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Exam 3: Forecasting

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Question 41

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Detecting non-randomness in errors can be done using:

A) MSEs
B) MAPs
C) Control Charts
D) Correlation Coefficients
E) Strategies

F) B) and E)
G) A) and B)

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Question 42

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Forecasts for groups of items tend to be less accurate than forecasts for individual items because forecasts for individual items don't include as many influencing factors.

A) True
B) False

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Question 43

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A proactive approach to forecasting views forecasts as probable descriptions of future demand, and requires action to be taken to meet that demand.

Current information on _________ can have a significant impact on forecast accuracy:

For the data given below, what would the naive forecast be for the next period (period #5) ?

A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend.

What is this year's forecast using the naive approach?

Seasonal relatives can be used to de-seasonalize data or incorporate seasonality in a forecast.

What is this week's forecast using a three-week simple moving average?

What is this year's forecast using the naive approach?

The shorter the forecast period, the more accurately the forecasts tend to track what actually happens.

The two most important factors in choosing a forecasting technique are:

Forecasts based on an average tend to exhibit less variability than the original data.

Forecasts help managers plan both the system itself and provide valuable information for using the system.

What is this year's forecast using a four-year simple moving average?

What is the forecast for this year using trend adjusted (double) smoothing with alpha = 0.3 and beta = 0.2, if the forecast for last year was 310, the forecast for two years ago was 430, and the trend estimate for last year's forecast was -150?

What is the forecast for this year using the naive approach?

Which of the following changes would tend to shorten the time frame for short term forecasting?

Forecasts based on judgment and opinion don't include

Forecasting techniques such as moving averages, exponential smoothing, and the naive approach all represent smoothed (averaged) values of time series data.