Opperations Management – Chapter 3

24 June 2024
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Forecasting techniques generally assume an existing causal system that will continue to exist in the future.
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TRUE Forecasts 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.
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FALSE If growth is strong, alpha should be large so that the model will catch up more quickly.
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Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast.
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FALSE Flexibility to accommodate major changes is important to good forecasting.
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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.
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FALSE Forecasting for an individual item is more difficult than forecasting for a number of items.
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Forecasts help managers both to plan the system itself and to provide valuable information for using the system.
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TRUE Both planning and use are shaped by forecasts.
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Organizations that are capable of responding quickly to changing requirements can use a shorter forecast horizon and therefore benefit from more accurate forecasts.
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TRUE If an organization can react more quickly, its forecasts need not be so long term.
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When new products or services are introduced, focus forecasting models are an attractive option.
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FALSE Because focus forecasting models depend on historical data, they're not so attractive for newly introduced products or services.
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The purpose of the forecast should be established first so that the level of detail, amount of resources, and accuracy level can be understood.
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TRUE All of these considerations are shaped by what the forecast will be used for.
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Forecasts based on time-series (historical) data are referred to as associative forecasts.
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FALSE Forecasts based on time-series data are referred to as time-series forecasts.
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Time-series techniques involve the identification of explanatory variables that can be used to predict future demand.
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FALSE Associative forecasts involve identifying explanatory variables.
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A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys.
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FALSE Most people do not enjoy participating in surveys.
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The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast.
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TRUE A consensus among divergent perspectives is developed using questionnaires.
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Exponential smoothing adds a percentage (called alpha) of the last period's forecast to estimate the next period's demand.
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FALSE Exponential smoothing adds a percentage to the last period's forecast error.
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The shorter the forecast period, the more accurately the forecasts tend to track what actually happens.
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TRUE Long-term forecasting is much more difficult to do accurately.
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Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values.
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FALSE Time-series forecasts assume that future patterns in the series will mimic past patterns in the series.
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Trend-adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period's actual demand.
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FALSE Trend-adjusted smoothing smoothes both random and trend-related variation.
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Forecasts based on an average tend to exhibit less variability than the original data.
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TRUE Averaging is a way of smoothing out random variability.
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The naive approach to forecasting requires a linear trend line.
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FALSE The naive approach is useful in a wider variety of settings.
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The naive forecast is limited in its application to series that reflect no trend or seasonality.
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FALSE When a trend or seasonality is present, the naive forecast is more limited in its application.
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The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques.
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TRUE Often the naive forecast performs reasonably well when compared to more complex techniques.
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A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average.
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FALSE More data points reduce a moving average forecast's responsiveness.
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In order to update a moving average forecast, the values of each data point in the average must be known.
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TRUE The moving average cannot be updated until the most recent value is known.
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Forecasts of future demand are used by operations people to plan capacity.
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TRUE Capacity decisions are made for the future and therefore depend on forecasts.
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An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago.
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TRUE Weighted moving averages can be adjusted to make more recent data more important in setting the forecast.
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Exponential smoothing is a form of weighted averaging.
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TRUE The most recent period is given the most weight, but prior periods also factor in.
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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.
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FALSE Smaller smoothing constants result in less reactive forecast models.
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The T in the model TAF = S + T represents the time dimension (which is usually expressed in weeks or months).
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FALSE The T represents the trend dimension.
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Trend-adjusted exponential smoothing requires selection of two smoothing constants.
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TRUE One is for the trend and one is for the random error.
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An advantage of trend-adjusted exponential smoothing over the linear trend equation is its ability to adjust over time to changes in the trend.
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TRUE A linear trend equation assumes a constant trend; trend-adjusted smoothing allows for changes in the underlying trend.
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A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend.
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TRUE Seasonal relatives are used when the seasonal effect is multiplicative rather than additive.
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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.
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TRUE Computing seasonal relatives depends on past data being available.
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Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative.
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TRUE Deseasonalized data points have been adjusted for seasonal influences.
