Question

Which forecasting method is most appropriate when the data exhibits trend but no seasonality?

• A. Simple moving average
• B. Naive forecasting
• C. Double exponential smoothing
• D. Seasonal index method

Hint:

Remember:
- Single Smoothing $\to$ No Trend, No Seasonality
- Double Smoothing (Holt's) $\to$ Trend present, No Seasonality
- Triple Smoothing (Winters's) $\to$ Trend and Seasonality present

Answer 1

The Correct Option is C

Step 1: Understanding the Question:
This question focuses on selecting the appropriate quantitative forecasting technique in inventory and production planning depending on the characteristics of historical demand data.

Step 2: Key Formula or Approach:
Data characteristics play a primary role in forecasting model selection:
- Constant mean $\to$ Simple Moving Average Single Exponential Smoothing
- Linear trend without seasonality $\to$ Double Exponential Smoothing (Holt's Linear Method)
- Trend and seasonality $\to$ Triple Exponential Smoothing (Winters' Method)

Step 3: Detailed Explanation:

• Option A (Simple moving average) is suitable only for stationary data where there is no trend or seasonality. If a trend exists, simple moving average forecasts will lag significantly behind the actual values.

• Option B (Naive forecasting) simply uses the previous period's actual demand as the current forecast, which does not model trends effectively.

• Option D (Seasonal index method) is designed specifically to model seasonal fluctuations.

• Option C (Double exponential smoothing, or Holt's method) introduces a second smoothing constant ($\beta$) to track and update the trend component alongside the level parameter ($\alpha$). This makes it ideal for data with a clear trend but no seasonal patterns.

Step 4: Final Answer:
Double exponential smoothing is the most appropriate forecasting method when the data exhibits a trend but no seasonality.