The degree to which a company’s anticipated demand matches actual customer demand is known as forecast accuracy. Because erroneous forecasts can result in either stockouts or excess inventory, it is a crucial performance metric in inventory optimization. Forecast Bias, Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) are frequently used metrics to assess forecast accuracy. Better planning for production, logistics, and procurement is made possible by high forecast accuracy, which eventually lowers costs and enhances customer satisfaction. To improve forecast accuracy, businesses frequently use seasonality, promotional calendars, historical sales data, and outside variables like market trends.
