Exam Prep Forecasting Ch.12

Chapter 12:
Forecasting

True/False

1. Forecasts based on mathematical formulas are referred to as
   1. opinion forecasts.
   2. past experience forecasts.
   3. qualitative forecasts.
   4. quantitative forecasts.
2. Because of globalization of markets, managers are finding it increasingly more difficult to create accurate demand forecasts.

Difficulty: Moderate

Learning Objective: LO 1

1. Forecasting customer demand is often a key to providing good quality service.

Difficulty: Easy

Learning Objective: LO 1

1. One way to deal with the bullwhip effect is to develop and share the forecasts with other supply chain members.

Difficulty: Easy

Learning Objective: LO 1

1. Qualitative forecasts use mathematical techniques and statistical formulas.

Difficulty: Moderate

Learning Objective: LO 1

1. In today’s competitive environment, effective supply chain management requires absolute demand forecasts.

Difficulty: Moderate

Learning Objective: LO 1

1. Sharing demand forecasts with supply chain members has resulted in an increased bullwhip effect.

Difficulty: Moderate

Learning Objective: LO 1

1. Because of advances in technology, many service industries no longer require accurate forecasts to provide high quality service.

Difficulty: Moderate

Learning Objective: LO 1

1. The type of forecasting method used depends entirely on whether the supply chain is continuous replenishment or not.

Difficulty: Moderate

Learning Objective: LO 1

1. Continuous replenishment systems rely heavily on extremely accurate long-term forecasts.

Difficulty: Easy

Learning Objective: LO 1

1. The type of forecasting method selected depends on
   1. time frame.
   2. demand behavior.
   3. causes of behavior.
   4. All of these answer choices are correct.
2. A gradual, long-term up or down movement of demand over time is referred to as a
   1. trend.
   2. seasonal pattern.
   3. cycle.
   4. correlation.
3. A seasonal pattern is an oscillating movement in demand that occurs periodically over the short-run and is repetitive.

Difficulty: Moderate

Learning Objective: LO 2

1. Short-midrange forecasts tend to use quantitative models that forecast demand based on historical demand.

Difficulty: Moderate

Learning Objective: LO 2

1. Long-range quantitative forecasts are used to determine future demand for all of the following EXCEPT
   1. new products.
   2. markets.
   3. customers.
   4. facilities.
2. The trend toward continuous replenishment in supply chain design has shifted the need for accurate forecasts from
   1. long-term to short-term.
   2. short-term to long-term.
   3. short-term to mid-term.
   4. long-term to mid-term.
3. Because of heightened competition resulting from globalization, most companies find little strategic value in long-range forecasts.

Difficulty: Moderate

Learning Objective: LO 2

1. Movements in demand that do not follow a given pattern are referred to as
   1. assignable variations.
   2. random variations.
   3. cycles.
   4. trends.
2. Many companies are shifting from long-term to short-term forecasts for strategic planning.

Difficulty: Moderate

Learning Objective: LO 2

1. The demand behavior for skis is considered cyclical.

Difficulty: Moderate

Learning Objective: LO 2

1. The long-term strategic planning process is dependent upon qualitative forecasting methods.

Difficulty: Moderate

Learning Objective: LO 2

1. The Delphi method generates forecasts based on informed judgments and opinions from knowledgeable individuals.

Difficulty: Moderate

Learning Objective: LO 2

1. The most common type of forecasting method for long-term strategic planning is based on quantitative modeling

Difficulty: Moderate

Learning Objective: LO 2

1. Time series methods use historical data to predict future demand.

Difficulty: Moderate

Learning Objective: LO 3

1. Which of the following is best defined as soliciting forecasts about technological advances from experts?
   1. Delphi method
   2. data mining
   3. regression forecasting
   4. exponential smoothing
2. ___________ is an averaging method for forecasting that reacts more strongly to recent changes in demand.
   1. Moving average
   2. Exponential smoothing
   3. Linear trend line
   4. Linear regression
3. Time series methods assume that demand patterns in the past are a good predictor of demand in the future.

