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OPRE Chapter 3 part 1 (1-100)

Forecasting techniques generally assume an existing causal system that will continue to exist in the future. True False
Forecasts depend on the rules of the game remaining reasonably constant.
For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques. True False
If growth is strong, alpha should be large so that the model will catch up more quickly.
Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast. True False
Flexibility to accommodate major changes is important to good forecasting.
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. True False
Forecasting for an individual item is more difficult than forecasting for a number of items.
Forecasts help managers plan both the system itself and provide valuable information for using the system. True False
Both planning and use are shaped by forecasts.
Organizations that are capable of responding quickly to changing requirements can use a shorter forecast horizon and therefore benefit from more accurate forecasts. True False
If an organization can react quicker, its forecasts need not be so long term.
When new products or services are introduced, focus forecasting models are an attractive option. True False
Because focus forecasting models depend on historical data, they’re not so attractive for newly introduced products or services.
The purpose of the forecast should be established first so that the level of detail, amount of resources, and accuracy level can be understood. True False
All of these considerations are shaped by what the forecast will be used for.
Forecasts based on time series (historical) data are referred to as associative forecasts. True False
Forecasts based on time series data are referred to as time-series forecasts.
Time series techniques involve identification of explanatory variables that can be used to predict future demand. True False
Associate forecasts involve identifying explanatory variables.
A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys. True False
Most people do not enjoy participating in surveys.
The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast. True False
A consensus among divergent perspectives is developed using questionnaires.
Exponential smoothing adds a percentage (called alpha) of last period’s forecast to estimate next period’s demand. True False
Exponential smoothing adds a percentage to the last period’s forecast error.
The shorter the forecast period, the more accurately the forecasts tend to track what actually happens. True False
Long-term forecasting is much more difficult to do accurately.
Forecasting techniques that are based on time series data assume that future values of the series will duplicate past values. True False
Time-series forecast assume that future patterns in the series will mimic past patterns in the series.
Trend adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period’s actual demand. True False
Trend adjusted smoothing smoothes both random and trend-related variation.
Forecasts based on an average tend to exhibit less variability than the original data. True False
Averaging is a way of smoothing out random variability.
The naive approach to forecasting requires a linear trend line. True False
The naïve approach is useful in a wider variety of settings.
The naive forecast is limited in its application to series that reflect no trend or seasonality. True False
When a trend or seasonality is present, the naïve forecast is more limited in its application.
The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques. True False
Often the naïve forecast performs reasonably well when compared to more complex techniques.
A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average. True False
More data points reduce a moving average forecast’s responsiveness.
In order to update a moving average forecast, the values of each data point in the average must be known. True False
The moving average cannot be updated until the most recent value is known.
Forecasts of future demand are used by operations people to plan capacity. True False
Capacity decisions are made for the future and therefore depend on forecasts.
An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago. True False
Weighted moving averages can be adjusted to make more recent data more important in setting the forecast.
. Exponential smoothing is a form of weighted averaging. True False
The most recent period is given the most weight, but prior periods also factor in
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
Smaller smoothing constants result in less reactive forecast models.
The T in the model TAF = S+T represents the time dimension (which is usually expressed in weeks or months). True False
The T represents the trend dimension.
. Trend adjusted exponential smoothing requires selection of two smoothing constants. True False
One is for the trend and one is for the random error.
An advantage of “trend adjusted exponential smoothing” over the “linear trend equation” is its ability to adjust over time to changes in the trend. True False
One is for the trend and one is for the random error.
A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend. True False
Seasonal relatives are used when the seasonal effect is multiplicative rather than additive.
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 can’t be used. True False
Computing seasonal relatives depends on past data being available.
Removing the seasonal component from a data series (de-seasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative. True False
Deseasonalized data points have been adjusted for seasonal influences.
If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis. True False
Patterns reflect influences such as trends or seasonality that go against regression analysis assumptions.
Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are non-linear or involve more than one predictor variable. True False
Regression analysis can be used in a variety of settings.
The sample standard deviation of forecast error is equal to the square root of MSE. True False
The MSE is equal to the sample variance of the forecast error.
Correlation measures the strength and direction of a relationship between variables. True False
The association between two variations is summarized in the correlation coefficient.
