Forecasting Essay

5440 words - 22 pages

MAN 3520 - Fall 2011CP2 - Forecasting: Page 44 of 44FORECASTING FUNDAMENTALSForecast: A prediction, projection, or estimate of some future activity, event, or occurrence.Types of ForecastsEconomic forecastsPredict a variety of economic indicators, like money supply, inflation rates, interest rates, etc.Technological forecastsPredict rates of technological progress and innovation.Demand forecastsPredict the future demand for a company's products or services.Since virtually all the operations management decisions (in both the strategic category and the tactical category) require as input a good estimate of future demand, this is the type of forecasting that is emphasized in our textbook and in this course.TYPES OF FORECASTING METHODSQualitative methods: These types of forecasting methods are based on judgments, opinions, intuition, emotions, or personal experiences and are subjective in nature. They do not rely on any rigorous mathematical computations.Quantitative methods: These types of forecasting methods are based on mathematical (quantitative) models, and are objective in nature. They rely heavily on mathematical computations.QUALITATIVE FORECASTING METHODSQualitative MethodsDelphi Method Approach in which consensus agreement is reached among a group of experts Sales Force Composite Approach in which each salesperson estimates sales in his or her region Executive Opinion Approach in which a group of managers meet and collectively develop a forecast Market Survey Approach that uses interviews and surveys to judge preferences of customer and to assess demand QUANTITATIVE FORECASTING METHODSQua ntit ative MethodsTime-Series Models Time series models look at past patterns of data and attempt to predict the future based upon the underlying patterns contained within those data. Associative Models Associative models (often called causal models) assume that the variable being forecasted is related to other variables in the environment. They try to project based upon those associations. TIME SERIES MODELS
Model

Description

Naïve

Uses last period's actual value as a forecast

Simple Mean (Average)

Uses an average of all past data as a forecast

Simple Moving Average

Uses an average of a specified number of the most recent observations, with each observation receiving the same emphasis (weight)

Weighted Moving Average

Uses an average of a specified number of the most recent observations, with each observation receiving a different emphasis (weight)

Exponential Smoothing

A weighted average procedure with weights declining exponentially as data become older

Trend Projection

Technique that uses the least squares method to fit a straight line to the data

Seasonal Indexes

A mechanism for adjusting the forecast to accommodate any seasonal patterns inherent in the data

DECOMPOSITION OF A TIME SERIESPatterns that may be present in a time seriesTrend: Data exhibit a steady growth or decline over time.Seasonality: Data exhibit...

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