Chapter 8: Forecasting and Demand Printing
Result 100 %
Multiple Choice
1. Forecasting is not a function which contributes to:
a) deciding which business market to pursue
b) deciding which product to produce
c) deciding how bonuses should be allocated
d) deciding how much inventory to carry
e) deciding how many people to hire
Ans:
Section Ref: Introduction
Level: easy
2. When evaluating forecasting models it is accurate to say:
a) they all rely on the same data sets
b) they will provide the same results
c) they are usually accurate
d) they differ in their degree of complexity
e) they do not differ in their degree of complexity
Ans:
Section Ref: Principles of Forecasting
Level: moderate
3. Which of the following is not a feature common to all forecasting models?
a) This period’s forecast error is needed to compute next period’s forecast.
b) Forecasts are rarely perfect.
c) Forecasts are more accurate for groups of items rather than for individual items.
d) Forecasts are more accurate for shorter rather than for longer time horizons.
e) All of the above features are common to all forecasting models.
Ans:
Section Ref: Principles of Forecasting
Level: moderate
4. The first step in forecasting is:
a) determine what data is available
b) decide what to forecast
c) evaluate and analyze appropriate data
d) select and test the forecast model
e) establish the forecast accuracy requirements
Ans:
Section Ref: Steps in the Forecasting Process
Level: moderate
5. Which of the following companies helps businesses use weather data to make their business plans?
a) i2 technologies
b) Manugistics
c) Planalytics
d) Algorithmics
e) SAP
Ans:
Section Ref: Types of Forecasting Methods
Level: hard
6. Qualitative forecasting methods
a) are made objectively by the forecaster
b) are made subjectively by the forecaster
c) are made using existing data sources
d) are based on mathematical models
e) are only used in parallel with quantitative models
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
7. Under which forecasting method does a group of managers meet to generate a forecast?
a) Market research
b) Executive opinion
c) Delphi method
d) Naïve method
e) Gamma method
Ans:
Section Ref: Types of Forecasting Methods
Level: easy
8. Which forecasting method seeks to develop a consensus among a group of experts?
a) Market research
b) Executive opinion
c) Delphi method
d) Naïve method
e) Gamma method
Ans:
Section Ref: Types of Forecasting Methods
Level: easy
9. One quantitative forecasting models limitation is
a) it is objective
b) they are consistent
c) they are based on mathematical formulas
d) they are limited on the quality of available data
e) they can work around bad data
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
10. Which forecasting method is particular good for predicting technological changes and scientific advances?
a) Market research
b) Executive opinion
c) Delphi method
d) Naïve method
e) Gamma method
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
11. Which forecasting method is particularly good for determining customer preferences?
a) Market research
b) Executive opinion
c) Delphi method
d) Naïve method
e) Gamma method
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
12. Which forecasting method suffers from the possibility of having one person’s opinion dominate the forecast?
a) Market research
b) Executive opinion
c) Delphi method
d) Naïve method
e) Gamma method
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
13. Which of the following forecasting methods is most likely to be implemented to change an existing quantitative forecast to account for a new competitor in the marketplace?
a) Market research
b) Executive opinion
c) Delphi method
d) Naïve method
e) Gamma method
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
14. Which of the following forecasting methods is specifically designed to go through several rounds of modification before generating a final forecast?
a) Exponential smoothing
b) Executive opinion
c) Delphi method
d) Naïve method
e) Gamma method
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
15. What are the two categories of quantitative models?
a) Delphi and noncausal
b) Causal and noncausal
c) Delphi and time series
d) Causal and time series
e) Causal and Delphi
Ans:
