## ISDS 3115

A naive forecast for September sales of a product would be equal to the forecast for August.

FALSE

The forecasting time horizon and the forecasting techniques used to vary over the life cycle of a product.

TRUE

Demand (sales) forecasts serve as inputs to financial, marketing, and personnel planning.

TRUE

Forecasts of individual products tend to be more accurate than forecasts of product families.

FALSE

Most forecasting techniques assume that there is some underlying stability in the system.

TRUE

The sales force composite forecasting method relies on salespersons’ estimates of expected sales.

TRUE

A time-series model uses a series of past data points to make the forecast

TRUE

The quarterly “make meeting” of Lexus dealers is an example of a sales force composite forecast.

TRUE

Cycles and random variation are both components of time series.

TRUE

A naive forecast for September sales of a product would be equal to the sales in August.

TRUE

One advantage of exponential smoothing is the limited amount of record keeping involved.

TRUE

The larger the number of periods in the simple moving average forecasting method, the greater the method’s responsiveness to changes in demand.

FALSE

Forecast including trend is an exponential smoothing technique that utilizes two smoothing constants: one for the average level of the forecast and one for its trend.

TRUE

Mean squared error and Coefficient of Correlation are two measures of the overall error of a forecasting model.

FALSE

In trend projection, the trend component is the slope of the regression equation.

TRUE

In trend projection, a negative regression slope is mathematically impossible.

FALSE

Seasonal indexes adjust raw data for patterns that repeat at regular time intervals.

TRUE

If a quarterly seasonal index has been calculated at 1.55 for the October-December quarter, then raw data for that quarter must be multiplied by 1.55 so that the quarter can be fairly compare to other quarters.

FALSE

The best way to forecast a business cycle is by finding a leading variable.

TRUE

Linear regression analysis is a straight-line mathematical model to describe the functional relationships between independent and dependent variables.

TRUE

The larger the standard error of the estimate, the more accurate the forecasting model.

FALSE

A trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y for every unit of time that passes.

TRUE

In a regression equation where Y is demand and X is advertising, a coefficient of determination (r squared) of .70 means that 70% of the variance in advertising is explained by demand.

FALSE

Demand cycles for individual products can be driven by product life cycles.

TRUE

If a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased.

TRUE

Focus forecasting tries a variety of computer models and selects the best one for a particular application.

TRUE

Many service firms use point-of-sale computers to collect detailed records needed for accurate short-term forecasts.

TRUE

What two numbers are contained in the daily report to the CEO of Walt Disney Parks & Resorts regarding the six Orlando parks?

yesterday’s forecasted attendance and yesterday’s actual attendance

Using an exponential smoothing modeal with smoothing constant alpha= .20, how much weight would be assigned to the 2nd most recent period?

.16

Forecasts:

are rarely perfect

One use of short-range forecasts is to determine

job assignments

Forecasts are usually classified by time horizon into three categories:

short-range, medium-range, and long-range

A forecast with a time horizon of about 3 months to 3 years is typically called a

medium-range forecast

Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a…

long-range time horizon

The three major types of forecasts used by business organizations are:

economic, technological, and demand

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

eliminate any assumptions

The two general approaches to forecasting are:

qualitative and quantitative

Which of the following uses three types of participants: decision makers, staff personnel, and respondents?

the delphi method

The forecasting model that pools the opinions of a group of experts or managers is known as the:

jury of executive opinion modeal

Which of the following is NOT a type of qualitative forecasting?

moving average

Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand?

Associative models

Which of the following statements about time series forecasting is true?

The analysis of past demand helps predict future demand.

Time series data may exhibit which of the following behaviors?

They may exhibit all of the above.

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

trends

Which of the following is not present in a time series?

operational variations

The fundamental difference between cycles and seasonality is the:

duration of the repeating patterns

In time series, which of the following cannot be predicted?

random fluctuation

What is the approximate forecast for May using a four-month moving average?

Nov=39, Dec=36, Jan=40, Feb=42, Mar=48, April=46?

Nov=39, Dec=36, Jan=40, Feb=42, Mar=48, April=46?

44

What time series model below assumes that demand in the next period will be equal to the most recent period’s demand?

naive approach

John’s House of Pancakes uses a weighted moving average method to forecast pancake sales. It assigns a weight of 5 to the previous month’s demand, 3 to demand two months ago, and 1 to demand three months ago. If sales amounted to 1000 pancakes in May, 2200 pancakes in June, and 3000 pancakes in July, what should be the forecast for August?

2511

A six-month moving average forecast is better than a three-month moving average forecast if demand:

is rather stable

Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of:

responsiveness to changes

Which of the following statements comparing the weighted moving average technique and exponential smoothing is true:

Exponential smoothing typically requires less record keeping of past data.

Which time series model uses past forecasts and past demand data to generate a new forecast?

exponential smoothing

Which is NOT a characteristic of exponential smoothing?

weights each historical value equally

Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?

1.0

Given an actual demand of 103, a previous forecast value of 99, and an alpha of 0.4, the exponential smoothing forecast for the next period would be:

100.6

A forecast based on the previous forecast plus a percentage of the forecast error is an:

exponentially smoothed forecast

Given an actual demand of 61, a previous forecast of an alpha=0.3, what would the forecast for the next period be using simple exponential smoothing?

58.9

Which of the following values of alpha would cause exponential smoothing to respond the most slowly to forecast errors?

0.10

(the lowest number)

(the lowest number)

The primary purpose of the mean absolute deviation (MAD) in forecasting is to:

measure forecast accuracy

Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?

4

The last four months of sales were 8, 10, 15, and 9 units. The last four forecast 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) is:

3.5

A time series trend equation is 25.3 + 2.1 X. what is your forecast for period 7?

40.0

For a given product demand, the time series trend equation is 53 – 4X. The negative sign on the slope of the equation:

is an indication that product demand is declining

Yamaha manufacturers which set of products with complementary demands to address seasonal fluctuations:

jet skis and snowmobiles

Which of the following is TRUE regarding two smoothing constants of the Forecast Including Trend (FIT) model?

Their values are determined independently

Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January?

1000 units

A seasonal index for a monthly series is about to be calculated on the basis of three years’ accumulation of data. The three previous July values were 110, 150, and 130. The average over all months is 190. The approximate seasonal index for July is:

0.684

A fundamental distinction between trend projection and linear regression is that :

In trend projection the independent variable is time; in linear regression the independent variable need not be time, but can be any variable with explanatory power.

The percent of variation in the dependent variable that is explained by the regression equation is measured by the:

coefficient of determination

The degree or strength of a linear relationship is shown by the

correlation coefficient

If two variables were perfectly correlated, the correlation coefficient r would equal:

b or c

(-1 and 1)

(-1 and 1)

The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustrate:

bias

The tracking signal is the

ratio RSFE/MAD

Computer monitoring of tracking signals and self-adjustment is a signal passes a preset limit is characteristic of:

adaptive smoothing

Many services maintain records of sales noting:

all of the above

(the day of week, unusual events, weather, holidays)

(the day of week, unusual events, weather, holidays)

Taco Bell’s unique employee scheduling parties are partly the result of using

a and c

(point-of-sale computers to track food sales in 15 minute intervals AND a six-week moving average forecasting technique)

(point-of-sale computers to track food sales in 15 minute intervals AND a six-week moving average forecasting technique)

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