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If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis.
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TRUE Patterns reflect influences such as trends or seasonality that go against regression analysis assumptions.
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Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are nonlinear or involve more than one predictor variable.
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TRUE Regression analysis can be used in a variety of settings.
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The sample standard deviation of forecast error is equal to the square root of MSE.
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TRUE The MSE is equal to the sample variance of the forecast error.
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Correlation measures the strength and direction of a relationship between variables.
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TRUE The association between two variations is summarized in the correlation coefficient.
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MAD is equal to the square root of MSE, which is why we calculate the easier MSE and then calculate the more difficult MAD.
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FALSE MAD is the mean absolute deviation.
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In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naive forecast would yield.
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TRUE With alpha equal to 1 we are using a naive forecasting method.
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A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern.
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FALSE Forecast methods are generally considered to be performing adequately when the errors appear to be randomly distributed.
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A control chart involves setting action limits for cumulative forecast error.
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FALSE Control charts set action limits for the tracking signal.
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A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD.
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TRUE Large absolute values of the tracking signal suggest a fundamental change in the forecast model's performance.
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The use of a control chart assumes that errors are normally distributed about a mean of zero.
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TRUE Over time, a forecast model's tracking signal should fluctuate randomly about a mean of zero.
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Bias exists when forecasts tend to be greater or less than the actual values of time series.
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TRUE A tendency in one direction is defined as bias.
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Bias is measured by the cumulative sum of forecast errors.
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TRUE Bias would result in the cumulative sum of forecast errors being large in absolute value.
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Seasonal relatives can be used to deseasonalize data or incorporate seasonality in a forecast.
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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.
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The best forecast is not necessarily the most accurate.
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TRUE More accuracy often comes at too high a cost to be worthwhile.
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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.
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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.
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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
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B. 397 The forecast will be (.1 * 380) + (.2 * 410) + (.3 * 390) + (.4 * 400) = 397.
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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.
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D. 2050 The forecast for will be (.2 * 2,200) + (.3 * 1,950) + (.5 * 2,050) = 2,050.
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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.
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E. cost and accuracy. The trade-off between cost and accuracy is the critical consideration when choosing a forecasting technique.
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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.
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A. subjective estimates. New products and services lack historical data, so forecasts for them must be based on subjective estimates.
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Forecasts based on judgment and opinion do not include: A. executive opinion. B. salesperson opinion. C. second opinions. D. customer surveys. E. Delphi methods.
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C. second opinions. Second opinions generally refer to medical diagnoses, not demand forecasting.
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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
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E. demand forecasts Demand forecasts are direct inputs into capacity, sales, and production plans.
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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.
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C. Historical data is available on which to base the forecast. In some forecasting situations historical data are not available.
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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.
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B. Eliminate all assumptions. We cannot eliminate all assumptions.
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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.
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D. least squares estimation. Least squares estimations minimize the sum of squared deviations around the estimated regression function.
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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.
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B. qualitative and quantitative. Forecast approaches are either quantitative or qualitative.
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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
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E. time series analysis Time series analysis is a quantitative approach.
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Accuracy in forecasting can be measured by: A. MSE. B. MRP. C. MPS. D. MTM. E. MTE.
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A. MSE MSE is mean squared error.
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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.
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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.
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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
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C. series of questionnaires The questionnaires are a way of fostering a consensus among divergent perspectives.
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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.
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C. the Delphi method. Anonymity is important in Delphi efforts.
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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.
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A. avoid premature consensus (bandwagon effect). A bandwagon can lead to popular but potentially inaccurate viewpoints to drown out other important considerations.
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Detecting nonrandomness in errors can be done using: A. MSEs. B. MAPs. C. control charts. D. correlation coefficients. E. strategies.
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C. control charts. Control charts graphically depict the statistical behavior of forecast errors.
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Gradual, long-term movement in time series data is called: A. seasonal variation. B. cycles. C. irregular variation. D. trend. E. random variation.
