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.