Forecasting Flashcards
Process of predicting a future event
Forecasting
Underlying basis of all business decisions
Production
Inventory
Personnel
Facilities
Up to 1 year; usually less than 3 months
Job scheduling, worker assignments
Short-range forecast
3 months to 3 years
Sales & production planning, budgeting
Medium-range forecast
3+ years
New product planning, facility location
Long-range forecast
Address business cycle, e.g., inflation rate, money supply etc.
Economic forecasts
Predict rate of technological progress
Predict acceptance of new product
Technological forecasts
Predict sales of existing product
Demand forecasts
Seven steps in forecasting
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
True or False. Most forecasting methods assume that there is some underlying stability in the system
True
> Used when situation is vague & little data exist:
New products
New technology
Involves intuition, experience:
e.g., forecasting sales on Internet
Qualitative methods
> Used when situation is ‘stable’ & historical data exist
Existing products
Current technology
Involves mathematical techniques
e.g., forecasting sales of color televisions
Quantitative methods
Pool opinions of high-level executives, sometimes augment by statistical models
Jury of executive opinion
Panel of experts, queried iteratively
Delphi method
Estimates from individual salespersons are reviewed for reasonableness, then aggregated
Sales force composite
Ask the customer
Consumer Market Survey
Time-series models
Naïve approach
Moving averages
Exponential smoothing
Trend projection
Associative models
Linear regression
> 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
Time series
Time series components
Trend, cyclical, seasonal, random
Persistent, overall upward or downward pattern
Due to population, technology etc.
Several years duration
Trend component
Regular pattern of up & down fluctuations
Due to weather, customs etc.
Occurs within 1 year
Seasonal component
Repeating up & down movements
Due to interactions of factors influencing economy
Usually 2-10 years duration
Cyclical component
Erratic, unsystematic, ‘residual’ fluctuations
Due to random variation or unforeseen events
Short duration & nonrepeating
Random component
Assumes demand in next period is the same as demand in most recent period
Sometimes cost effective & efficient
Naive approach
is a series of arithmetic means
Used if little or no trend
Used often for smoothing
Provides overall impression of data over time
Moving Average method
Equation of Moving average method
MA= sum(demand in prev n periods)/n
Used when trend is present
Older data usually less important
Weights based on intuition
Often lay between 0 & 1, & sum to 1.0
Weighted moving average method
Equation of weighted moving average
WMA= sum(weightforperiodn)(demand in period n)/sum(weights)
True or False. Moving Average does not forecast trend well
True
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
Exponential Smoothing method
Exponential Smoothing equation
Ft = Ft-1 + (At-1 - Ft-1)
Use for computing forecast
Used for forecasting linear trend line
Assumes relationship between response variable, Y, and time, X, is a linear function
Linear trend projection
