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Operations Management Forecasting techniques

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

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

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

FALSEIf 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

FALSEFlexibility 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

FALSEForecasting 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

TRUEBoth 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

TRUEIf 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

FALSEBecause 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

TRUEAll 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

FALSEForecasts 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

FALSEAssociate 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

FALSEMost 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

TRUEA 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

FALSEExponential 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

TRUELong-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

FALSETime-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

FALSETrend 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

TRUEAveraging is a way of smoothing out random variability.

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

FALSEThe 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

FALSEWhen 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

TRUEOften 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

FALSEMore 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

TRUEThe 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

TRUECapacity 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

TRUEWeighted 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

TRUEThe 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

FALSESmaller 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

FALSEThe T represents the trend dimension.

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

TRUEOne 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

TRUEOne 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

TRUESeasonal 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

TRUEComputing 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

TRUEDeseasonalized 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

TRUEPatterns 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

TRUERegression 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

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

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

TRUEThe 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

FALSEMAD 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

TRUEWith 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

FALSEForecast 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

FALSEControl 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

TRUELarge 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

TRUEOver 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

TRUEA tendency in one direction is defined as bias.

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

TRUEBias 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

TRUESeasonal 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

TRUEMore 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

FALSEProactive approaches involve taking action to influence demand.

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

FALSESimple linear regression involves only one independent variable.

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

FALSERegardless 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

FALSEThe 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

TRUELarger values for alpha result in more responsive models.

Forecasts based on judgment and opinion don’t includeA. executive opinionB. salesperson opinionC. second opinionsD. customer surveysE. Delphi methods

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

In business, forecasts are the basis for:A. capacity planningB. budgetingC. sales planningD. production planningE. all of the above

E. all of the aboveA 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 forecastIn 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 requiredB. eliminate all assumptionsC. establish a time horizonD. select a forecasting modelE. monitor the forecast

B. eliminate all assumptionsWe cannot eliminate all assumptions.

Minimizing the sum of the squared deviations around the line is called:A. mean squared error techniqueB. mean absolute deviationC. double smoothingD. least squares estimationE. predictor regression

D. least squares estimationLeast squares estimations minimizes the sum of squared deviations around the estimated regression function.

The two general approaches to forecasting are:A. mathematical and statisticalB. qualitative and quantitativeC. judgmental and qualitativeD. historical and associativeE. precise and approximation

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

Which of the following is not a type of judgmental forecasting?A. executive opinionsB. sales force opinionsC. consumer surveysD. the Delphi methodE. time series analysis

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

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.

The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:A. sales force opinionsB. consumer surveysC. the Delphi methodD. time series analysisE. executive opinions


. One reason for using the Delphi method in forecasting is toA. avoid premature consensus (bandwagon effect)B. achieve a high degree of accuracyC. maintain accountability and responsibilityD. be able to replicate resultsE. prevent hurt feelings


Detecting non-randomness in errors can be done using:

Control charts

Gradual, long-term movement in time series data is called

(Trends move the time series in a long-term direction)

The primary difference between seasonality and cycles isA. the duration of the repeating patternsB. the magnitude of the variationC. the ability to attribute the pattern to a causeD. the direction of the movementE. there are only 4 seasons but 30 cycles


Averaging techniques are useful for:A. distinguishing between random and non-random variationsB. smoothing out fluctuations in time seriesC. eliminating historical dataD. providing accuracy in forecastsE. average people


Using the latest observation in a sequence of data to forecast the next period is

a naive forecast

Moving average forecasting techniques do the following:A. immediately reflect changing patterns in the dataB. lead changes in the dataC. smooth variations in the dataD. operate independently of recent dataE. assist when organizations are relocating


Which is not a characteristic of simple moving averages applied to time series data?A. smoothes random variations in the dataB. weights each historical value equallyC. lags changes in the dataD. requires only last period’s forecast and actual dataE. smoothes real variations in the 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. decreasedB. increasedC. multiplied by a larger alphaD. multiplied by a smaller alphaE. eliminated if the MAD is greater than the MSE

(Fewer data points result in more responsive moving averages.)

A forecast based on the previous forecast plus a percentage of the forecast error is

Exponential smoothing

The most recent period of demand is given the most weight in exponential smoothing. True False


Which alpha smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?

An alpha of 1.0 leads to a naïve forecast.

In trend-adjusted exponential smoothing, the trend adjusted forecast (TAF) consists of

A. an exponentially smoothed forecast and a smoothed trend factorBoth 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.

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?a. double smoothingB. DelphiC. Mean Squared Error (MSE)D. centered moving averageE. exponential smoothing


A persistent tendency for forecasts to be greater than or less than the actual values is called:A. biasB. trackingC. control chartingD. positive correlationE. linear regression


The primary method for associative forecasting is:

regression analysis
(Regression analysis is an associative forecasting technique)

Which term most closely relates to associative forecasting techniques?
A. time series dataB. expert opinionsC. Delphi techniqueD. consumer surveyE. predictor variables

(Associate techniques use predictor variables)

Which of the following corresponds to the predictor variable in simple linear regression?A. regression coefficientB. dependent variableC. independent variableD. predicted variableE. demand coefficient

(Demand is the typical dependent variable when forecasting with simple linear regression)

The mean absolute deviation (MAD) is used to:

measure forecast accuracy

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:A. cost and time horizonB. accuracy and time horizonC. cost and accuracyD. quantity and qualityE. objective and subjective components

(More accurate forecasts cost more but may not be worth the additional cost)

Customer service levels can be improved by better:A. mission statementsB. control chartingC. short term forecast accuracyD. exponential smoothingE. customer selection


Which of the following forecasting techniques generates trend forecasts?a.Delphi methodb.Weighted moving averagec.Moving averagesd.Single exponential smoothinge. None of the above


Select the statement about moving averages and exponential smoothing that is not true.a.Both tend to lag changes in a time series.b.Both smooth data.c.Both involve fairly simple calculationsd.Both can be used obtain seasonal index numbers.e.both are easy to interpret


If a particular season of the year shows greater than average sales, the seasonal relative for that season is greater than 1.00. True False


Forecast accuracy decreases as the time horizon increases. True False


A double exponentially smoothed model is more responsive to trend in the data series than a single smoothed model. True False


The tracking signal is supposed to run between the control limits. True False


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