Sunday, January 29, 2017

MBA Operations Management - Test Bank - Chapter 04a

Operations Management, 12e (Heizer/Render/Munson)
Chapter 4  Forecasting

 Section 1   What is Forecasting?

1) Forecasts may be influenced by a product's position in its life cycle.
Answer:  TRUE
Diff: 1
Learning Outcome:  Describe major approaches to forecasting

2) Demand forecasts serve as inputs to financial, marketing, and personnel planning.
Answer:  TRUE
Diff: 2
Key Term:  Demand forecasts
Learning Outcome:  Describe major approaches to forecasting

3) What two numbers are contained in the daily report to the CEO of Walt Disney Parks & Resorts regarding the six Orlando parks?
  1. A) yesterday's forecasted attendance and yesterday's actual attendance
  2. B) yesterday's actual attendance and today's forecasted attendance
  3. C) yesterday's forecasted attendance and today's forecasted attendance
  4. D) yesterday's actual attendance and last year's actual attendance
  5. E) yesterday's forecasted attendance and the year-to-date average daily forecast error
Answer:  A
Diff: 2
Learning Outcome:  Describe major approaches to forecasting

4) As compared to long-range forecasts, short-range forecasts:
  1. A) are less accurate.
  2. B) deal with less comprehensive issues supporting management decisions.
  3. C) employ similar methodologies.
  4. D) all of the above
  5. E) none of the above
Answer:  B
Diff: 2
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting

5) One use of short-range forecasts is to determine:
  1. A) planning for new products.
  2. B) capital expenditures.
  3. C) research and development plans.
  4. D) facility location.
  5. E) job assignments.
Answer:  E
Diff: 2
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting

6) Forecasts are usually classified by time horizon into which three categories?
  1. A) short-range, medium-range, and long-range
  2. B) finance/accounting, marketing, and operations
  3. C) strategic, tactical, and operational
  4. D) exponential smoothing, regression, and time series
  5. E) departmental, organizational, and industrial
Answer:  A
Diff: 1
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting

7) A forecast with a time horizon of about 3 months to 3 years is typically called a:
  1. A) long-range forecast.
  2. B) medium-range forecast.
  3. C) short-range forecast.
  4. D) weather forecast.
  5. E) strategic forecast.
Answer:  B
Diff: 2
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting

8) Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a:
  1. A) short-range time horizon.
  2. B) medium-range time horizon.
  3. C) long-range time horizon.
  4. D) naive method, because there is no data history.
  5. E) trend extrapolation.
Answer:  C
Diff: 2
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting

9) The three major types of forecasts used by organizations in planning future operations are:
  1. A) strategic, tactical, and operational.
  2. B) economic, technological, and demand.
  3. C) exponential smoothing, Delphi, and regression.
  4. D) causal, time-series, and seasonal.
  5. E) departmental, organizational, and territorial.
Answer:  B
Diff: 2
Learning Outcome:  Describe major approaches to forecasting

10) Which of the following most requires long-range forecasting (as opposed to short-range or medium-range forecasting) for its planning purposes?
  1. A) job scheduling
  2. B) production levels
  3. C) cash budgeting
  4. D) capital expenditures
  5. E) purchasing
Answer:  D
Diff: 2
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting

11) Short-range forecasts tends to ________ longer-range forecasts.
  1. A) be less accurate than
  2. B) be more accurate than
  3. C) have about the same level of accuracy as
  4. D) employ the same methodologies as
  5. E) deal with more comprehensive issues than
Answer:  B
Diff: 2
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting

12) ________ forecasts are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment.
Answer:  Technological
Diff: 1
Key Term:  Technological forecasts
Learning Outcome:  Describe major approaches to forecasting

13) ________ forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators.
Answer:  Economic
Diff: 2
Key Term:  Economic forecasts
Learning Outcome:  Describe major approaches to forecasting

14) A skeptical manager asks what short-range forecasts can be used for. Give her three possible uses/purposes.
Answer:  Any three of: planning purchasing, job scheduling, workforce levels, job assignments, and production levels.
Diff: 2
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting


15) A skeptical manager asks what long-range forecasts can be used for. Give her three possible uses/purposes.
Answer:  Any three of: planning for new products, capital expenditures, facility location or expansion, and research and development.
Diff: 2
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting
16) Describe the three forecasting time horizons and their use.
Answer:  Forecasting time horizons are: short range–generally less than three months, used for planning purchasing, job scheduling, workforce levels, job assignments, and production levels; medium range–usually from three months up to three years, used for sales planning, production planning and budgeting, cash budgeting, analysis of various operating plans; long range–usually three years or more, used for planning for new products, capital expenditures, facility location or expansion, and R&D.
Diff: 3
Objective:  LO 4.1 Understand the three time horizons and which models apply for each
Learning Outcome:  Describe major approaches to forecasting

17) List and briefly describe the three major types of forecasts that organizations use in planning future operations.
Answer:  The three types are economic, technological, and demand. Economic forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators. Technological forecasts are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment. Demand forecasts are projections of demand for a company's products or services.
Diff: 3
Learning Outcome:  Describe major approaches to forecasting

Section 2   The Strategic Importance of Forecasting

1) What forecasting systems combine the intelligence of multiple supply chain partners?
  1. A) FORE
  2. B) MULTISUP
  3. C) CPFR
  4. D) SUPPLY
  5. E) MSCP
Answer:  C
Diff: 3
Learning Outcome:  Describe major approaches to forecasting

Section 3   Seven Steps in the Forecasting System

1) Forecasts of individual products tend to be more accurate than forecasts of product families.
Answer:  FALSE
Diff: 2
Learning Outcome:  Describe major approaches to forecasting


2) Most forecasting techniques assume that there is some underlying stability in the system.
Answer:  TRUE
Diff: 2
Learning Outcome:  Describe major approaches to forecasting
3) Which of the following is NOT a step in the forecasting process?
  1. A) Determine the use of the forecast.
  2. B) Eliminate any assumptions.
  3. C) Determine the time horizon of the forecast.
  4. D) Select the forecasting model.
  5. E) Validate and implement the results.
Answer:  B
Diff: 2
Learning Outcome:  Describe major approaches to forecasting

