Operations Management 13th Edition by William J Stevenson – Test Bank

 

To Purchase this Complete Test Bank with Answers Click the link Below

 

https://tbzuiqe.com/product/operations-management-13th-edition-by-william-j-stevenson-test-bank/

 

If face any problem or Further information contact us At tbzuiqe@gmail.com

 

 

Sample Test

Chapter 03 Test Bank – Static

1.	Forecasting techniques generally assume an existing causal system that will continue to exist in the future. TRUE   Forecasts depend on the rules of the game remaining reasonably constant.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-01 List features common to all forecasts. Topic: Features Common to All Forecasts

 

2.	For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques. FALSE   If growth is strong, alpha should be large so that the model will catch up more quickly.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

3.	Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast. FALSE   Flexibility to accommodate major changes is important to good forecasting.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 1 Easy Learning Objective: 03-04 Outline the steps in the forecasting process. Topic: Steps in the Forecasting Process

 

 

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

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 1 Easy Learning Objective: 03-01 List features common to all forecasts. Topic: Features Common to All Forecasts

 

5.	Forecasts help managers both to plan the system itself and to provide valuable information for using the system. TRUE   Both planning and using the system are shaped by forecasts.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-01 List features common to all forecasts. Topic: Forecasting

 

6.	Organizations that are capable of responding quickly to changing requirements can use a shorter forecast horizon and therefore benefit from more accurate forecasts. TRUE   If an organization can react more quickly, its forecasts need not be so long term.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-04 Outline the steps in the forecasting process. Topic: Steps in the Forecasting Process

 

7.	When new products or services are introduced, focus forecasting models are an attractive option. FALSE   Because focus forecasting models depend on historical data, they’re not so attractive for newly introduced products or services.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Topic: Forecasts Based on Time-Series Data

 

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

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-04 Outline the steps in the forecasting process. Topic: Steps in the Forecasting Process

 

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

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Topic: Approaches to Forecasting

 

10.	Time-series techniques involve the identification of explanatory variables that can be used to predict future demand. FALSE   Associative forecasts involve identifying explanatory variables.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Topic: Approaches to Forecasting

 

11.

A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys. FALSE   Most people do not enjoy participating in surveys.Surveys can be expensive and time consuming; and actual consumer behavior may not match their survey responses

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-06 Describe four qualitative forecasting techniques. Topic: Qualitative Forecasts

 

12.	The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast. TRUE   A consensus among divergent perspectives is developed using questionnaires.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-06 Describe four qualitative forecasting techniques. Topic: Qualitative Forecasts

 

13.	Exponential smoothing adds a percentage (called alpha) of the last period’s forecast to estimate the next period’s demand. FALSE   Exponential smoothing adds a percentage of the last period’s forecast error.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

14.	The shorter the forecast period, the more accurately the forecasts tend to track what actually happens. TRUE Long-term forecasting is much more difficult to do accurately.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 1 Easy Learning Objective: 03-01 List features common to all forecasts. Topic: Features Common to All Forecasts

 

15.	Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values. FALSE   Time-series forecasts assume that future patterns in the series will mimic past patterns in the series.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Topic: Forecasts Based on Time-Series Data

 

16.	Trend-adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period’s actual demand. FALSE   Trend-adjusted smoothing smoothes both random and trend-related variation.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

17.	Forecasts based on an average tend to exhibit less variability than the original data. TRUE   Averaging is a way of smoothing out random variability.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-08 Prepare a moving average forecast. Topic: Forecasts Based on Time-Series Data

 

18.	The naive approach to forecasting requires a linear trend line. FALSE   The naive approach uses a single previous value of a time series as the basis of a forecast.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-07 Use a naive method to make a forecast. Topic: Forecasts Based on Time-Series Data

 

19.	The naive forecast is limited in its application to series that reflect no trend or seasonality. FALSE   When a trend or seasonality is present, the naive forecast uses the most recent observation of trend and/or the most recent observation from the most recent similar season.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 1 Easy Learning Objective: 03-07 Use a naive method to make a forecast. Topic: Forecasts Based on Time-Series Data

