What is the primary purpose of the mean absolute deviation (MAD) in forecasting?

The mean absolute deviation (MAD) is primarily utilized to measure forecast accuracy. This statistical tool calculates the average of the absolute differences between actual observed values and the values forecasted by a model. By focusing on the magnitude of errors without regard to their direction—meaning it doesn't differentiate between over-forecasting and under-forecasting—MAD provides a clear and straightforward way to assess how well a forecasting model performs.

In practice, a lower MAD indicates a more accurate forecast, which can aid organizations in evaluating and refining their forecasting methods to improve operational decisions. The focus on accuracy makes MAD a vital metric in the field of supply chain and operations management, where precise demand forecasting is crucial for effective inventory management, production planning, and overall efficiency.

Using MAD in this way also helps practitioners identify trends in forecasting errors, leading to improvements in future forecasting efforts. This distinction in purpose reinforces the critical nature of accuracy in achieving strategic supply chain objectives.