4) Identify the seven steps involved in forecasting.
Answer:
  1. Determine the use of the forecast.
  2. Select the items to be forecasted.
  3. Determine the time horizon of the forecast.
  4. Select the forecasting model(s).
  5. Gather the data needed to make the forecast.
  6. Make the forecast.
  7. Validate and implement the results.
Diff: 2
Learning Outcome:  Describe major approaches to forecasting

5) What are the three realities of forecasting that companies face?
Answer:  First, outside factors that we cannot predict or control often impact the forecast. Second, most forecasting techniques assume that there is some underlying stability in the system. Finally, both product family and aggregated forecasts are more accurate than individual product forecasts.
Diff: 3
Learning Outcome:  Describe major approaches to forecasting

Section 4   Forecasting Approaches

1) The sales force composite forecasting method relies on salespersons' estimates of expected sales.
Answer:  TRUE
Diff: 1
Key Term:  Sales force composite
Objective:  LO 4.2 Explain when to use each of the four qualitative models
Learning Outcome:  Describe major approaches to forecasting

2) A time-series model uses a series of past data points to make the forecast.
Answer:  TRUE
Diff: 2
Key Term:  Time series
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

3) The quarterly "make meeting" of Lexus dealers is an example of a sales force composite forecast.
Answer:  TRUE
Diff: 2
Key Term:  Sales force composite
Objective:  LO 4.2 Explain when to use each of the four qualitative models
Learning Outcome:  Describe major approaches to forecasting
4) The two general approaches to forecasting are:
  1. A) qualitative and quantitative.
  2. B) mathematical and statistical.
  3. C) judgmental and qualitative.
  4. D) historical and associative.
  5. E) judgmental and associative.
Answer:  A
Diff: 1
Learning Outcome:  Describe major approaches to forecasting

5) Which of the following uses three types of participants: decision makers, staff personnel, and respondents?
  1. A) jury of executive opinion
  2. B) sales force composite
  3. C) Delphi method
  4. D) associative models
  5. E) time series
Answer:  C
Diff: 2
Key Term:  Delphi method
Objective:  LO 4.2 Explain when to use each of the four qualitative models
Learning Outcome:  Describe major approaches to forecasting

6) The forecasting technique that pools the opinions of a group of experts or managers is known as:
  1. A) the expert judgment model.
  2. B) multiple regression.
  3. C) jury of executive opinion.
  4. D) market survey.
  5. E) management coefficients.
Answer:  C
Diff: 2
Key Term:  Jury of executive opinion
Objective:  LO 4.2 Explain when to use each of the four qualitative models
Learning Outcome:  Describe major approaches to forecasting


7) Which of the following is not a type of qualitative forecasting?
  1. A) jury of executive opinion
  2. B) sales force composite
  3. C) market survey
  4. D) Delphi method
  5. E) moving average
Answer:  E
Diff: 2
Key Term:  Qualitative forecasts
Objective:  LO 4.2 Explain when to use each of the four qualitative models
Learning Outcome:  Describe major approaches to forecasting
8) Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand?
  1. A) associative models
  2. B) exponential smoothing
  3. C) weighted moving average
  4. D) moving average
  5. E) trend projection
Answer:  A
Diff: 2
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

9) ________ forecasts employ one or more mathematical models that rely on historical data and/or associative variables to forecast demand.
Answer:  Quantitative
Diff: 2
Key Term:  Quantitative forecasts
Learning Outcome:  Describe major approaches to forecasting

10) ________ is a forecasting technique based upon salespersons' estimates of expected sales.
Answer:  Sales force composite
Diff: 2
Key Term:  Sales force composite
Objective:  LO 4.2 Explain when to use each of the four qualitative models
Learning Outcome:  Describe major approaches to forecasting

11) ________ forecasts use a series of past data points to make a forecast.
Answer:  Time-series
Diff: 2
Key Term:  Time series
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

12) What are the differences between quantitative and qualitative forecasting methods?
Answer:  Quantitative methods use mathematical models to analyze historical data. Qualitative methods incorporate such factors as the decision maker's intuition, emotions, personal experiences, and value systems in determining the forecast.
Diff: 2
Learning Outcome:  Describe major approaches to forecasting

13) Identify four quantitative forecasting methods.
Answer:  The list includes naive, moving averages, exponential smoothing, trend projection, and linear regression.
Diff: 2
Key Term:  Quantitative forecasts
Learning Outcome:  Describe major approaches to forecasting

14) What is a time-series forecasting model?
Answer:  A time-series forecasting model uses a series of past data points to make a forecast.
Diff: 1
Key Term:  Time series
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
15) What is the difference between an associative model and a time-series model?
Answer:  A time-series model uses only historical values of the quantity of interest to predict future values of that quantity. The associative model, on the other hand, incorporates the variables or factors that might influence the quantity being forecast.
Diff: 2
Key Term:  Time series
Learning Outcome:  Describe major approaches to forecasting

16) Name and discuss three qualitative forecasting methods.
Answer:  Qualitative forecasting methods include: jury of executive opinion, where high-level managers arrive at a group estimate of demand; sales force composite, where salespersons' estimates are aggregated; Delphi method, which uses a group process that allows experts to make forecasts; and market survey, where consumers are queried about their future purchasing plans.
Diff: 2
Key Term:  Qualitative forecasts
Objective:  LO 4.2 Explain when to use each of the four qualitative models
Learning Outcome:  Describe major approaches to forecasting

Section 5   Time-Series Forecasting

1) A naïve forecast for September sales of a product would be equal to the forecast for August.
Answer:  FALSE
Diff: 2
Key Term:  Naive approach
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

2) Cycles and random variations are both components of time series.
Answer:  TRUE
Diff: 1
Key Term:  Time series
Learning Outcome:  Describe major approaches to forecasting


3) A naïve forecast for September sales of a product would be equal to the sales in August.
Answer:  TRUE
Diff: 1
Key Term:  Naive approach
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

4) One advantage of exponential smoothing is the limited amount of record keeping involved.
Answer:  TRUE
Diff: 2
Key Term:  Exponential smoothing
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
5) The larger the number of periods in the simple moving average forecasting method, the greater the method's responsiveness to changes in demand.
Answer:  FALSE
Diff: 2
Key Term:  Moving averages
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

6) Mean squared error and exponential smoothing are two measures of the overall error of a forecasting model.
Answer:  FALSE
Diff: 1
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting

