Logistics and Supply Chain Management Exam 1 Practice

Baseline forecasting includes simple methods that set the next period’s forecast in different ways. The naive approach is defined by using the most recent observation as the forecast for the next period. It’s the simplest possible forecast because it assumes nothing changes from one period to the next beyond what you’ve just seen.

That’s why this is the best answer: the method literally carries forward the last known value as the prediction, with no smoothing, no averaging, and no modeling of trends or seasonality.

In contrast, a moving average smooths data by averaging a window of past values, which reduces short-term fluctuations but lags changing patterns. Exponential smoothing assigns weights to past observations that decay over time, so newer data matter more but all past values still influence the forecast. ARIMA builds a statistical model that can capture trends, seasonality, and autocorrelation, producing forecasts based on learned parameters rather than just the most recent point.