Forecasting in OM (Lecture#2)

7/25/2019 Forecasting in OM (Lecture#2)

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OperationsManagementForecastingForecasting


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Forecasting & PredictionForecasting & Prediction

Forecasting

Objective

Scientific

Free from BIAS

Reproducible

Error Analyi !oible

Prediction

Subjective

Intuitive

Individual BIAS

"on # Reproducible

Error Analyi $imited


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Forecasting at Disney WorldForecasting at Disney World

Global portfolio includes parks in HongGlobal portfolio includes parks in HongKong, Paris, okyo, Orlando, andKong, Paris, okyo, Orlando, and

!na"eim!na"eim

#e$enues are deri$ed from people % "o#e$enues are deri$ed from people % "omany $isitors and "o t"ey spend t"eirmany $isitors and "o t"ey spend t"eirmoneymoney

Daily management report contains onlyDaily management report contains onlyt"e forecast and actual attendance att"e forecast and actual attendance ateac" parkeac" park


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Disney generates daily, eekly, mont"ly,Disney generates daily, eekly, mont"ly,annual, and '(year forecastsannual, and '(year forecasts

Forecast used by labor management,Forecast used by labor management,maintenance, operations, finance, andmaintenance, operations, finance, and

park sc"edulingpark sc"eduling

Forecast used to ad)ust opening times,Forecast used to ad)ust opening times,

rides, s"os, staffing le$els, and guestsrides, s"os, staffing le$els, and guestsadmittedadmitted


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*+ of customers come from outside t"e*+ of customers come from outside t"e-.!-.!

/conomic model includes gross/conomic model includes grossdomestic product, cross(e0c"ange rates,domestic product, cross(e0c"ange rates,arri$als into t"e -.!arri$als into t"e -.!

! staff of 1' analysts and 2+ field people! staff of 1' analysts and 2+ field people

sur$ey 3 million park guests, employees,sur$ey 3 million park guests, employees,and tra$el professionals eac" yearand tra$el professionals eac" year


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4nputs to t"e forecasting model include4nputs to t"e forecasting model includeairline specials, Federal #eser$eairline specials, Federal #eser$e

policies, Wall .treet trends,policies, Wall .treet trends,

$acation5"oliday sc"edules for 1,+++$acation5"oliday sc"edules for 1,+++sc"ool districts around t"e orldsc"ool districts around t"e orld

!$erage forecast error for t"e '(year!$erage forecast error for t"e '(yearforecast is 'forecast is '

!$erage forecast error for annual!$erage forecast error for annualforecasts is beteen + and 1forecasts is beteen + and 1


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W"at is Forecasting6W"at is Forecasting6

Process ofProcess ofpredicting a futurepredicting a futuree$entse$ents

-nderlying basis of-nderlying basis ofall businessall businessdecisionsdecisions

ProductionProduction

4n$entory4n$entory

PersonnelPersonnel

FacilitiesFacilities

66


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."ort(range forecast."ort(range forecast

-p to 3 year, generally less t"an 1 mont"s-p to 3 year, generally less t"an 1 mont"s

Purc"asing, )ob sc"eduling, orkforce le$els,Purc"asing, )ob sc"eduling, orkforce le$els,

)ob assignments, production le$els)ob assignments, production le$els Medium(range forecastMedium(range forecast

1 mont"s to 1 years1 mont"s to 1 years

.ales and production planning, budgeting.ales and production planning, budgeting

7ong(range forecast7ong(range forecast 1188yearsyears

9e product planning, facility location,9e product planning, facility location,researc" and de$elopmentresearc" and de$elopment

Forecasting ime Hori:onsForecasting ime Hori:ons


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Distinguis"ing DifferencesDistinguis"ing Differences

Medium5long rangeMedium5long rangeforecasts deal it"forecasts deal it"more compre"ensi$e issues and supportmore compre"ensi$e issues and supportmanagement decisions regardingmanagement decisions regarding

planning and products, plants andplanning and products, plants andprocessesprocesses

."ort(term."ort(termforecasting usually employsforecasting usually employsdifferent met"odologies t"an longer(termdifferent met"odologies t"an longer(term

forecastingforecasting."ort(term."ort(termforecasts tend to be moreforecasts tend to be more

accurate t"an longer(term forecastsaccurate t"an longer(term forecasts


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4nfluence of Product 7ife4nfluence of Product 7ife

;ycle;ycle

4ntroduction and grot" re


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Product 7ife ;ycleProduct 7ife ;ycle

=est period to=est period to

increase marketincrease market

s"ares"are

#&D engineering is#&D engineering iscriticalcritical

Practical to c"angePractical to c"ange

price or



Product 7ife ;ycleProduct 7ife ;ycle

Product designProduct designandandde$elopmentde$elopmentcriticalcritical

Fre


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ypes of Forecastsypes of Forecasts

