# OPM CH 3 HW

Time series forecasting models make predictions about the future based on analysis of past data.

correct True

False

False

Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model.

correct True

False

Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model.

False

Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model.

MAD statistics can be used to generate tracking signals.

correct True

False

In recent years, MAD has made a comeback because of its simplicity and usefulness in obtaining tracking signals.

False

In recent years, MAD has made a comeback because of its simplicity and usefulness in obtaining tracking signals.

Qualitative forecasting techniques generally take advantage of the knowledge of experts and therefore do not require much judgment.

True

correct False

Qualitative forecasting techniques generally take advantage of the knowledge of experts and require much judgment.

correct False

Qualitative forecasting techniques generally take advantage of the knowledge of experts and require much judgment.

Market research is a quantitative method of forecasting.

True

correct False

Market research is used mostly for product research in the sense of looking for new product ideas, likes and dislikes about existing products, which competitive products within a particular class are preferred, and so on. Again, the data collection methods are primarily surveys and interviews. It is a discussed under the qualitative techniques in forecasting topic area.

correct False

Market research is used mostly for product research in the sense of looking for new product ideas, likes and dislikes about existing products, which competitive products within a particular class are preferred, and so on. Again, the data collection methods are primarily surveys and interviews. It is a discussed under the qualitative techniques in forecasting topic area.

Decomposition of a time series means identifying and separating the time series data into its components.

correct True

False

Decomposition of a time series means identifying and separating the time series data into its components.

False

Decomposition of a time series means identifying and separating the time series data into its components.

A time series is defined in the text as chronologically ordered data that may contain one or more components of demand variation: trend, seasonal, cyclical, autocorrelation, and random.

correct True

False

A time series can be defined as chronologically ordered data that may contain one or more components of demand: trend, seasonal, cyclical, autocorrelation, and random

False

A time series can be defined as chronologically ordered data that may contain one or more components of demand: trend, seasonal, cyclical, autocorrelation, and random

We usually associate the word “seasonal” with recurrent periods of repetitive activity that happen on other than an annual cycle.

True

correct False

We usually associate seasonal with a period of the year characterized by some particular activity. We use the word cyclical to indicate other than annual recurrent periods of repetitive activity.

correct False

We usually associate seasonal with a period of the year characterized by some particular activity. We use the word cyclical to indicate other than annual recurrent periods of repetitive activity.

Which of the following is not one of the basic types of forecasting?

Time series analysis

Qualitative

Simulation

Force field analysis

Causal relationships

Time series analysis

Qualitative

Simulation

Force field analysis

Causal relationships

Time series analysis

Qualitative

Simulation

correct Force field analysis

Causal relationships

Forecasting can be classified into four basic types: qualitative, time series analysis, causal relationships, and simulation.

Qualitative

Simulation

correct Force field analysis

Causal relationships

Forecasting can be classified into four basic types: qualitative, time series analysis, causal relationships, and simulation.

Which of the following forecasting methodologies is considered a time series forecasting technique?

Simulation

Simple moving average

Market research

Leading indicators

Historical analogy

Simulation

Simple moving average

Market research

Leading indicators

Historical analogy

Simulation

correct Simple moving average

Market research

Leading indicators

Historical analogy

Simple moving average is the only choice that attempts to predict future values of demand based upon past dat

correct Simple moving average

Market research

Leading indicators

Historical analogy

Simple moving average is the only choice that attempts to predict future values of demand based upon past dat

In business forecasting, what is usually considered a short-term time period?

One year

Four weeks or less

More than three months

Six months or more

correct Less than three months

In business forecasting short term usually refers to under three months.

Four weeks or less

More than three months

Six months or more

correct Less than three months

In business forecasting short term usually refers to under three months.

In general, which forecasting time frame compensates most effectively for random variation and short term changes?

correct

correct

Short-term forecasts

Rapid change forecasts

Long range forecasts

Medium term forecasts

Quick-time forecasts

In general, the short-term models compensate for random variation and adjust for short-term changes (such as consumers’ responses to a new product).

Rapid change forecasts

Long range forecasts

Medium term forecasts

Quick-time forecasts

In general, the short-term models compensate for random variation and adjust for short-term changes (such as consumers’ responses to a new product).

A company wants to forecast demand using the simple moving average. If the company uses four prior yearly sales values (i.e., year 2010 = 100, year 2011 = 120, year 2012 = 140, and year 2013 = 210), which of the following is the simple moving average forecast for year 2014?

140.0

100.5

145.5

correct 142.5

155.0

Forecast for 2014 = (100+120+140+210)/4 = 570/4 = 142.5

100.5

145.5

correct 142.5

155.0

Forecast for 2014 = (100+120+140+210)/4 = 570/4 = 142.5

A company wants to forecast demand using the weighted moving average. If the company uses two prior yearly sales values (i.e., year 2012 = 110 and year 2013 = 130), and we want to weight year 2012 at 10% and year 2013 at 90%, which of the following is the weighted moving average forecast for year 2014?

