Q&A Hero

Q: Which of the following might be used to indicate the cyclical component of a forecast? A. leading variable B. mean squared error C. Delphi technique D. exponential smoothing E. mean absolute deviation

Q: 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.

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

Q: 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

Q: 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.

Q: 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

Q: 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.

Q: 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

Q: 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.

Q: Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast? A. 0 B. .01 C. .1 D. .5 E. 1.0

Q: 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

Q: 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.

Q: 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.

Q: 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

Q: 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.

Q: For the data given below, what would the naive forecast be for period 5? A. 58 B. 62 C. 59.5 D. 61 E. cannot tell from the data given

Q: 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.

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

Q: 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.

Q: 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.

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

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

Q: 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.

Q: 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.

Q: 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

Q: 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.

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

Q: 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

Q: 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.

Q: 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.

Q: 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.

Q: 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.

Q: 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

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

Q: 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.

Q: 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.

Q: 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

Q: 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

Q: 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.

Q: The best forecast is not necessarily the most accurate.

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

Q: Bias is measured by the cumulative sum of forecast errors.

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

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

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

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

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

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

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

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

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

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

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

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

Q: 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.

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

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

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

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

Q: Exponential smoothing is a form of weighted averaging.

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

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

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

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

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

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

Q: The naive approach to forecasting requires a linear trend line.

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

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

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

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

Q: A manager wants to choose one of two forecasting alternatives. Each alternative was tested using historical data. The resulting forecast errors for the two are shown in the table. Analyze the data and recommend a course of action to the manager.

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

Q: A new car dealer has been using exponential smoothing with an alpha of .2 to forecast weekly new car sales. Given the data below, would a naive forecast have provided greater accuracy? Explain. Assume an initial exponential forecast of 60 units in period 2 (i.e., no forecast for period 1).

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

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

Q: Use of simple linear regression analysis assumes that: A. variations around the line are nonrandom. B. deviations around the line are normally distributed. C. predictions can easily be made beyond the range of observed values of the predictor variable. D. all possible predictor variables are included in the model. E. the variance of error terms (deviations) varies directly with the predictor variable. That deviations conform to the normal distribution is a very important assumption underpinning simple linear regression.

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

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

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