7) In trend projection, the trend component is the slope of the regression equation.
Answer:  TRUE
Diff: 1
Key Term:  Trend projection
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

8) In trend projection, a negative regression slope is mathematically impossible.
Answer:  FALSE
Diff: 2
Key Term:  Trend projection
AACSB:  Reflective thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


9) Seasonal indices adjust raw data for patterns that repeat at regular time intervals.
Answer:  TRUE
Diff: 2
Key Term:  Seasonal variations
AACSB:  Reflective thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

10) A trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y per period.
Answer:  TRUE
Diff: 2
Key Term:  Trend projection
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

11) Demand for individual products can be driven by product life cycles.
Answer:  TRUE
Diff: 2
Learning Outcome:  Describe major approaches to forecasting
12) Which of the following statements about time-series forecasting is true?
  1. A) It is always based on the assumption that future demand will be the same as past demand.
  2. B) It makes extensive use of the data collected in the qualitative approach.
  3. C) It is based on the assumption that the analysis of past demand helps predict future demand.
  4. D) Because it accounts for trends, cycles, and seasonal patterns, it is always more powerful than associative forecasting.
  5. E) All of the above are true.
Answer:  C
Diff: 2
Key Term:  Time series
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

13) Time-series data may exhibit which of the following behaviors?
  1. A) trend
  2. B) random variations
  3. C) seasonality
  4. D) cycles
  5. E) They may exhibit all of the above.
Answer:  E
Diff: 2
Key Term:  Time series
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


14) Gradual upward or downward movement of data over time is called:
  1. A) seasonality.
  2. B) a cycle.
  3. C) a trend.
  4. D) exponential variation.
  5. E) random variation.
Answer:  C
Diff: 2
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

15) Which of the following is not present in a time series?
  1. A) seasonality
  2. B) operational variations
  3. C) trend
  4. D) cycles
  5. E) random variations
Answer:  B
Diff: 2
Key Term:  Time series
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
16) The fundamental difference between cycles and seasonality is the:
  1. A) duration of the repeating patterns.
  2. B) magnitude of the variation.
  3. C) ability to attribute the pattern to a cause.
  4. D) all of the above
  5. E) none of the above
Answer:  A
Diff: 2
Learning Outcome:  Describe major approaches to forecasting

17) In time series, which of the following cannot be predicted?
  1. A) large increases in demand
  2. B) cycles
  3. C) seasonal fluctuations
  4. D) random variations
  5. E) large decreases in demand
Answer:  D
Diff: 2
Key Term:  Time series
Learning Outcome:  Describe major approaches to forecasting


18) What is the forecast for May using a four-month moving average?

Nov. Dec. Jan. Feb. Mar. April
39 36 40 42 48 46

  1. A) 38
  2. B) 42
  3. C) 43
  4. D) 44
  5. E) 47
Answer:  D
Diff: 2
Key Term:  Moving averages
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

19) Which time-series model below assumes that demand in the next period will be equal to the most recent period's demand?
  1. A) naïve approach
  2. B) moving average approach
  3. C) weighted moving average approach
  4. D) exponential smoothing approach
  5. E) trend projection
Answer:  A
Diff: 1
Key Term:  Naive approach
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
20) 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?
  1. A) 2400
  2. B) 2511
  3. C) 2067
  4. D) 3767
  5. E) 1622
Answer:  B
Diff: 2
Key Term:  Moving averages
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


21) A six-month moving average forecast is generally better than a three-month moving average forecast if demand:
  1. A) is rather stable.
  2. B) has been changing due to recent promotional efforts.
  3. C) follows a downward trend.
  4. D) exceeds one million units per year.
  5. E) follows an upward trend.
Answer:  A
Diff: 2
Key Term:  Moving averages
AACSB:  Reflective thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

22) Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of:
  1. A) manager understanding.
  2. B) accuracy.
  3. C) stability.
  4. D) sensitivity to real changes in the data.
  5. E) All of the above are diminished when the number of periods increases.
Answer:  D
Diff: 2
Key Term:  Moving averages
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
23) Which of the following statements comparing exponential smoothing to the weighted moving average technique is TRUE?
  1. A) Exponential smoothing is more easily used in combination with the Delphi method.
  2. B) More emphasis can be placed on recent values using the weighted moving average.
  3. C) Exponential smoothing is considerably more difficult to implement on a computer.
  4. D) Exponential smoothing typically requires less record keeping of past data.
  5. E) Exponential smoothing allows one to develop forecasts for multiple periods, whereas the weighted moving average technique does not.
Answer:  D
Diff: 2
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


24) Which time-series model uses BOTH past forecasts and past demand data to generate a new forecast?
  1. A) naïve
  2. B) moving average
  3. C) weighted moving average
  4. D) exponential smoothing
  5. E) trend projection
Answer:  D
Diff: 2
Key Term:  Exponential smoothing
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

25) Which of the following is NOT a characteristic of exponential smoothing?
  1. A) smoothes random variations in the data
  2. B) uses an easily altered weighting scheme
  3. C) weights each historical value equally
  4. D) has minimal data storage requirements
  5. E) uses the previous period's forecast
Answer:  C
Diff: 2
Key Term:  Exponential smoothing
AACSB:  Reflective thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

26) Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?
  1. A) 0
  2. B) 1 divided by the number of periods
  3. C) 0.5
  4. D) 1.0
  5. E) cannot be determined
Answer:  D
Diff: 3
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

27) Given an actual demand this period of 103, a forecast value for this period of 99, and an alpha of .4, what is the exponential smoothing forecast for next period?
  1. A) 94.6
  2. B) 97.4
  3. C) 100.6
  4. D) 101.6
  5. E) 103.0
Answer:  C
Diff: 2
Key Term:  Exponential smoothing
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

28) A forecast based on the previous forecast plus a percentage of the forecast error is a(n):
  1. A) qualitative forecast.
  2. B) naive forecast.
  3. C) moving average forecast.
  4. D) weighted moving average forecast.
  5. E) exponential smoothing forecast.
Answer:  E
Diff: 2
Key Term:  Exponential smoothing
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