/conomic forecasts/conomic forecasts

!ddress business cycle % inflation rate,!ddress business cycle % inflation rate,money supply, "ousing starts, etcBmoney supply, "ousing starts, etcB

ec"nological forecastsec"nological forecasts

Predict rate of tec"nological progressPredict rate of tec"nological progress

4mpacts de$elopment of ne products4mpacts de$elopment of ne products

Demand forecastsDemand forecasts

Predict sales of e0isting products andPredict sales of e0isting products andser$icesser$ices



.trategic 4mportance of.trategic 4mportance of

ForecastingForecasting

Human #esources % Hiring, training,Human #esources % Hiring, training,

laying off orkerslaying off orkers ;apacity % ;apacity s"ortages can;apacity % ;apacity s"ortages can

result in undependable deli$ery, lossresult in undependable deli$ery, lossof customers, loss of market s"areof customers, loss of market s"are

.upply ;"ain Management % Good.upply ;"ain Management % Goodsupplier relations and pricesupplier relations and pricead$antagesad$antages


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.e$en .teps in Forecasting.e$en .teps in Forecasting

Determine t"e use of t"e forecastDetermine t"e use of t"e forecast

.elect t"e items to be forecasted.elect t"e items to be forecasted

Determine t"e time "ori:on of t"eDetermine t"e time "ori:on of t"eforecastforecast

.elect t"e forecasting modelCs.elect t"e forecasting modelCs

Gat"er t"e dataGat"er t"e data Make t"e forecastMake t"e forecast

Aalidate and implement resultsAalidate and implement results


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"e #ealitiesE"e #ealitiesE

Forecasts are seldom perfectForecasts are seldom perfect

Most tec"ni


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Forecasting !pproac"esForecasting !pproac"es

-sed "en situation is $ague-sed "en situation is $ague

and little data e0istand little data e0ist 9e products9e products

9e tec"nology9e tec"nology

4n$ol$es intuition, e0perience4n$ol$es intuition, e0perience

eBgB, forecasting sales on 4nterneteBgB, forecasting sales on 4nternet

ualitati$e Met"odsualitati$e Met"ods


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Forecasting !pproac"esForecasting !pproac"es

-sed "en situation is stable and-sed "en situation is stable and

"istorical data e0ist"istorical data e0ist /0isting products/0isting products

;urrent tec"nology;urrent tec"nology

4n$ol$es mat"ematical tec"ni


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O$er$ie of ualitati$eO$er$ie of ualitati$e

Met"odsMet"ods

Iury of e0ecuti$e opinionIury of e0ecuti$e opinion

Pool opinions of "ig"(le$el e0perts,Pool opinions of "ig"(le$el e0perts,sometimes augment by statisticalsometimes augment by statisticalmodelsmodels

Delp"i met"odDelp"i met"od

Panel of e0perts,


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O$er$ie of ualitati$eO$er$ie of ualitati$e

Met"odsMet"ods

.ales force composite.ales force composite

/stimates from indi$idual/stimates from indi$idualsalespersons are re$ieed forsalespersons are re$ieed forreasonableness, t"en aggregatedreasonableness, t"en aggregated

;onsumer Market .ur$ey;onsumer Market .ur$ey

!sk t"e customer!sk t"e customer


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4n$ol$es small group of "ig"(le$el e0perts4n$ol$es small group of "ig"(le$el e0pertsand managersand managers

Group estimates demand by orkingGroup estimates demand by orkingtoget"ertoget"er

;ombines managerial e0perience it";ombines managerial e0perience it"statistical modelsstatistical models

#elati$ely


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.ales Force ;omposite.ales Force ;omposite

/ac" salesperson pro)ects "is or/ac" salesperson pro)ects "is or"er sales"er sales

;ombined at district and national;ombined at district and nationalle$elsle$els

.ales reps kno customers ants.ales reps kno customers ants

ends to be o$erly optimisticends to be o$erly optimistic


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Delp"i Met"odDelp"i Met"od

4terati$e group4terati$e groupprocess,process,continues untilcontinues until

consensus isconsensus isreac"edreac"ed

1 types of1 types ofparticipantsparticipants Decision makersDecision makers

.taff.taff

#espondents#espondents

.taffC!dministering

sur$ey

Decision MakersC/$aluate

responses andmake decisions

#espondentsCPeople "o canmake $aluable

)udgments


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;onsumer Market .ur$ey;onsumer Market .ur$ey

!sk customers about purc"asing!sk customers about purc"asingplansplans

W"at consumers say, and "atW"at consumers say, and "att"ey actually do are often differentt"ey actually do are often different

.ometimes difficult to anser.ometimes difficult to anser


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O$er$ie of uantitati$eO$er$ie of uantitati$e

!pproac"es!pproac"es3B3B 9ai$e approac"9ai$e approac"

*B*B Mo$ing a$eragesMo$ing a$erages1B1B /0ponential/0ponential

smoot"ingsmoot"ing

JBJB rend pro)ectionrend pro)ection

'B'B 7inear regression7inear regression

ime(.eriesime(.eriesModelsModels

!ssociati$e!ssociati$eModelModel


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.et of e$enly spaced numerical data.et of e$enly spaced numerical data