 

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

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 1 Easy Learning Objective: 03-07 Use a naive method to make a forecast. Topic: Forecasts Based on Time-Series Data

 

21.	A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average. FALSE   More data points reduce a moving average forecast’s responsiveness.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-08 Prepare a moving average forecast. Topic: Forecasts Based on Time-Series Data

 

22.	In order to update a moving average forecast, the values of each data point in the average must be known. TRUE   The oldest value in the average must be dropped before updating the moving average when a new data value becomes available.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 3 Hard Learning Objective: 03-08 Prepare a moving average forecast. Topic: Forecasts Based on Time-Series Data

 

23.	Forecasts of future demand are used by operations people to plan capacity. TRUE   Capacity decisions are made for the future and therefore depend on forecasts.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-04 Outline the steps in the forecasting process. Topic: Forecasting

 

24.	An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago. TRUE   Weighted moving averages can be adjusted to make more recent data more important in setting the forecast.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-09 Prepare a weighted-average forecast. Topic: Forecasts Based on Time-Series Data

 

25.	Exponential smoothing is a form of weighted averaging. TRUE   The most recent period is given the most weight, but prior periods also factor in.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

26.	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 responsive forecast models.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 3 Hard Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

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

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

28.	Trend-adjusted exponential smoothing requires selection of two smoothing constants. TRUE   One is for the trend and one is for the random error.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

29.	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   A linear trend equation assumes a constant trend; trend-adjusted smoothing allows for changes in the underlying trend.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

30.	A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend. TRUE   Seasonal relatives are used when the seasonal effect is multiplicative rather than additive.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-13 Compute and use seasonal relatives. Topic: Forecasts Based on Time-Series Data

 

31.	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. TRUE   Computing seasonal relatives depends on past data being available.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-13 Compute and use seasonal relatives. Topic: Forecasts Based on Time-Series Data

 

32.

Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative. TRUE   Deseasonalized data points have been adjusted to remove seasonal influences in order to obtain a clearer picture of the nonseasonal (trend and average) components.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-13 Compute and use seasonal relatives. Topic: Forecasts Based on Time-Series Data

 

33.	If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of simple linear regression. TRUE   Patterns reflect influences such as trends or seasonality that go against regression analysis assumptions.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-14 Compute and use regression and correlation coefficients. Topic: Associative Forecasting Techniques

 

34.

Nonlinear and multiple regression procedures permit us to extend associative models to relationships that are nonlinear or involve more than one predictor variable. TRUE   Regression analysis can be used in a variety of settings, even when the relationship between variables is nonlinear or when multiple predictor variables are involved

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-14 Compute and use regression and correlation coefficients. Topic: Associative Forecasting Techniques

 

35.	The sample standard deviation of forecast error is estimated by the square root of MSE. TRUE   The MSE is an estimate of the sample variance of the forecast error.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Topic: Monitoring Forecast Error

 

36.	Correlation measures the strength and direction of a relationship between variables. TRUE   The association between two variables is summarized in the correlation coefficient.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-14 Compute and use regression and correlation coefficients. Topic: Associative Forecasting Techniques

 

37.	MAD is equal to the square root of MSE, which is why we calculate the easier MSE and then calculate the more difficult MAD. FALSE   MAD is the mean absolute deviation while MSE is the mean squared error.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Topic: Forecast Accuracy

 

38.	In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naive forecast would yield. TRUE   With alpha equal to 1 we are using a naive forecasting method.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

39.	A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern. FALSE   Forecast methods are generally considered to be performing adequately when the errors appear to be randomly distributed.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Topic: Monitoring Forecast Error

 

40.	A control chart involves setting action limits for cumulative forecast error. FALSE   Control charts set action limits for the individual observations of forecast error.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Topic: Monitoring Forecast Error

 

41.	A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD. TRUE   Large absolute values of the tracking signal suggest a fundamental change in the forecast model’s performance.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Topic: Monitoring Forecast Error

 

