Forecast bias measures the tendency of a forecast to consistently overestimate or underestimate actual demand. A positive bias indicates over-forecasting, while a negative bias indicates under-forecasting. Bias can lead to significant inefficiencies—over-forecasting results in excess inventory and higher holding costs, whereas under-forecasting can cause stockouts and lost sales. Identifying and correcting forecast bias is essential for improving inventory planning, setting realistic safety stock levels, and maintaining service performance. Advanced analytics and machine learning can help detect patterns and reduce bias over time.