29) Given an actual demand this period of 61, a forecast for this period of 58, and an alpha of 0.3, what would the forecast for the next period be using exponential smoothing?
  1. A) 45.5
  2. B) 57.1
  3. C) 58.9
  4. D) 61.0
  5. E) 65.5
Answer:  C
Diff: 2
Key Term:  Exponential smoothing
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


30) Which of the following values of alpha would cause exponential smoothing to respond the SLOWEST to forecast errors?
  1. A) 0.10
  2. B) 0.2246
  3. C) 0.50
  4. D) 0.90
  5. E) cannot be determined
Answer:  A
Diff: 2
Key Term:  Exponential smoothing
AACSB:  Reflective thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
31) 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

  1. A) -0.2
  2. B) -1.0
  3. C) 0.0
  4. D) 1.2
  5. E) 8.6
Answer:  D
Diff: 2
Key Term:  Mean absolute deviation (MAD)
AACSB:  Analytical thinking
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting

32) The primary purpose of the mean absolute deviation (MAD) in forecasting is to:
  1. A) estimate the trend line.
  2. B) eliminate forecast errors.
  3. C) measure forecast accuracy.
  4. D) seasonally adjust the forecast.
  5. E) remove random variations.
Answer:  C
Diff: 2
Key Term:  Mean absolute deviation (MAD)
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting


33) Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?
  1. A) 2
  2. B) 3
  3. C) 4
  4. D) 8
  5. E) 16
Answer:  C
Diff: 2
Key Term:  Mean absolute deviation (MAD)
AACSB:  Analytical thinking
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting
34) Suppose that the last four months of sales were 8, 10, 15, and 9 units, respectively. Suppose further that the last four forecasts were 5, 6, 11, and 12 units, respectively. What is the Mean Absolute Deviation (MAD) of these forecasts?
  1. A) 2
  2. B) -10
  3. C) 3.5
  4. D) 9
  5. E) 10.5
Answer:  C
Diff: 2
Key Term:  Mean absolute deviation (MAD)
AACSB:  Analytical thinking
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting

35) A time-series trend equation is 25.3 + 2.1x. What is your forecast for period 7?
  1. A) 23.2
  2. B) 25.3
  3. C) 27.4
  4. D) 40.0
  5. E) 179.2
Answer:  D
Diff: 2
Key Term:  Trend projection
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


36) For a given product demand, the time-series trend equation is 53 - 4x. The negative sign on the slope of the equation:
  1. A) is a mathematical impossibility.
  2. B) is an indication that the forecast is biased, with forecast values lower than actual values.
  3. C) is an indication that product demand is declining.
  4. D) implies that the coefficient of determination will also be negative.
  5. E) implies that the cumulative error will be negative.
Answer:  C
Diff: 2
Key Term:  Trend projection
AACSB:  Reflective thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
37) Yamaha manufactures which set of products with complementary demands to address seasonal variations?
  1. A) golf clubs and skis
  2. B) swimming suits and winter jackets
  3. C) jet skis and snowmobiles
  4. D) pianos and guitars
  5. E) ice skates and water skis
Answer:  C
Diff: 2
Key Term:  Seasonal variations
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

38) Which of the following is TRUE regarding the two smoothing constants of the Forecast Including Trend (FIT) model?
  1. A) One constant is positive, while the other is negative.
  2. B) They are called MAD and cumulative error.
  3. C) Alpha is always smaller than beta.
  4. D) One constant smoothes the regression intercept, whereas the other smoothes the regression slope.
  5. E) Their values are determined independently.
Answer:  E
Diff: 2
Key Term:  Exponential smoothing
AACSB:  Reflective thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


39) 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. A) 640 units
  2. B) 798.75 units
  3. C) 801.25 units
  4. D) 1000 units
  5. E) 83.33 units
Answer:  D
Diff: 2
Key Term:  Seasonal variations
AACSB:  Analytical thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting
40) 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 demand over all months during the three-year time period was 190. What is the approximate seasonal index for July?
  1. A) 0.487
  2. B) 0.684
  3. C) 1.462
  4. D) 2.053
  5. E) cannot be calculated with the information given
Answer:  B
Diff: 2
Key Term:  Seasonal variations
AACSB:  Analytical thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

41) Suppose that the 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.)
  1. A) 9.00
  2. B) 3.72
  3. C) 9.48
  4. D) 5.00
  5. E) 6.12
Answer:  E
Diff: 2
Key Term:  Exponential smoothing
AACSB:  Analytical thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting


42) ________ expresses the error as a percent of the actual values.
  1. A) MAD
  2. B) MSE
  3. C) MAPE
  4. D) FIT
  5. E) The smoothing constant
Answer:  C
Diff: 2
Key Term:  Mean absolute percent error (MAPE)
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting
43) If Brandon Edward were working to develop a forecast using a moving averages approach, but he noticed a detectable trend in the historical data, he should:
  1. A) use weights to place more emphasis on recent data.
  2. B) use weights to minimize the importance of the trend.
  3. C) change to an associative multiple regression approach.
  4. D) use a simple moving average.
  5. E) change to a qualitative approach.
Answer:  A
Diff: 2
Key Term:  Moving averages
AACSB:  Reflective thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

44) A(n) ________ forecast uses an average of the most recent periods of data to forecast the next period.
Answer:             moving average (or simple moving average)
Diff: 2
Key Term:  Moving averages
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

45) The smoothing constant is a weighting factor used in ________.
Answer:  exponential smoothing
Diff: 2
Key Term:  Exponential smoothing
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

46) A measure of forecast error that does not depend upon the magnitude of the item being forecast is the ________.
Answer:             mean absolute percent error (or MAPE)
Diff: 1
Key Term:  Mean absolute percent error (MAPE)
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting


47) When one constant is used to smooth the forecast average and a second constant is used to smooth the trend, the forecasting method is ________.
Answer:  exponential smoothing with trend adjustment or trend-adjusted smoothing or second-order smoothing or double smoothing
Diff: 2
Key Term:  Exponential smoothing
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

48) ________ is a time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts.
Answer:  Trend projection
Diff: 1
Key Term:  Trend projection
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
49) Simple ________ forecasts only work well if we can assume that market demands will stay fairly steady over time.
Answer:  moving average
Diff: 1
Key Term:  Moving averages
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