138

120

142

correct 128

133

Forecast for 2014 = (110×0.1) + (130×0.9) = 11 + 117 = 128

120

142

correct 128

133

Forecast for 2014 = (110×0.1) + (130×0.9) = 11 + 117 = 128

The exponential smoothing method requires which of the following data to forecast the future?

Overall industry demand data

The most recent forecast

Precise actual demand for the past several years

Tracking values

The value of the smoothing constant delta

Overall industry demand data

The most recent forecast

Precise actual demand for the past several years

Tracking values

The value of the smoothing constant delta

Overall industry demand data

correct The most recent forecast

Precise actual demand for the past several years

Tracking values

The value of the smoothing constant delta

In the exponential smoothing method, only three pieces of data are needed to forecast the future: the most recent forecast, the actual demand that occurred for that forecast period, and a smoothing constant alpha.

correct The most recent forecast

Precise actual demand for the past several years

Tracking values

The value of the smoothing constant delta

In the exponential smoothing method, only three pieces of data are needed to forecast the future: the most recent forecast, the actual demand that occurred for that forecast period, and a smoothing constant alpha.

If a firm produced a product that was experiencing growth in demand, the smoothing constant alpha (reaction rate to differences) used in an exponential smoothing forecasting model would tend to be which of the following?

The more rapid the growth, the lower the percentage

50 % or more

A very low percentage, less than 10%

The more rapid the growth, the higher the percentage

Close to zero

The more rapid the growth, the lower the percentage

50 % or more

A very low percentage, less than 10%

The more rapid the growth, the higher the percentage

Close to zero

The more rapid the growth, the lower the percentage

50 % or more

A very low percentage, less than 10%

correct The more rapid the growth, the higher the percentage

Close to zero

If a firm were experiencing growth, it would be desirable to have a higher reaction rate, perhaps 15 to 30 percentage points, to give greater importance to recent growth experience. The more rapid the growth, the higher the reaction rate should be.

50 % or more

A very low percentage, less than 10%

correct The more rapid the growth, the higher the percentage

Close to zero

If a firm were experiencing growth, it would be desirable to have a higher reaction rate, perhaps 15 to 30 percentage points, to give greater importance to recent growth experience. The more rapid the growth, the higher the reaction rate should be.

As a consultant you have been asked to generate a unit demand forecast for a product for year 2014 using exponential smoothing. The actual demand in year 2013 was 750. The forecast demand in year 2013 was 960. Using this data and a smoothing constant alpha of 0.3, which of the following is the resulting year 2014 forecast value?

813

1,120

897

766

1,023

813

1,120

897

766

1,023

813

1,120

correct 897

766

1,023

Forecast = 960 + 0.3 x (960-750) = 897

1,120

correct 897

766

1,023

Forecast = 960 + 0.3 x (960-750) = 897

Heavy sales of umbrellas during a rain storm is an example of which of the following?

A causal relationship

A statistical correlation

A coincidence

A trend

A fad

A causal relationship

A statistical correlation

A coincidence

A trend

A fad

correct A causal relationship

A statistical correlation

A coincidence

A trend

A fad

We can expect that an extended period of rain will increase sales of umbrellas and raincoats. The rain causes the sale of rain gear. This is a causal relationship, where one occurrence causes another.

A statistical correlation

A coincidence

A trend

A fad

We can expect that an extended period of rain will increase sales of umbrellas and raincoats. The rain causes the sale of rain gear. This is a causal relationship, where one occurrence causes another.

Statistical information is very important in building the forecasts. Qualitative input is secondary in importance.

True

correct False

Market knowledge and business savvy are critically important in understanding future business leve

correct False

Market knowledge and business savvy are critically important in understanding future business leve

Marketing and Sales both have specific and different inputs into the Demand Plan.

correct True

False

Marketing is focused on changing customer behavior. Sales focuses on closing orders.

False

Marketing is focused on changing customer behavior. Sales focuses on closing orders.

Statistical inputs are important and represent the most significant forecast input affecting trends and accuracy, easily more important than qualitative inputs.

True

correct False

While statistical data are important, it is market and customer knowledge that defines improved accuracy.

correct False

While statistical data are important, it is market and customer knowledge that defines improved accuracy.

The Demand Plan has several inputs. These include (choose best answer):

Business Plan inputs and Marketing inputs

None of the above.

Production inputs

Direct-feed customer input to the final demand plan without modification.

Business Plan inputs and Marketing inputs

None of the above.

Production inputs

Direct-feed customer input to the final demand plan without modification.

correct Business Plan inputs and Marketing inputs

None of the above.

Production inputs

Direct-feed customer input to the final demand plan without modification.

There are several inputs that go into the Demand Plan. Not all of the listed ones were on the instructor slide showing inputs. Some were.

None of the above.

Production inputs

Direct-feed customer input to the final demand plan without modification.

There are several inputs that go into the Demand Plan. Not all of the listed ones were on the instructor slide showing inputs. Some were.

The operations manager or VP off Operations needs to be the process owner for the Demand Plan.

True

correct False

correct False

Pyramid forecasting techniques are rare but offer some advantages in accuracy over exponential smoothing when data are difficult to find.

True

correct False

correct False

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