Obtained by obser$ing responseObtained by obser$ing response

$ariable at regular time periods$ariable at regular time periods

Forecast based only on past $alues,Forecast based only on past $alues,no ot"er $ariables importantno ot"er $ariables important

!ssumes t"at factors influencing!ssumes t"at factors influencingpast and present ill continuepast and present ill continueinfluence in futureinfluence in future

ime .eries Forecastingime .eries Forecasting


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.rend

Seaonal

/yclical

Random

ime .eries ;omponentsime .eries ;omponents


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;omponents of Demand;omponents of Demand

Demandforproductorser$ice

3 * 1 J

Lear

!$erage

demand o$erfour years

.easonal peaks

rendcomponent

!ctualdemand

#andom$ariation

Fi,ure 4-1Fi,ure 4-1


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Persistent, o$erall upard orPersistent, o$erall upard ordonard patterndonard pattern

;"anges due to population,;"anges due to population,tec"nology, age, culture, etcBtec"nology, age, culture, etcB

ypically se$eral yearsypically se$eral years

durationduration

rend ;omponentrend ;omponent


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#egular pattern of up and#egular pattern of up anddon fluctuationsdon fluctuations

Due to eat"er, customs, etcBDue to eat"er, customs, etcB

Occurs it"in a single yearOccurs it"in a single year

.easonal ;omponent.easonal ;omponent

9umber ofPeriod 7engt" .easons

Week Day (Mont" Week 4#4-&Mont" Day 2)#%1Lear uarter 4Lear Mont" 12

Lear Week &2


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#epeating up and don mo$ements#epeating up and don mo$ements

!ffected by business cycle,!ffected by business cycle,

political, and economic factorspolitical, and economic factors Multiple years durationMultiple years duration

Often causal orOften causal orassociati$eassociati$erelations"ipsrelations"ips

;yclical ;omponent;yclical ;omponent

++ '' 3+3+ 3'3' *+*+


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/rratic, unsystematic, residual/rratic, unsystematic, residualfluctuationsfluctuations

Due to random $ariation orDue to random $ariation orunforeseen e$entsunforeseen e$ents

."ort duration and."ort duration andnonrepeatingnonrepeating

#andom ;omponent#andom ;omponent

MM WW FF


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9ai$e !pproac"9ai$e !pproac"

!ssumes demand in ne0t!ssumes demand in ne0tperiod is t"e same asperiod is t"e same as

demand in most recent perioddemand in most recent period eBgB, 4f Ianuary sales ere ?, t"eneBgB, 4f Ianuary sales ere ?, t"en

February sales ill be ?February sales ill be ?

.ometimes cost effecti$e and.ometimes cost effecti$e andefficientefficient

;an be good starting point;an be good starting point


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M! is a series of arit"metic meansM! is a series of arit"metic means

-sed if little or no trend-sed if little or no trend

-sed often for smoot"ing-sed often for smoot"ing

Pro$ides o$erall impression of dataPro$ides o$erall impression of datao$er timeo$er time

Mo$ing !$erage Met"odMo$ing !$erage Met"od

Mo$ing a$erage NMo$ing a$erage N00demand in pre$ious n periodsdemand in pre$ious n periods

nn


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IanuaryIanuary 1+1+

FebruaryFebruary 1212Marc"Marc" 1%1%

!pril!pril 1'1'

MayMay 1*1*

IuneIune 2%2%IulyIuly 2'2'

!ctual!ctual 1(Mont"1(Mont"Mont"Mont" ."ed .ales."ed .ales Mo$ing !$erageMo$ing !$erage

12 1% 1'3% 5 1%12 1% 1'3% 5 1% 22%%

1% 1' 1*3% 5 1'1% 1' 1*3% 5 1'1' 1* 2%3% 5 1*1' 1* 2%3% 5 1* 11%%

Mo$ing !$erage /0ampleMo$ing !$erage /0ample

1+1+

12121%1%

1+1+ 1212 1%1%3% 5 113% 5 11 22%%


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Grap" of Mo$ing !$erageGrap" of Mo$ing !$erage

II FF MM !! MM II II !! .. OO 99 DD

."ed.ales

."ed.ales

1+1+ %

** %

*?*? %

*J*J %

**** %

*+*+ %

33 %

3?3? %

3J3J %3*3* %

3+3+ %

!ctual!ctual

.ales.ales

Mo$ingMo$ing!$erage!$erageForecastForecast


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-sed "en trend is present-sed "en trend is present

Older data usually less importantOlder data usually less important

Weig"ts based on e0perience andWeig"ts based on e0perience andintuitionintuition

Weig"ted Mo$ing !$erageWeig"ted Mo$ing !$erage

Weig"tedWeig"tedmo$ing a$eragemo$ing a$erage

NN

00eig"t for period neig"t for period n33 00 demand in period ndemand in period n33

00eig"tseig"ts


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IanuaryIanuary 1+1+

FebruaryFebruary 1212

Marc"Marc" 1%1%

!pril!pril 1'1'MayMay 1*1*

IuneIune 2%2%

IulyIuly 2'2'