Difficulty: Moderate

Learning Objective: LO 3

1. The moving average method is used for creating forecasts when there is no variation in demand.

Difficulty: Moderate

Learning Objective: LO 3

1. Because of ease of use and simplicity, exponential smoothing is preferred over smoothing average.

Difficulty: Moderate

Learning Objective: LO 3

1. The average, absolute difference between the forecast and demand is a popular measure of forecast error.

Difficulty: Moderate

Learning Objective: LO 4

1. The larger the mean absolute deviation (MAD) the more accurate the forecast.

Difficulty: Moderate

Learning Objective: LO 4

1. __________ is measured by the per-period average of the sum of forecast errors.
   1. Cumulative error
   2. Mean absolute deviation
   3. Tracking signal
   4. Mean absolute percent deviation
2. Because of the development of advanced forecasting models, managers no longer track forecast error.

Difficulty: Moderate

Learning Objective: LO 4

1. ­­­­­­­____________ is used for forecasting when there is a relationship between the dependent variable (demand) and one or more independent (explanatory) variables.
   1. Moving average
   2. Exponential smoothing
   3. Regression analysis
   4. Data mining
2. Correlation in linear regression is a measure of the strength of the relationship between the dependent variable, demand, and an independent (explanatory) variable.

Difficulty: Moderate

Learning Objective: LO 6

1. A linear regression model that relates demand to time is known as a linear trend line.

Difficulty: Moderate

Learning Objective: LO 6

1. Linear regression relates two variables using a linear model.

Difficulty: Moderate

Learning Objective: LO 6

1. A correlation coefficient is a measure of the strength of the linear relationship between an independent and a dependent variable.

Difficulty: Moderate

Learning Objective: LO 6

1. Multiple regression analysis can be used to relate demand to two or more dependent variables.

Difficulty: Moderate

Learning Objective: LO 6

Multiple Choice

1. Forecast methods based on judgment, opinion, past experiences, or best guesses are known as ___________ methods.
2. quantitative
3. qualitative
4. time series
5. regression

Difficulty: Easy

Learning Objective: LO 1

1. A forecast
   a. predicts what will occur in the future.

b. results from an uncertain process.

c. support strategic planning.

d. All of these answer choices are correct.

Difficulty: Easy

Learning Objective: LO 1

1. Forecasts of product demand determine how much
   a. inventory is needed.

b. product to make.
c. material to purchase from suppliers.
d. All of these answer choices are correct.

Difficulty: Easy

Learning Objective: LO 1

1. The ______________ effect is caused in part by distortion in product demand information caused by inaccurate forecasts.
   a. bullwhip
   b. regression
   c. error
   d. None of these answer choices is correct.

Difficulty: Easy

Learning Objective: LO 1

1. Continuous replenishment relies heavily on ____________term forecast.
   a. short-
   b. medium-
   c. long-
   d. All of these answer choices are correct.

Difficulty: Easy

Learning Objective: LO 1

1. In ___________________ replenishment, the supplier and customer care continuously update data.
   a. demand
   b. ongoing
   c. continuous
   d. forecasted

Difficulty: Easy

Learning Objective: LO 1

1. ________________ demand is a key to providing good-quality service.
   a. Predicted
   b. Forecasted
   c. Anticipated
   d. Unknown

Difficulty: Easy

Learning Objective: LO 1

1. A long-range forecast would normally not be used to
2. design the supply chain.
3. implement strategic programs.
4. determine production schedules.
5. plan new products for changing markets.

Difficulty: Moderate

Learning Objective: LO 2

1. A forecast where the current period’s demand is used as the next period’s forecast is known as a
2. moving average forecast.
3. naïve forecast.
4. weighted moving average forecast.
5. Delphi method.

