Glossary
Forecast Accuracy
Tags: Glossary
A measurement of the level of accuracy inherent in your forecast, as a percent of actual units or dollars shipped, is called forecast accuracy. In the supply chain, forecast accuracy is typically measured using the Mean Absolute Percent Error (MAPE). However, there are confusions between the statistical definition of MAPE and its application among supply chain planners. Statistically, MAPE is defined as the average of percentage errors. Most practitioners, however, define and use the MAPE as the Mean Absolute Deviation divided by Average Sales. You can think of this as a volume-weighted MAPE. In some references, this is also referred to as the mean absolute difference (MAD)/mean ratio.
What is Forecast Accuracy?
Forecast Accuracy
Forecast accuracy is a crucial aspect of logistics and supply chain management. It refers to the measurement of how accurately a forecast predicts the actual units or dollars shipped. Accurate forecasting is essential for effective planning, inventory management, and meeting customer demands.
In the supply chain, forecast accuracy is commonly evaluated using a metric called Mean Absolute Percent Error (MAPE). MAPE calculates the average percentage error between the forecasted and actual values. However, there can be confusion regarding the statistical definition of MAPE and its practical application among supply chain planners.
Statistically, MAPE is defined as the average of percentage errors. However, in practice, most practitioners define and use MAPE as the Mean Absolute Deviation divided by Average Sales. This approach is often referred to as the volume-weighted MAPE or the mean absolute difference (MAD)/mean ratio.
The volume-weighted MAPE takes into account the sales volume of each product, giving more weight to higher-selling items. This approach provides a more accurate representation of forecast accuracy, as it considers the impact of different product volumes on overall performance.
Forecast accuracy is crucial for various aspects of logistics and supply chain management. It helps in determining optimal inventory levels, reducing stockouts, minimizing excess inventory, and improving customer satisfaction. Accurate forecasts enable companies to plan their production, procurement, and distribution activities more efficiently, leading to cost savings and improved operational performance.
To improve forecast accuracy, companies can employ various strategies and techniques. These may include using advanced forecasting models, incorporating historical data, considering market trends and seasonality, collaborating with suppliers and customers, and continuously monitoring and adjusting forecasts based on real-time information.
In conclusion, forecast accuracy is a vital measure in logistics and supply chain management. It quantifies the level of accuracy in predicting actual units or dollars shipped. While the statistical definition of MAPE differs from its practical application in supply chain planning, the volume-weighted MAPE provides a more accurate representation of forecast accuracy. Improving forecast accuracy is crucial for optimizing inventory management, reducing costs, and enhancing customer satisfaction.