Section Ref: Types of Forecasting Methods
Level: easy
16. A causal research model is based on the assumption that
a) the independent variable is related to the dependent variable
b) there is a relationship between the time series and the dependent variable
c) the variable being forecast is related to other variables in the environment
d) there is a relationship between the time series and the independent variable
e) the information is contained in a time series of data
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
17. Which of the following is a causal forecasting method?
a) Naïve
b) Moving average
c) Weighted moving average
d) Trend adjusted exponential smoothing
e) Linear regression
Ans:
Section Ref: Types of Forecasting Methods
Level: easy
18. Which of the following is the least useful sales forecasting model to use when sales are increasing?
a) Trend adjusted exponential smoothing
b) Simple mean
c) Exponential smoothing
d) Weighted moving average
e) Naïve
Ans:
Section Ref: Types of Forecasting Methods
Level: hard
19. Over the long term, which of the following forecasting models will likely require carrying the least amount of data?
a) Naïve
b) Simple mean
c) Exponential smoothing
d) Weighted moving average
e) Moving average
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
20. In looking at seasonal indexes one weakness to watch for is
a) use of the wrong alpha
b) incorrect selection of weights
c) a clear lack of linear relationship
d) seasonality is not present
e) significant increase in computational requirements
Ans:
Section Ref: Types of Forecasting Methods
Level: easy
21. Which of the following is not considered to be one of the four basic patterns of time series data?
a) Horizontal
b) Trend
c) Vertical
d) Seasonality
e) Cycle
Ans:
Response: See pages 259260
Level: easy
22. Which is typically the most difficult data pattern to predict?
a) Horizontal
b) Trend
c) Level
d) Seasonality
e) Cycle
Ans:
Section Ref: Time Series Models
Level: moderate
23. Which forecasting method assumes that next period’s forecast is equal to this period’s actual value?
a) Simple mean
b) Ignorant
c) Basic
d) Naïve
e) Nescient
Ans:
Section Ref: Time Series Models
Level: easy
24. The OM supervisor informs you, the researcher, that the data has a large standard deviation. What data pattern would you expect to observe once you generated a time series trend?
a) horizontal
b) seasonal
c) positive/negative trend
d) cycle
e) insufficient information to derive a valid response
Ans:
Section Ref: Time Series Models
Level: moderate
25. Suppose that you are using the naïve forecasting method with trend to forecast sales. If sales have been declining by 20% per week, and this week’s sales amounted to $200, what would your forecast be for next week?
a) $200
b) $ 40
c) $240
d. $180
e) $160
Ans:
Section Ref: Time Series Models
Level: moderate
26. Suppose that you are using the simple mean to make a forecast. This period’s forecast was equal to 100 units, and it was based on 6 periods of demand. This period’s actual demand was 86 units. What is your forecast for next period?
a) 98
b) 100
c) 93
d) 86
e) Not enough information is given to answer the question.
Ans:
Section Ref: Time Series Models
Level: hard
27. Suppose that you are using the fourperiod simple moving average method to forecast sales, and sales have been decreasing by 10% every period. How will your forecasts perform?
a) Forecasts will be lower than actual.
b) Forecasts will be higher than actual.
c) Forecasts will equal actual.
d) Forecasts will be increasing.
e) Forecasts will be decreasing by 2.5% every period.
Ans:
Section Ref: Time Series Models
Level: hard
28. Suppose that you are using the fourperiod weighted moving average forecasting method to forecast sales and you know that sales will be increasing every period for the foreseeable future. What of the following would be the best set of weights to use (listed in order from the most recent period to four periods ago, respectively)?
a) 0.25, 0.25, 0.25, 0.25
b) 0.40, 0.30, 0.20, 0.10
c) 1.00, 0.00, 0.00, 0.00
d) 0.10, 0.20, 0.30, 0.40
e) 0.00, 0.00, 0.00, 1.00
Ans:
Section Ref: Time Series Models
Level: hard
29. The following sales figures show actual sales over the identified time period. What can be determined by comparing a simple mean forecast and a six month moving average forecast
December 4,000
January 5,000
February 4,000
March 4,500
April 5,500
May 5,000
a) moving average develops a smoother forecast
b) 4.7, 5
c) 4.7, 4
d) 4,4
e) 4, 4.7
Ans:
