Certified Supply Chain Professional (CSCP) Practice Exam 2026 - Free CSCP Practice Questions and Study Guide

MAPE, or Mean Absolute Percentage Error, is widely regarded as a useful metric in demand forecasting because it quantifies the accuracy of forecasting models in terms of percentage. This allows organizations to understand the extent of errors relative to the actual demand over a specific period. By expressing forecasting errors as a percentage, MAPE provides a clear, intuitive measure that can be easily interpreted by stakeholders at various levels. This percentage format helps in comparing forecast accuracy across different products, time periods, or even different forecasting methods, making it a versatile tool in supply chain management.

The other options, while related to aspects of demand planning and supply chain management, do not capture the specific role of MAPE in forecasting. For instance, summarizing past sales trends is more about historical analysis rather than error measurement. Calculating future inventory needs involves projections based on forecasts rather than directly addressing forecasting accuracy, and determining shipping methods relates to logistics rather than forecasting performance. Therefore, the significance of MAPE lies specifically in its ability to measure and communicate the accuracy of demand forecasts through the lens of percentage error.