!ctual!ctual 1(Mont" Weig"ted1(Mont" Weig"ted

Mont"Mont" ."ed .ales."ed .ales Mo$ing !$erageMo$ing !$erage

6% 7 1'3 2 7 1%3 1238' 5 146% 7 1'3 2 7 1%3 1238' 5 1411%%6% 7 1*3 2 7 1'3 1%38' 5 1(6% 7 1*3 2 7 1'3 1%38' 5 1(

6% 7 2%3 2 7 1*3 1'38' 5 2+6% 7 2%3 2 7 1*3 1'38' 5 2+1122

Weig"ted Mo$ing !$erageWeig"ted Mo$ing !$erage

1+1+

1212

1%1%

6% 76% 7 1%1%3 2 73 2 7 12123 3 1+1+38' 5 1238' 5 1211

''

Weig"ts !pplied Period

1 7ast mont"

* o mont"s ago

3 "ree mont"s ago

? .um of eig"ts


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4ncreasing n smoot"s t"e forecast4ncreasing n smoot"s t"e forecast

but makes it less sensiti$e tobut makes it less sensiti$e toc"angesc"anges

Do not forecast trends ellDo not forecast trends ell

#e


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Mo$ing !$erage !ndMo$ing !$erage !ndWeig"ted Mo$ing !$erageWeig"ted Mo$ing !$erage

1+1+ %

*'*' %

*+*+ %

3'3' %

3+3+ %

'' %

.alesdeman

d

.alesdeman

d


!ctual!ctualsalessales

Mo$ingMo$inga$eragea$erage

Weig"tedWeig"tedmo$ingmo$inga$eragea$erage



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Form of eig"ted mo$ing a$erageForm of eig"ted mo$ing a$erage

Weig"ts decline e0ponentiallyWeig"ts decline e0ponentially

Most recent data eig"ted mostMost recent data eig"ted most #e


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/0ponential .moot"ing/0ponential .moot"ing

9e forecast N9e forecast N 7ast periods forecast7ast periods forecast

88

7ast periods actual demand7ast periods actual demand

%% 7ast periods forecast7ast periods forecast33

FFttN FN Ftt 1 1!!tt 1 1##FFtt 1 133

"ere"ere FFtt 55 ne forecastne forecastFFtt 1 1 55 pre$ious forecastpre$ious forecast

55 smoot"ing Cor eig"tingsmoot"ing Cor eig"ting

constantconstant ++ 99991313


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/0ample/0amplePredicted demandPredicted demand 5 1425 142Ford MustangsFord Mustangs

!ctual demand!ctual demand 5 1&%5 1&%

.moot"ing constant.moot"ing constant5 -2+5 -2+


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9e forecast9e forecast 5 142 -21&% 14235 142 -21&% 1423


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9e forecast9e forecast 5 142 -21&% 14235 142 -21&% 1423

5 142 2-25 142 2-25 144-2 : 144 car5 144-2 : 144 car


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/ffect of/ffect of

.moot"ing ;onstants.moot"ing ;onstants

Weig"t !ssigned toWeig"t !ssigned to

MostMost *nd Most*nd Most 1rd Most1rd Most Jt" MostJt" Most 't" Most't" Most#ecent#ecent #ecent#ecent #ecent#ecent #ecent#ecent #ecent#ecent

.moot"ing.moot"ing PeriodPeriod PeriodPeriod PeriodPeriod PeriodPeriod PeriodPeriod;onstant;onstant

33

1 #1 #

33

1 #1 #

3322

1 #1 #

33%%

1 #1 #

3344

5 -15 -1 -1-1 -+*-+* -+)1-+)1 -+(%-+(% -+''-+''

5 -&5 -& -&-& -2&-2& -12&-12& -+'%-+'% -+%1-+%1


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4mpact of Different4mpact of Different

**'**' %

*++*++ %

32'32' %

3'+3'+ %

33 ** 11 JJ '' ?? 22

uarteruarter

Demand

Demand

5 -15 -1

!ctual!ctualdemanddemand

5 -&5 -&


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4mpact of Different4mpact of Different

**'**' %

*++*++ %

32'32' %

3'+3'+ %

33 ** 11 JJ '' ?? 22

uarteruarter

Demand

Demand

5 -15 -1

!ctual!ctualdemanddemand

5 -&5 -&;"ose "ig" $alues of;"ose "ig" $alues of

"en underlying a$erage"en underlying a$erageis likely to c"angeis likely to c"ange

;"oose lo $alues of;"oose lo $alues of

"en underlying a$erage"en underlying a$erageis stableis stable


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;"oosing;"oosing

"e ob)ecti$e is to obtain t"e most"e ob)ecti$e is to obtain t"e mostaccurate forecast no matter t"eaccurate forecast no matter t"e

tec"ni


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;ommon Measures of /rror;ommon Measures of /rror

Mean !bsolute De$iationMean !bsolute De$iation M!DM!D33

M!D NM!D N 00!ctual ( Forecast!ctual ( Forecastnn

Mean .