Section Ref: Time Series Models
Level: moderate
30. What are the most frequently used forecasting techniques?
a) Linear regression
b) Simple mean
c) Exponential smoothing
d) Weighted moving average
e) Econometric models
Ans:
Section Ref: Time Series Models
Level: moderate
31. In exponential smoothing, what values can the smoothing constant, a, have?
a) [1, 1]
b) [1, ¥]
c) [0, ¥]
d) [0, 1]
e) [¥, ¥]
Ans:
Section Ref: Time Series Models
Level: easy
32. In exponential smoothing, which of the following values for a would generate the most stable forecast?
a) 0.10
b) 0.25
c) 0.50
d) 0.75
e) 1.00
Ans:
Section Ref: Time Series Models
Level: moderate
33. Suppose that you are interested in trendadjusted exponential smoothing. Which of the following values of the trend smoothing constant, b, would most likely be seen in practice?
a) 0.10
b) 0.50
c) 0.75
d) 0.90
e) 1.00
Ans:
Section Ref: Time Series Models
Level: moderate
34. In linear regression, what are we trying to forecast?
a) Beta parameter
b) Dependent variable
c) Independent variable
d) Yintercept of the line
e) Slope of the line
Ans:
Section Ref: Causal Models
Level: moderate
35. What does the linear regression line do?
a) Minimizes sum of errors
b) Minimizes product of squared errors
c) Minimizes sum of squared errors
d) Minimizes product of errors
e) Minimizes sum of absolute value of errors
Ans:
Section Ref: CausalCausal Models
Level: moderate
36. What value of the correlation coefficient implies that there is a perfect positive linear relationship between the two variables of a linear regression model?
a) 1
b) 0
c) 0.5
d) 1
e) ¥
Ans:
Section Ref: CausalCausal Models
Level: easy
37. In linear regression, an r^{2} of .984 implies what?
a) 98.4% of the variability of the independent variable is explained by the dependent variable
b) 98.4% of the variability of the dependent variable is explained by the independent variable
c) 1.6% of the variability of the independent variable is explained by the dependent variable
d) 1.6% of the variability of the dependent variable is explained by the independent variable
e) 99.2% of the variability of the dependent variable is explained by the independent variable
Ans:
Section Ref: CausalCausal Models
Level: moderate
38. What value of the correlation coefficient implies that there is no relationship between the two variables of a linear regression model?
a) 1
b) 0
c) 0.5
d) 1
e) ¥
Ans:
Section Ref: CausalCausal Models
Level: easy
39. What is the statistic that measures the direction and strength of the linear relationship between two variables?
a) r^{2}
b) Coefficient of variation
c) Variance
d) Coefficient of kurtosis
e) Correlation coefficient
Ans:
Section Ref: CausalCausal Models
Level: moderate
40. Which of the following is true with respect to the correlation coefficient r?
a) r^{2} £ r
b) r^{2} £  r 
c) r^{2} ³ r
d) r^{2} ³  r 
e) r^{2} can never equal r
Ans:
Section Ref: CausalCausal Models
Level: moderate
41. Which of the following values of the correlation coefficient implies that the value of the dependent variable decreases as the value of the independent variable increases?
a) 0.2
b) 0
c) 0.2
d) 1
e) 0.5
Ans:
Section Ref: CausalCausal Models
Level: easy
42. The following correlation coefficient values come from five different linear regression models. Which model “fits” the data the best?
a) 0.99
b) 0.5
c) 0
d) 0.8
e) 1
Ans:
Section Ref: CausalCausal Models
Level: moderate
43. For what is a tracking signal used?
a) To identify trends in actual data
b) To identify seasonality in actual data
c) To identify the effect of business cycles on actual data
d) To compute the value of the smoothing constant, a, for exponential smoothing
e) To identify forecast bias
Ans:
Section Ref: Selecting The Right Forecasting Model
Level: moderate
44. Suppose that Sally’s company uses exponential smoothing to make forecasts. Further suppose that last period’s demand forecast was for 20,000 units, and last period’s actual demand was 21,000 units. Sally’s company uses a smoothing constant (α) equal to 40%. What should be the forecast for this period?
