question

Forecasting techniques generally assume an existing causal system that will continue to exist in the future.

answer

TRUE
Forecasts depend on the rules of the game remaining reasonably constant.

question

For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques.

answer

FALSE
If growth is strong, alpha should be large so that the model will catch up more quickly.

question

Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast.

answer

FALSE
Flexibility to accommodate major changes is important to good forecasting.

question

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.

answer

FALSE
Forecasting for an individual item is more difficult than forecasting for a number of items.

question

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

answer

TRUE
Both planning and use are shaped by forecasts.

question

Organizations that are capable of responding quickly to changing requirements can use a shorter forecast horizon and therefore benefit from more accurate forecasts.

answer

TRUE
If an organization can react more quickly, its forecasts need not be so long term.

question

When new products or services are introduced, focus forecasting models are an attractive option.

answer

FALSE
Because focus forecasting models depend on historical data, they're not so attractive for newly introduced products or services.

question

The purpose of the forecast should be established first so that the level of detail, amount of resources, and accuracy level can be understood.

answer

TRUE
All of these considerations are shaped by what the forecast will be used for.

question

Forecasts based on time-series (historical) data are referred to as associative forecasts.

answer

FALSE
Forecasts based on time-series data are referred to as time-series forecasts.

question

Time-series techniques involve the identification of explanatory variables that can be used to predict future demand.

answer

FALSE
Associative forecasts involve identifying explanatory variables.

question

A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys.

answer

FALSE
Most people do not enjoy participating in surveys.

question

The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast.

answer

TRUE
A consensus among divergent perspectives is developed using questionnaires.

question

Exponential smoothing adds a percentage (called alpha) of the last period's forecast to estimate the next period's demand.

answer

FALSE
Exponential smoothing adds a percentage to the last period's forecast error.

question

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

answer

TRUE
Long-term forecasting is much more difficult to do accurately.

question

Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values.

answer

FALSE
Time-series forecasts assume that future patterns in the series will mimic past patterns in the series.

question

Trend-adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period's actual demand.

answer

FALSE
Trend-adjusted smoothing smoothes both random and trend-related variation.

question

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

answer

TRUE
Averaging is a way of smoothing out random variability.

question

The naive approach to forecasting requires a linear trend line.

answer

FALSE
The naive approach is useful in a wider variety of settings.

question

The naive forecast is limited in its application to series that reflect no trend or seasonality.

answer

FALSE
When a trend or seasonality is present, the naive forecast is more limited in its application.

question

The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques.

answer

TRUE
Often the naive forecast performs reasonably well when compared to more complex techniques.

question

A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average.

answer

FALSE
More data points reduce a moving average forecast's responsiveness.

question

In order to update a moving average forecast, the values of each data point in the average must be known.

answer

TRUE
The moving average cannot be updated until the most recent value is known.

question

Forecasts of future demand are used by operations people to plan capacity.

answer

TRUE
Capacity decisions are made for the future and therefore depend on forecasts.

question

An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago.

answer

TRUE
Weighted moving averages can be adjusted to make more recent data more important in setting the forecast.

question

Exponential smoothing is a form of weighted averaging.

answer

TRUE
The most recent period is given the most weight, but prior periods also factor in.

question

A smoothing constant of .1 will cause an exponential smoothing forecast to react more quickly to a sudden change than a smoothing constant value of .3.

answer

FALSE
Smaller smoothing constants result in less reactive forecast models.

question

The T in the model TAF = S + T represents the time dimension (which is usually expressed in weeks or months).

answer

FALSE
The T represents the trend dimension.

question

Trend-adjusted exponential smoothing requires selection of two smoothing constants.

answer

TRUE
One is for the trend and one is for the random error.

question

An advantage of trend-adjusted exponential smoothing over the linear trend equation is its ability to adjust over time to changes in the trend.

answer

TRUE
A linear trend equation assumes a constant trend; trend-adjusted smoothing allows for changes in the underlying trend.

question

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

answer

TRUE
Seasonal relatives are used when the seasonal effect is multiplicative rather than additive.

question

In order to compute seasonal relatives, the trend of past data must be computed or known, which means that for brand-new products this approach cannot be used.

answer

TRUE
Computing seasonal relatives depends on past data being available.

question

Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative.

answer

TRUE
Deseasonalized data points have been adjusted for seasonal influences.

question

If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis.

answer

TRUE
Patterns reflect influences such as trends or seasonality that go against regression analysis assumptions.

question

Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are nonlinear or involve more than one predictor variable.

answer

TRUE
Regression analysis can be used in a variety of settings.