MAD is equal to the square root of MSE which is why we calculate the easier MSE and then calculate the more difficult MAD. True False
MAD is the mean absolute deviation.
In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naïve forecast would yield. True False
With alpha equal to 1 we are using a naïve forecasting method.
A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern. True False
Forecast methods are generally considered to be performing adequately when the errors appear to be randomly distributed.
A control chart involves setting action limits for cumulative forecast error. True False
Control charts set action limits for the tracking signal.
A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD. True False
Large absolute values of the tracking signal suggest a fundamental change in the forecast model’s performance.
The use of a control chart assumes that errors are normally distributed about a mean of zero. True False
Over time, a forecast model’s tracking signal should fluctuate randomly about a mean of zero.
Bias exists when forecasts tend to be greater or less than the actual values of time series. True False
A tendency in one direction is defined as bias.
Bias is measured by the cumulative sum of forecast errors. True False
Bias would result in the cumulative sum of forecast errors being large in absolute value.
Seasonal relatives can be used to de-seasonalize data or incorporate seasonality in a forecast. True False
Seasonal relatives are used to de-seasonalize data to forecast future values of the underlying trend, and they are also used to re-seasonalize de-seasonalized forecasts.
The best forecast is not necessarily the most accurate. True False
More accuracy often comes at too high a cost to be worthwhile.
A proactive approach to forecasting views forecasts as probable descriptions of future demand, and requires action to be taken to meet that demand. True False
Proactive approaches involve taking action to influence demand.
. Simple linear regression applies to linear relationships with no more than three independent variables. True False
Simple linear regression involves only one independent variable.
n important goal of forecasting is to minimize the average forecast error. True False
Regardless of the model chosen, so long as there is no fundamental bias average forecast error will be zero.
Forecasting techniques such as moving averages, exponential smoothing, and the naive approach all represent smoothed (averaged) values of time series data. True False
The naïve approach involves no smoothing.
In exponential smoothing, an alpha of .30 will cause a forecast to react more quickly to a large error than will an alpha of .20. True False
Larger values for alpha result in more responsive models.
Forecasts based on judgment and opinion don’t include A. executive opinion B. salesperson opinion C. second opinions D. customer surveys E. Delphi methods
C. second opinions
Second opinions generally refer to medical diagnoses, not demand forecasting.
In business, forecasts are the basis for: A. capacity planning B. budgeting C. sales planning D. production planning E. all of the above
E. all of the above
A wide variety of areas depend on forecasting.
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.
C. Historical data is available on which to base the forecast
In some forecasting situations historical data are not available.
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
B. eliminate all assumptions
We cannot eliminate all assumptions.
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
D. least squares estimation
Least squares estimations minimizes the sum of squared deviations around the estimated regression function.
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
B. qualitative and quantitative
Forecast approaches are either quantitative or qualitative.
Which of the following is not a type of judgmental forecasting?
A. executive opinions
B. sales force opinions
C. consumer surveys
D. the Delphi method
E. time series analysis
E. time series analysis
Time series analysis is a quantitative approach.
Accuracy in forecasting can be measured by:.
E. A & C
E. A & C
MSE is mean squared error; MAPE is mean absolute percent error.
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.
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.
Which phrase most closely describes the Delphi technique
C. series of questionnaires
The questionnaires are a way of fostering a consensus among divergent perspectives.
The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:
C. the Delphi method
Anonymity is important in Delphi efforts.
. One reason for using the Delphi method in forecasting is to:
A. avoid premature consensus (bandwagon effect)
A bandwagon can lead to popular but potentially inaccurate viewpoints to drown up other important considerations.
. Detecting non-randomness in errors can be done using:
C. Control Cha
Control charts graphically depict the statistical behavior of forecast errors.
. Gradual, long-term movement in time series data is called
D. trend
Trends move the time series in a long-term direction
The primary difference between seasonality and cycles is:
A. the duration of the repeating patterns
Seasons happen within time periods; cycles happen across multiple time periods.
Averaging techniques are useful for:
B. smoothing out fluctuations in time series
Smoothing helps forecasters see past random error.
. Putting forecast errors into perspective is best done using
MAPE depicts the forecast error relative to what was being forecast.
Using the latest observation in a sequence of data to forecast the next period is
B. a naive forecast
Only one piece of information is needed for a naïve forecast.