a) 20,000
b) 21,000
c) 20,600
d) 20,400
e) 19,600
Ans:
Section Ref: Time Series Models
Level: moderate
45. Suppose that Jane’s company uses exponential smoothing to make forecasts. Further suppose that last period’s demand forecast was for 500 units, and last period’s actual demand was 480 units. In addition, yesterday Jane found out that this period’s actual demand will be for 550 units. Jane’s company uses an α value of .20. Today Jane’s boss asked her to prepare a forecast for this period. What should that forecast be?
a) 504
b) 496
c) 510
d) 484
e) 550
Ans:
Section Ref: Time Series Models
Level: hard
46. A firm has the following order history over the last 6 months.
January 120
February 95
March 100
April 75
May 100
June 50
What would be a 3month weighted moving average forecast for July, using weights of 40% for the most recent month, 30% for the month preceding the most recent month, and 30% for the month preceding that one?
a) 75
b) 72.5
c) 50
d) 90
e) 106.5
Answer: b
Section Ref: Time Series Models
Level: moderate
47. What is the mean absolute deviation of the following forecasts?
Month Actual Sales Forecast
Jan. 614 600
Feb. 480 480
Mar. 500 550
Apr. 500 600
a) 3174
b) 164
c) 41
d) 136
e) 34
Ans:
Section Ref: Measuring Forecast Accuracy
Level: moderate
48. What is the mean absolute deviation and mean squared error of the following forecast
Day Sales 
Sale Forecast 
24 
37 
31 
41 
27 
46 
29 
47 
25 
50 
a) 13, 157
b) 14, 321
c) 16, 312
d) 17, 313
e) 18, 321
Ans:
Section Ref: Measuring Forecast Accuracy
Level: moderate
49. When is exponential smoothing equivalent to the “naïve” approach to forecasting?
a) When the smoothing constant is chosen randomly
b) α = 0
c) α = 1
d) α = .5
e) When next month’s forecast equals this month’s forecast
Ans:
Section Ref: Time Series Models
Level: hard
50. Consider the demand data listed below. What is the 4month moving average forecast for June?
Month Actual Demand
Jan. 10,000
Feb. 12,000
Mar. 24,000
Apr. 8,000
May 14,000
a) 14,000
b) Not enough information is given to answer the question.
c) 14,500
d) 13,500
e) 15,333
Ans:
Section Ref: Time Series Models
Level: moderate
51. Suppose that you want to set up a 3month weighted moving average forecasting system. You want the weights to be percentages (that add to 100%). Furthermore, you want weights for the most recent two months to be equal but you want each of those weights to be twice as large as the weight for the oldest month. What should the weight be for the oldest month?
a) 33%
b) 25%
c) 80%
d) 50%
e) 20%
Ans:
Section Ref: Time Series Models
Level: hard
52. Given the following data, use exponential smoothing (α = .2) to develop a demand forecast for period 3. Assume the forecast for the initial period is 5. What is the forecast?
Period Demand
1 7
2 9
a) 9.00
b) 3.72
c) 9.48
d) 5.00
e) 6.12
Ans:
Section Ref: Time Series Models
Level: moderate
53. Which of the following forecasting methods would be best (most accurate) if demand were rapidly decreasing?
a) 3month moving average
b) 6month moving average
c) 12month moving average
d) Simple mean
e) Exponential smoothing with a = 0.001
Ans:
Section Ref: Types of Forecasting Methods
Level: hard
54. Suppose that you are using the exponential smoothing forecasting method, and this period’s forecast (F_{t}) was 100% accurate (i.e., no error). If α = .5, which of the following is definitely true?
a) Next period’s forecast will also be 100% accurate.
b) Next period’s forecast equals this period’s actual.
c) This period’s forecast must be thrown out, and next period’s forecast equals
F_{t1} + α (A_{t1} − F_{t1}).
d) Next period’s forecast equals 50% of this period’s forecast.
e) Next period’s forecast equals 50% more than this period’s forecast.