question

The sample standard deviation of forecast error is equal to the square root of MSE.

answer

TRUE
The MSE is equal to the sample variance of the forecast error.

question

Correlation measures the strength and direction of a relationship between variables.

answer

TRUE
The association between two variations is summarized in the correlation coefficient.

question

MAD is equal to the square root of MSE, which is why we calculate the easier MSE and then calculate the more difficult MAD.

answer

FALSE
MAD is the mean absolute deviation.

question

In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naive forecast would yield.

answer

TRUE
With alpha equal to 1 we are using a naive forecasting method.

question

A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern.

answer

FALSE
Forecast methods are generally considered to be performing adequately when the errors appear to be randomly distributed.

question

A control chart involves setting action limits for cumulative forecast error.

answer

FALSE
Control charts set action limits for the tracking signal.

question

A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD.

answer

TRUE
Large absolute values of the tracking signal suggest a fundamental change in the forecast model's performance.

question

The use of a control chart assumes that errors are normally distributed about a mean of zero.

answer

TRUE
Over time, a forecast model's tracking signal should fluctuate randomly about a mean of zero.

question

Bias exists when forecasts tend to be greater or less than the actual values of time series.

answer

TRUE
A tendency in one direction is defined as bias.

question

Bias is measured by the cumulative sum of forecast errors.

answer

TRUE
Bias would result in the cumulative sum of forecast errors being large in absolute value.

question

Seasonal relatives can be used to deseasonalize data or incorporate seasonality in a forecast.

answer

TRUE
Seasonal relatives are used to deseasonalize data to forecast future values of the underlying trend, and they are also used to reseasonalize deseasonalized forecasts.

question

The best forecast is not necessarily the most accurate.

answer

TRUE
More accuracy often comes at too high a cost to be worthwhile.

question

Which of the following is a potential shortcoming of using sales force opinions in demand forecasting?
A. Members of the sales force often have substantial histories of working with and understanding their customers.
B. Members of the sales force often are well aware of customers' future plans.
C. Members of the sales force have direct contact with consumers.
D. Members of the sales force can have difficulty distinguishing between what customers would like to do and what they actually will do.
E. Customers often are quite open with members of the sales force with regard to future plans.

answer

D. Members of the sales force can have difficulty distinguishing between what customers would like to do and what they actually will do.
Customers themselves may be unclear regarding what they'd like to do versus what they'll actually do.

question

Suppose a four-period weighted average is being used to forecast demand. Weights for the periods are as follows: wt-4 = 0.1, wt-3 = 0.2, wt-2 = 0.3 and wt-1 = 0.4. Demand observed in the previous four periods was as follows: At-4 = 380, At-3 = 410, At-2 = 390, At-1 = 400. What will be the demand forecast for period t?
A. 402
B. 397
C. 399
D. 393
E. 403

answer

B. 397
The forecast will be (.1 * 380) + (.2 * 410) + (.3 * 390) + (.4 * 400) = 397.

question

Suppose a three-period weighted average is being used to forecast demand. Weights for the periods are as follows: wt-3 = 0.2, wt-2 = 0.3 and wt-1 = 0.5. Demand observed in the previous three periods was as follows: At-3 = 2,200, At-2 = 1,950, At-1 = 2,050. What will be the demand forecast for period t?
A. 2,000
B. 2,095
C. 1,980
D. 2,050
E. 1,875
The forecast for will be (.2 * 2,200) + (.3 * 1,950) + (.5 * 2,050) = 2,050.

answer

D. 2050
The forecast for will be (.2 * 2,200) + (.3 * 1,950) + (.5 * 2,050) = 2,050.

question

When choosing a forecasting technique, a critical trade-off that must be considered is that between:
A. time series and associative.
B. seasonality and cyclicality.
C. length and duration.
D. simplicity and complexity.
E. cost and accuracy.

answer

E. cost and accuracy.
The trade-off between cost and accuracy is the critical consideration when choosing a forecasting technique.

question

The more novel a new product or service design is, the more forecasters have to rely on:
A. subjective estimates.
B. seasonality.
C. cyclicality.
D. historical data.
E. smoothed variation.

answer

A. subjective estimates.
New products and services lack historical data, so forecasts for them must be based on subjective estimates.

question

Forecasts based on judgment and opinion do not include:
A. executive opinion.
B. salesperson opinion.
C. second opinions.
D. customer surveys.
E. Delphi methods.

