Exam 18: Forecasting

A company hires you to develop a linear regression forecasting model. Based on the company's historical sales information, you determine the intercept value of the model to be 1,200. You also find the slope value is minus 50. If, after developing the model, you are given a value of X = 10, which of the following is the resulting forecast value using this model?

In a forecasting model using simple exponential smoothing, the data pattern should remain stationary.

Given a prior forecast demand value of 1,100, a related actual demand value of 1,000, and a smoothing constant alpha of 0.3, what is the exponential smoothing forecast value?

As a consultant, you have been asked to generate a unit demand forecast for a product for year 2020 using exponential smoothing. The actual demand in year 2019 was 750. The forecast demand in year 2019 was 960. Using these data and a smoothing constant alpha of 0.3, which of the following is the resulting year 2020 forecast value?

Exponential smoothing models require a great deal of computation.

You are using an exponential smoothing model for forecasting. The running sum of the forecast error statistics (RSFE) is calculated each time a forecast is generated. You find the last RSFE to be 34. Originally, the forecasting model used was selected because of its relatively low MAD of 0.4. To determine when it is time to re-evaluate the usefulness of the exponential smoothing model, you compute tracking signals. Which of the following is the resulting tracking signal?

CPFR stands for "collective planning, forecasting, and reordering."

When average, trend, and seasonality are removed from the data, random variations are what remains.

Exponential smoothing with trend requires two different smoothing constants.

In causal relationship forecasting, leading indicators are used to forecast occurrences.

In a forecasting model using simple moving average, the shorter the time span used for calculating the moving average, the closer the average follows volatile trends.

Exponential smoothing forecasts always lag behind the actual occurrence but can be corrected somewhat with a trend adjustment.

Linear regression is not useful for aggregate planning.

Which of the following is the percentage of observations you would expect to see lying within a plus or minus 3 MAD range?

When forecast errors occur in a normally distributed pattern, the ratio of the mean absolute deviation to the standard deviation is 2 to 1, or 2 × MAD = 1 standard deviation.

In the simple exponential smoothing forecasting model, you need at least 30 observations to set the smoothing constant alpha.

Which of the following forecasting methodologies is considered a causal forecasting technique?

The equation for exponential smoothing states that the new forecast is equal to the old forecast plus the error of the old forecast.

Which of the following forecasting methodologies is considered a time series forecasting technique?

In time series data depicting demand, which of the following is not considered a component of demand variation?