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;ommon Measures of /rror;ommon Measures of /rror

Mean !bsolute Percent /rrorMean !bsolute Percent /rror M!P/M!P/33

M!P/ NM!P/ N

001++1++!ctual!ctualii( Forecast( Forecastii5!ctual5!ctualii

nn

nn

ii 5 15 1


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;omparison of Forecast;omparison of Forecast/rror/rror

#ounded#ounded !bsolute!bsolute #ounded#ounded !bsolute!bsolute!ctual!ctual ForecastForecast De$iationDe$iation ForecastForecast De$iationDe$iationonnageonnage it"it" forfor it"it" forfor

uarteruarter -nloaded-nloaded

5 -1+5 -1+

5 -1+5 -1+

5 -&+5 -&+

5 -&+5 -&+

11 1)+1)+ 1(&1(& &-++&-++ 1(&1(& &-++&-++

22 1')1') 1(&-&1(&-& (-&+(-&+ 1((-&+1((-&+ *-&+*-&+%% 1&*1&* 1(4-(&1(4-(& 1&-(&1&-(& 1(2-(&1(2-(& 1%-(&1%-(&

44 1(&1(& 1(%-1)1(%-1) 1-)21-)2 1'&-))1'&-)) *-12*-12

&& 1*+1*+ 1(%-%'1(%-%' 1'-'41'-'4 1(+-441(+-44 1*-&'1*-&'

'' 2+&2+& 1(&-+21(&-+2 2*-*)2*-*) 1)+-221)+-22 24-()24-()

(( 1)+1)+ 1()-+21()-+2 1-*)1-*) 1*2-'11*2-'1 12-'112-'1)) 1)21)2 1()-221()-22 %-()%-() 1)'-%+1)'-%+ 4-%+4-%+

)2-4&)2-4& *)-'2*)-'2


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5 -1+5 -1+

5 -1+5 -1+

5 -&+5 -&+

5 -&+5 -&+

11 1)+1)+ 1(&1(& &-++&-++ 1(&1(& &-++&-++

22 1')1') 1(&-&1(&-& (-&+(-&+ 1((-&+1((-&+ *-&+*-&+%% 1&*1&* 1(4-(&1(4-(& 1&-(&1&-(& 1(2-(&1(2-(& 1%-(&1%-(&

44 1(&1(& 1(%-1)1(%-1) 1-)21-)2 1'&-))1'&-)) *-12*-12

&& 1*+1*+ 1(%-%'1(%-%' 1'-'41'-'4 1(+-441(+-44 1*-&'1*-&'

'' 2+&2+& 1(&-+21(&-+2 2*-*)2*-*) 1)+-221)+-22 24-()24-()

(( 1)+1)+ 1()-+21()-+2 1-*)1-*) 1*2-'11*2-'1 12-'112-'1)) 1)21)2 1()-221()-22 %-()%-() 1)'-%+1)'-%+ 4-%+4-%+

)2-4&)2-4& *)-'2*)-'2

M!D N0de$iations

n

5 )2-4&) 5 1+-%1

For 5 -1+

5 *)-'2) 5 12-%%

For 5 -&+


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5 -1+5 -1+

5 -1+5 -1+

5 -&+5 -&+

5 -&+5 -&+

11 1)+1)+ 1(&1(& &-++&-++ 1(&1(& &-++&-++

22 1')1') 1(&-&1(&-& (-&+(-&+ 1((-&+1((-&+ *-&+*-&+%% 1&*1&* 1(4-(&1(4-(& 1&-(&1&-(& 1(2-(&1(2-(& 1%-(&1%-(&

44 1(&1(& 1(%-1)1(%-1) 1-)21-)2 1'&-))1'&-)) *-12*-12

&& 1*+1*+ 1(%-%'1(%-%' 1'-'41'-'4 1(+-441(+-44 1*-&'1*-&'

'' 2+&2+& 1(&-+21(&-+2 2*-*)2*-*) 1)+-221)+-22 24-()24-()

(( 1)+1)+ 1()-+21()-+2 1-*)1-*) 1*2-'11*2-'1 12-'112-'1)) 1)21)2 1()-221()-22 %-()%-() 1)'-%+1)'-%+ 4-%+4-%+

)2-4&)2-4& *)-'2*)-'2

;A


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5 -1+5 -1+

5 -1+5 -1+

5 -&+5 -&+

5 -&+5 -&+

11 1)+1)+ 1(&1(& &-++&-++ 1(&1(& &-++&-++

22 1')1') 1(&-&1(&-& (-&+(-&+ 1((-&+1((-&+ *-&+*-&+%% 1&*1&* 1(4-(&1(4-(& 1&-(&1&-(& 1(2-(&1(2-(& 1%-(&1%-(&

44 1(&1(& 1(%-1)1(%-1) 1-)21-)2 1'&-))1'&-)) *-12*-12

&& 1*+1*+ 1(%-%'1(%-%' 1'-'41'-'4 1(+-441(+-44 1*-&'1*-&'

'' 2+&2+& 1(&-+21(&-+2 2*-*)2*-*) 1)+-221)+-22 24-()24-()

