi pipe dream to real-deal opportunity for businesses to improve efficiency and service. As everyday shoppers, we experience the effect of AI as recipients of personalized advertising and product recommendations. That’s not all it can do, though, as AI is becoming a powerful tool for in-store retailers to turn imprecise and inefficient backend processes into value-drivers.

But why do we need AI for this? It’s not like retailers have been starved for data analysis or don’t know how to run their businesses based on decades of experience and customer relationship building. And yes, technology has been improving nearly every aspect of retail for years. But even when retailers adapted advanced rules-based algorithms to ease the burden, their improvements were capped by the limited power of historical data to tell the full story – which it really never does.

In this two-part series, we will explore three ways that AI can impact store processes in ways no other technology quite can. Let’s take a deeper dive into the first benefit we’ll explore - replenishment.

Replenishment

Today’s retail workforce is heavily manual. Retailers rely on their staff to manually perform time-consuming tasks that allow them to make decisions based on the best available information they can gather, instead of the right information.

One great example of this is the process of analyzing buy cycles of certain products in order to manage replenishment – both for restocking shelves and the store as a whole.

At a foundational level, a retailer’s ERP system will track how many items are left in the store based on POS and supply data. But the ERP can’t distinguish between the shelf and the back room – it just knows how many items have come in and how many have been sold. This makes it challenging to know if stock levels are too low or high in one area, or if poor sales are due to true out of stocks, or just a lack of product on the shelf.

Figuring this all out usually requires hourly associate walks through the aisles, check stock levels and note which products need a boost. Then, they head to the stock room to resupply, relying on a generic, consistent standard of how many of an item need to be available to handle coming demand. This isn’t exactly an exact science…

With AI, retailers can automate and optimize sales and replenishment recommendations based not only on exactly what’s on the shelf and in reserve – as separate units of measurement – but also adjust these needs dynamically in combination with POS and supply data analysis. The AI engine predicts future product sales based on historical data and prevailing trends and provides automatic real-time adjustments in replenishment commands in the instance of some unexpected macro event such as a storm or holiday.

AI also helps retailers recognize micro-moments, like a staple product experiencing a sudden drop, which the system could immediately recognize as a stock level issue, alerting employees to check for an in-aisle problem.

Now that you know how AI can streamline the replenishment process and make efficient use of merchandising tasks, be sure to stay tuned for part two of this blog series as we explore how two marketing processes can also be improved with this powerful technology. To find out how GK Software’s AI-enabled platform can help your business, please feel free to reach out.