A statement about the future value of a variable of interest such as demand.
Forecasting is used to make informed decisions.
Forecasts affect decisions and activities throughout an organization
Accounting, finance
Human resources
Product/service design
Features of Forecasts
Assumes causal system
past ==> future
Forecasts rarely perfect because of randomness
Forecasts more accurate for
groups vs. individuals
Forecast accuracy decreases
as time horizon increases
Forecasting Approaches
Qualitative Forecasting
Qualitative techniques permit the inclusion of soft information such as:
Human factors
Personal opinions
These factors are difficult, or impossible, to quantify
Quantitative Forecasting
Quantitative techniques involve either the projection of historical data or the development of
associative methods that attempt to use causal variables to make a forecast
These techniques rely on hard data
Types of Forecasts
Judgmental: uses subjective inputs
- Executive opinions
- Sales force opinions
- Consumer surveys
- Outside opinion
- Delphi method
o Opinions of managers and staff
o Achieves a consensus forecast
Time series: uses historical data, assuming the future will be like the past
- Trend
- Seasonality
- Cycles
- Irregular variations
- Random variations
A long-term upward or downward movement in data
- Population shifts
- Changing income
Short-term, fairly regular variations related to the calendar or time of day
Restaurants, service call centers, and theaters all experience seasonal demand
Wavelike variations lasting more than one year
These are often related to a variety of economic, political, or even agricultural conditions
Irregular variation
Due to unusual circumstances that do not reflect typical behavior
- Labor strike
- Weather event
Random Variation
Residual variation that remains after all other behaviors have been accounted for
Time-Series Forecasting
 Naive Forecasts
The Naive forecast ignores all data points in a time series except the last one.
Forecast = Last value
 Averaging
Simple Moving Average
Weighted Moving Average
Exponential Smoothing
Associative models: uses explanatory variables to predict the future
Linear Regression Assumptions
Variations around the line are random
Deviations around the line normally distributed
Predictions are being made only within the range of observed values
For best results:
Always plot the data to verify linearity
Check for data being time-dependent
Small correlation may imply that other variables are important

FORECAST - Assumption University