# 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

TRUE

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

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

FALSE

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

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

FALSE

Flexibility to accommodate major changes is important to good forecasting.

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

FALSE

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

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

TRUE

Both planning and use are shaped by forecasts.

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

TRUE

If an organization can react quicker, its forecasts need not be so long term.

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

FALSE

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

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

TRUE

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

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

FALSE

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

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

FALSE

Associate forecasts involve identifying explanatory variables.

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

FALSE

Most people do not enjoy participating in surveys.

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

TRUE

A consensus among divergent perspectives is developed using questionnaires.

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

FALSE

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

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

TRUE

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

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

FALSE

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

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

FALSE

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

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

TRUE

Averaging is a way of smoothing out random variability.

Averaging is a way of smoothing out random variability.

The naive approach to forecasting requires a linear trend line. True False

FALSE

The naïve approach is useful in a wider variety of settings.

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

FALSE

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

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

TRUE

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

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

FALSE

More data points reduce a moving average forecast’s responsiveness.

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

TRUE

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

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

TRUE

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

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

TRUE

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

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

TRUE

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

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

FALSE

Smaller smoothing constants result in less reactive forecast models.

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

FALSE

The T represents the trend dimension.

The T represents the trend dimension.

. Trend adjusted exponential smoothing requires selection of two smoothing constants. True False

TRUE

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

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

TRUE

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

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

TRUE

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

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

TRUE

Computing seasonal relatives depends on past data being available.

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

TRUE

Deseasonalized data points have been adjusted for seasonal influences.

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

TRUE

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

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

TRUE

Regression analysis can be used in a variety of settings.

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

TRUE

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

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

TRUE

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

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

FALSE

MAD is the mean absolute deviation.

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

TRUE

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

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

FALSE

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

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

FALSE

Control charts set action limits for the tracking signal.

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

TRUE

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

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

TRUE

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

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

TRUE

A tendency in one direction is defined as bias.

A tendency in one direction is defined as bias.

Bias is measured by the cumulative sum of forecast errors. True False

TRUE

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

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

TRUE

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.

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

TRUE

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

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

FALSE

Proactive approaches involve taking action to influence demand.

Proactive approaches involve taking action to influence demand.

. Simple linear regression applies to linear relationships with no more than three independent variables. True False

FALSE

Simple linear regression involves only one independent variable.

Simple linear regression involves only one independent variable.

n important goal of forecasting is to minimize the average forecast error. True False

FALSE

Regardless of the model chosen, so long as there is no fundamental bias average forecast error will be zero.

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

FALSE

The naïve approach involves no smoothing.

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

TRUE

Larger values for alpha result in more responsive models.

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.

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.

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.

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.

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

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.

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

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.

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

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.

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

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.

Time series analysis is a quantitative approach.

Accuracy in forecasting can be measured by:.

A. MSE

B. MRP

C. MAPE

D. MTM

E. A & C

A. MSE

B. MRP

C. MAPE

D. MTM

E. A & C

E. A & C

MSE is mean squared error; MAPE is mean absolute percent error.

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.

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.

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 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.

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.

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.

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

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.

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.

Smoothing helps forecasters see past random error.

. Putting forecast errors into perspective is best done using

B. MAPE

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

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.

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.

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.

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.

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.

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.

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

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.

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.

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).

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.

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.

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.

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.

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.

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

Regression analysis is an associative forecasting technique

Which term most closely relates to associative forecasting techniques?

E. predictor variables

Associate techniques use 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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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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