Ans:
Section Ref: Time Series Models
Level: hard
55. A firm has had the following order history over the last 4 months:
November 140
December 80
January 100
February 150
What is the weighted moving average forecast for March, assuming a weight of 60% for the most recent month, 30% for the month preceding the most recent month, and 10% for the month preceding that one?
a) 117.5
b) 228.1
c) 118.0
d) 128.0
e) 132.4
Ans:
Section Ref: Time Series Models
Level: moderate
56. What is the mean absolute deviation of the following forecasts?
Month Actual Sales Forecast
January 68 60
February 48 50
March 50 60
April 30 30
a) 1
b) 5
c) 20
d) 1
e) 42
Ans:
Section Ref: Selecting the Right Forecasting Model
Level: moderate
57. What is the mean squared error of the following forecasts?
Month Actual Sales Forecast
Jan. 614 600
Feb. 480 480
Mar. 500 450
Apr. 500 600
a) 3174
b) 164
c) 41
d) 136
e) 12,696
Ans:
Section Ref: Selecting the Right Forecasting Model
Level: moderate
58. What is the mean squared error of the following forecasts?
Month Actual Sales Forecast
Jan. 68 60
Feb. 48 50
Mar. 50 60
Apr. 30 30
a) 168
b) 5
c) 20
d) 1
e) 42
Ans:
Section Ref: Selecting the Right Forecasting Model
Level: moderate
59. Suppose that you are using exponential smoothing with a = 0.5, and your initial forecast 5 months ago was for 100 units. If the actual demand last month was 0 units, which of the following is definitely true?
a) The forecast for this month should be 0.
b) The model blew up. You can’t use exponential smoothing anymore.
c) The forecast for last month was 0.
d) The forecast for this month should be 50.
e) We need more information to determine this month’s forecast.
Ans:
Section Ref: Time Series Models
Level: hard
60. Suppose that you are using the fourperiod simple moving average method to forecast sales, and sales have been increasing by 20% every period. How will your forecasts perform?
a) Forecasts will be lower than actual.
b) Forecasts will be higher than actual.
c) Forecasts will equal actual.
d) Forecasts will be decreasing.
e) Forecasts will be increasing by 5.0% every period.
Ans:
Section Ref: Time Series Models
Level: hard
61. Suppose that you are using the fourperiod simple moving average method to forecast sales, and sales have been increasing by 40% every period. How will your forecasts perform?
a) Forecasts will be increasing by 40.0% every period.
b) Forecasts will be higher than actual.
c) Forecasts will equal actual.
d) Forecasts will be decreasing.
e) Forecasts will be increasing by 10.0% every period.
Ans:
Section Ref: Time Series Models
Level: hard
62. Suppose that you are using the naïve forecasting method with trend to forecast sales. If sales have been increasing by 40% per month, and this month’s sales amounted to $1200, what would your forecast be for next month?
a) $1200
b) $ 480
c) $1680
d. $ 720
e) $1600
Ans:
Section Ref: Time Series Models
Level: moderate
63. Suppose that you are using the naïve forecasting method with trend to forecast sales. Sales have been increasing by 10% per week. Two weeks ago, sales amounted to $100. What should your forecast be for this week?
a) $100
b) $ 10
c) $110
d. $121
e) $120
Ans:
Section Ref: Time Series Models
Level: moderate
64. Suppose that you are using the simple mean to make a forecast. This period’s forecast was equal to 200 units, and it was based on 5 periods of demand. This period’s actual demand was 300 units. What is your forecast for next period?
a) 217
b) 250
c) 260
d) 300
e) 200
Ans:
Section Ref: Time Series Models
Level: hard
65. Suppose that you are using the simple mean to make a forecast. This period’s forecast was equal to 1000 units, and it was based on 99 periods of demand. This period’s actual demand was 0 units. What is your forecast for next period?
a) 1000
b) 990
c) 0
d) 1010
e) 999
Ans:
Section Ref: Time Series Models
Level: hard
66. A firm has the following order history over the last 6 months.
January 120
February 95
March 100
April 75
May 100
June 50
67. What would be the 4month simple moving average forecast for July?
a) 97.5
b) 325
c) 90
d) 81.25
e) 50
Ans:
Section Ref: Time Series Models
Level: moderate
68. Given the following data, use exponential smoothing (α = .1) to develop a demand forecast for period 3. Assume the forecast for the initial period is 500. What is the forecast?