(( 1)+1)+ 1()-+21()-+2 1-*)1-*) 1*2-'11*2-'1 12-'112-'1)) 1)21)2 1()-221()-22 %-()%-() 1)'-%+1)'-%+ 4-%+4-%+

)2-4&)2-4& *)-'2*)-'2

;A

For 5 -1+

5 &4-+&) 5 '-('>

For 5 -&+

M!P/ N

01++de$iationi

5actuali

n

n

i 5 1


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5 -1+5 -1+

5 -1+5 -1+

5 -&+5 -&+

5 -&+5 -&+

11 1)+1)+ 1(&1(& &-++&-++ 1(&1(& &-++&-++

22 1')1') 1(&-&1(&-& (-&+(-&+ 1((-&+1((-&+ *-&+*-&+%% 1&*1&* 1(4-(&1(4-(& 1&-(&1&-(& 1(2-(&1(2-(& 1%-(&1%-(&

44 1(&1(& 1(%-1)1(%-1) 1-)21-)2 1'&-))1'&-)) *-12*-12

&& 1*+1*+ 1(%-%'1(%-%' 1'-'41'-'4 1(+-441(+-44 1*-&'1*-&'

'' 2+&2+& 1(&-+21(&-+2 2*-*)2*-*) 1)+-221)+-22 24-()24-()

(( 1)+1)+ 1()-+21()-+2 1-*)1-*) 1*2-'11*2-'1 12-'112-'1)) 1)21)2 1()-221()-22 %-()%-() 1)'-%+1)'-%+ 4-%+4-%+

)2-4&)2-4& *)-'2*)-'2

;A&-&*> '-('>'-('>


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2008 Prentice Hall, Inc. 4 &(

/0ponential .moot"ing it"/0ponential .moot"ing it"

rend !d)ustmentrend !d)ustmentW"en a trend is present, e0ponentialW"en a trend is present, e0ponentialsmoot"ing must be modifiedsmoot"ing must be modified

ForecastForecastincludingincluding F4F4tt33NN

trendtrend

/0ponentially/0ponentially /0ponentially/0ponentiallysmoot"edsmoot"ed FFtt33 88 tt33 smoot"edsmoot"ed

forecastforecast trendtrend


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2008 Prentice Hall, Inc. 4 &)

/0ponential .moot"ing it"/0ponential .moot"ing it"

rend !d)ustmentrend !d)ustment

FFtt55 !!tt# 1# 13 1 #3 1 # 33FFtt# 1# 1 tt# 1# 133

tt55 FFtt ## FFtt# 1# 13 1 #3 1 # 33tt# 1# 1

.tep 3 ;ompute F.tep 3 ;ompute Ftt

.tep * ;ompute .tep * ;ompute tt

.tep 1 ;alculate t"e forecast F4.tep 1 ;alculate t"e forecast F4tt55FFtt tt


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/0ponential .moot"ing it"/0ponential .moot"ing it"rend !d)ustment /0amplerend !d)ustment /0ample

ForecastForecast!ctual!ctual .moot"ed.moot"ed .moot"ed.moot"ed 4ncluding4ncluding

Mont"Mont"tt33 DemandDemand !!tt33 Forecast, FForecast, Ftt rend, rend, tt rend, F4rend, F4tt

11 1212 1111 22 1%-++1%-++

22 1(1(

%% 2+2+

44 1*1*

&& 2424

'' 2121

(( %1%1)) 2)2)

** %'%'

1+1+

.able 4-1.able 4-1


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11 1212 1111 22 1%-++1%-++

22 1(1(

%% 2+2+

44 1*1*

&& 2424

'' 2121

(( %1%1)) 2)2)

** %'%'

1+1+

.able 4-1.able 4-1

F2 5 !

1 1 # 3F

1

13

F2 5 -23123 1 # -2311 23

5 2-4 1+-4 5 12-) unit

.tep 3 Forecast for Mont" *


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11 1212 1111 22 1%-++1%-++

22 1(1( 12-)+12-)+

%% 2+2+

44 1*1*

&& 2424

'' 2121

(( %1%1)) 2)2)

** %'%'

1+1+

.able 4-1.able 4-1

2 5 F

2# F

13 1 # 3

1

2 5 -4312-) # 113 1 # -4323

5 -(2 1-2 5 1-*2 unit

.tep * rend for Mont" *


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11 1212 1111 22 1%-++1%-++

22 1(1( 12-)+12-)+ 1-*21-*2

%% 2+2+

44 1*1*

&& 2424

'' 2121

(( %1%1)) 2)2)

** %'%'

1+1+

.able 4-1.able 4-1

F42

5 F2

1

F42 5 12-) 1-*2

5 14-(2 unit

.tep 1 ;alculate F4 for Mont" *


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11 1212 1111 22 1%-++1%-++

22 1(1( 12-)+12-)+ 1-*21-*2 14-(214-(2

%% 2+2+

44 1*1*

&& 2424

'' 2121

(( %1%1)) 2)2)

** %'%'

1+1+

.able 4-1.able 4-1

1&-1)1&-1) 2-1+2-1+ 1(-2)1(-2)