. For the data given below, what would the naive forecast be for the next period
Period 5’s forecast would be period 4’s demand.
Moving average forecasting techniques do the following:
C. smooth variations in the data
Variation is smoothed out in moving average forecasts.
. Which is not a characteristic of simple moving averages applied to time series data?
D. requires only last period’s forecast and actual data
Simple moving averages can require several periods of data.
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. decrease
Fewer data points result in more responsive moving averages.
. A forecast based on the previous forecast plus a percentage of the forecast error is
D. an exponentially
Exponential smoothing uses the previous forecast error to shape the next forecast.
Which is not a characteristic of exponential smoothing
B. weights each historical value equally
The most recent period of demand is given the most weight in exponential smoothing.
Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?
An alpha of 1.0 leads to a naïve forecast.
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 previous period’s forecast error of 4 units would lead to a change in the forecast of 0.6 if alpha equals 0.15.
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?
D. 62
Multiply the previous period’s forecast error (-5) by alpha and then add to the previous period’s forecast
. 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
C. 100
Multiply the previous period’s forecast error (8) by alpha and then add to the previous period’s forecast.
Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?
E. .15
Larger values for alpha correspond with greater responsiveness.
. 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
July would be period 3, so the forecast would be 40,000 + 150(3).
In trend-adjusted exponential smoothing, the trend adjusted forecast (TAF) consists of
A. an exponentially smoothed forecast and a smoothed trend factor
Both random variation and the trend are smoothed in TAF models.
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
The additive model simply adds a seasonal adjustment to the de-seasonalized forecast. The multiplicative model adjusts the de-seasonalized forecast by multiplying it by a season relative or index.
Which technique is used in computing seasonal relatives?
D. centered moving average
The centered moving average serves as the basis point for computing seasonal relatives.
A persistent tendency for forecasts to be greater than or less than the actual values is called:
A. bias
Bias is a tendency for a forecast to be above (or below) the actual value.
Which of the following might be used to indicate the cyclical component of a forecast?
A. leading variable
Leading variables, such as births in a given year, can correlate strongly with long-term phenomena such as cycles.
The primary method for associative forecasting is:
B. regression analysis
Regression analysis is an associative forecasting technique
Which term most closely relates to associative forecasting techniques?
E. predictor variables
Associate techniques use predictor variables.
Which of the following corresponds to the predictor variable in simple linear regression?
C. independent variable
Demand is the typical dependent variable when forecasting with simple linear regression.
The mean absolute deviation (MAD) is used to:
C. measure forecast accuracy
MAD is one way of evaluating forecast performance.
Given forecast errors of 4, 8, and – 3, what is the mean absolute deviation
C. 5
Convert each error into an absolute value and then average.
. Given forecast errors of 5, 0, – 4, and 3, what is the mean absolute deviation?
B. 3
Convert each error into an absolute value and then average.
Given forecast errors of 5, 0, – 4, and 3, what is the bias?
B. 4
Sum the forecast errors.
. Which of the following is used for constructing a control chart?
B. mean squared error (MSE
The mean squared error leads to an estimate for the sample forecast standard deviation.
The two most important factors in choosing a forecasting technique are:
C. cost and accurac
More accurate forecasts cost more but may not be worth the additional cost.
The degree of management involvement in short range forecasts is
B. low
Short range forecasting tends to be fairly routine.
. Which of the following is not necessarily an element of a good forecast?
D. low cost
A good forecast can be quite costly if necessary.
Current information on _________ can have a significant impact on forecast accuracy:
E. all of the above
Demand in the future could be subject to decision-making prompted by prices, promotions, inventory or competition. Accuracy will be affected if these are (or are not) taken into consideration.
. A managerial approach toward forecasting which seeks to actively influence demand is:
B. proactive
imply responding to demand is a reactive approach.
Customer service levels can be improved by better:
C. short term forecast accuracy
More accurate short-term forecasts enable organization’s to better accommodate customer requests.
. Given the following historical data, what is the simple three-period moving average forecast for period 6?
D. 68
Average demand from periods 3 through 5.
Given the following historical data and weights of .5, .3, and .2, what is the three-period moving average forecast for period 5?
B. 144
Multiply period 4 (144) by .5, period 3 (148) by .3 and period 2 (142) by .2, then sum these products.

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