Conestoga College

OPER 8025

CHAPTER 4

1)What two numbers are contained in the daily report to the CEO of Walt Disney Parks & Resorts regarding the six Orlando parks?

1. yesterday's forecasted attendance and yesterday's actual attendance
2. yesterday's actual attendance and today's forecasted attendance
3. yesterday's forecasted attendance and today's forecasted attendance
4. yesterday's actual attendance and last year's actual attendance
5. yesterday's forecasted attendance and the year-to-date average daily forecast error

2. Forecasts

1. become more accurate with longer time horizons.
2. are rarely perfect.

3. are more accurate for individual items than for groups of items.
4. are more accurate for new products than for existing products.
5. are impossible to make.

3. One use of short-range forecasts is to determine

1. planning for new products.
2. capital expenditures.
3. research and development plans.
4. facility location.
5. job assignments.

4. Forecasts are usually classified by time horizon into three categories

1. short-range, medium-range, and long-range.
2. finance/accounting, marketing, and operations.
3. strategic, tactical, and operational.
4. exponential smoothing, regression, and time series.
5. departmental, organizational, and industrial.

5. A forecast with a time horizon of about 3 months to 3 years is typically called a

1. long-range forecast.
2. medium-range forecast.
3. short-range forecast.
4. weather forecast.
5. strategic forecast.

6. Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a

1. short-range time horizon.
2. medium-range time horizon.
3. long-range time horizon.
4. naive method, because there is no data history.

5. strategic forecast.

7. Organizations use which three major types of forecasts, including two that may fall outside the role of the operations manager?

1. strategic, tactical, and operational
2. economic, technological, and demand
3. exponential smoothing, Delphi, and regression
4. causal, time-series, and seasonal
5. departmental, organizational, and territorial

8. Which of the following is not a step in the forecasting process?

1. Determine the use of the forecast.
2. Eliminate any assumptions.
3. Determine the time horizon.
4. Select forecasting model.
5. Validate and implement the results.

9. The two general approaches to forecasting are

1. qualitative and quantitative.
2. mathematical and statistical.
3. judgmental and qualitative.
4. historical and associative.
5. judgmental and associative.

10. Which of the following uses three types of participants: decision makers, staff personnel, and respondents?

1. executive opinions
2. sales force composites
3. the Delphi method
4. associative models
5. time series analysis

11. The forecasting model that pools the opinions of a group of experts or managers is known as the

1. expert judgment model.
2. multiple regression model.
3. jury of executive opinion model.
4. consumer market survey model.
5. management coefficients model.

12. Which of the following is not a type of qualitative forecasting?

1. executive opinions
2. consumer market surveys
3. sales force composite
4. the Delphi method
5. moving average

13. Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand?

1. associative models
2. exponential smoothing
3. weighted moving average
4. simple moving average
5. time series  

14. Which of the following statements about time-series forecasting is true?

1. It is based on the assumption that future demand will be the same as past demand.
2. It makes extensive use of the data collected in the qualitative approach.
3. It is based on the assumption that the analysis of past demand helps predict future demand.
4. Because it accounts for trends, cycles, and seasonal patterns, it is always more powerful than associative forecasting.
5. All of the above are true.

15. Time-series data may exhibit which of the following behaviors?

1. trend
2. random variations
3. seasonality
4. cycles
5. They may exhibit all of the above.

16. Gradual movement in time-series data over time is called

1. seasonal variation.
2. a cycle.
3. a trend.
4. exponential variation.
5. random variation.

17. Which of the following is not present in a time series?

1. seasonality
2. operational variations
3. trend
4. cycles
5. random variations

18. The fundamental difference between cycles and seasonality is the

1. duration of the repeating patterns.
2. magnitude of the variation.
3. ability to attribute the pattern to a cause.
4. speed of the variation.
5. cycles happen more often than seasonality.

