Study Guides (248,639)
Canada (121,653)
Administration (1,205)
ADM3301 (6)
All (6)

forecastingtimeseriesdemand.doc

14 Pages
122 Views

Department
Administration
Course Code
ADM3301
Professor
All

This preview shows pages 1,2,3. Sign up to view the full 14 pages of the document.
Description
Forecasting Time Series DemandForecastprediction of future conditionsThe expected cost of developing a forecast should not exceed the expected benefitsTypes of forecasts includeWeatherStock MarketSporting eventsTechnologicalEconomicDemandDemand ForecastsFactors that influence demand includeBusiness cycleoGrowth oRecessionoRecoveryoStagnationProduct Life cycleoIntroductionoGrowthoMaturityoDeclineCustomers own conditionsTypes and Characteristics of Demand ForecastsLong range5 years or longerProduct planningResearch programmingCapital planningPlant location and expansionIntermediate range1 to 2 yearsAnalysis of alternative operating plansIntermediate operations planningoCapital and cash budgetsoSales planningoProduction aggregate planningoProduction and inventory budgets1Short range1 day to 1 yearAdjustment of production and employmentJob schedulingProject assignmentOvertime decisionsForecasting of demand for goods or services can be done in many different waysFigure 1 illustrates how the various techniques can be classifiedFORECASTINGQUANTITATIVE QUALITATIVE TECHNIQUESTECHNIQUESCAUSALTIME SERIES EXPERT GROUP MODELSMODELSOPINIONSPROCESSESFigure 1Classification of forecasting techniquesQualitative techniques rely on experience that has not been captured in the form of hard dataQuantitative techniques rely on historical dataCausal models are based on finding a cause and effect relationshipFor example housing permits have a cause relation to demand for building materials and appliances and the level of disposable income has a cause relationship to purchases of luxury itemsIt is important that indicator ie it can be measured in advance of the demand it is the cause variable is a leading assumed to causeCausal models are usually developed using a linear regression approach based on the method of least squaresOur focus here is on time series modelsTime series demand is viewed as a sequence of observed values which we will denote byAAA1 2 nThese models attempt to identify patterns that have been present in the past and assume they will continue in the futureThese models are often termed extrapolation modelsWe typically search for four major components in past demand1Average or base2Trend3Seasonal4Cyclical2
More Less
Unlock Document

Only pages 1,2,3 are available for preview. Some parts have been intentionally blurred.

Unlock Document
You're Reading a Preview

Unlock to view full version

Unlock Document

Log In


OR

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


OR

By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.


Submit