1. Evaluate inventory more accurately
When it comes to crunching numbers, AI rarely gets it wrong, making it a very accurate predictor of inventory needs. This is true at every stage of the retail journey: AI-Driven Forecasting can reduce supply chain errors by 20 to 50 percent, according to McKinseyresulting in a 65% increase in efficiency through a reduction in lost sales and unavailable products.
Concrete example: 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.
In addition to this, today machine learning algorithms improve on their own. The more actions they perform, the more they learn and the better they will perform in the future. This means even more accurate and sensitive forecasts that optimize inventory.
2. Anticipate customer needs faster
The shopping experience can quickly become frustrating if there is a long queue, no customer support or an item is out of stock. When this happens, customers may turn to other stores to make their purchases. In a split second, a retailer can lose a customer’s brand loyalty and lose a sale.
Machine learning can help prevent stock-outs by providing better inventory tracking. It also helps at the end of the retail journey: AI can track inventory in real timemeaning customers can be alerted when the stock of items in their digital cart is low. Research published in Harvard Business Review suggests that greater transparency about inventory levels improves customer reviews. Customers prefer to know if a product is out of stock in real time because it allows them to adjust their purchasing strategies more quickly.
20-50%
The percentage of supply chain errors that can be avoided with AI-driven forecasting
Source: McKinsey, “Forecasting AI-Driven Operations in Data-Light Environments,” February 15, 2022
3. Plan accurately and optimize pricing
Some products sell like hot cakes, while others hang around on the shelves. Excess inventory can be very expensive for retailers: if they stay with products too long, they are forced to sell them at a reduced price.
Demand forecasting helps retailers plan accurately. It takes into account purchasing trends, consumer preferences and seasonality to avoid these losses. All this data helps retailers optimize prices for their products and maintain accurate inventory levels.
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4. Reallocate resources to customer experiences
Inventory management, just like data analysisis no small feat. It is complex, long and laborious. By bring AI In the warehouse, retailers are better able to assign their employees to more innovative and strategic tasks.
According to the National Retail FoundationRetail supports 1 in 4 jobs in the United States. Diverting this collective brain power from inventory to deepening customer experience and developing omnichannel capabilities will result in better brands and happier customers.
Between its optimization potential supply chain networksimprove customer satisfaction and reduce labor hours, AI-driven demand forecasting offers retailers a solution to some of their longest-standing problems. It also creates the conditions for IT managers to create the “surprise and delight” factor that buyers value most.
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