1(-)21(-)2 2-%22-%2 2+-142+-14

1*-*11*-*1 2-2%2-2% 22-1422-14

22-&122-&1 2-%)2-%) 24-)*24-)*

24-1124-11 2-+(2-+( 2'-1)2'-1)2(-142(-14 2-4&2-4& 2*-&*2*-&*

2*-2)2*-2) 2-%22-%2 %1-'+%1-'+

%2-4)%2-4) 2-')2-') %&-1'%&-1'


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Fi,ure 4-%Fi,ure 4-%

33 ** 11 JJ '' ?? 22

ime Cmont"ime Cmont"

Productdemand

Productdemand

1'1' %

1+1+ %

*'*' %

*+*+ %

3'3' %

3+3+ %

'' %

++ %

!ctual demand!ctual demand !!tt33

Forecast including trendForecast including trend F4F4tt33

it"it"5 -25 -2 andand5 -45 -4


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rend Pro)ectionsrend Pro)ections

Fitting a trend line to "istorical data pointsFitting a trend line to "istorical data pointsto pro)ect into t"e medium to long(rangeto pro)ect into t"e medium to long(range

7inear trends can be found using t"e least7inear trends can be found using t"e leasts


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7east .


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7east .


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7east .


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7east .


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2008 Prentice Hall, Inc. 4 (+

7east .


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7east .


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.easonal Aariations 4n Data.easonal Aariations 4n Data

"e multiplicati$e"e multiplicati$e

seasonal modelseasonal modelcan ad)ust trendcan ad)ust trenddata for seasonaldata for seasonal

$ariations in$ariations indemanddemand


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2008 Prentice Hall, Inc. 4 (4

.easonal Aariations 4n Data.easonal Aariations 4n Data

3B3B Find a$erage "istorical demand for eac"Find a$erage "istorical demand for eac"seasonseason

*B*B ;ompute t"e a$erage demand o$er all;ompute t"e a$erage demand o$er allseasonsseasons

1B1B ;ompute a seasonal inde0 for eac" season;ompute a seasonal inde0 for eac" season

JBJB /stimate ne0t years total demand/stimate ne0t years total demand'B'B Di$ide t"is estimate of total demand by t"eDi$ide t"is estimate of total demand by t"e

number of seasons, t"en multiply it by t"enumber of seasons, t"en multiply it by t"eseasonal inde0 for t"at seasonseasonal inde0 for t"at season

.teps in t"e process.teps in t"e process


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.easonal 4nde0 /0ample.easonal 4nde0 /0ample

IanIan )+)+ )&)& 1+&1+& *+*+ *4*4

FebFeb (+(+ )&)& )&)& )+)+ *4*4

MarMar )+)+ *%*% )2)2 )&)& *4*4

!pr!pr *+*+ *&*& 11&11& 1++1++ *4*4MayMay 11%11% 12&12& 1%11%1 12%12% *4*4

IunIun 11+11+ 11&11& 12+12+ 11&11& *4*4

IulIul 1++1++ 1+21+2 11%11% 1+&1+& *4*4

!ug!ug )))) 1+21+2 11+11+ 1++1++ *4*4

.ept.ept )&)& *+*+ *&*& *+*+ *4*4

OctOct (((( ()() )&)& )+)+ *4*4

9o$9o$ (&(& (2(2 )%)% )+)+ *4*4

DecDec )2)2 ()() )+)+ )+)+ *4*4

DemandDemand !$erage!$erage !$erage!$erage .easonal.easonalMont"Mont" *++'*++' *++?*++? *++2*++2 *++'(*++2*++'(*++2 Mont"lyMont"ly 4nde04nde0


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2008 Prentice Hall, Inc. 4 ('


IanIan )+)+ )&)& 1+&1+& *+*+ *4*4

FebFeb (+(+ )&)& )&)& )+)+ *4*4

MarMar )+)+ *%*% )2)2 )&)& *4*4

!pr!pr *+*+ *&*& 11&11& 1++1++ *4*4MayMay 11%11% 12&12& 1%11%1 12%12% *4*4

IunIun 11+11+ 11&11& 12+12+ 11&11& *4*4

IulIul 1++1++ 1+21+2 11%11% 1+&1+& *4*4

!ug!ug )))) 1+21+2 11+11+ 1++1++ *4*4

.ept.ept )&)& *+*+ *&*& *+*+ *4*4

OctOct (((( ()() )&)& )+)+ *4*4

9o$9o$ (&(& (2(2 )%)% )+)+ *4*4

DecDec )2)2 ()() )+)+ )+)+ *4*4


+-*&(+-*&(

.easonal inde0 N

a$erage *++'(*++2 mont"ly demand

a$erage mont"ly demand

5 *+*4 5 -*&(


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2008 Prentice Hall, Inc. 4 ((


