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Operations

ManagementMS29PForecasting

D. Anthony Chevers

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Lecture # ! Forecasting De"nition Panning hori$on

Forecasting techni%ues & comparison Simpe moving average 'eighte( moving average )*ponentia smoothing Forecast errors +egression anaysis )*ercises

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Learning O,-ectivesWhen you complete this chapterWhen you complete this chapteryou should be able to :you should be able to :

1. Understand the three timehorizons and which models applyfor each use

2. Explain when to use each of thethree qualitative models

. !pply movin" avera"e#exponential smoothin"# and

re"ression analysis

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Learning O,-ectivesWhen you complete this chapterWhen you complete this chapteryou should be able to :you should be able to :

$. %ompute two measures offorecast accuracy

&. %ompare and contrast each

technique tau"ht

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Forecasting ! De"ne( 'orecastin" is the prediction(estimation offuture activities./ypes o0 0orecast )conomic

/echnoogica an( )emand

/he "rst step in panning is 0orecasting or estimatingthe 0uture (eman( 0or pro(ucts an( services an( theresources necessary to pro(uce these outputs. ).g. +isingStar "nas 324456 Cra0t 7iage 8e :gn 0or 'or( Cup Cric;et 244< !cosure & Diana +oss 3=a$$ Festiva=an 244>56 ?ir you have @ntimate potentia ?regory @saacs 3244>5

Operations managers nee( ong range 0orecasts to

ma;e strategic (ecisions a,out pro(ucts processesan( 0aciities. Operations managers nee( short range 0orecasts to

assist them in ma;ing (ecisions a,out pro(uctionissues that span ony the ne*t 0e ee;s.

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Panning Bori$on/as;s &+esponsi,iities /op )*ecutives Long range pans over 1 year

+ & D 8e pro(uct ine

Capita e*pen(iture Faciity ocatione*pansion

Operations Managers Mi((e term pans !1> months Saes panning Pro(uction panning

Setting inventory eves Supervisors Short term pans up to months

=o, assignments Or(ering =o, sche(uingDispatching

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Forecasting/echni%ues *ualitative +ethods

+ar,et research Dephi Pane consensus

?rass roots & Bistorica anaogy -ime eries +ethods

+ovin" avera"e# Exponential smoothin"Eo* =en;ins & /ren( pro-ections

%ausal +ethods /e"ression analysis )conometric mo(es

@nputOutput mo(es Li0e!Cyce anaysis &Simuation

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Anaysis o0 some

Forecasting /echni%ues.. Mar;et +esearch

Accuracy G )*ceent Cost G Bigh Avaia,iity!historica (ata G 8one Avaia,iity!competent men G Hes /ime nee(e( 0or anaysis G Long Forecast time hori$on G Long

Moving Average Accuracy G ?oo(

Cost G Lo Avaia,iity!historica (ata G Hes Avaia,iity!competent men G Partia /ime nee(e( 0or anaysis G Short Forecast time hori$on G Short

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Anaysis o0 some

Forecasting /echni%ues.#2 )*ponentia Smoothing I/utoriaJ

Accuracy G K Cost G K Avaia,iity historica (ata G K Avaia,iity competent men G K /ime nee(e( 0or anaysis G K Forecast time hori$on G K

Exercise: Rank exponential smoothing in terms of factors above

+egression Anaysis

Accuracy G 7ery goo( Cost G Me(ium Avaia,iity!historica (ata G Hes Avaia,iity!competent men G Hes /ime nee(e( 0or anaysis G Me(ium

Forecast time hori$on G Long

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)%uations ! Simpi"e(

SMA G Deman( n 'MA G 3Deman( * 'eight5 'eight

)*p. Smooth FtG Ft!1N 3At!1 Ft!15

MAD G 3 Forecast error 5 n MS) G 3Forecast error52 n

G a N ,

a = y bx

Forecast error G Actua ! Forecast

Y

2X2 nX

YXnXYb

=

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Simpe MovingAverageI Months Moving Average 3M.A.5J =anuary

Fe,ruary

March Apri

May

=une Forecast??

=uy KKKK

Actua ta,esso(

9 > 14

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1

+earrange( Deman(Sche(ue I Months M.A.J 9 14

11

G =uyQs Avg.

11 14

9 >

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1

Simpe MovingAverages

Moving Average 3MA5 G (eman( inprevious n perio(s n

'here n is the num,er o0 perio(s in the M.A. )*ampe 1 Phone Saes

Month Actua Saes !month MA =an 144

Fe, 124 Mar 14 Apr 160 3144N124N145G11.< May 190 3124N14N145G1..= >.> .= 1=.=11.= 11.= 11. 11.=

444 Aug 1444 Sept 1444 32N24N1>N15 G 1#2&= Oct 1444 8ov 9444

Dec 12444

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Soution /utoria #> R24.>ISimpe & 'eighte( Moving AveragesJ

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Soution /utoria #9

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Soution /utoria#14

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Soution /utoria #11

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