Question 1

Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast.

Accepted Answer

A) True 
 B)False

Question 2

Given forecast errors of -5, -10, and +15, the MAD is:

Accepted Answer

A)  0. 
B)  10. 
C)  30. 
D)  175. 
E)  225. 
A)  0. 
B)  10. 
C)  30. 
D)  175. 
E)  225.

Question 3

Given the following historical data and weights of .5, .3, and .2, what is the three-period moving average forecast for period 5? $$\begin{array} { l l l l } 
\text { Period } & \text { Value } & \text { Period } & \text { Value } \
\hline  1 & 138 & 3 & 148 \
2 & 142 & 4 & 144
\end{array}$$

Accepted Answer

A)  144.20 
B)  144.80 
C)  144.67 
D)  143.00 
E)  144.00 
A)  144.20 
B)  144.80 
C)  144.67 
D)  143.00 
E)  144.00

Question 4

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: $$\begin{array} { l l } 
\text { Year } & \text { Demand } \
\hline \text { 5 years ago } & 900 \
\text { 4 years ago } & 700 \
\text { 3 years ago } & 600 \
\text { 2 years ago } & 500 \
\text { Last year } & 300
\end{array}$$ What is the forecast for this year using a three-year weighted moving average with weights of .5, .3, and .2?

Accepted Answer

A)  163 
B)  180 
C)  300 
D)  420 
E)  510 
A)  163 
B)  180 
C)  300 
D)  420 
E)  510

Question 5

The president of State University wants to forecast student enrollments for this academic year based on the following historical data: $$\begin{array} { l l } 
\text { Year } & \text { Enrollments } \
\hline \text { 5 years ago } & 15,000 \
4 \text { years ago } & 16,000 \
\text { 3 years ago } & 18,000 \
2 \text { years ago } & 20,000 \
\text { Last year } & 21,000
\end{array}$$ What is the forecast for this year using a four-year simple moving average?

Accepted Answer

A)  18,750 
B)  19,500 
C)  21,000 
D)  22,650 
E)  22,800 
A)  18,750 
B)  19,500 
C)  21,000 
D)  22,650 
E)  22,800

Question 6

The primary method for associative forecasting is:

Accepted Answer

A)  sensitivity analysis. 
B)  regression analysis. 
C)  simple moving averages. 
D)  centered moving averages. 
E)  exponential smoothing. 
A)  sensitivity analysis. 
B)  regression analysis. 
C)  simple moving averages. 
D)  centered moving averages. 
E)  exponential smoothing.

Question 7

The shorter the forecast period, the more accurately the forecasts tend to track what actually happens.

Accepted Answer

A) True 
 B)False

Question 8

A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data: $$\begin{array} { l l } 
\text { Year } & \text { Attendance } \
\hline \text { Four years ago } & 10,000 \
\text { Three years ago } & 12,000 \
\text { Two years ago } & 18,000 \
\text { Last Year } & 20,000
\end{array}$$ What is this year's forecast using the naive approach?

Accepted Answer

A)  22,000 
B)  20,000 
C)  18,000 
D)  15,000 
E)  12,000 
A)  22,000 
B)  20,000 
C)  18,000 
D)  15,000 
E)  12,000

Question 9

For the data given below, what would the naive forecast be for period 5? $$\begin{array} { l l } 
\text { Period } & \text { Demand } \
\hline 1 & 58 \
2 & 59 \
3 & 60 \
4 & 61
\end{array}$$

Accepted Answer

A)  58 
B)  62 
C)  59.5 
D)  61 
E)  cannot tell from the data given 
A)  58 
B)  62 
C)  59.5 
D)  61 
E)  cannot tell from the data given

Question 10

Consider the data below: $$\begin{array} { l l } 
\text { Period } & \text { Demand } \
\hline 11 & 81 \
12 & 75 \
13 & 82
\end{array}$$ Using exponential smoothing with alpha = .2, and assuming the forecast for period 11 was 80, what would the forecast for period 14 be?

Accepted Answer

\[\begin{array} { l l l } 
\te

Question 11

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

Accepted Answer

A) True 
 B)False

Question 12

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

Accepted Answer

A) True 
 B)False

Question 13

Which of the following might be used to indicate the cyclical component of a forecast?

Accepted Answer

A)  leading variable 
B)  mean squared error 
C)  Delphi technique 
D)  exponential smoothing 
E)  mean absolute deviation 
A)  leading variable 
B)  mean squared error 
C)  Delphi technique 
D)  exponential smoothing 
E)  mean absolute deviation

Question 14

A manager is using exponential smoothing to predict merchandise returns at a suburban branch of a department store chain. Given a previous forecast of 140 items, an actual number of returns of 148 items, and a smoothing constant equal to .15, what is the forecast for the next period?