50) If a barbershop operator noted that Tuesday's business was typically twice as heavy as Wednesday's, and that Friday's business was typically the busiest of the week, business at the barbershop is subject to ________.
Answer:             seasonal variations (or seasonality)
Diff: 2
Key Term:  Seasonal variations
AACSB:  Reflective thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

51) Identify the four components of a time series. Which one of these is rarely forecast? Why is this so?
Answer:  Trend, seasonality, cycles, and random variation. Since random variations follow no discernible pattern, they cannot be predicted, and thus are not forecast.
Diff: 2
Key Term:  Time series
Learning Outcome:  Describe major approaches to forecasting

52) Compare seasonal effects and cyclical effects.
Answer:  A cycle is longer (typically several years) than a season (typically days, weeks, months, or quarters). A cycle has variable duration, while a season has fixed duration and regular repetition. Cycles include a wide variety of factors that cause the economy to go from recession to expansion to recession over a period of years.
Diff: 2
Learning Outcome:  Describe major approaches to forecasting


53) Distinguish between a weighted moving average model and an exponential smoothing model.
Answer:  Exponential smoothing is a weighted moving average model wherein previous values are weighted in a specific manner--in particular, all previous values are weighted with a set of weights that decline exponentially. Also, exponential smoothing involves little record keeping of past data.
Diff: 2
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

54) Describe three popular measures of forecast accuracy.
Answer:  Measures of forecast accuracy include: (a) MAD (mean absolute deviation) is a sum of the absolute values of individual errors divided by the number of periods of data; (b) MSE (mean squared error) is the average of the squared differences between the forecast and observed values; and (c) MAPE (mean absolute percent error) is the average of the absolute differences between the forecast and actual values, expressed as a percent of actual values.
Diff: 2
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting
55) Give an example, other than a restaurant or other food-service firm, of an organization that experiences an hourly seasonal pattern. (That is, each hour of the day has a pattern that tends to repeat day after day.) Explain.
Answer:  Answer will vary. However, two non-food examples would be banks and movie theaters.
Diff: 2
Key Term:  Seasonal variations
AACSB:  Reflective thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

56) Explain the role of regression models (time series and otherwise) in forecasting. That is, how is trend projection able to forecast? How is regression used for causal forecasting?
Answer:  For trend projection, the independent variable is time. The trend projection equation has a slope that is the change in demand per period. To forecast the demand for period x, perform the calculation a + bx. For causal forecasting, the independent variables are predictors of the forecast value or dependent variable. The slope of the regression equation is the change in the Y variable per unit change in the X variable.
Diff: 3
Learning Outcome:  Describe major approaches to forecasting

57) Identify two advantages of the moving average forecasting model. Identify two disadvantages of the moving average forecasting model.
Answer:  Advantages of the model include: it uses simple calculations, it smoothes out sudden fluctuations, and it is easy for users to understand. Disadvantages include: increasing the size of n makes the method less sensitive to real changes in demand, they require extensive record keeping of past data, and they do not pick up on trends very well.
Diff: 2
Key Term:  Moving averages
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


58) What does it mean to "decompose" a time series?
Answer:  To decompose a time series means to break past data down into components of trend, seasonality, cycles, and random variations, and then to project them forward.
Diff: 2
Key Term:  Time series
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

59) What is the key difference between weighted moving average and simple moving average approaches to forecasting?
Answer:  Simple moving averages are useful where there is no identifiable trend in the historical data, i.e., demand has been fairly steady over time. If there were an identifiable trend, weighted moving averages would provide a more accurate forecast because higher weights would be put on the more recent data.
Diff: 2
Key Term:  Moving averages
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
60) Weekly sales of ten-grain bread at the local organic food market are provided in the table below. Based on these data, forecast week 9 using a five-week moving average.

Week              Sales
    1                    415
    2                    389
    3                    420
    4                    382
    5                    410
    6                    432
    7                    405
    8                    421
Answer:  (382 + 410 + 432 + 405 + 421)/5 = 410.0
Diff: 1
Key Term:  Moving averages
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


61) Given the following data, calculate the three-year moving averages for years 4 through 10.

Year Demand
1 74
2 90
3 59
4 91
5 140
6 98
7 110
8 123
9 99

Answer:
Year Demand 3-Year Moving Ave.
1 74
2 90
3 59
4 91  74.33
5 140  80.00
6 98  96.67
7 110 109.67
8 123 116.00
9 99 110.33
10 110.67

Diff: 2
Key Term:  Moving averages
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting
62) What is the forecast for May based on a weighted moving average applied to the following past demand data and using the weights: 4, 3, 2 (largest weight is for most recent data)?

Nov. Dec. Jan. Feb. Mar. April
37 36 40 42 47 43

Answer:  2 × 42 + 3 × 47 + 4 × 43 = 84 + 141 + 172 = 397; 397/9 = 44.1
Diff: 1
Key Term:  Moving averages
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


63) Weekly sales of copy paper at Cubicle Suppliers are provided in the table below. Compute a three-period moving average and a four-period moving average for weeks 5, 6, and 7. Compute the MAD for both forecasting methods. Which model is more accurate? Forecast week 8 with the more accurate method.

Week               Sales (cases)
    1                            17
    2                            21
    3                            27
    4                            31
    5                            19
    6                            17
    7                            21
Answer:
Week Sales (cases) 3MA |error| 4MA |error|
1 17
2 21
3 27
4 31
5 19 26.3 7.3 24.0 5.0
6 17 25.7 8.7 24.5 7.5
7 21 22.3 1.3 23.5 2.5
8 22.0

The MAD for the 3-week moving average is (7.3 + 8.7 + 1.3) / 3 = 5.77. The MAD for the 4-week moving average is (5.0 + 7.5 + 2.5) / 3 = 5.00. The four-week moving average is more accurate. The forecast with the 4-moving average is 22.0.
Diff: 2
AACSB:  Analytical thinking
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting
64) The last four weekly values of sales were 80, 100, 105, and 90 units, respectively. The last four forecasts (for the same four weeks) were 60, 80, 95, and 75 units, respectively. Calculate the MAD, MSE, and MAPE for these four weeks.

