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Category: Business and Industry

Date Submitted: 09/12/2011 07:49 PM

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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

* Judgment and opinion

* Sales force

* Consumer surveys

* Delphi technique

* Quantitative

* Regression and Correlation (associative)

* Time series

Forecasts Based on Time Series Data

* What is Time Series?

* Components (behavior) of Time Series data

* Trend

* Cycle

* Seasonal

* Irregular

* 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

* Moving Averages

* 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:

* Deseasonalizing data

* Seasonalizing data

* How indexes are computed (see Example 7 on page 109)

Accuracy and Control of Forecasts

Measures of Accuracy

* Mean Absolute Deviation (MAD)

* Mean Squared Error (MSE)

* Mean...