Difficulty: Moderate

Learning Objective: LO 2

1. Which of the following is not a type of predictable demand behavior?
2. trend
3. random variation
4. cycle
5. seasonal pattern

Difficulty: Moderate

Learning Objective: LO 2

1. A ___________ is an up-and-down movement in demand that repeats itself over a period of more than a year.
2. trend
3. seasonal pattern
4. random variation
5. cycle

Difficulty: Easy

Learning Objective: LO 2

1. Selecting the type of forecasting method to use depends on
2. the time frame of the forecast.
3. the behavior of demand and demand patterns.
4. the causes of demand behavior.
5. All of these answer choices are correct.

Difficulty: Easy

Learning Objective: LO 2

1. A qualitative procedure used to develop a consensus forecast is known as
2. exponential smoothing.
3. regression methods.
4. the Delphi technique.
5. naïve forecasting.

Difficulty: Moderate

Learning Objective: LO 2

1. The sum of the weights in a weighted moving average forecast must
2. equal the number of periods being averaged.
3. equal 1.00.
4. be less than 1.00.
5. be greater than 1.00.

Difficulty: Easy

Learning Objective: LO 3

1. An exponential smoothing forecasting technique requires all of the following except
2. the forecast for the current period.
3. the actual demand for the current period.
4. a smoothing constant.
5. large amounts of historical demand data.

Difficulty: Moderate

Learning Objective: LO 3

1. Given the demand and forecast values below, the naïve forecast for September is

1. 100.6.
2. 99.0.
3. 102.0.
4. cannot be determined.

Difficulty: Moderate

Solution: Naïve forecast=99.0

Learning Objective: LO 3

1. The smoothing constant, α, in the exponential smoothing forecast
2. must always be a value greater than 1.0.
3. must always be a value less than 0.10.
4. must be a value between 0.0 and 1.0.
5. should be equal to the time frame for the forecast.

Difficulty: Moderate

Learning Objective: LO 3

1. The closer the smoothing constant, α, is to 1.0 the
2. greater the reaction to the most recent demand.
3. greater the dampening, or smoothing, effect.
4. more accurate the forecast.
5. less accurate the forecast.

Difficulty: Moderate

Learning Objective: LO 3

1. The exponential smoothing model produces a naïve forecast when the smoothing constant, α, is equal to
2. 0.00.
3. 1.00.
4. 0.50.
5. 2.00

Difficulty: Moderate

Learning Objective: LO 3

1. The _______ method uses demand in the first period to forecast demand in the next period.
2. naïve
3. moving average
4. exponential smoothing
5. linear trend

Difficulty: Moderate

Learning Objective: LO 3

1. The _________________ forecast method consists of an exponential smoothing forecast with a trend adjustment factor added to it.
2. exponentially smoothed
3. adjusted exponential smoothing
4. time series
5. moving average

Difficulty: Moderate

Learning Objective: LO 3

1. Given the following demand data for the past five months, the three-period moving average forecast for June is

1. 103.33.
2. 99.00.
3. 95.00.
4. 92.50

Difficulty: Moderate

Solution: Moving Average, MA₃=95.00

Learning Objective: LO 3

1. Given the following demand data for the past five months, the four-period moving average forecast for June is

1. 96.25.
2. 99.00.
3. 110.00.
4. 93.75.

Difficulty: Moderate

Solution: Moving Average, MA₄=93.75

Learning Objective: LO 3

1. A company wants to produce a weighted moving average forecast for April with the weights 0.40, 0.35, and 0.25 assigned to March, February, and January respectively. If the company had demands of 5,000 in January, 4,750 in February, and 5,200 in March, then April’s forecast is
2. 4983.33.
3. 4992.50.
4. 4962.50.
5. 5000.00.

Difficulty: Moderate

Solution: Weighted Moving Average, WMA₃=4992.50

Learning Objective: LO 3

1. The weighted moving average forecast for the fifth period, with weights of 0.15 for period 1, 0.20 for period 2, 0.25 for period 3, and 0.40 for period 4, using the demand data shown below is

1. 3,760.
2. 3,700.
3. 3,650.
4. 3,325.

Difficulty: Moderate

Solution: Weighted Moving Average, WMA₃=3,760

Learning Objective: LO 3

1. For the demand values and the January forecast shown in the table below the exponential smoothing forecast for March using α = 0.30 is

1. 489.
2. 486.
3. 483.
4. 480.