42.	The use of a control chart assumes that random errors are normally distributed about a mean of zero. TRUE   Over time, a forecast model’s errors should fluctuate randomly about a mean of zero.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 3 Hard Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Topic: Monitoring Forecast Error

 

43.	Bias exists when forecasts tend to be greater or less than the actual values of time series. TRUE   A tendency in one direction is defined as bias.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Topic: Monitoring Forecast Error

 

44.	Bias is measured by the ratio of the cumulative sum of forecast errors to the mean absolute deviation (MAD). TRUE   Bias would result in the ratio of the cumulative sum of forecast errors to MAD being large in absolute value.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Topic: Monitoring Forecast Error

 

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

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-13 Compute and use seasonal relatives. Topic: Forecasts Based on Time-Series Data

 

46.	The best forecast is not necessarily the most accurate. TRUE   More accuracy often comes at too high a cost to be worthwhile.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Topic: Choosing a Forecasting Technique

 

 

47.

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.     Customers themselves may be unclear regarding what they’d like to do versus what they’ll actually do.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 1 Easy Learning Objective: 03-06 Describe four qualitative forecasting techniques. Topic: Qualitative Forecasts

 

48.

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     The forecast will be (.1 * 380) + (.2 * 410) + (.3 * 390) + (.4 * 400) = 397.

 

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Difficulty: 2 Medium Learning Objective: 03-09 Prepare a weighted-average forecast. Topic: Forecasts Based on Time-Series Data

 

49.

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.

 

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Difficulty: 2 Medium Learning Objective: 03-09 Prepare a weighted-average forecast. Topic: Forecasts Based on Time-Series Data

 

50.

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.     The trade-off between cost and accuracy is the critical consideration when choosing a forecasting technique.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 1 Easy Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Topic: Choosing a Forecasting Technique

 

51.

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.     New products and services lack historical data, so forecasts for them must be based on subjective estimates.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 1 Easy Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Topic: Choosing a Forecasting Technique

 

52.	Forecasts based on judgment and opinion do not include: A. executive opinion.   B. salesperson opinion.   C. second opinions.   D. customer surveys.   E. Delphi methods.     Second opinions generally refer to medical diagnoses, not demand forecasting.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-06 Describe four qualitative forecasting techniques. Topic: Qualitative Forecasts

 

53.	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     Demand forecasts are direct inputs into capacity, sales, and production plans.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-01 List features common to all forecasts. Topic: Features Common to All Forecasts

 

54.

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.   In some forecasting situations historical data are not available.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 3 Hard Learning Objective: 03-01 List features common to all forecasts. Topic: Features Common to All Forecasts

 

55.	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.     We cannot eliminate all assumptions.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-04 Outline the steps in the forecasting process. Topic: Features Common to All Forecasts

 

56.

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.   Least squares estimations minimize the sum of squared deviations around the estimated regression function.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-14 Compute and use regression and correlation coefficients. Topic: Associative Forecasting Techniques

 

57.	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.   Forecast approaches are either quantitative or qualitative.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Topic: Approaches to Forecasting

 

58.	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   Time series analysis is a quantitative approach.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-06 Describe four qualitative forecasting techniques. Topic: Qualitative Forecasts

 

59.	Accuracy in forecasting can be measured by: A. MSE.   B. MRP.   C. MPS.   D. MTM.   E. MTE.   MSE is mean squared error.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 3 Hard Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Topic: Forecast Accuracy

 

60.

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.   Members of the sales force should be the organization’s tightest link with its customers.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 3 Hard Learning Objective: 03-06 Describe four qualitative forecasting techniques. Topic: Qualitative Forecasts

 

61.	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   The questionnaires are a way of fostering a consensus among divergent perspectives.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-06 Describe four qualitative forecasting techniques. Topic: Qualitative Forecasts

 

62.	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.   Anonymity is important in Delphi efforts.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-06 Describe four qualitative forecasting techniques. Topic: Qualitative Forecasts

 

63.

