Forecasting Flashcards

1
Q

Process of predicting a future event

A

Forecasting

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2
Q

Underlying basis of all business decisions

A

Production
Inventory
Personnel
Facilities

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3
Q

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

A

Short-range forecast

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4
Q

3 months to 3 years
Sales & production planning, budgeting

A

Medium-range forecast

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5
Q

3+ years
New product planning, facility location

A

Long-range forecast

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6
Q

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

A

Economic forecasts

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7
Q

Predict rate of technological progress
Predict acceptance of new product

A

Technological forecasts

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8
Q

Predict sales of existing product

A

Demand forecasts

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9
Q

Seven steps in forecasting

A

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

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10
Q

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

A

True

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11
Q

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

A

Qualitative methods

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12
Q

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

A

Quantitative methods

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13
Q

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

A

Jury of executive opinion

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14
Q

Panel of experts, queried iteratively

A

Delphi method

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15
Q

Estimates from individual salespersons are reviewed for reasonableness, then aggregated

A

Sales force composite

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16
Q

Ask the customer

A

Consumer Market Survey

17
Q

Time-series models

A

Naïve approach
Moving averages
Exponential smoothing
Trend projection

18
Q

Associative models

A

Linear regression

19
Q

> 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

A

Time series

20
Q

Time series components

A

Trend, cyclical, seasonal, random

21
Q

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

A

Trend component

22
Q

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

A

Seasonal component

23
Q

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

A

Cyclical component

24
Q

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

A

Random component

25
Q

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

A

Naive approach

26
Q

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

A

Moving Average method

27
Q

Equation of Moving average method

A

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

28
Q

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

A

Weighted moving average method

29
Q

Equation of weighted moving average

A

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

30
Q

True or False. Moving Average does not forecast trend well

A

True

31
Q

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

A

Exponential Smoothing method

32
Q

Exponential Smoothing equation

A

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

33
Q

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

A

Linear trend projection