Artificial intelligence is now touching every industry, including retail, and retailers of all sizes are tapping into the technology to improve their operations. While AI can give all aspects of a retail business a boost, there’s one particular area that’s capturing the limelight right now, which is inventory management.
By improving inventory management, retailers can hold the right number of products in the right colors and sizes at the right locations, enabling them to better meet customer demand while minimizing warehouse costs and maximizing profitability due to reduced stockouts.
Using AI to better forecast demand leads to improved accuracy and customer satisfaction, making this technology a critical part of today’s inventory management systems.
How AI Gives Inventory Management a Boost
Demand forecasting is an essential component of inventory management, as is real-time visibility into where products are located. Other use cases for AI within inventory management include developing what-if scenarios, managing warehouse operations and suppliers, detecting anomalies, and automating replenishment.
In the area of demand forecasting, AI enables retailers to respond dynamically to the changing market. The technology grants them deeper insights into how customers are behaving and what demand is looking like. As a result, demand forecasting becomes more precise, enabling retailers to adjust their inventory levels in real time.
“AI enhances demand forecasting and inventory management by rapidly analyzing large data sets from various sources in real time to deliver accurate forecasts and data-driven inventory recommendations,” explained 7thonline CEO Max Ma. “… With faster insights — down to style, color, size — brands and retailers are able to make agile inventory decisions that align with demand in real-time and optimize stock levels across channels by predicting what products are needed, where and when.”
Real Results from AI in Demand Forecasting
Retailers have already been reaping the benefits of using AI in inventory management. In fact, some are reporting actual numbers demonstrating the improvements they’ve seen.
For example, earlier this year, Walmart announced it was reengineering its global supply chain using real-time AI and automation. Early deployments in markets like Costa Rica were a success, so the big-box retailer began rolling the technologies out to other locations.
Walmart’s Self-Healing Inventory system has been in place in Mexico City for some time. Shelf space is scarce there, so timing is critical. Self-Healing Inventory watches for overstocks and then automatically reroutes supply to other stores before that excess inventory turns into waste. According to the retailer, this system alone has already saved over $55 million.
Merchants selling on Amazon have also benefited from AI technologies for demand forecasting. For example, India’s More Retail reported that Amazon Forecast enabled it to improve its forecasting accuracy from 27% to 76% and reduce wastage in fresh produce by 20%.
According to 7thonline, one retailer managing over 8,000 stores was able to boost its inventory accuracy from 60% to 90% using 7thonline’s AI-powered forecasting. The retailer reported that they were better able to analyze regional demand and predict what products will sell, down to style, color, size per store, to reduce costly reallocation/ transfer efforts.
The Future of AI in Inventory Management
Going forward, we can expect AI to improve more and more over time, both in general and for each individual retailer. The more a retailer uses AI for things like demand forecasting, the better it will get at predicting customer flows and demand. AI models improve as they gain more data, so we expect dramatic improvements in accuracy, decision making, and real-time capabilities.
Ma also predicts a widening gap between the haves and have-nots — retailers that use AI and those that don’t.
“AI for demand forecasting and inventory management will further the divide between large enterprises and smaller retailers,” he explained. “But clean data and early adoption can serve as a saving grace.”
Ma also believes that AI personalization for shopping will rapidly evolve until it doesn’t feel like AI.
“Chatbots may be gone, but personalization efforts that people don’t associate with AI (such as FYPs) will take off,” he added.