answer

C. second opinions.
Second opinions generally refer to medical diagnoses, not demand forecasting.

question

Which of the following is/are a primary input into capacity, sales, and production planning?
A. product design
B. market share
C. ethics
D. globalization
E. demand forecasts

answer

E. demand forecasts
Demand forecasts are direct inputs into capacity, sales, and production plans.

question

Which of the following features would not generally be considered common to all forecasts?
A. Assumption of a stable underlying causal system.
B. Actual results will differ somewhat from predicted values.
C. Historical data is available on which to base the forecast.
D. Forecasts for groups of items tend to be more accurate than forecasts for individual items.
E. Accuracy decreases as the time horizon increases.

answer

C. Historical data is available on which to base the forecast.
In some forecasting situations historical data are not available.

question

Which of the following is not a step in the forecasting process?
A. Determine the purpose and level of detail required.
B. Eliminate all assumptions.
C. Establish a time horizon.
D. Select a forecasting model.
E. Monitor the forecast.

answer

B. Eliminate all assumptions.
We cannot eliminate all assumptions.

question

Minimizing the sum of the squared deviations around the line is called:
A. mean squared error technique.
B. mean absolute deviation.
C. double smoothing.
D. least squares estimation.
E. predictor regression.

answer

D. least squares estimation.
Least squares estimations minimize the sum of squared deviations around the estimated regression function.

question

The two general approaches to forecasting are:
A. mathematical and statistical.
B. qualitative and quantitative.
C. judgmental and qualitative.
D. historical and associative.
E. precise and approximation.

answer

B. qualitative and quantitative.
Forecast approaches are either quantitative or qualitative.

question

Which of the following is not a type of judgmental forecasting?
A. executive opinion.
B. sales force opinions
C. consumer surveys
D. the Delphi method
E. time series analysis

answer

E. time series analysis
Time series analysis is a quantitative approach.

question

Accuracy in forecasting can be measured by:
A. MSE.
B. MRP.
C. MPS.
D. MTM.
E. MTE.

answer

A. MSE
MSE is mean squared error.

question

Which of the following would be an advantage of using a sales force composite to develop a demand forecast?
A. The sales staff is least affected by changing customer needs.
B. The sales force can easily distinguish between customer desires and probable actions.
C. The sales staff is often aware of customers' future plans.
D. Salespeople are least likely to be influenced by recent events.
E. Salespeople are least likely to be biased by sales quotas.

answer

C. The sales staff is often aware of customers' future plans.
Members of the sales force should be the organization's tightest link with its customers.

question

Which phrase most closely describes the Delphi technique?
A. associative forecast
B. consumer survey
C. series of questionnaires
D. developed in India
E. historical data

answer

C. series of questionnaires
The questionnaires are a way of fostering a consensus among divergent perspectives.

question

The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:
A. sales force opinions.
B. consumer surveys.
C. the Delphi method.
D. time series analysis.
E. executive opinions.

answer

C. the Delphi method.
Anonymity is important in Delphi efforts.

question

One reason for using the Delphi method in forecasting is to:
A. avoid premature consensus (bandwagon effect).
B. achieve a high degree of accuracy.
C. maintain accountability and responsibility.
D. be able to replicate results.
E. prevent hurt feelings.

answer

A. avoid premature consensus (bandwagon effect).
A bandwagon can lead to popular but potentially inaccurate viewpoints to drown out other important considerations.

question

Detecting nonrandomness in errors can be done using:
A. MSEs.
B. MAPs.
C. control charts.
D. correlation coefficients.
E. strategies.

answer

C. control charts.
Control charts graphically depict the statistical behavior of forecast errors.

question

Gradual, long-term movement in time series data is called:
A. seasonal variation.
B. cycles.
C. irregular variation.
D. trend.
E. random variation.

answer

D. trend
Trends move the time series in a long-term direction.

question

The primary difference between seasonality and cycles is:
A. the duration of the repeating patterns.
B. the magnitude of the variation.
C. the ability to attribute the pattern to a cause.
D. the direction of the movement.
E. there are only four seasons but 30 cycles.

answer

A. the duration of the repeating patterns.
Seasons happen within time periods; cycles happen across multiple time periods.

question

Averaging techniques are useful for:
A. distinguishing between random and nonrandom variations.
B. smoothing out fluctuations in time series.
C. eliminating historical data.
D. providing accuracy in forecasts.
E. average people.

answer

B. smoothing out fluctuations in time series.
Smoothing helps forecasters see past random error.

question

Putting forecast errors into perspective is best done using
A. exponential smoothing.
B. MAPE.
C. linear decision rules.
D. MAD.
E. hindsight.

