Retailers Forecast Demand with AI and Data Analytics
1. Assess Inventory More Accurately
When it comes to crunching numbers, AI is rarely wrong, making it a highly accurate predictor of inventory needs. That’s true at every point of the retail journey: AI-driven forecasting can reduce supply chain errors by between 20 and 50 percent, according to McKinsey, leading to a 65 percent boost in efficiency through fewer lost sales and unavailable products.
Case in point: Danone’s AI-powered demand model has helped consumer packaged goods manufacturers more accurately predict customer demand. The result: a 30 percent reduction in lost sales.
On top of that, today’s machine learning algorithms are self-improving. The more actions they execute, the more they learn and the better they perform in the future. This means even more accurate, more sensitive predictions that optimize stock.
2. Anticipate Customer Needs Faster
The shopping experience can quickly turn frustrating if there’s a long line, no customer support or an item is out of stock. When this happens, customers may turn elsewhere to make their purchases. In a split second, a retailer can lose a customer’s brand loyalty — and a sale.
Machine learning can help prevent stockouts by offering improved inventory tracking. It also helps at the end of the retail journey: AI can track stock in real time, meaning that customers can be alerted when inventory of items in their digital carts is running low. Research published in Harvard Business Review suggests that more transparency about inventory levels improves customer ratings. Customers would rather know whether a product is running low in real time as it allows them to adjust their shopping strategies faster.
20-50%
The percentage of supply chain errors that can be prevented with AI-driven forecasting
Source: McKinsey, “AI-Driven Operations Forecasting in Data-light Environments,” February 15, 2022
3. Plan with Precision and Optimize Prices
There are some products that sell like hotcakes, while others linger on the shelves. Excess inventory can cost retailers a great deal: If they are left with products for too long, they are forced to sell them at a discount.
Demand forecasting helps retailers plan with precision. It considers shopping trends, consumer preferences and seasonality to avoid these losses. All of this data helps retailers optimize prices for their products and keep inventory levels accurate.
DISCOVER: Learn five steps to success with analytics and artificial intelligence.
4. Reallocate Resources to Customer Experiences
Inventory management, much like data analysis, is no small task. It’s complex, time-consuming, and labor intensive. By bringing AI into the stockroom, retailers are better able to assign their workers to more innovative, strategic tasks.
According to the National Retail Foundation, retail supports 1 in 4 jobs in the United States. Diverting that collective brain power from inventory to deepening the customer experience and building omnichannel capabilities will result in better brands and happier customers.
Between its potential to optimize supply chain networks, improve customer satisfaction and reduce labor hours, AI-driven demand forecasting offers retailers a solution to some of their oldest pain points. It’s also creating the conditions for IT leaders to create the “surprise and delight” factor that shoppers love most.
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