Sales Forecast Error Error squared Pct. error
80 60 20 400 .25
100 80 20 400 .20
105 95 10 100 .095
90 75 15 225 .167

Answer:  MAD = 65/4 = 16.25; MSE = 1125/4 = 281.25; MAPE = 0.712/4 = .178 or 17.8%
Diff: 2
AACSB:  Analytical thinking
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting

65) A management analyst is using exponential smoothing to predict merchandise returns at an upscale branch of a department store chain. Given an actual number of returns of 154 items in the most recent period completed, a forecast of 172 items for that period, and a smoothing constant of 0.3, what is the forecast for the next period? How would the forecast be changed if the smoothing constant were 0.6? Explain the difference in terms of alpha and responsiveness.
Answer:  166.6; 161.2  The larger the smoothing constant in an exponentially smoothed forecast, the more responsive the forecast.
Diff: 2
Key Term:  Exponential smoothing
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

66) The following trend projection is used to predict quarterly demand: y-hat = 250 - 2.5x, where x = 1 in the first quarter. Seasonal (quarterly) indices are Quarter 1 = 1.5; Quarter 2 = 0.8; Quarter 3 = 1.1; and Quarter 4 = 0.6. What is the seasonally adjusted forecast for the next four quarters?
Answer:
 Quarter     Projection         Adjusted
      1                247.5                371.25
      2                  245                    196
      3                242.5                266.75
      4                  240                    144
Diff: 3
AACSB:  Analytical thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting
67) Favors Distribution Company purchases small imported trinkets in bulk, packages them, and sells them to retail stores. The managers are conducting an inventory control study of all their items. The following data are for one such item, which is not seasonal.

  1. Use a trend projection to estimate the relationship between time and sales (state the equation).
  2. Calculate forecasts for the first four months of the next year.

1 2 3 4 5 6 7 8 9 10 11 12
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Sales 51 55 54 57 50 68 66 59 67 69 75 73

Answer:  The trend projection equation is y-hat = 48.32 + 2.105x. The next four months are forecast to be 75.68, 77.79, 79.89, and 82.00
Diff: 2
Key Term:  Trend projection
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

68) Use exponential smoothing with trend adjustment to forecast deliveries for period 10. Let alpha = 0.4, beta = 0.2, and let the initial trend value be 4 and the initial forecast be 200.

Period Actual
Demand
1 200
2 212
3 214
4 222
5 236
6 221
7 240
8 244
9 250
10 266

Answer:
Actual Forecast Trend FIT
1 200 200.00 4.00
2 212 202.40 3.68 206.08
3 214 208.45 4.15 212.60
4 222 213.16 4.27 217.43
5 236 219.26 4.63 223.89
6 221 228.73 5.60 234.33
7 240 229.00 4.53 233.53
8 244 236.12 5.05 241.17
9 250 242.30 5.28 247.58
10 266 248.55 5.47 254.02

Diff: 3
Key Term:  Exponential smoothing
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting

69) A restaurant has tracked the number of meals served at lunch over the last four weeks. The data show little in terms of trends, but do display substantial variation by day of the week. Use the following information to determine the seasonal (daily) indices for this restaurant.

Week
Day 1 2 3 4
Sunday 40 35 39 43
Monday 54 55 51 59
Tuesday 61 60 65 64
Wednesday 72 77 78 69
Thursday 89 80 81 79
Friday 91 90 99 95
Saturday 80 82 81 83

Answer:
Day Index
Sunday 0.5627
Monday 0.7855
Tuesday 0.8963
Wednesday 1.0618
Thursday 1.1800
Friday 1.3444
Saturday 1.1692

Diff: 3
Key Term:  Seasonal variations
AACSB:  Analytical thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

70) Demand for a certain product is forecast to be 8,000 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?
Answer:  8,000 × 1.25 = 10,000
Diff: 1
Key Term:  Seasonal variations
AACSB:  Analytical thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

71) 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, 135, and 130. The average over all months is 160. What is the approximate seasonal index for July?
Answer:  (110 + 135 + 130)/3 = 125; 125/160 = 0.781
Diff: 2
Key Term:  Seasonal variations
AACSB:  Analytical thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

72) Marie Bain is the production manager at a company that manufactures hot water heaters. Marie needs a demand forecast for the next few years to help decide whether to add new production capacity. The company's sales history (in thousands of units) is shown in the table below. Use exponential smoothing with trend adjustment to forecast demand for period 6. The initial forecast for period 1 was 11 units; the initial estimate of trend was 0. The smoothing constants are α = .3 and β = .3

Period Actual
1 12
2 15
3 16
4 16
5 18
6 20

Answer:
Period Actual Forecast Trend FIT
1 12 11.00 0.00
2 15 11.30 0.09 11.39
3 16 12.47 0.41 12.89
4 16 13.82 0.69 14.52
5 18 14.96 0.83 15.79
6 20 16.45 1.03 17.48

Diff: 3
Key Term:  Exponential smoothing
AACSB:  Analytical thinking
Objective:  LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods
Learning Outcome:  Describe major approaches to forecasting


73) The quarterly sales for specific educational software over the past three years are given in the following table. Compute the four seasonal factors.

YEAR 1 YEAR 2 YEAR 3
Quarter 1 1710 1820 1830
Quarter 2 960 910 1090
Quarter 3 2720 2840 2900
Quarter 4 2430 2200 2590

Answer:
Avg. Sea. Fact.
Quarter 1 1786.67 0.8933
Quarter 2 986.67 0.4933
Quarter 3 2820.00 1.4100
Quarter 4 2406.67 1.2033
Grand Average 2000.00

Diff: 2
Key Term:  Seasonal variations
AACSB:  Analytical thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

74) The last seven weeks of demand at a new car dealer are shown below. Use a three-period weighted-moving average forecast to determine a forecast for the 8th week using weights of 3, 2, and 1 (where the most recent week receives the highest weight). (Round all forecasts to the nearest whole unit.) Calculate the MAD for this forecast (covering all weeks in which error comparisons can be made). What does the MAD indicate?

