Forecasting

Forecasting

Why forecast?

Features Common to all Forecasts

• Conditions in the past will continue in the future
• Rarely perfect
• Forecasts for groups tend to be more accurate than forecasts for individuals
• Forecast accuracy declines as time horizon increases

Elements of a Good Forecast

• Timely
• Accurate
• Reliable (should work consistently)
• Forecast expressed in meaningful units
• Communicated in writing
• Simple to understand and use

Steps in Forecasting Process

• Determine purpose of the forecast
• Establish a time horizon
• Select forecasting technique
• Gather and analyze the appropriate data
• Prepare the forecast
• Monitor the forecast

Types of Forecasts

• Qualitative o Judgment and opinion o Sales force o Consumer surveys o Delphi technique

• Quantitative o Regression and Correlation (associative) o Time series Forecasts Based on Time Series Data

• What is Time Series?
• Components (behavior) of Time Series data o Trend o Cycle o Seasonal o Irregular o Random variations

Naïve Methods

Naïve Forecast – uses a single previous value of a time series as the basis of a forecast.

Techniques for Averaging

• What is the purpose of averaging?
• Common Averaging Techniques o Moving Averages o Exponential smoothing

Moving Average

Exponential Smoothing

Techniques for Trend

Linear Trend Equation

Curvilinear Trend Equation

Techniques for Seasonality

• What is seasonality?

• What are seasonal relatives or indexes?

• How seasonal indexes are used: o Deseasonalizing data o Seasonalizing data

• How indexes are computed (see Example 7 on page 109) Accuracy and Control of Forecasts

Measures of Accuracy o Mean Absolute Deviation (MAD) o Mean Squared Error (MSE) o Mean Absolute Percentage Error (MAPE)

Forecast Control Measure o Tracking Signal

Mean Absolute Deviation (MAD)

Mean