Period Demand
1 600
2 200
a) 569
b) 470
c) 541
d) 551
e) 479
Ans:
Section Ref: Time Series Models
Level: moderate
69. Which of the following is the simplest forecasting method?
a) Naïve
b) Moving average
c) Weighted moving average
d) Trend adjusted exponential smoothing
e) Linear regression
Ans:
Section Ref: Types of Forecasting Methods
Level: easy
70. Which of the following would NOT be a consideration for selecting a forecasting software package?
a) How easy is the package to learn
b) Is it possible to implement new methods
c) Do you require repetitive forecasting
d) Does the supplier support a local conference
e) Is there any local support
Ans:
Section Ref: Forecasting Software
Level: moderate
71. Combined forecasting involves a rule that
a) you must work with different vendors
b) you need different forecasters
c) you must always use a quantitative and qualitative method
d) the results are not comparable to a single forecast
e) the forecasting methods should be different
Ans:
Section Ref: Combining Forecasts
Level: easy
72. Which of the following is not typically done jointly by CPFR trading partners?
a) set forecasts
b) plan production
c) replenish inventories
d) raise capital
e) evaluate their success in the marketplace
Ans:
Section Ref: Collaborative Planning, Forecasting, and Replenishment (CPFR)
Level: hard
73. ___________________________ is a collaborative process between two trading partners that establishes formal guidelines for joint forecasting and planning.
a) Collaborative Planning Forecasting and Replenishment (CPFR)
b) Supply Chain Planning Forecasting and Replenishment (SCPFR)
c) Supply Chain Optimization (SCO)
d) Collaborative Creation of Guidelines (CCG)
e) Joint Planning and Forecasting (JPP)
Ans:
Section Ref: Collaborative Planning, Forecasting, and Replenishment (CPFR)
Level: moderate
74. “Inside information” is most likely garnered through which of the following forecasting methods?
a) exponential smoothing
b) seasonal indexes
c) naïve
d) Delphi
e) multiple regression
Ans:
Section Ref: Types of Forecasting Methods
Level: hard
75. Which of the following is not one of the nine steps utilized in the most complete form of CPFR?
a) identify exceptions for order forecasts
b) create a sales forecast
c) create order forecast
d) create separate business plans
e) generate order
Ans:
Section Ref: Collaborative Planning, Forecasting, and Replenishment (CPFR)
Level: hard
True/False
1. Forecasts are more accurate for individual items than for groups or families of items.
Ans:
Section Ref: Principles of Forecasting
Level: moderate
2. Forecasting demand and forecasting sales are the same thing.
Ans:
Section Ref: Steps in the Forecasting Process
Level: moderate
3. A qualitative forecast is made subjectively by the forecaster.
Ans:
Section Ref: Types of Forecasting Methods
Level: easy
4. Planalytics is a company that helps businesses use weather data to make their business plans.
Ans:
Section Ref: Types of Forecasting Methods
Level: hard
5. Executive opinion is a forecasting method designed to preserve anonymity among the forecasters.
Ans:
Section Ref: Types of Forecasting Methods
Level: easy
6. With the Delphi method, a group of managers meets and collectively generates a forecast.
Ans:
Section Ref: Types of Forecasting Methods
Level: easy
7. The Delphi method of forecasting is preferred to the executive opinion method if an important consideration is eliminating any one person’s dominant opinion.
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
8. Time series models are generally more difficult to use than causal models.
Ans:
Section Ref: Types of Forecasting Methods
Level: moderate
9. A cycle is any data pattern that regularly repeats itself and is constant in length.
Ans:
Section Ref: Time Series Models
Level: moderate
10. A cycle is typically the most difficult data pattern to predict.
Ans:
Section Ref: Time Series Models
Level: moderate
11. The naïve forecasting method assumes that next period’s actual value will be equal to this period’s forecast.
Ans:
Section Ref: Time Series Models
Level: hard
12. The naïve forecasting method assumes that next period’s actual value will be equal to this period’s actual value.
Ans:
Section Ref: Time Series Models
Level: hard
13. The simple moving average forecasting method uses fewer periods of data than the simple mean forecasting method does.