Week              Sales
    1                     25
    2                     30
    3                     27
    4                     31
    5                     27
    6                     29
    7                     30
Answer:
Week       Sales                         3WMA               |error|
    1              25
    2              30
    3              27
    4              31                                28                         3
    5              27                                30                         3
    6              29                                28                         1
    7              30                                29                         1
    8                                                  29

MAD = 8/4 = 2
An MAD of 2 means that the forecasting technique used was typically off by 2 units each period.
Diff: 2
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting

75) A small family-owned restaurant uses a seven-day moving average model to determine manpower requirements. These forecasts need to be seasonalized because each day of the week has its own demand pattern. The seasonal indices for each day of the week are: Monday 0.445; Tuesday 0.791; Wednesday 0.927; Thursday 1.033; Friday 1.422; Saturday 1.478; and Sunday 0.903. Average daily demand based on the most recent moving average is 194 patrons. What is the seasonalized forecast for each day of next week?
Answer:  The average value multiplied by each day's seasonal index. Monday: 194 × .445 = 86; Tuesday: 194 × .791 = 153; Wednesday: 194 × .927 = 180; Thursday: 194 × 1.033 = 200; Friday: 194 × 1.422 = 276; Saturday: 194 × 1.478 = 287; and Sunday: 194 × .903 = 175.
Diff: 2
Key Term:  Seasonal variations
AACSB:  Analytical thinking
Objective:  LO 4.5 Develop seasonal indices
Learning Outcome:  Describe major approaches to forecasting

76) The department manager using a combination of methods has forecast sales of toasters at a local department store. Calculate the MAD for the manager's forecast. Compare the manager's forecast against a naive forecast covering the same time period. Which is better?

Month Unit Sales Manager's Forecast
January 52
February 61
March 73
April 79
May 66
June 51
July 47 50
August 44 55
September 30 52
October 55 42
November 74 60
December 125 75

Answer:
Month Actual Manager's Abs. Error Naive Abs. Error
January 52
February 61
March 73
April 79
May 66
June 51
July 47 50 3 51 4
August 44 55 11 47 3
September 30 52 22 44 14
October 55 42 13 30 25
November 74 60 14 55 19
December 125 75 50 74 51

The manager's forecast has a MAD of 18.83, while the naive is 19.33. Therefore, the manager's forecast is slightly better than the naive.
Diff: 2
Key Term:  Mean absolute deviation (MAD)
AACSB:  Analytical thinking
Objective:  LO 4.4 Compute three measures of forecast accuracy
Learning Outcome:  Describe major approaches to forecasting


Section 6   Associative Forecasting Methods: Regression and Correlation Analysis

1) Linear-regression analysis is a straight-line mathematical model to describe the functional relationships between independent and dependent variables.
Answer:  TRUE
Diff: 1
Key Term:  Linear-regression analysis
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting
2) The larger the standard error of the estimate, the more accurate the forecasting model.
Answer:  FALSE
Diff: 1
Key Term:  Standard error of the estimate
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

3) In a regression equation where y-hat is demand and x is advertising, a coefficient of determination (R2) of .70 means that 70% of the variance in advertising is explained by demand.
Answer:  FALSE
Diff: 2
Key Term:  Coefficient of determination
AACSB:  Analytical thinking
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

4) Regression lines graphically depict "cause-and-effect" relationships.
Answer:  FALSE
Diff: 3
Key Term:  Linear-regression analysis
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

5) A fundamental distinction between trend projection and linear regression is that:
  1. A) trend projection uses least squares while linear regression does not.
  2. B) only linear regression can have a negative slope.
  3. C) 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. D) trend projection can be a function of several variables, while linear regression can only be a function of one variable.
  5. E) trend projection uses two smoothing constants, not just one.
Answer:  C
Diff: 2
Learning Outcome:  Describe major approaches to forecasting


6) The degree or strength of a relationship between two variables is shown by the:
  1. A) alpha.
  2. B) mean.
  3. C) mean absolute deviation.
  4. D) coefficient of correlation.
  5. E) cumulative error.
Answer:  D
Diff: 2
Key Term:  Coefficient of correlation
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting
7) If two variables were perfectly correlated, what would the coefficient of correlation r equal?
  1. A) 0
  2. B) -1
  3. C) 1
  4. D) B or C
  5. E) none of the above
Answer:  D
Diff: 3
Key Term:  Coefficient of correlation
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

8) Linear regression is known as a(n) ________ model because it incorporates variables or factors that might influence the quantity being forecast.
Answer:  associative forecasting
Diff: 1
Key Term:  Linear-regression analysis
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

9) The ________ measures the strength of the relationship between two variables.
Answer:  coefficient of correlation
Diff: 2
Key Term:  Coefficient of correlation
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

10) Distinguish a dependent variable from an independent variable.
Answer:  The dependent variable is the factor or behavior that we are trying to explain or predict. The independent variables are believed to impact the dependent variable and thus affect its value.
Diff: 2
Key Term:  Linear-regression analysis
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting


11) Explain, in your own words, the meaning of the coefficient of determination.
Answer:  The coefficient of determination measures the amount (percent) of total variation in the data that is explained by the model.
Diff: 2
Key Term:  Coefficient of determination
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting
12) A firm has modeled its experience with industrial accidents and found that the number of accidents per year (y-hat) is related to the number of employees (x) by the regression equation:
y-hat = 3.3 + 0.049x. The r-squared value is 0.68. The regression is based on 20 annual observations. The firm intends to employ 480 workers next year. How many accidents do you project? How much confidence do you have in that forecast?
Answer:  y-hat = 3.3 + 0.049(480) = 3.3 + 23.52 = 26.82 accidents. This is not a time series, so next year = year 21 is of no relevance. Confidence comes from the coefficient of determination; the model explains 68% of the variation in number of accidents, which seems respectable.
Diff: 2
Key Term:  Linear-regression analysis
AACSB:  Analytical thinking
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

13) An innovative restaurateur owns and operates a dozen "Ultimate Low-Carb" restaurants in northern Arkansas. His signature item is a cheese-encrusted beef medallion wrapped in lettuce. Sales (x, in millions of dollars) is related to Profits (y-hat, in hundreds of thousands of dollars) by the regression equation y-hat = 8.21 + 0.76 x. What is your forecast of profit for a store with sales of $40 million? $50 million?
Answer:  Students must recognize that "sales" is the independent variable and "profits" is dependent; the problem is not a time series. A store with $40 million in sales: 40 × 0.76 = 30.4; 30.4 + 8.21 = 38.61, or $3,861,000 in profit. Similarly, $50 million in sales is estimated to produce a profit of 46.21 or $4,621,000.
Diff: 2
Key Term:  Linear-regression analysis
AACSB:  Analytical thinking
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

14) Arnold Tofu owns and operates a chain of 12 vegetable protein "hamburger" restaurants in northern Louisiana. Sales figures and profits for the stores are provided in the table below. Sales are given in millions of dollars; profits are in hundreds of thousands of dollars. Calculate a regression line for the data. What is your forecast of profit for a store with sales of $24 million? $30 million?