answer

B. MAPE.
MAPE depicts the forecast error relative to what was being forecast.

question

Using the latest observation in a sequence of data to forecast the next period is:
A. a moving average forecast.
B. a naive forecast.
C. an exponentially smoothed forecast.
D. an associative forecast.
E. regression analysis.

answer

B. a naive forecast.
Only one piece of information is needed for a naive forecast.

question

For the data given below, what would the naive forecast be for period 5?
1 - 58
2- 59
3 - 60
4 - 61

answer

D. 61
Period 5's forecast would be period 4's demand.

question

Moving average forecasting techniques do the following:
A. Immediately reflect changing patterns in the data.
B. Lead changes in the data.
C. Smooth variations in the data.
D. Operate independently of recent data.
E. Assist when organizations are relocating.
Variation is smoothed out in moving average forecasts.

answer

C. Smooth variations in the data.
Variation is smooth out in moving average forecasts.

question

Which is not a characteristic of simple moving averages applied to time series data?
A. smoothes random variations in the data
B. weights each historical value equally
C. lags changes in the data
D. requires only last period's forecast and actual data
E. smoothes real variations in the data

answer

D. requires only last period's forecast and actual data
Simple moving averages can require several periods of data.

question

In order to increase the responsiveness of a forecast made using the moving average technique, the number of data points in the average should be:
A. decreased.
B. increased.
C. multiplied by a larger alpha.
D. multiplied by a smaller alpha.
E. eliminated if the MAD is greater than the MSE.

answer

A. decreased
Fewer data points result in more responsive moving averages.

question

A forecast based on the previous forecast plus a percentage of the forecast error is:
A. a naive forecast.
B. a simple moving average forecast.
C. a centered moving average forecast.
D. an exponentially smoothed forecast.
E. an associative forecast.

answer

D. an exponentially smoothed forecast.
Exponential smoothing uses the previous forecast error to shape the next forecast.

question

Which is not a characteristic of exponential smoothing?
A. smoothes random variations in the data
B. weights each historical value equally
C. has an easily altered weighting scheme
D. has minimal data storage requirements
E. smoothes real variations in the data

answer

B. weights each historical value equally
The most recent period of demand is given the most weight in exponential smoothing.

question

Simple exponential smoothing is being used to forecast demand. The previous forecast of 66 turned out to be four units less than actual demand. The next forecast is 66.6, implying a smoothing constant, alpha, equal to:
A. .01.
B. .10.
C. .15.
D. .20.
E. .60.

answer

C .15
A previous period's forecast error of 4 units would lead to a change in the forecast of 0.6 if alpha equals 0.15.

question

Given an actual demand of 59, a previous forecast of 64, and an alpha of .3, what would the forecast for the next period be using simple exponential smoothing?
A. 36.9
B. 57.5
C. 60.5
D. 62.5
E. 65.5

answer

D. 62.5
Multiply the previous period's forecast error (-5) by alpha and then add to the previous period's forecast.

question

Given an actual demand of 105, a forecasted value of 97, and an alpha of .4, the simple exponential smoothing forecast for the next period would be:
A. 80.8.
B. 93.8.
C. 100.2.
D. 101.8.
E. 108.2.

answer

C. 100.2.
Multiply the previous period's forecast error (8) by alpha and then add to the previous period's forecast.

question

Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?
A. 0
B. .01
C. .05
D. .10
E. .15

answer

E. .15
Larger values for alpha correspond with greater responsiveness.

question

A manager uses the following equation to predict monthly receipts: Yt = 40,000 + 150t. What is the forecast for July if t = 0 in April of this year?
A. 40,450
B. 40,600
C. 42,100
D. 42,250
E. 42,400

answer

A. 40,450
July would be period 3, so the forecast would be 40,000 + 150(3).

question

In trend-adjusted exponential smoothing, the trend-adjusted forecast consists of:
A. an exponentially smoothed forecast and a smoothed trend factor.
B. an exponentially smoothed forecast and an estimated trend value.
C. the old forecast adjusted by a trend factor.
D. the old forecast and a smoothed trend factor.
E. a moving average and a trend factor.

answer

A. an exponentially smoothed forecast and a smoothed trend factor.
Both random variation and the trend are smoothed in TAF models.

question

In the additive model for seasonality, seasonality is expressed as a ______________ adjustment to the average; in the multiplicative model, seasonality is expressed as a __________ adjustment to the average.
A. quantity; percentage
B. percentage; quantity
C. quantity; quantity
D. percentage; percentage
E. qualitative; quantitative

answer

A. quantity; percentage
The additive model simply adds a seasonal adjustment to the deseasonalized forecast. The multiplicative model adjusts the deseasonalized forecast by multiplying it by a season relative or index.