Ans:
Section Ref: Time Series Models
Level: moderate
14. Suppose that you are using the threeperiod simple moving average method to forecast sales, and sales have been increasing by 10% every period. Then your forecasts will be increasing by 10% every period.
Ans:
Section Ref: Time Series Models
Level: hard
15. Suppose that you are using the threeperiod simple moving average method to forecast sales, and sales have been increasing by 10% every period. Then your forecasts will be lower than the actual sales.
Ans:
Section Ref: Time Series Models
Level: moderate
16. Moving average forecasts with a larger number of observations are more responsive than those with a smaller number of observations.
Ans:
Section Ref: Time Series Models
Level: moderate
17. Moving average forecasts with a smaller number of observations are more subject to random changes in the data than those with a larger number of observations.
Ans:
Section Ref: Time Series Models
Level: moderate
18. The MSE is always greater than or equal to the MAD.
Ans:
Section Ref: Selecting the Right Forecasting Model
Level: hard
19. Exponential smoothing forecasting methods requires a small amount of historical data.
Ans:
Section Ref: Selecting the Right Forecasting Model
Level: hard
20. Studies have shown that combining forecasts can lead to forecast accuracy that is better than that of the individual forecasts.
Ans:
Section Ref: Collaborative Planning, Forecasting, and Replenishment (CPFR)
Level: moderate
21. Focus forecasting needs to test the rule set once for the highest level of accuracy.
Ans:
Section Ref: Focus Forecasting
Level: moderate
22. CPFR is an iterative process.
Ans:
Section Ref: Collaborative Planning, Forecasting, and Replenishment (CPFR)
Level: moderate
23. Forecasting only impacts the business functions.
Ans:
Section Ref: Forecasting Within OM: How It All Fits Together
24. Economics relies on forecasting to predict the duration of economic turning points.
Ans:
Section Ref: Forecasting Across the Organization
Level: easy
Essay
1. Why are forecasts more accurate for groups or families of items rather than for individual items?
Ans:
2. What are the five basic steps in the forecasting process?
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3. What is the objective of the Delphi method?
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4. What are the two categories of quantitative models?
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5. Describe a data pattern with seasonality.
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6. Describe the naïve forecasting method.
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7. Explain the impact on forecasting that the number of observations used in a simple moving average has.
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8. What is a correlation coefficient?
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9. Discuss why the degree of accuracy is important in selecting the right forecasting model.
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10 Describe the idea behind focus forecasting.
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11. What is one of the simplest ways to combine forecasts?
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12. What are the 9 steps utilized in the most complete form of CPFR?
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Problems
1. Hoops, Inc. produces videos on the art of shooting in basketball. The firm has experienced the following demand for the first 6 months of the year.
Month Demand
Jan. 4,000
Feb. 6,000
Mar. 10,000
Apr. 2,000
May 20,000
June 30,000
What is the forecast for July using a 5month simple moving average forecast?
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2. Hoops, Inc., produces videos on the art of shooting in basketball. The firm has experienced the following demand for the first 6 months of the year.
Month Demand
Jan. 4,000
Feb. 6,000
Mar. 10,000
Apr. 2,000
May 20,000
June 30,000
What is the forecast for July using a 4month weighted moving average forecast, where (1) the weight for the most recent month is three times more than the weight for the period that’s four months in the past, (2) the weight for two periods ago is the same as the weight for the most recent period, (3) the weight for three periods ago is the same as the weight for the demand four periods ago, and (4) the weights sum to 100%?
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3. Hoops, Inc., produces videos on the art of shooting in basketball. The firm has experienced the following demand for the most recent four months.