Objective: LO5 Develop seasonal indices

19. In time series, which of the following cannot be predicted?

1. large increases in demand
2. cycles
3. seasonal fluctuations
4. random fluctuations
5. large decreases in demand

20. What is the approximate forecast for May using a four-month moving average? Nov. Dec. Jan. Feb. Mar. April

39 36 40 42 48 46

1. 38
2. 42
3. 43
4. 44
5. 47  

21. Which time-series model below assumes that demand in the next period will be equal to the most recent period's demand?

1. naive approach
2. moving average approach
3. weighted moving average approach
4. exponential smoothing approach
5. random approach

22. John's House of Pancakes uses a weighted moving average method to forecast pancake sales. It assigns a weight of 5 to the previous month's demand, 3 to demand two months ago, and 1 to demand three months ago. If sales amounted to 1000 pancakes in May, 2200 pancakes in June,

and 3000 pancakes in July, what should be the forecast for August? A) 2400

B) 2511

C) 2067

D) 3767

E) 1622

23. A six-month moving average forecast is generally better than a three-month moving average forecast if demand

1. is rather stable.
2. has been changing due to recent promotional efforts.
3. follows a downward trend.
4. exceeds one million units per year.
5. follows an upward trend.

24. Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of

1. manager understanding.
2. accuracy.
3. stability.
4. responsiveness to changes.
5. reliability.  

25. Which of the following statements comparing the weighted moving average technique and exponential smoothing is true?

1. Exponential smoothing is more easily used in combination with the Delphi method.
2. More emphasis can be placed on recent values using the weighted moving average.
3. Exponential smoothing is considerably more difficult to implement on a computer.
4. Exponential smoothing typically requires less record keeping of past data.
5. Exponential smoothing allows one to develop forecasts for multiple periods, whereas weighted moving averages does not.

26. Which time-series model uses past forecasts and past demand data to generate a new forecast?

1. naive
2. moving average
3. weighted moving average

4. exponential smoothing
5. regression analysis

27. Which is not a characteristic of exponential smoothing?

1. smooths random variations in the data
2. easily altered weighting scheme
3. weights each historical value equally
4. has minimal data storage requirements
5. None of the above; they are all characteristics of exponential smoothing.

28. Which of the following smoothing constants would make an exponential smoothing forecast

equivalent to a naive forecast?

1. 0
2. 1 divided by the number of periods

C) 0.5

D) 1.0

E) cannot be determined

29. Given an actual demand of 103, a previous forecast value of 99, and an alpha of .4, the exponential smoothing forecast for the next period would be

A) 94.6.

B) 97.4.

C) 100.6.

D) 101.6.

E) 103.0.

30. A forecast based on the previous forecast plus a percentage of the forecast error is a(n)

1. qualitative forecast.
2. naive forecast.
3. moving average forecast.
4. weighted moving average forecast.

5. exponentially smoothed forecast.

31. Given an actual demand of 61, a previous forecast of 58, and an alpha of .3, what would the forecast for the next period be using simple exponential smoothing?

A) 45.5

B) 57.1

C) 58.9

D) 61.0

E) 65.5

32. Which of the following values of alpha would cause exponential smoothing to respond the most slowly to forecast errors?

A) 0.10

B) 0.20

C) 0.40

D) 0.80

E) cannot be determined

33. A forecasting method has produced the following over the past five months. What is the mean absolute deviation?

Actual Forecast Error |Error| 10 11 -1 1

8 10 -2 2

10 8 2 2

6 6 0 0

9 8 1 1

A) -0.2

B) -1.0

C) 0.0

D) 1.2

E) 8.6

34. The primary purpose of the mean absolute deviation (MAD) in forecasting is to

1. estimate the trend line.
2. eliminate forecast errors.
3. measure forecast accuracy.
4. seasonally adjust the forecast.
5. all of the above.

35. Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?

1. 2
2. 3
3. 4
4. 8
5. 16  

36. The last four months of sales were 8, 10, 15, and 9 units. The last four forecasts were 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) is

A) 2.

B) -10.

C) 3.5.

4. 9.

