OPMA 3306
What time horizons are used for strategic forecasts?
Medium & long-term
A forecast based on average past demand is a
Moving average forecast
When the amount of seasonal variation depends upon the trend or average amount, the seasonality is described as
Multiplicative Seasonality
What are the sources or random error?
error that is not explained by the forecasting model
What are the sources of bias error?
the failure to include the right variables; the use of the wrong relationships among variables; employing of the wrong trend line; a mistaken shift in the seasonal demand from where it normally occurs; and the existence of some undetected secular trend
In the formula for exponential smoothing (At-1 - Ft-1) represents
the forecast error for last period
When you are trying to determine whether a forecast is keeping pace with changes in demand, you should use which measure?
tracking signal
Which time series forecasting model uses weights that decrease at a rate of (1- a) for each past period?
Exponential
A qualitative forecasting technique that is useful for identifying what consumers like and dislike about a product is
Market research
What is the process for forecasting the future values of each component
Project the trend component into the future, multiply the trend component by the seasonal component
Which is the larger measure, the standard deviation or MAD?
Standard deviation
What type of forecast is used for day-to-day decision making?
Tactical
all of the following are reasons why exponential has become well accepted except: formulating the model is easy, low computer storage requirements, tests for accuracy are difficult, models are surprising accurate
Tests for accuracy are difficult
True or false: the weights in a weighted moving average can be adjusted to account for seasonal data
True
What is the process for decomposing the time series into its components?
find the seasonal component, deseasonalize the demand, find the trend component
smoothing constants must be given a value between
0 and 1
The weight on each element in a weighted moving average must equal
1
Which of the following is true about the relationship between standard deviation and mean absolute deviation? MAD and standard deviation are equal, there is no relationship, 1 standard deviation is approximately 1.25 MAD, 1 MAD is approximately 0.8 standard deviation
1 standard deviation is approximately 1.25 MAD, 1 MAD is approximately 0.8 standard deviation
When the change in demand due to seasonally is a constant amount, regardless of trend or average, the seasonal variation is describes as
Additive Seasonal Variation
Which smoothing constant controls the speed of reaction to differences between forecasts and actual demand?
Alpha
Using the number of cars passing by a restaurant to predict sales is an example of which type of forecasting ?
Causal relationship forecasting
What term describes the process of identifying & separating time series data into components?
Decomposition
True or False: Qualitative forecasts typically involve mathematical calculations
False
Which measure of error calculates the average absolute value of the actual forecast error?
MAD
Which measurement of error represent the average error measured as a percentage of average demand?
MAPE
When considering more than one variable, along with the effects of each variable on the item of interest, which forecasting model should be used?
Multiple regression
Which forecasting model is based upon average past demand?
Simple moving average
What type of forecast should be used for strategy, sourcing & location decisions?
Strategic
The exponentially smoothed trend for period t (Tt) includes the error between
The previous forecast and the previous forecast including trend
True or False: Mean Absolute Deviation calculates the average absolute value of the forecast error
True
True or False: The seasonal factor is also known as the seasonal index
True
True or false: regression relationships are usually developed from observed data
True
Bias errors occur when
a forecast is consistently too high (or too low)
Mean Absolute Percent Error represents the average error as
a percentage of average demand
The main disadvantage of the moving average is that
all individual data elements must be carried as data
A simple moving average gives ________ weight to each component of the forecast; whereas a weighted moving average gives __________ weight to each element
equal; varying
Which of the following statements is true about linear regression forecasting? linear regression is used for time series forecasting, linear regression is used for causal forecasting, linear regression estimates demand using a line of the form Y = a+bt , linear regression has no serious drawbacks
linear regression is used for time series forecasting, linear regression is used for causal forecasting, linear regression estimates demand using a line of the form Y = a+bt
Which of the following are examples of qualitative forecasting? market research, panel consensus, linear regression analysis, time series analysis
market research, panel consensus
What are attributes of the panel consensus forecasting technique?
may be difficult to get full participation from all members, relies on larger group to develop accurate forecast
To adjust for trends, exponential smoothing uses a second smoothing constant called
delta
What are attributes of the marketing research forecasting technique?
often involves marketing surveys, useful for identifying attractive attributes of competitive products
What are attributes of the delphi method forecasting technique?
requires a moderator, may require several rounds to achieve results
The exponential forecasting model uses all the following data except: actual demand for most recent forecast period, smoothing constant alpha, most recent forecast, smoothing constant delta
smoothing constant delta
When including trend effects in exponential smoothing, how many smoothing constants are required?
two (alpha and delta)
One way to overcome the major restriction of linear regression forecasting is to
use a shorter time period
What are attributes of the historical analogy forecasting technique?
useful when a similar product already exists