Difficulty: Hard

Solution: Exponential Smoothing, F₃=489

Learning Objective: LO 3

1. For the demand values and the January forecast shown in the table below the exponential smoothing forecast for March using α = 0.40 is

1. 1,200.
2. 1,220.
3. 1,222.
4. 1,225.

Difficulty: Hard

Solution: Exponential Smoothing, F₃=1,222.

Learning Objective: LO 3

1. If the forecast for July was 3,300 and the actual demand for July was 3,250, then the exponential smoothing forecast for August using α = 0.20 is
2. 3,300.
3. 3,290.
4. 3,275.
5. 3,250.

Difficulty: Moderate

Solution: Exponential Smoothing, F₂=3,290.

Learning Objective: LO 3

1. Given the demand and forecast values shown in the following table,

calculate the three-period moving average forecast for November.

1. 516.
2. 528.
3. 524.
4. 515.

Difficulty: Moderate

Solution: Moving Average, MA₃=524

Learning Objective: LO 3

1. Given the demand and forecast values shown in the table below,

the exponential smoothing forecast for November using α = 0.35 is

• 1. 552.45.
  2. 553.50.
  3. 554.55.
  4. 557.50.

Difficulty: Moderate

Solution: Exponential Smoothing, F₆=552.45
Learning Objective: LO 3

1. Given the demand and forecast values shown in the table below,

the forecast error for September is

1. 10.00.
2. −10.00.
3. 1.00.
4. 39.00.

Difficulty: Moderate

Solution: E = −10.00

Learning Objective: LO 4

1. If forecast errors are normally distributed then
2. 1 MAD = 1σ
3. 1 MAD ≈ 0.8 σ
4. 0.8 MAD ≈ 1σ
5. 1 MAD ≈ 1.96 σ

Difficulty: Moderate

Learning Objective: LO 4

1. A forecasting model has produced the following forecasts,

the forecast error for February is

1. 10.
2. −10.
3. −15.
4. −5

Difficulty: Moderate

Solution: E = −5

Learning Objective: LO 4

1. Given the demand and forecast values shown in the table below,

the mean absolute deviation (MAD) through the end of October is

1. 9.20
2. -9.20
3. 1.00
4. 7.00

Difficulty: Hard

Solution: MAD=9.20

Learning Objective: LO 4

1. The per-period average of cumulative error is called
2. cumulative forecast variation.
3. absolute error.
4. average error.
5. noise.

Difficulty: Easy

Learning Objective: LO 4

1. A forecasting model has produced the following forecasts:

The mean absolute deviation (MAD) for the end of May is

1. 7.0.
2. 7.5.
3. 10.0.
4. 3.0

Difficulty: Hard

Solution: MAD=7.0

Learning Objective: LO 4

1. A forecasting model has produced the following forecasts:

The mean absolute percent deviation (MAPD) for the end of May is

1. 0.0250.
2. 0.0583.
3. 0.5830.
4. 0.6670.

Difficulty: Hard

Solution: MAPD=0.0583

Learning Objective: LO 4

1. A forecasting model has produced the following forecasts:

At the end of May the average error would be

1. 7.
2. 5.
3. 3.
4. 1.

Difficulty: Moderate

Solution: Ebar=3

Learning Objective: LO 4

1. A forecasting model has produced the following forecasts:

At the end of May the tracking signal would be

1. 0.000.
2. 0.667.
3. 1.333.
4. 2.143.

Difficulty: Hard

Solution: Tracking Signal=2.143

Learning Objective: LO 4

1. The mean absolute percentage deviation (MAPD) measures the absolute error as a percentage of
2. all errors.
3. per-period demand.
4. total demand.
5. the average error.

Difficulty: Moderate

Learning Objective: LO 4

1. A large positive cumulative error indicates that the forecast is probably
2. higher than the actual demand.
3. lower than the actual demand.
4. unbiased.
5. biased.

