Which forecasting method uses a correlated leading indicator and is more useful for large aggregations?

• A. Extrinsic forecasting method
• B. Judgmental forecasting method
• C. Time-series forecasting method
• D. Intrinsic forecasting method

Answer: (A)

The concept being tested is using external information to forecast. The extrinsic forecasting approach builds forecasts from external data that tends to move ahead of demand, a correlated leading indicator. Because these indicators reflect broad market or economic trends, they provide a strong, top-down signal that’s especially useful when forecasting at higher levels of aggregation, like product families or overall demand, where many items respond to the same market forces. This helps reduce the noise and variability that can plague item-level histories.

In contrast, time-series or intrinsic methods rely mainly on internal historical data from the specific item, trying to identify patterns or trends within that history, which can be unreliable for items with sparse or volatile data. Judgmental forecasting depends on expert opinion, not systematically on external indicators. So for large aggregations, external leading indicators offer a clearer, more stable signal than relying solely on internal history or subjective judgment.