SCMS 331 Ch. 4
Suppose that the demand in period 1 was 7 units and the demand in period 2 was 9 units. Assume that the forecast for period 1 was for 5 units. If the firm uses exponential smoothing with an alpha value of .20, what should be the forecast for period 3? (Round answers to two decimal places.)
6.12
A skeptical manager asks what long-range forecasts can be used for. Give her three possible uses/purposes.
Any three of: planning for new products, capital expenditures, facility location or expansion, and research and development.
A skeptical manager asks what short-range forecasts can be used for. Give her three possible uses/purposes.
Any three of: planning purchasing, job scheduling, workforce levels, job assignments, and production levels.
Which of the following statements comparing exponential smoothing to the weighted moving average technique is TRUE?
Exponential smoothing typically requires less record keeping of past data.
A naïve forecast for September sales of a product would be equal to the forecast for August.
FALSE
Forecasts of individual products tend to be more accurate than forecasts of product families.
FALSE
In a regression equation where y-hat is demand and x is advertising, a coefficient of determination (R2) of .70 means that 70% of the variance in advertising is explained by demand.
FALSE
In trend projection, a negative regression slope is mathematically impossible.
FALSE
Mean squared error and exponential smoothing are two measures of the overall error of a forecasting model.
FALSE
Regression lines graphically depict "cause-and-effect" relationships.
FALSE
The larger the number of periods in the simple moving average forecasting method, the greater the method's responsiveness to changes in demand.
FALSE
The larger the standard error of the estimate, the more accurate the forecasting model.
FALSE
________ forecasts employ one or more mathematical models that rely on historical data and/or associative variables to forecast demand.
Quantitative
What are the differences between quantitative and qualitative forecasting methods?
Quantitative methods use mathematical models to analyze historical data. Qualitative methods incorporate such factors as the decision maker's intuition, emotions, personal experiences, and value systems in determining the forecast.
________ is a forecasting technique based upon salespersons' estimates of expected sales.
Sales force composite
A naïve forecast for September sales of a product would be equal to the sales in August.
TRUE
A time-series model uses a series of past data points to make the forecast.
TRUE
A trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y per period.
TRUE
If a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased.
TRUE
The quarterly "make meeting" of Lexus dealers is an example of a sales force composite forecast.
TRUE
Time-series data may exhibit which of the following behaviors?
They may exhibit all of the above.
________ forecasts use a series of past data points to make a forecast.
Time-series
________ is a time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts.
Trend projection
Identify the four components of a time series. Which one of these is rarely forecast? Why is this so?
Trend, seasonality, cycles, and random variation. Since random variations follow no discernible pattern, they cannot be predicted, and thus are not forecast.
When one constant is used to smooth the forecast average and a second constant is used to smooth the trend, the forecasting method is ________.
exponential smoothing with trend adjustment or trend-adjusted smoothing or second-order smoothing or double smoothing
For a given product demand, the time-series trend equation is 53 - 4x. The negative sign on the slope of the equation:
is an indication that product demand is declining.
A six-month moving average forecast is generally better than a three-month moving average forecast if demand:
is rather stable.
Yamaha manufactures which set of products with complementary demands to address seasonal variations?
jet skis and snowmobiles
The forecasting technique that pools the opinions of a group of experts or managers is known as:
jury of executive opinion.
Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a:
long-range time horizon.
A measure of forecast error that does not depend upon the magnitude of the item being forecast is the ________.
mean absolute percent error (or MAPE)
The primary purpose of the mean absolute deviation (MAD) in forecasting is to:
measure forecast accuracy.
A forecast with a time horizon of about 3 months to 3 years is typically called a:
medium-range forecast.
Simple ________ forecasts only work well if we can assume that market demands will stay fairly steady over time.
moving average
A(n) ________ forecast uses an average of the most recent periods of data to forecast the next period.
moving average (or simple moving average)
Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of:
sensitivity to real changes in the data.
Which of the following is NOT a characteristic of exponential smoothing?
weights each historical value equally
Which of the following uses three types of participants: decision makers, staff personnel, and respondents?
Delphi method
What is a time-series forecasting model?
A time-series forecasting model uses a series of past data points to make a forecast.
What forecasting systems combine the intelligence of multiple supply chain partners?
CPFR
Short-range forecasts tends to ________ longer-range forecasts.
be more accurate than
A fundamental distinction between trend projection and linear regression is that:
in trend projection the independent variable is time; in linear regression the independent variable need not be time, but can be any variable with explanatory power.
One use of short-range forecasts is to determine:
job assignments.
Which of the following is not a type of qualitative forecasting?
moving average
Which time-series model below assumes that demand in the next period will be equal to the most recent period's demand?
naïve approach
Which of the following is not present in a time series?
operational variations
The two general approaches to forecasting are:
qualitative and quantitative.
In time series, which of the following cannot be predicted?
random variations
The tracking signal is the:
ratio of cumulative error / MAD.
If a barbershop operator noted that Tuesday's business was typically twice as heavy as Wednesday's, and that Friday's business was typically the busiest of the week, business at the barbershop is subject to ________.
seasonal variations (or seasonality)
Forecasts are usually classified by time horizon into which three categories?
short-range, medium-range, and long-range
If Brandon Edward were working to develop a forecast using a moving averages approach, but he noticed a detectable trend in the historical data, he should:
use weights to place more emphasis on recent data.