Difficulty: Moderate

Learning Objective: LO 4

1. Which of the following statements concerning average error is true?
2. A positive value indicates high bias, and a negative value indicates low bias.
3. A positive value indicates zero bias, and a negative value indicates low bias.
4. A negative value indicates zero bias, and a negative value indicates high bias.
5. A positive value indicates low bias, and a negative value indicates high bias.

Difficulty: Moderate

Learning Objective: LO 4

1. Which of the following is a reason why a forecast can go out of control?
2. a change in trend
3. an irregular variation such as unseasonable weather
4. a promotional campaign
5. All of these answer choices are correct.

Difficulty: Moderate

Learning Objective: LO 4

1. Which of the following can be used to monitor a forecast to see if it is biased high or low?
2. a tracking signal
3. the mean absolute deviation (MAD)
4. the mean absolute percentage deviation (MAPD)
5. a linear trend line model

Difficulty: Moderate

Learning Objective: LO 4

1. A tracking signal is computed by
2. multiplying the cumulative error by MAD.
3. multiplying the absolute error by MAD.
4. dividing MAD by the cumulative absolute error.
5. dividing the cumulative error by MAD.

Difficulty: Moderate

Learning Objective: LO 4

1. Regression forecasting methods relate _________to other factors that cause demand behavior.
2. supply
3. demand
4. time
5. money

Difficulty: Moderate

Learning Objective: LO 6

1. Correlation is a measure of the strength of the
2. nonlinear relationship between two dependent variables.
3. nonlinear relationship between a dependent and independent variable.
4. linear relationship between two dependent variables.
5. linear relationship between a dependent and independent variable.

Difficulty: Moderate

Learning Objective: LO 6

1. A mathematical technique for forecasting that relates the dependent variable to an independent variable is
2. correlation analysis.
3. exponential smoothing.
4. linear regression.
5. weighted moving average.

Difficulty: Easy

Learning Objective: LO 6

(Use the following information for the next five problems.)

The owner of Koffi, the sole coffee house located in a resort area, wants to develop a forecast based on the relationship between tourism and coffee drinks sold. He has generated the following data over the past 12 months:

The data from using Data Analysis on Excel is as follows:

1. What is the approximate intercept, a?
   a. −11.6
   b. 11.6
   c. 6.4
   d. −6.4

Difficulty: Moderate

Learning Objective: LO 6

1. What is the approximate slope, b?
   a. −11.6
   b. 11.6
   c. 6.4
   d. −6.4

Difficulty: Moderate

Learning Objective: LO 6

1. What is the forecasted number of coffee drinks sold if the number of tourists is 25 (thousand)?
   a. 128
   b. 138
   c. 148
   d. 158

Ans. C
Difficulty: Moderate

Learning Objective: LO 6

1. What is the correlation?
   a. 0.95
   b. 0.91
   c. 0.90
   d. 19

Ans. A
Difficulty: Moderate

Learning Objective: LO 6

1. What is the coefficient of determination?
   a. 0.95
   b. 0.91
   c. 0.90
   d. 18

Ans. C
Difficulty: Moderate

Learning Objective: LO 6

1. Data mining uses and analyzes data that is stored in
   a. databases.

b. data warehouses.
c. data marts.

d. All of these answer choices are correct.

Ans. D
Difficulty: Moderate

Learning Objective: LO 6

1. _______________ can be subdivided into ________________ that store subsets of data.
   a. Databases, data warehouses
   b. Data warehouses, data marts
   c. Databases, data marts
   d. Data warehouses, databases

Ans. B
Difficulty: Moderate

Learning Objective: LO 6

1. Association rule learning is a data-mining technique that
2. discovers trends, predicts future events, and assesses possible course of action.
3. searches for relationships between variables.
4. identifies groups of data that fall naturally together.
5. None of these answer choices is correct.

Ans. B
Difficulty: Moderate

Learning Objective: LO 6

1. Cluster analysis is a tool that
2. discovers trends, predicts future events, and assesses possible course of action.
3. searches for exact relationships between variables.
4. identifies groups of data that fall naturally together.
5. None of these answer choices is correct.