One reason for using the Delphi method in forecasting is to: A. reduce the risk that one individual’s opinion will prevail.   B. achieve a high degree of accuracy.   C. maintain accountability and responsibility.   D. be able to replicate results.   E. prevent hurt feelings.   Since responses are anonymous, there is less risk that a domineering personality can push potentially inaccurate viewpoints to drown out other important considerations.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-06 Describe four qualitative forecasting techniques. Topic: Qualitative Forecasts

 

64.	Detecting nonrandomness in errors can be done using: A. MSEs.   B. MAPs.   C. control charts.   D. correlation coefficients.   E. strategies.   Control charts graphically depict the statistical behavior of forecast errors.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Topic: Monitoring Forecast Error

 

65.	Gradual, long-term movement in time series data is called: A. seasonal variation.   B. cycles.   C. irregular variation.   D. trend.   E. random variation.   Trends move the time series in a long-term direction.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-11 Prepare a linear trend forecast. Topic: Forecasts Based on Time-Series Data

 

66.

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.   Seasons happen within time periods; cycles happen across multiple time periods.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 3 Hard Learning Objective: 03-13 Compute and use seasonal relatives. Topic: Forecasts Based on Time-Series Data

 

67.	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.   Smoothing helps forecasters see past random error.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 3 Hard Learning Objective: 03-08 Prepare a moving average forecast. Topic: Forecasts Based on Time-Series Data

 

68.	Putting forecast errors into perspective is best done using A. exponential smoothing.   B. MAPE.   C. linear decision rules.   D. MAD.   E. hindsight.   MAPE depicts the forecast error relative to what was being forecast.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Topic: Forecast Error

 

69.	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.   Only one piece of information is needed for a naive forecast.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 1 Easy Learning Objective: 03-07 Use a naive method to make a forecast. Topic: Forecasts Based on Time-Series Data

 

70.	For the data given below, what would the naive forecast be for period 5? Period Value 1 58 2 59 3 60 4 61   A. 58   B. 62   C. 59.5   D. 61   E. cannot tell from the data given   Period 5’s forecast would be period 4’s demand.

 

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Difficulty: 1 Easy Learning Objective: 03-07 Use a naive method to make a forecast. Topic: Forecasts Based on Time-Series Data

 

71.

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.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-08 Prepare a moving average forecast. Topic: Forecasts Based on Time-Series Data

 

72.

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   Simple moving averages can require several periods of data.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-08 Prepare a moving average forecast. Topic: Forecasts Based on Time-Series Data

 

73.

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.   Fewer data points result in more responsive moving averages.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-08 Prepare a moving average forecast. Topic: Forecasts Based on Time-Series Data

 

74.

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.   Exponential smoothing uses the previous forecast error to shape the next forecast.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

75.

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

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 3 Hard Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

76.	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   An alpha of 1.0 leads to a naive forecast.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

77.

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.   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.

 

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Difficulty: 3 Hard Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

78.

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   Multiply the previous period’s forecast error (-5) by alpha and then add to the previous period’s forecast.

 

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Difficulty: 2 Medium Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

79.

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.   Multiply the previous period’s forecast error (8) by alpha and then add to the previous period’s forecast.

 

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Difficulty: 2 Medium Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

80.	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   Larger values for alpha correspond with greater responsiveness.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Difficulty: 2 Medium Learning Objective: 03-10 Prepare an exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

81.	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   July would be period 3, so the forecast would be 40,000 + 150(3).

 

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Difficulty: 2 Medium Learning Objective: 03-11 Prepare a linear trend forecast. Topic: Forecasts Based on Time-Series Data

 

82.

In trend-adjusted exponential smoothing, the trend-adjusted forecast consists of: A. an exponentially smoothed forecast and a smoothed trend factor.   B. the most recent actual value 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.   Both random variation and the trend the forecast error and the error in the trend estimate are smoothed in TAF models.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 3 Hard Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Topic: Forecasts Based on Time-Series Data

 

 

83.

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   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.

 

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Difficulty: 2 Medium Learning Objective: 03-13 Compute and use seasonal relatives. Topic: Forecasts Based on Time-Series Data