E) 10.5.

37. A time series trend equation is 25.3 + 2.1 X. What is your forecast for period 7? A) 23.2

B) 25.3

C) 27.4

D) 40.0

5. cannot be determined

38. For a given product demand, the time series trend equation is 53 - 4 X. The negative sign on

the slope of the equation

1. is a mathematical impossibility.
2. is an indication that the forecast is biased, with forecast values lower than actual values.
3. is an indication that product demand is declining.
4. implies that the coefficient of determination will also be negative.
5. implies that the cumulative error will be negative.

39. Yamaha manufactures which set of products with complementary demands to address seasonal fluctuations?

1. golf clubs and skis
2. swimming suits and winter jackets
3. jet skis and snowmobiles
4. pianos and guitars
5. ice skates and water skis

40. Which of the following is true regarding the two smoothing constants of the Forecast Including Trend (FIT) model?

1. One constant is positive, while the other is negative.
2. They are called MAD and cumulative error.
3. Alpha is always smaller than beta.
4. One constant smooths the regression intercept, whereas the other smooths the regression slope.
5. Their values are determined independently.

41. Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January?

1. 640 units
2. 798.75 units
3. 801.25 units
4. 1000 units
5. 88.33 units  

42. A seasonal index for a monthly series is about to be calculated on the basis of three years' accumulation of data. The three previous July values were 110, 150, and 130. The average over

all months is 190. The approximate seasonal index for July is A) 0.487.

B) 0.684.

C) 1.462.

D) 2.053.

E) cannot be calculated with the information given.

43. A fundamental distinction between trend projection and linear regression is that

1. trend projection uses least squares while linear regression does not.
2. only linear regression can have a negative slope.
3. in trend projection the independent variable is time; in linear regression the independent variable need not be time, but can be any variable with explanatory power.
4. trend projection can be a function of several variables, while linear regression can only be a function of one variable.
5. trend projection uses two smoothing constants, not just one.

44. The degree or strength of a relationship between two variables is shown by the

1. alpha.
2. mean.
3. mean absolute deviation.
4. correlation coefficient.
5. cumulative error.

45. If two variables were perfectly correlated, the correlation coefficient r would equal

A) 0.

B) -1.

3. 1.
4. 1 or -1.

E) -2.

46. The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts

were 60, 80, 95, and 75 units. These forecasts illustrate

1. qualitative methods.
2. adaptive smoothing.
3. slope.
4. bias.
5. trend projection.

47. The tracking signal is the

1. standard error of the estimate.
2. absolute deviation of the last period's forecast.
3. mean absolute deviation (MAD).
4. ratio of cumulative error/MAD.
5. mean absolute percentage error (MAP.

48. Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic of

1. exponential smoothing including trend.
2. adaptive smoothing.
3. trend projection.
4. focus forecasting.
5. multiple regression analysis.

49. Many services maintain records of sales noting

1. the day of the week.
2. unusual events.
3. weather.
4. holidays.
5. all of the above.

50. Taco Bell's unique employee scheduling practices are partly the result of using

1. point-of-sale computers to track food sales in 15 minute intervals and a six-week moving average forecasting technique.
2. focus forecasting and multiple regression.
3. a six-week moving average forecasting technique and multiple regression.
4. singular regression.
5. work breakdown structures.

51. Which of the following most requires long-range forecasting (as opposed to short-range or medium-range forecasting) for its planning purposes?

1. job scheduling
2. production levels
3. cash budgeting
4. capital expenditures
5. purchasing  

52. Suppose that demand in period 1 was 7 units and the demand in period 2 was 9 units. Assume that the forecast for period 1 was for 5 units. If the firm uses exponential smoothing with an alpha value of .20, what should be the forecast for period 3? (Round answers to two decimal places.)

A) 9.00

B) 3.72

C) 9.48

D) 5.00

E) 6.12

53.                 expresses the error as a percent of the actual values, undistorted by a single large value.

1. MAD
2. MSE
3. MAPE