What two numbers are contained in the daily report to the CEO of Walt Disney Parks & Resorts regarding the six Orlando parks?
yesterday's forecasted attendance and yesterday's actual attendance
________ forecasting tries a variety of computer models and selects the best one for a particular application.
Focus
________ expresses the error as a percent of the actual values.
MAPE
Cycles and random variations are both components of time series.
TRUE
Demand for individual products can be driven by product life cycles.
TRUE
Demand forecasts serve as inputs to financial, marketing, and personnel planning.
TRUE
Focus forecasting tries a variety of computer models and selects the best one for a particular application.
TRUE
Forecasts may be influenced by a product's position in its life cycle.
TRUE
In trend projection, the trend component is the slope of the regression equation.
TRUE
Linear-regression analysis is a straight-line mathematical model to describe the functional relationships between independent and dependent variables.
TRUE
Most forecasting techniques assume that there is some underlying stability in the system.
TRUE
One advantage of exponential smoothing is the limited amount of record keeping involved.
TRUE
Seasonal indices adjust raw data for patterns that repeat at regular time intervals.
TRUE
The sales force composite forecasting method relies on salespersons' estimates of expected sales.
TRUE
________ forecasts are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment.
Technological
Which of the following is TRUE regarding the two smoothing constants of the Forecast Including Trend (FIT) model?
Their values are determined independently.
Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic of:
adaptive smoothing.
Many services maintain records of sales noting:
all of the above.
Which of the following values of alpha would cause exponential smoothing to respond the SLOWEST to forecast errors?
0.10
Taco Bell's unique employee scheduling practices are partly the result of using:
A and C are both correct.
Given an actual demand this period of 61, a forecast for this period of 58, and an alpha of 0.3, what would the forecast for the next period be using exponential smoothing?
58.9
A seasonal index for a monthly series is about to be calculated on the basis of three years' accumulation of data. The three previous July values were 110, 150, and 130. The average demand over all months during the three-year time period was 190 . What is the approximate seasonal index for July?
0.684
Identify the seven steps involved in forecasting.
1. Determine the use of the forecast. 2. Select the items to be forecasted. 3. Determine the time horizon of the forecast. 4. Select the forecasting model(s). 5. Gather the data needed to make the forecast. 6. Make the forecast. 7. Validate and implement the results.
Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?
1.0
A forecasting method has produced the following over the past five months. What is the mean absolute deviation?
1.2
Given an actual demand this period of 103, a forecast value for this period of 99, and an alpha of .4, what is the exponential smoothing forecast for next period?
100.6
Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January?
1000 units
John's House of Pancakes uses a weighted moving average method to forecast pancake sales. It assigns a weight of 5 to the previous month's demand, 3 to demand two months ago, and 1 to demand three months ago. If sales amounted to 1000 pancakes in May, 2200 pancakes in June, and 3000 pancakes in July, what should be the forecast for August?
2511
Suppose that the last four months of sales were 8, 10, 15, and 9 units, respectively. Suppose further that the last four forecasts were 5, 6, 11, and 12 units, respectively. What is the Mean Absolute Deviation (MAD) of these forecasts?
3.5
Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?
4
A time-series trend equation is 25.3 + 2.1x. What is your forecast for period 7?
40
What is the forecast for May using a four-month moving average?
44
What is the difference between an associative model and a time-series model?
A time-series model uses only historical values of the quantity of interest to predict future values of that quantity. The associative model, on the other hand, incorporates the variables or factors that might influence the quantity being forecast.
If two variables were perfectly correlated, what would the coefficient of correlation r equal?
B or C
________ forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators.
Economic
Which of the following is NOT a step in the forecasting process?
Eliminate any assumptions.
Which of the following statements about time-series forecasting is true?
It is based on the assumption that the analysis of past demand helps predict future demand.
Identify four quantitative forecasting methods.
The list includes naive, moving averages, exponential smoothing, trend projection, and linear regression.
Gradual upward or downward movement of data over time is called:
a trend.
Linear regression is known as a(n) ________ model because it incorporates variables or factors that might influence the quantity being forecast.
associative forecasting
Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand?
associative models
The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustrate:
bias.
Which of the following most requires long-range forecasting (as opposed to short-range or medium-range forecasting) for its planning purposes?
capital expenditures
The ________ measures the strength of the relationship between two variables.
coefficient of correlation
The degree or strength of a relationship between two variables is shown by the:
coefficient of correlation.
As compared to long-range forecasts, short-range forecasts:
deal with less comprehensive issues supporting management decisions.
The fundamental difference between cycles and seasonality is the:
duration of the repeating patterns.
The three major types of forecasts used by organizations in planning future operations are:
economic, technological, and demand.
The smoothing constant is a weighting factor used in ________.
exponential smoothing
Which time-series model uses BOTH past forecasts and past demand data to generate a new forecast?
exponential smoothing
A forecast based on the previous forecast plus a percentage of the forecast error is a(n):
exponential smoothing forecast.