Accepted Answer

The answer of A manager is using exponential smoothing to...

Question 15

Correlation measures the strength and direction of a relationship between variables.

Accepted Answer

A) True 
 B)False

Question 16

Data on the last three years of demand are available as follows: $$\begin{array} { l l l l l } 
&&& { \text { Season } } \
\text { Year } & \text { Spring } & \text { Summer } & \text { Fall } & \text { Winter } \
\hline  \text { Three years ago } & 18 & 10 & 26 & 42 \
\text { Two years ago } & 26 & 18 & 34 & 50 \
\text { Last year } & 34 & 26 & 42 & 58
\end{array}$$ What is the linear regression trend line for these data (t = 0 for spring, three years ago)?

Accepted Answer

The answer of Data on the last three years of...

Question 17

The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data: $$\begin{array} { l l } 
\text { Month } & \text { Number of Visits } \
\hline  \text { April } & 100 \
\text { May } & 140 \
\text { June } & 110 \
\text { July } & 150 \
\text { August } & 120 \
\text { September } & 160
\end{array}$$ What is this month's forecast using a four-month weighted moving average with weights of .4, .3, .2, and .1?

Accepted Answer

A)  120 
B)  129 
C)  141 
D)  135 
E)  140 
A)  120 
B)  129 
C)  141 
D)  135 
E)  140

Question 18

A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD.

Accepted Answer

A) True 
 B)False

Question 19

Which of the following is a potential shortcoming of using sales force opinions in demand forecasting?

Accepted Answer

A)  Members of the sales force often have substantial histories of working with and understanding their customers. 
B)  Members of the sales force often are well aware of customers' future plans. 
C)  Members of the sales force have direct contact with consumers. 
D)  Members of the sales force can have difficulty distinguishing between what customers would like to do and what they actually will do. 
E)  Customers often are quite open with members of the sales force with regard to future plans. 
A)  Members of the sales force often have substantial histories of working with and understanding their customers. 
B)  Members of the sales force often are well aware of customers' future plans. 
C)  Members of the sales force have direct contact with consumers. 
D)  Members of the sales force can have difficulty distinguishing between what customers would like to do and what they actually will do. 
E)  Customers often are quite open with members of the sales force with regard to future plans.

Question 20

Given the following data, develop a linear regression model for y as a function of x. $$\begin{array} { l l } 
\mathrm { x } & \mathrm { y } \
\hline  2 & 20 \
4 & 25 \
6 & 30 \
6 & 32 \
8 & 40
\end{array}$$

Accepted Answer

None

Exam 3: Forecasting

Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast.

Given forecast errors of -5, -10, and +15, the MAD is:

Given the following historical data and weights of .5, .3, and .2, what is the three-period moving average forecast for period 5? Period Value Period Value 1 138 3 148 2 142 4 144

The primary method for associative forecasting is:

The shorter the forecast period, the more accurately the forecasts tend to track what actually happens.

A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data: Year Attendance Four years ago 10,000 Three years ago 12,000 Two years ago 18,000 Last Year 20,000 What is this year's forecast using the naive approach?

For the data given below, what would the naive forecast be for period 5? Period Demand 1 58 2 59 3 60 4 61

Consider the data below: Period Demand 11 81 12 75 13 82 Using exponential smoothing with alpha = .2, and assuming the forecast for period 11 was 80, what would the forecast for period 14 be?

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

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

Which of the following might be used to indicate the cyclical component of a forecast?

A manager is using exponential smoothing to predict merchandise returns at a suburban branch of a department store chain. Given a previous forecast of 140 items, an actual number of returns of 148 items, and a smoothing constant equal to .15, what is the forecast for the next period?

Correlation measures the strength and direction of a relationship between variables.

Data on the last three years of demand are available as follows: Season Year Spring Summer Fall Winter Three years ago 18 10 26 42 Two years ago 26 18 34 50 Last year 34 26 42 58 What is the linear regression trend line for these data (t = 0 for spring, three years ago)?

A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD.

Which of the following is a potential shortcoming of using sales force opinions in demand forecasting?

Given the following data, develop a linear regression model for y as a function of x. 2 20 4 25 6 30 6 32 8 40

Introduction to Operations Management

Competitiveness, Strategy, and Productivity

Product and Service Design

Extension: Reliability

Strategic Capacity Planning for Products and Services

Extension: Decision Theory

Process Selection and Facility Layout

Work Design and Measurement

Extension: Learning Curves

Location Planning and Analysis

Extension: The Transportation Model

Management of Quality

Quality Control

Extension: Acceptance Sampling

Aggregate Planning and Master Scheduling

MRP and ERP

Inventory Management

Jit and Lean Operations

Extension: Maintenance

Supply Chain Management

Scheduling

Project Management

Management of Waiting Lines

Linear Programming

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