question

Which technique is used in computing seasonal relatives?
A. double smoothing
B. Delphi
C. mean squared error
D. centered moving average
E. exponential smoothing

answer

D. centered moving average
The centered moving average serves as the basis point for computing seasonal relatives.

question

A persistent tendency for forecasts to be greater than or less than the actual values is called:
A. bias.
B. tracking.
C. control charting.
D. positive correlation.
E. linear regression.

answer

A. bias.
Bias is a tendency for a forecast to be above (or below) the actual value.

question

The primary method for associative forecasting is:
A. sensitivity analysis.
B. regression analysis.
C. simple moving averages.
D. centered moving averages.
E. exponential smoothing.

answer

B. regression analysis.
Regression analysis is an associative forecasting technique.

question

Which term most closely relates to associative forecasting techniques?
A. time series data
B. expert opinions
C. Delphi technique
D. consumer survey
E. predictor variables

answer

E. predictor variables
Associative techniques use predictor variables.

question

Which of the following corresponds to the predictor variable in simple linear regression?
A. regression coefficient
B. dependent variable
C. independent variable
D. predicted variable
E. demand coefficient

answer

C. independent variable
Demand is the typical dependent variable when forecasting with simple linear regression.

question

The mean absolute deviation is used to:
A. estimate the trend line.
B. eliminate forecast errors.
C. measure forecast accuracy.
D. seasonally adjust the forecast.
E. compute periodic forecast errors.

answer

C. measure forecast accuracy.
MAD is one way of evaluating forecast performance.

question

Given forecast errors of 4, 8, and -3, what is the mean absolute deviation?
A. 4
B. 3
C. 5
D. 6
E. 12

answer

C. 5
Convert each error into an absolute value and then average.

question

Given forecast errors of 5, 0, -4, and 3, what is the mean absolute deviation?
A. 4
B. 3
C. 2.5
D. 2
E. 1

answer

B. 3
Convert each error into an absolute value and then average.

question

Given forecast errors of 5, 0, -4, and 3, what is the bias?
A. -4
B. 4
C. 5
D. 12
E. 6

answer

B. 4
Sum the forecast errors.

question

Which of the following is used for constructing a control chart?
A. mean absolute deviation
B. mean squared error
C. tracking signal
D. bias

answer

B. mean squared error
The mean squared error leads to an estimate for the sample forecast standard deviation.

question

The two most important factors in choosing a forecasting technique are:
A. cost and time horizon.
B. accuracy and time horizon.
C. cost and accuracy.
D. quantity and quality.
E. objective and subjective components.

answer

C. cost and accuracy.
More accurate forecasts cost more but may not be worth the additional cost.

question

The degree of management involvement in short-range forecasts is:
A. none.
B. low.
C. moderate.
D. high.
E. total.

answer

B. low.
Short-range forecasting tends to be fairly routine.

question

Which of the following is not necessarily an element of a good forecast?
A. estimate of accuracy
B. timeliness
C. meaningful units
D. low cost
E. written
A good forecast can be quite costly if necessary.

answer

D. low cost
A good forecast can be quite costly if necessary.

question

Forecasting techniques generally assume:
A. the absence of randomness.
B. continuity of some underlying causal system.
C. a linear relationship between time and demand.
D. accuracy that increases the farther out in time the forecast projects.
E. accuracy that is better when individual items, rather than groups of items, are being considered.

answer

B. continuity of some underlying causal system.
Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future.

question

A managerial approach toward forecasting which seeks to actively influence demand is:
A. reactive.
B. proactive.
C. influential.
D. protracted.
E. retroactive.

answer

B. proactive.
Simply responding to demand is a reactive approach.

question

Customer service levels can be improved by better:
A. mission statements.
B. control charting.
C. short-term forecast accuracy.
D. exponential smoothing.
E. customer selection.

answer

C. short-term forecast accuracy.
More accurate short-term forecasts enable organizations to better accommodate customer requests.

question

Given the following historical data, what is the simple three-period moving average forecast for period 6?
A. 67
B. 115
C. 69
D. 68
E. 68.67
Average demand from periods 3 through 5.

answer

D. 68
Average demand from periods 3 through 5.

question

The president of State University wants to forecast student enrollments for this academic year based on the following historical data
What is the forecast for this year using the least squares trend line for these data?
A. 18,750
B. 19,500
C. 21,000
D. 22,650
E. 22,800

answer

E. 22,800
Treat 5 years ago as period 0.