Question 1

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

Accepted Answer

A) True 
 B)False

Question 2

Which of the following helps improve supply chain forecasting performance?

Accepted Answer

A)  contracts that require supply chain members to formulate long-term forecasts 
B)  penalties for supply chain members that adjust forecasts 
C)  sharing forecasts or demand data across the supply chain 
D)  increasing lead times for critical supply chain members 
E)  increasing the number of suppliers at critical junctures in the supply chain 
A)  contracts that require supply chain members to formulate long-term forecasts 
B)  penalties for supply chain members that adjust forecasts 
C)  sharing forecasts or demand data across the supply chain 
D)  increasing lead times for critical supply chain members 
E)  increasing the number of suppliers at critical junctures in the supply chain

Question 3

A control chart involves setting action limits for cumulative forecast error.

Accepted Answer

A) True 
 B)False

Question 4

The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques.

Accepted Answer

A) True 
 B)False

Question 5

Which of the following changes would tend to shorten the time frame for short-term forecasting?

Accepted Answer

A)  bringing greater variety into the product mix 
B)  increasing the flexibility of the production system 
C)  ordering fewer weather-sensitive items 
D)  adding more special-purpose equipment 
E)  investing in the production system to make it more task-specific 
A)  bringing greater variety into the product mix 
B)  increasing the flexibility of the production system 
C)  ordering fewer weather-sensitive items 
D)  adding more special-purpose equipment 
E)  investing in the production system to make it more task-specific

Question 6

Suppose a four-period weighted average is being used to forecast demand. Weights for the periods are as follows: wt-4 = 0.1, wt-3 = 0.2, wt-2 = 0.3 and wt-1 = 0.4. Demand observed in the previous four periods was as follows: At-4 = 380, At-3 = 410, At-2 = 390, At-1 = 400. What will be the demand forecast for period t?

Accepted Answer

A)  402 
B)  397 
C)  399 
D)  393 
E)  403 
A)  402 
B)  397 
C)  399 
D)  393 
E)  403

Question 7

The primary difference between seasonality and cycles is:

Accepted Answer

A)  the duration of the repeating patterns. 
B)  the magnitude of the variation. 
C)  the ability to attribute the pattern to a cause. 
D)  the direction of the movement. 
E)  there are only four seasons but 30 cycles. 
A)  the duration of the repeating patterns. 
B)  the magnitude of the variation. 
C)  the ability to attribute the pattern to a cause. 
D)  the direction of the movement. 
E)  there are only four seasons but 30 cycles.

Question 8

For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques.

Accepted Answer

A) True 
 B)False

Question 9

Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:  
What is this week's forecast using exponential smoothing with alpha = .2, if the forecast for two weeks ago was 90?

Accepted Answer

A)  49 
B)  50 
C)  52 
D)  65 
E)  78 
A)  49 
B)  50 
C)  52 
D)  65 
E)  78

Question 10

The president of State University wants to forecast student enrollments for this academic year based on the following historical data: $$\begin{array} { | l | c | } 
\hline \text { Year } & \text { Enrollments } \
\hline \text { 5 Years ago } & 15,000 \
\hline \text { 4 Years ago } & 16,000 \
\hline \text { 3 Years ago } & 18,000 \
\hline \text { 2 Years ago } & 20,000 \
\hline \text { Last Year } & 21,000 \
\hline
\end{array}$$
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 11

Using the latest observation in a sequence of data to forecast the next period is:

Accepted Answer

A)  a moving average forecast. 
B)  a naive forecast. 
C)  an exponentially smoothed forecast. 
D)  an associative forecast. 
E)  regression analysis. 
A)  a moving average forecast. 
B)  a naive forecast. 
C)  an exponentially smoothed forecast. 
D)  an associative forecast. 
E)  regression analysis.

Question 12

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: $$\begin{array} { | l | c | } 
\hline{ \text { Month } } & \text { Number of Visits } \
\hline \text { April } & 100 \
\hline \text { May } & 140 \
\hline \text { June } & 110 \
\hline \text { July } & 150 \
\hline \text { August } & 120 \
\hline \text { September } & 160 \
\hline
\end{array}$$
What is this month's forecast using the naive approach?

Accepted Answer

A)  100 
B)  160 
C)  130 
D)  140 
E)  120 
A)  100 
B)  160 
C)  130 
D)  140 
E)  120

Question 13

The president of State University wants to forecast student enrollments for this academic year based on the following historical data: $$\begin{array} { | c | c | } 
\hline \text { Year } & \text { Enrollments } \
\hline \text { 5 Years ago } & 15,000 \
\hline \text { 4 Years ago } & 16,000 \
\hline \text { 3 Years ago } & 18,000 \
\hline \text { 2 Years ago } & 20,000 \
\hline \text { Last Year } & 21,000 \
\hline
\end{array}$$
What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .05 and beta = .3, if the forecast for last year was 21,000, the forecast for two years ago was 19,000, and the trend estimate for last year's forecast was 1,500?

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 14

Given forecast errors of 5, 0, -4, and 3, what is the mean absolute deviation?