Ans. C
Difficulty: Moderate

Learning Objective: LO 6

1. Data mining
2. discovers trends, predicts future events, and assesses possible course of action.
3. searches for exact relationships between variables.
4. identifies groups of data that fall naturally together.
5. None of these answer choices is correct.

Ans. A
Difficulty: Moderate

Learning Objective: LO 6

Short Answer

1. Discuss the importance of accurate forecasts in supply chain management.

Difficulty: Hard

Learning Objective: LO 1

1. Compare and contrast short-mid-range forecasts and long-range forecasts.

Difficulty: Moderate

Learning Objective: LO 2

1. Explain the difference between qualitative and quantitative forecasting methods.

Difficulty: Moderate

Learning Objective: LO 2

1. Explain how and why time series and regression forecasting methods differ.

Difficulty: Moderate

Learning Objective: LO 3

103. The following table provides information about actual and forecasted demand for January through March:

The MAD for the three-month period is 2250. Then the tracking signal is __________.

a) −1.67

b) 0.6

c) 1.67

d) There is not enough information to determine the tracking signal.

104. The following table provides information about two variables: the independent variable X and the dependent variable Y.

Assume that p × q ≠ 0. The linear regression equation for this data set is __________.

a) y = q – (1/p) x

b) y = q(1 – 1/p x)

c) y = p – (1/q) x

d) There is not enough information to determine the linear regression equation.

105. The following table provides information about two variables: the independent variable X and the dependent variable Y.

Assume that p × q ≠ 0. The coefficient of determination for the linear regression is __________.

a) 0

b) 1

c) p/q

d) There is not enough information to determine the linear regression equation.

106. The following table provides information about two variables: the independent variable X and the dependent variable Y.

Assume that p × q ≠ 0. The correlation coefficient for the linear regression is __________.

a) 0

b) ±1, but there is not enough information to determine the sign

c) −1

d) –(p × q)/|p × q|

107. The following table provides information about two variables: the independent variable X and the dependent variable Y.

Assume that p × q ≠ 0. The linear regression equation predicts that when x = p/2, y = __________.

a) 2q

b) q/2

c) ±2q, but there is not enough information to determine the sign

d) ±q/2, but there is not enough information to determine the sign

108. The following table provides information about two variables: the independent variable X and the dependent variable Y.

Assume that p × q ≠ 0. The linear regression equation predicts that when x = p/3, y = __________.

a) 2q/3

b) q/3

c) ±2q/3, but there is not enough information to determine the sign

d) ±q/3, but there is not enough information to determine the sign

109. The following table provides information about two variables: the independent variable X and the dependent variable Y.

Assume that p × q ≠ 0. The sum of errors squared (Σε² = Σ(y_actual − y_predicted)²) for the linear regression equation is __________.

a) (p + q)/2

b) 1

c) 1/2

d) 0

110. You want to develop a forecast of the number of drinks sold each day at your specialty drinks shop as a function of the maximum temperature of the day. The following table provides information about the max temperature and the number of drinks sold for the last ten days:

Use MS Excel, a statistical program, or simply the web to do a linear regression. The approximate slope of the linear regression equation is __________.

a) 3.27

b) 4.17

c) 4.38

d) 4.45

111. You want to develop a forecast of the number of drinks sold each day at your specialty drinks shop as a function of the maximum temperature of the day. The following table provides information about the max temperature and the number of drinks sold for the last ten days:

Use MS Excel, a statistical program, or simply the web to do a linear regression. The coefficient of determination is __________.

a) 0.96

b) 0.93

c) 0.86

d) 0.75

112. You want to develop a forecast of the number of drinks sold each day at your specialty drinks shop as a function of the maximum temperature of the day. The following table provides information about the max temperature and the number of drinks sold for the last ten days:

Use MS Excel, a statistical program, or simply the web to do a linear regression. The forecasted number of drinks sold if the max temperature reached 85 degrees is __________.

a) 311

b) 310

c) 309

d) 308