Store Profits Sales
1 14 6
2 11 3
3 15 5
4 16 5
5 24 15
6 28 18
7 22 17
8 21 12
9 26 15
10 43 20
11 34 14
12 9 5

Answer:  Students must recognize that "sales" is the independent variable and "profits" is dependent. Store number is not a variable, and the problem is not a time series. The regression equation is:
y-hat = 5.936 + 1.421x (y-hat = profit, x = sales). A store with $24 million in sales is estimated to produce a profit of 40.04 or $4,004,000. Similarly, $30 million in sales should yield 48.566 or $4,856,600 in profit.
Diff: 3
Key Term:  Linear-regression analysis
AACSB:  Analytical thinking
Objective:  LO 4.6 Conduct a regression and correlation analysis
Learning Outcome:  Describe major approaches to forecasting

Section 7   Monitoring and Controlling Forecasts

1) If a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased.
Answer:  TRUE
Diff: 2
Key Term:  Bias
Objective:  LO 4.7 Use a tracking signal
Learning Outcome:  Describe major approaches to forecasting

2) Focus forecasting tries a variety of computer models and selects the best one for a particular application.
Answer:  TRUE
Diff: 2
Key Term:  Focus forecasting
Learning Outcome:  Describe major approaches to forecasting

3) 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. A) qualitative methods.
  2. B) adaptive smoothing.
  3. C) slope.
  4. D) bias.
  5. E) trend projection.
Answer:  D
Diff: 1
Key Term:  Bias
AACSB:  Analytical thinking
Objective:  LO 4.7 Use a tracking signal
Learning Outcome:  Describe major approaches to forecasting

4) The tracking signal is the:
  1. A) standard error of the estimate.
  2. B) absolute deviation of the last period's forecast.
  3. C) MAD.
  4. D) ratio of cumulative error / MAD.
  5. E) MAPE.
Answer:  D
Diff: 2
Key Term:  Tracking signal
Objective:  LO 4.7 Use a tracking signal
Learning Outcome:  Describe major approaches to forecasting

5) Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic of:
  1. A) exponential smoothing including trend.
  2. B) adaptive smoothing.
  3. C) trend projection.
  4. D) focus forecasting.
  5. E) multiple regression analysis.
Answer:  B
Diff: 2
Key Term:  Adaptive smoothing
Objective:  LO 4.7 Use a tracking signal
Learning Outcome:  Describe major approaches to forecasting

6) ________ forecasting tries a variety of computer models and selects the best one for a particular application.
Answer:  Focus
Diff: 2
Key Term:  Focus forecasting
Learning Outcome:  Describe major approaches to forecasting


7) An approach to exponential smoothing in which the smoothing constant is automatically changed to keep errors to a minimum is called ________.
Answer:  adaptive smoothing
Diff: 2
Key Term:  Adaptive smoothing
Objective:  LO 4.7 Use a tracking signal
Learning Outcome:  Describe major approaches to forecasting
8) What is a tracking signal? Explain the connection between adaptive smoothing and tracking signals.
Answer:  A tracking signal is a measure of how well the forecast actually predicts. The larger the absolute tracking signal, the worse the forecast is performing. Adaptive smoothing sets limits for the tracking signal and makes changes to its forecasting models when the tracking signal goes beyond those limits.
Diff: 2
Key Term:  Tracking signal
Objective:  LO 4.7 Use a tracking signal
Learning Outcome:  Describe major approaches to forecasting

9) What is focus forecasting?
Answer:  It is a forecasting method that tries a variety of computer models and selects the one that is best for a particular application.
Diff: 2
Key Term:  Focus forecasting
Learning Outcome:  Describe major approaches to forecasting

10) Jim's department at a local department store has tracked the sales of a product over the last ten weeks. Forecast demand using exponential smoothing with an alpha of 0.4, and an initial forecast of 28.0 for period 1. Calculate the MAD. Calculate the tracking signal. What do you recommend?

Period Demand
1 24
2 23
3 26
4 36
5 26
6 30
7 32
8 26
9 25
10 28

Answer:
Period Demand Forecast Error Absolute
1 24 28.00
2 23 26.40 -3.40 3.40
3 26 25.04 0.96 0.96
4 36 25.42 10.58 10.58
5 26 29.65 -3.65 3.65
6 30 28.19 1.81 1.81
7 32 28.92 3.08 3.08
8 26 30.15 -4.15 4.15
9 25 28.49 -3.49 3.49
10 28 27.09 0.91 0.91
Total 2.64 32.03
Average 0.29 3.56
Bias MAD

The tracking signal RSFE/MAD = 2.64/3.56 = .742 is low; therefore, keep using the forecasting method.
Diff: 3
AACSB:  Analytical thinking
Objective:  LO 4.7 Use a tracking signal
Learning Outcome:  Describe major approaches to forecasting


Section 8   Forecasting in the Service Sector

1) Many services maintain records of sales noting:
  1. A) the day of the week.
  2. B) unusual events.
  3. C) the weather.
  4. D) holiday impacts.
  5. E) all of the above.
Answer:  E
Diff: 2
Learning Outcome:  Describe major approaches to forecasting
2) Taco Bell's unique employee scheduling practices are partly the result of using:
  1. A) point-of-sale computers to track food sales in 15 minute intervals.
  2. B) focus forecasting.
  3. C) a six-week moving average forecasting technique.
  4. D) multiple regression.
  5. E) A and C are both correct.
Answer:  E
Diff: 2
Learning Outcome:  Describe major approaches to forecasting

----------------------------------
OPERATIONS MANAGEMENT - 2017 - COLLECTION
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Principles of Operations Management: Sustainability and Supply Chain Management, 10th Edition, 2017
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Introduction to Operations and Supply Chain Management, 4th Edition, Cecil B. Bozarth, Robert B. Handfield, 2016 
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6. Operations Research: An Introduction, 10th Edition, Hamdy A. Taha, 2017
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