Accepted Answer

A)  4 
B)  3 
C)  2.5 
D)  2 
E)  1 
A)  4 
B)  3 
C)  2.5 
D)  2 
E)  1

Question 15

Forecasts based on an average tend to exhibit less variability than the original data.

Accepted Answer

A) True 
 B)False

Question 16

Which of the following would tend to decrease forecast accuracy?

Accepted Answer

A)  a reduction in demand variability 
B)  a shortening of the forecast time horizon 
C)  an attempt to forecast demand for a group of similar items rather than an individual item 
D)  a change in the underlying causal system 
E)  an increase in the flexibility of the production system 
A)  a reduction in demand variability 
B)  a shortening of the forecast time horizon 
C)  an attempt to forecast demand for a group of similar items rather than an individual item 
D)  a change in the underlying causal system 
E)  an increase in the flexibility of the production system

Question 17

The more novel a new product or service design is, the more forecasters have to rely on:

Accepted Answer

A)  subjective estimates. 
B)  seasonality. 
C)  cyclicality. 
D)  historical data. 
E)  smoothed variation. 
A)  subjective estimates. 
B)  seasonality. 
C)  cyclicality. 
D)  historical data. 
E)  smoothed variation.

Question 18

A manager uses the following equation to predict monthly receipts: Yt = 40,000 + 150t. What is the forecast for July if t = 0 in April of this year?

Accepted Answer

A)  40,450 
B)  40,600 
C)  42,100 
D)  42,250 
E)  42,400 
A)  40,450 
B)  40,600 
C)  42,100 
D)  42,250 
E)  42,400

Question 19

Given an actual demand of 105, a forecasted value of 97, and an alpha of .4, the simple exponential smoothing forecast for the next period would be:

Accepted Answer

A)  80.8. 
B)  93.8. 
C)  100.2. 
D)  101.8. 
E)  108.2. 
A)  80.8. 
B)  93.8. 
C)  100.2. 
D)  101.8. 
E)  108.2.

Question 20

The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast.

Accepted Answer

A) True 
 B)False

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

Which of the following helps improve supply chain forecasting performance?

A control chart involves setting action limits for cumulative forecast error.

The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques.

Which of the following changes would tend to shorten the time frame for short-term forecasting?

The primary difference between seasonality and cycles is:

For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques.

Using the latest observation in a sequence of data to forecast the next period is:

Given forecast errors of 5, 0, -4, and 3, what is the mean absolute deviation?

Forecasts based on an average tend to exhibit less variability than the original data.

Which of the following would tend to decrease forecast accuracy?

The more novel a new product or service design is, the more forecasters have to rely on:

A manager uses the following equation to predict monthly receipts: Yt = 40,000 + 150t. What is the forecast for July if t = 0 in April of this year?

Given an actual demand of 105, a forecasted value of 97, and an alpha of .4, the simple exponential smoothing forecast for the next period would be:

The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast.

Introduction to Operations Management

Competitiveness

Product and Service Design

RELIABILITY – Static

Strategic Capacity Planning for Products and Services

Decision Theory– Static

Process Selection and Facility Layout

Work Design and Measurement

learning curve– Static

Location Planning and Analysis

The Transportation Model– Static

Management of Quality

Quality Control

Acceptance Sampling– Static

Aggregate Planning and Master Scheduling

MRP and ERP

Inventory Management

JIT and Lean Operations

Maintenance– Static

Supply Chain Management

Scheduling

Project Management

Management of Waiting Lines

Linear Programming

Filters

Exam 3: Forecasting

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

Which of the following helps improve supply chain forecasting performance?

A control chart involves setting action limits for cumulative forecast error.

The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques.

Which of the following changes would tend to shorten the time frame for short-term forecasting?

The primary difference between seasonality and cycles is:

For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques.

Using the latest observation in a sequence of data to forecast the next period is:

Given forecast errors of 5, 0, -4, and 3, what is the mean absolute deviation?

Forecasts based on an average tend to exhibit less variability than the original data.

Which of the following would tend to decrease forecast accuracy?

The more novel a new product or service design is, the more forecasters have to rely on:

A manager uses the following equation to predict monthly receipts: Yt = 40,000 + 150t. What is the forecast for July if t = 0 in April of this year?

Given an actual demand of 105, a forecasted value of 97, and an alpha of .4, the simple exponential smoothing forecast for the next period would be:

The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast.

Introduction to Operations Management

Competitiveness

Product and Service Design

RELIABILITY – Static

Strategic Capacity Planning for Products and Services

Decision Theory– Static

Process Selection and Facility Layout

Work Design and Measurement

learning curve– Static

Location Planning and Analysis

The Transportation Model– Static

Management of Quality

Quality Control

Acceptance Sampling– Static

Aggregate Planning and Master Scheduling

MRP and ERP

Inventory Management

JIT and Lean Operations

Maintenance– Static

Supply Chain Management

Scheduling

Project Management

Management of Waiting Lines

Linear Programming

Filters