The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: \[\begin{array} { | l | c | } \hline{ \text { Year } } & \text { Enrollments } \\ \hline \text { 5 Years ago } & 900 \\ \hline \text { 4 Years ago } & 700 \\ \hline \text { 3 Years ago } & 600 \\ \hline \text { 2 Years ago } & 500 \\ \hline \text { Last Year } & 300 \\ \hline \end{array}\] What is the forecast for this year using exponential smoothing with alpha = .4, if the forecast for two years ago was 750?

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

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: \[\begin{array} { | l | c | } \hline \text { Year } & \text { Enrollments } \\ \hline \text { 5 Years ago } & 900 \\ \hline \text { 4 Years ago } & 700 \\ \hline \text { 3 Years ago } & 600 \\ \hline \text { 2 Years ago } & 500 \\ \hline \text { Last Year } & 300 \\ \hline \end{array}\] What is the forecast for this year using the least squares trend line for these data?

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

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: \[\begin{array} { | c | c | } \hline \text { Year } & \text { Demand } \\ \hline \text { 5 Years ago } & 900 \\ \hline \text { 4 Years ago } & 700 \\ \hline 3 \text { Years ago } & 600 \\ \hline \text { 2 Years ago } & 500 \\ \hline \text { Last Year } & 300 \\ \hline \end{array}\] What is the forecast for this year using the naive approach?

A) 100 B) 200 C) 300 D) 500 E) 600 A) 100 B) 200 C) 300 D) 500 E) 600

A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data: \[\begin{array} { | l | c | } \hline { \text { Year } } & \text { Attendance } \\ \hline \text { Four Years ago } & 10,000 \\ \hline \text { Three Years ago } & 12,000 \\ \hline \text { Two Years ago } & 18,000 \\ \hline \text { Last Year } & 20,000 \\ \hline \end{array}\] What is this year's forecast using exponential smoothing with alpha = .2, if last year's smoothed forecast was 15,000?

A) 20,000 B) 19,000 C) 17,500 D) 16,000 E) 15,000 A) 20,000 B) 19,000 C) 17,500 D) 16,000 E) 15,000

Exam 3: Forecasting

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: Year Enrollments 5 Years ago 900 4 Years ago 700 3 Years ago 600 2 Years ago 500 Last Year 300 What is the forecast for this year using exponential smoothing with alpha = .4, if the forecast for two years ago was 750?

(Multiple Choice)

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Question 61

In order to update a moving average forecast, the values of each data point in the average must be known.

(True/False)

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Question 62

Bias is measured by the ratio of the cumulative sum of forecast errors to the mean absolute deviation (MAD).

(True/False)

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Question 63

Trend-adjusted exponential smoothing requires selection of two smoothing constants.

(True/False)

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Question 64

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

(True/False)

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Question 65

Which is not a characteristic of exponential smoothing?

(Multiple Choice)

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Question 66

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

(True/False)

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Question 67

An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago.

(True/False)

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Question 68

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: Year Enrollments 5 Years ago 900 4 Years ago 700 3 Years ago 600 2 Years ago 500 Last Year 300 What is the forecast for this year using the least squares trend line for these data?

(Multiple Choice)

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Question 69

Which of the following is not a step in the forecasting process?

(Multiple Choice)

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Question 70

Gradual, long-term movement in time series data is called:

(Multiple Choice)

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Question 71

Given forecast errors of 4, 8, and -3, what is the mean absolute deviation?

(Multiple Choice)

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Question 72

A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys.

(True/False)

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Question 73

Forecasting techniques generally assume:

(Multiple Choice)

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Question 74

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: Year Demand 5 Years ago 900 4 Years ago 700 3 Years ago 600 2 Years ago 500 Last Year 300 What is the forecast for this year using the naive approach?

(Multiple Choice)

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Question 75

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

(True/False)

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Question 76

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 exponential smoothing with alpha = .2, if last year's smoothed forecast was 15,000?

(Multiple Choice)

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Question 77

Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative.

(True/False)

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Question 78

Which is not a characteristic of simple moving averages applied to time series data?

(Multiple Choice)

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Question 79

Use of simple linear regression analysis assumes that:

(Multiple Choice)

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Question 80

Exam 3: Forecasting

In order to update a moving average forecast, the values of each data point in the average must be known.

Bias is measured by the ratio of the cumulative sum of forecast errors to the mean absolute deviation (MAD).

Trend-adjusted exponential smoothing requires selection of two smoothing constants.

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

Which is not a characteristic of exponential smoothing?

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

An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago.

Which of the following is not a step in the forecasting process?

Gradual, long-term movement in time series data is called:

Given forecast errors of 4, 8, and -3, what is the mean absolute deviation?

A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys.

Forecasting techniques generally assume:

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: Year Demand 5 Years ago 900 4 Years ago 700 3 Years ago 600 2 Years ago 500 Last Year 300 What is the forecast for this year using the naive approach?

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

Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative.

Which is not a characteristic of simple moving averages applied to time series data?

Use of simple linear regression analysis assumes that:

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