Machine learning algorithms improve forecasting methods in accuracy and optimize replenishment processes. With the help of AI, brands can reduce the cost of cash-in-stock and out-of-stock scenarios.
The improved accuracy leads up to a 65% reduction in lost sales due to inventory out-of-stock situation
AI-powered forecasting can reduce errors by 30 to 50% in supply chain networks
The improved accuracy reduces the warehousing cost by 10% to 40%
Stockouts reduce customer satisfaction while being available with your product anytime boosts customer satisfaction. Thus it improves brand perception and increasing customer loyalty.
Certain products stay unsold longer than expected. This causes higher than expected inventory costs and increases the risk for these products to go out of fashion, thereby losing their value. In these scenarios brands sell their products with reduced margins. With accurate demand forecasting, such scenarios can be minimized.
Accurate demand forecasts help teams focus on strategic issues rather than firefighting to reduce/increase stocks and headcount to manage unexpected demand fluctuations.