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D. trend Trends move the time series in a long-term direction.
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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.
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A. the duration of the repeating patterns. Seasons happen within time periods; cycles happen across multiple time periods.
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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.
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B. smoothing out fluctuations in time series. Smoothing helps forecasters see past random error.
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Putting forecast errors into perspective is best done using A. exponential smoothing. B. MAPE. C. linear decision rules. D. MAD. E. hindsight.
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B. MAPE. MAPE depicts the forecast error relative to what was being forecast.
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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.
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B. a naive forecast. Only one piece of information is needed for a naive forecast.
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For the data given below, what would the naive forecast be for period 5? 1 - 58 2- 59 3 - 60 4 - 61
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D. 61 Period 5's forecast would be period 4's demand.
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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.
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C. Smooth variations in the data. Variation is smooth out in moving average forecasts.
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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
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D. requires only last period's forecast and actual data Simple moving averages can require several periods of data.
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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.
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A. decreased Fewer data points result in more responsive moving averages.
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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.
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D. an exponentially smoothed forecast. Exponential smoothing uses the previous forecast error to shape the next forecast.
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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
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B. weights each historical value equally The most recent period of demand is given the most weight in exponential smoothing.
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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.
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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.
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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
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D. 62.5 Multiply the previous period's forecast error (-5) by alpha and then add to the previous period's forecast.
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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.
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C. 100.2. Multiply the previous period's forecast error (8) by alpha and then add to the previous period's forecast.
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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
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E. .15 Larger values for alpha correspond with greater responsiveness.
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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
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A. 40,450 July would be period 3, so the forecast would be 40,000 + 150(3).
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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.
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A. an exponentially smoothed forecast and a smoothed trend factor. Both random variation and the trend are smoothed in TAF models.
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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
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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.
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Which technique is used in computing seasonal relatives? A. double smoothing B. Delphi C. mean squared error D. centered moving average E. exponential smoothing
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D. centered moving average The centered moving average serves as the basis point for computing seasonal relatives.
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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.
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A. bias. Bias is a tendency for a forecast to be above (or below) the actual value.
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The primary method for associative forecasting is: A. sensitivity analysis. B. regression analysis. C. simple moving averages. D. centered moving averages. E. exponential smoothing.
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B. regression analysis. Regression analysis is an associative forecasting technique.
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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
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E. predictor variables Associative techniques use predictor variables.
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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
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C. independent variable Demand is the typical dependent variable when forecasting with simple linear regression.
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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.
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C. measure forecast accuracy. MAD is one way of evaluating forecast performance.
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Given forecast errors of 4, 8, and -3, what is the mean absolute deviation? A. 4 B. 3 C. 5 D. 6 E. 12
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C. 5 Convert each error into an absolute value and then average.
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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
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B. 3 Convert each error into an absolute value and then average.
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Given forecast errors of 5, 0, -4, and 3, what is the bias? A. -4 B. 4 C. 5 D. 12 E. 6
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B. 4 Sum the forecast errors.
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Which of the following is used for constructing a control chart? A. mean absolute deviation B. mean squared error C. tracking signal D. bias
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B. mean squared error The mean squared error leads to an estimate for the sample forecast standard deviation.
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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.
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C. cost and accuracy. More accurate forecasts cost more but may not be worth the additional cost.
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The degree of management involvement in short-range forecasts is: A. none. B. low. C. moderate. D. high. E. total.
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B. low. Short-range forecasting tends to be fairly routine.
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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.
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D. low cost A good forecast can be quite costly if necessary.
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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.
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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.
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A managerial approach toward forecasting which seeks to actively influence demand is: A. reactive. B. proactive. C. influential. D. protracted. E. retroactive.
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B. proactive. Simply responding to demand is a reactive approach.
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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.
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C. short-term forecast accuracy. More accurate short-term forecasts enable organizations to better accommodate customer requests.
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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.
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D. 68 Average demand from periods 3 through 5.
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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
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E. 22,800 Treat 5 years ago as period 0.