Chapter 4

Front 1

Forecasting

Back 1

Art and science of predicting future events

Front 2

Future time Horizons

Back 2

Short-range forecast, Medium range, long-range.

Front 3

Short-range

Back 3

Span up to 1 year, used for planning purchasing, job scheduling, workforce levels, job assisgnments, and production levels

Front 4

Medium-range

Back 4

Spans from 3 months to 3 years. Useful in sales planning, production planning and budgeting, cash budgeting, and analyzing operating plans

Front 5

Long-range

Back 5

3 years or more, planning new products, capital expenditures, facility location or expansion, R&D

Front 6

Intermediate and long run forecasts

Back 6

Deal with more comprehensive issues and support management decisions

Front 7

Short Term forecasting

Back 7

Employs different methodologies. (moving averages, exponential smoothing, and trend extrapolation)

Front 8

Short-range forecasting

Back 8

Tend to be more accurate than longer-range forecasts.

Front 9

Product Life Cycles

Back 9

Introduction, growth, maturity, decline

Front 10

Type of forecasts

Back 10

Economic, technological, demand

Front 11

Strategic important of forecasting

Back 11

human resources, capacity, supply-chain management.

Front 12

Seven steps in forecasting

Back 12

Determing the use of the forecast

Front 13

Select the items to be forecasted

Back 13

Determine the time horizon of the forecast

Front 14

Select the forecasting models

Back 14

Gather the data

Front 15

Make the forecast

Back 15

Validate and implement results

Front 16

Realties

Back 16

Forecast are seldom perfect, most techniques assume an uderlying stability in the system, product family and aggregated forecasts are more accurate than individual product forecasts.

Front 17

Qualitative approach

Back 17

Decisions maker's intuition, emotions, personal experiences, and value system in reaching a forecast

Front 18

Quantitative approach

Back 18

variety of mathematical models that rely on historical data.

Front 19

Qualitative methods

Back 19

Jury of executive opinion - pool opinions of high-level executives

Front 20

Delphi Method

Back 20

- panel of experts

Front 21

Sales force composite

Back 21

Based on salespersons' estimates of expected sales

Front 22

Consumer market survey

Back 22

Forecasting method that solicits input from customers

Front 23

Quantitative Methods

Back 23

Naive approach, moving averages, exponential smoothing, trend projection, linear regression.

Front 24

Naive approach

Back 24

assumes next period is the same as next

Front 25

The forecasting time horizon that would typically be easiest to predict for would be the

Back 25

short-range

Front 26

A forecast that projects a company's sales is a(n)

Back 26

demand forecast

Front 27

Quantitative methods of forecasting include

Back 27

exponential smoothing

Front 28

The method that considers several variables that are related to the variable being predicted is

Back 28

multiple regression

Front 29

The forecasting model that is based upon salesperson's estimates of expected sales is

Back 29

sales force composite

Front 30

Decomposing a time series refers to breaking down past data into the components of

Back 30

trends, cycles, seasonal and random variations

Front 31

Realties

Back 31

Forecast are seldom perfect, most techniques assume an uderlying stability in the system, product family and aggregated forecasts are more accurate than individual product forecasts.

Front 32

Qualitative approach

Back 32

Decisions maker's intuition, emotions, personal experiences, and value system in reaching a forecast

Front 33

Quantitative approach

Back 33

variety of mathematical models that rely on historical data.

Front 34

Qualitative methods

Back 34

Jury of executive opinion - pool opinions of high-level executives

Front 35

Delphi Method

Back 35

- panel of experts

Front 36

Sales force composite

Back 36

Based on salespersons' estimates of expected sales

Front 37

Consumer market survey

Back 37

Forecasting method that solicits input from customers

Front 38

Quantitative Methods

Back 38

Naive approach, moving averages, exponential smoothing, trend projection, linear regression.

Front 39

Naive approach

Back 39

assumes next period is the same as next

Front 40

The forecasting time horizon that would typically be easiest to predict for would be the

Back 40

short-range

Front 41

A forecast that projects a company's sales is a(n)

Back 41

demand forecast

Front 42

Quantitative methods of forecasting include

Back 42

exponential smoothing

Front 43

The method that considers several variables that are related to the variable being predicted is

Back 43

multiple regression

Front 44

The forecasting model that is based upon salesperson's estimates of expected sales is

Back 44

sales force composite

Front 45

Decomposing a time series refers to breaking down past data into the components of

Back 45

trends, cycles, seasonal and random variations

Front 46

With regard to regression-based forecast, the standard error of the estimate gives a measure of

Back 46

the variability around the regression line

Front 47

When using exponential smoothing, the smoothing constant

Back 47

is typically between .05 and .50 for most business applications
indicates the amount of weight placed on the most recent period
can be determined using MAD

Front 48

Exponential smoothing is most similar to

Back 48

the weighted moving average method of forecasting

Front 49

Time series patterns that repeat themselves after a period of days or weeks are called

Back 49

seasonality

Front 50

Which of the following is not a time-series model?

Back 50

linear regression