Forecasting

Question 1

Process of predicting a future event

Answer 1

Forecasting

Question 2

Underlying basis of all business decisions

Answer 2

Production
Inventory
Personnel
Facilities

Question 3

Up to 1 year; usually less than 3 months
Job scheduling, worker assignments

Answer 3

Short-range forecast

Question 4

3 months to 3 years
Sales & production planning, budgeting

Answer 4

Medium-range forecast

Question 5

3+ years
New product planning, facility location

Answer 5

Long-range forecast

Question 6

Address business cycle, e.g., inflation rate, money supply etc.

Answer 6

Economic forecasts

Question 7

Predict rate of technological progress
Predict acceptance of new product

Answer 7

Technological forecasts

Question 8

Predict sales of existing product

Answer 8

Demand forecasts

Question 9

Seven steps in forecasting

Answer 9

1 Determine the use of the forecast
2 Select the items to be forecasted
3 Determine the time horizon of the forecast
4 Select the forecasting model(s)
5 Gather the data
6 Make the forecast
7 Validate and implement results

Question 10

True or False. Most forecasting methods assume that there is some underlying stability in the system

Answer 10

True

Question 11

> Used when situation is vague & little data exist:
New products
New technology
Involves intuition, experience:
e.g., forecasting sales on Internet

Answer 11

Qualitative methods

Question 12

> Used when situation is ‘stable’ & historical data exist
Existing products
Current technology
Involves mathematical techniques
e.g., forecasting sales of color televisions

Answer 12

Quantitative methods

Question 13

Pool opinions of high-level executives, sometimes augment by statistical models

Answer 13

Jury of executive opinion

Question 14

Panel of experts, queried iteratively

Answer 14

Delphi method

Question 15

Estimates from individual salespersons are reviewed for reasonableness, then aggregated

Answer 15

Sales force composite

Question 16

Ask the customer

Answer 16

Consumer Market Survey

Question 17

Time-series models

Answer 17

Naïve approach
Moving averages
Exponential smoothing
Trend projection

Question 18

Associative models

Answer 18

Linear regression

Question 19

> Set of evenly spaced numerical data
Obtained by observing response variable at regular time periods
Forecast based only on past values
Assumes that factors influencing past and present will continue influence in future

Answer 19

Time series

Question 20

Time series components

Answer 20

Trend, cyclical, seasonal, random

Question 21

Persistent, overall upward or downward pattern
Due to population, technology etc.
Several years duration

Answer 21

Trend component

Question 22

Regular pattern of up & down fluctuations
Due to weather, customs etc.
Occurs within 1 year

Answer 22

Seasonal component

Question 23

Repeating up & down movements
Due to interactions of factors influencing economy
Usually 2-10 years duration

Answer 23

Cyclical component

Question 24

Erratic, unsystematic, ‘residual’ fluctuations
Due to random variation or unforeseen events
Short duration & nonrepeating

Answer 24

Random component

Question 25

Assumes demand in next period is the same as demand in most recent period Sometimes cost effective & efficient

Answer 25

Naive approach

Question 26

is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time

Answer 26

Moving Average method

Question 27

Equation of Moving average method

Answer 27

MA= sum(demand in prev n periods)/n

Question 28

Used when trend is present Older data usually less important Weights based on intuition Often lay between 0 & 1, & sum to 1.0

Answer 28

Weighted moving average method

Question 29

Equation of weighted moving average

Answer 29

WMA= sum(weightforperiodn)(demand in period n)/sum(weights)

Question 30

True or False. Moving Average does not forecast trend well

Answer 30

True

Question 31

Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data

Answer 31

Exponential Smoothing method

Question 32

Exponential Smoothing equation

Answer 32

Ft = Ft-1 + (At-1 - Ft-1) Use for computing forecast

Question 33

Used for forecasting linear trend line Assumes relationship between response variable, Y, and time, X, is a linear function

Answer 33

Linear trend projection