Month Demand
Mar. 10,000
Apr. 2,000
May 20,000
June 30,000
Prepare an exponential smoothing forecast for July, using an a value of .40. Initiate the process by assuming that the forecast for March is 8,000 units.
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4. The Freewheel motorcycle dealer in the Chicago area wants to be able to forecast accurately the demand for the Freewheel Super Z12 motorcycle. From sales records, the dealer has accumulated the following data for the second half of 2000.
Month Sales
July 10
August 15
September 23
October 44
November 54
December 36
a. Compute a 3month moving average forecast of demand for January 2001.
b) Compute a 5month moving average forecast of demand for January 2001.
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5. John’s Office Supply Company has the following order history over the last 7 months.
April 65
May 180
June 30
July 90
August 120
September 190
October 70
Compute an exponential smoothing forecast for November, with a smoothing constant of α = .25. To start the procedure, assume that the forecast for April was 100. Round each forecast to two decimal places.
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6. John’s Office Supply Company has the following order history over the last 8 months.
April 50
May 75
June 160
July 120
August 80
September 120
October 180
November 90
Compute a 3month weighted moving average forecast for December, with a weight of 65% for the most recent month, 25% for the month preceding the most recent month, and 10% for the month preceding that one.
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7. Steve’s Steak House uses exponential smoothing with trend (alpha = .10 and beta = .40) to forecast its weekly demand for chopped steak in the metro area. Average sales have been 1000 steaks per week, and the recent trend has been an increase of 20 steaks per week. Actual demand last week was for 1040 steaks. What should the forecast be for this week?
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8. Joe’s Equipment Distributors sells “Low and Loud” brand lawnmowers. Total demand in 2002 is expected to be 2000 units. Given the historical sales figures listed below, derive a forecast for each quarter in 2002.
1999 2000 2001
Fall 50 80 120
Winter 150 450 510
Spring 500 600 700
Summer 400 490 610
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9. Central Heating needs to forecast the number of boilers they will sell in April. Using the following data and an alpha of .3 how many boilers should they have on hand?
Boilers sold 
forecast 

Nov 
12 
13 
Dec 
13.5 
12 
Jan 
14 
14 
Feb 
16 
15 
Mar 
17 
18 
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10. Cover Me, Inc. sells umbrellas in three cities. Management assumes that annual rainfall is the primary determinant of umbrella sales, and it wants to generate a linear regression equation to estimate potential sales in other cities. Given the data below, what is the regression equation?
Rainfall Sales
X Y
City A 36 in. 2300
City B 30 in. 2000
City C 12 in. 800
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11. Hoops, Inc. has the following actual demand and forecasted demand data.
Month Actual Demand Forecast
Jan. 1000 800
Feb. 200 880
Mar. 2000 600
Apr. 3000 1100
Calculate the mean squared error of those forecasts.
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12. Annie’s Smart Dog annual fair concession stand operates for five days. Last year they sold the following number of supreme smart dogs. Calculate (a) MAD, (MSE) and using a tracking signal of+/ 8 determine if the forecast should be reviewed.
Day Sales 
Sale Forecast 
24 
27 
31 
31 
27 
36 
29 
37 
25 
35 
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Short Answer
1. An observation's residual error is the ________________________ distance between itself and the linear regression line.
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2. Unlike MAD and MSE, the tracking signal's numerator allows positive and negative forecast errors to ________________________
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3. Developing a single forecast from several methods is called ________________________
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4. Companies that use collaborative planning, forecasting, and replenishment repeat its steps ________________________ and ________________________ annually.
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5. The basic principles of forecasting are: ________________________ , ________________________ , and ________________________ .
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6. What is the exponential smoothing formula? ________________________
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7. The most frequently used forecasting model is the _______
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8. The first round of an exponential smoothing process is often begun by setting the second period forecast equal to ________________________
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9. The difference between MSE and 2 is that ________________________
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10. The number of future periods forecast is called the ________________________
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11. Nearly all other business decisions depend on ________________________
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12. When developing the linear trend line you must calculate ____ before _____.
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13. Multiple regression is an _____ of linear regression.
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