This document discusses forecasting methods. It provides an overview of forecasting, its importance, and how to reduce uncertainties. The basic steps in forecasting are defined as: defining the metric, collecting matching data, calculating statistics/graphs, preliminary forecasts, choosing a method, evaluating forecast quality and usefulness. Forecasting methods are classified as qualitative or quantitative. Quantitative methods include time series analysis, averaging methods, moving averages, exponential smoothing, and causal modeling using linear regression.

1. Overview
What is forecasting?
Why is forecasting important?
Forecasting uncertainties and
how to effectively reduce them
Basic steps in forecasting

2. Basic Steps in Forecasting
Define the metric being forecast
Collect data that matches your metric
Calculate basic statistics and create
graphs of the data
Do some preliminary forecasts
Choose a forecasting method
Evaluate the quality of the forecast
Evaluate the usefulness of the forecast

3. Forecasting Methods
Classified into two groups:

4. Examples of Qualitative method
Jury of executive opinion
Delphi method
Sales force composite
Market survey

5. Essential Concepts of Quantitative Analysis
Variation
Classes of Data
Time Series
Cross-sectional
Longitudinal
Basic Statistics
Mean and Variance
Correlation Coefficient

6. Quantitative Methods (cont):
Time Series Analysis
 Averaging Methods
Simple Mean:
►The average of all available data - good for level patterns
Moving Average:
►The average value over a set time period
► (e.g.: the last four weeks)
►Each new forecast drops the oldest data point & adds a new
observation
►More responsive to a trend but still lags behind actual data

7. Quantitative Methods (cont):

8. Quantitative Methods (cont):

9. Causal Hypotheses
 Often, leading indicators can help to predict
changes in future demand e.g. housing starts
and appliance sales
 Causal models establish a cause-and-effect
relationship between independent and
dependent variables
 A common tool of causal modeling is linear
regression

10. Causal Hypotheses
 Often, leading indicators can help to predict
changes in future demand e.g. housing starts
and appliance sales
 Causal models establish a cause-and-effect
relationship between independent and
dependent variables
 A common tool of causal modeling is linear
regression