IanIan )+)+ )&)& 1+&1+& *+*+ *4*4 +-*&(+-*&(

FebFeb (+(+ )&)& )&)& )+)+ *4*4 +-)&1+-)&1

MarMar )+)+ *%*% )2)2 )&)& *4*4 +-*+4+-*+4

!pr!pr *+*+ *&*& 11&11& 1++1++ *4*4 1-+'41-+'4MayMay 11%11% 12&12& 1%11%1 12%12% *4*4 1-%+*1-%+*

IunIun 11+11+ 11&11& 12+12+ 11&11& *4*4 1-22%1-22%

IulIul 1++1++ 1+21+2 11%11% 1+&1+& *4*4 1-11(1-11(

!ug!ug )))) 1+21+2 11+11+ 1++1++ *4*4 1-+'41-+'4

.ept.ept )&)& *+*+ *&*& *+*+ *4*4 +-*&(+-*&(

OctOct (((( ()() )&)& )+)+ *4*4 +-)&1+-)&1

9o$9o$ (&(& (2(2 )%)% )+)+ *4*4 +-)&1+-)&1

DecDec )2)2 ()() )+)+ )+)+ *4*4 +-)&1+-)&1



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2008 Prentice Hall, Inc. 4 ()


IanIan )+)+ )&)& 1+&1+& *+*+ *4*4 +-*&(+-*&(

FebFeb (+(+ )&)& )&)& )+)+ *4*4 +-)&1+-)&1

MarMar )+)+ *%*% )2)2 )&)& *4*4 +-*+4+-*+4

!pr!pr *+*+ *&*& 11&11& 1++1++ *4*4 1-+'41-+'4MayMay 11%11% 12&12& 1%11%1 12%12% *4*4 1-%+*1-%+*

IunIun 11+11+ 11&11& 12+12+ 11&11& *4*4 1-22%1-22%

IulIul 1++1++ 1+21+2 11%11% 1+&1+& *4*4 1-11(1-11(

!ug!ug )))) 1+21+2 11+11+ 1++1++ *4*4 1-+'41-+'4

.ept.ept )&)& *+*+ *&*& *+*+ *4*4 +-*&(+-*&(

OctOct (((( ()() )&)& )+)+ *4*4 +-)&1+-)&1

9o$9o$ (&(& (2(2 )%)% )+)+ *4*4 +-)&1+-)&1

DecDec )2)2 ()() )+)+ )+)+ *4*4 +-)&1+-)&1


/0pected annual demand 5 1=2++

Ian 7 -*&( 5 *'1=2++

12

Feb 7 -)&1 5 )&1=2++12

Forecast for *++


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2008 Prentice Hall, Inc. 4 (*


3J+3J+ %

31+31+ %

3*+3*+ %

33+33+ %

3++3++ %

++ %

++ %

2+2+ %


imeime

Demand

Demand

*++ Forecast*++ Forecast

*++2 Demand*++2 Demand

*++? Demand*++? Demand

*++' Demand*++' Demand

. Di H i l. Di H it l


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.an Diego Hospital.an Diego Hospital

3+,*++3+,*++ %

3+,+++3+,+++ %

,++,++ %

,?++,?++ %

,J++,J++ %

,*++,*++ %

,+++,+++ %

IanIan FebFeb MarMar !pr!pr MayMayIuneIune IulyIuly!ug!ug .ept.ept OctOct 9o$9o$ DecDec?2?2 ?? ?? 2+2+ 2323 2*2* 2121 2J2J 2'2' 2?2? 2222 22

Mont"Mont"

4npatientDa

ys

4npatientDa

ys

*&%+*&%+

*&&1*&&1

*&(%*&(%

*&*4*&*4

*'1'*'1'

*'%(*'%(

*'&**'&*

*')+*')+

*(+2*(+2

*(24*(24

*(4&*(4&*(''*(''

Fi,ure 4-'Fi,ure 4-'

rend Datarend Data


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2008 Prentice Hall, Inc. 4 *'

Multiple #egressionMultiple #egression

!nalysis!nalysis

4f more t"an one independent $ariable is to be4f more t"an one independent $ariable is to beused in t"e model, linear regression can beused in t"e model, linear regression can be

e0tended to multiple regression toe0tended to multiple regression toaccommodate se$eral independent $ariablesaccommodate se$eral independent $ariables

yy 55 aa bb1100118 b8 b220022@@QQ

;omputationally, t"is is


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2008 Prentice Hall, Inc. 4 *(

Multiple #egressionMultiple #egression

!nalysis!nalysis

yy 5 1-)+ -%+5 1-)+ -%+0011# &-+# &-+0022QQ

4n t"e 9odel e0ample, including interest rates in4n t"e 9odel e0ample, including interest rates int"e model gi$es t"e ne e


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2008 Prentice Hall, Inc. 4 *)

Measures "o ell t"e forecast isMeasures "o ell t"e forecast ispredicting actual $aluespredicting actual $alues

#atio of running sum of forecast errors#atio of running sum of forecast errorsC#.F/ to mean absolute de$iation CM!DC#.F/ to mean absolute de$iation CM!D

Good tracking signal "as lo $aluesGood tracking signal "as lo $alues

4f forecasts are continually "ig" or lo, t"e4f forecasts are continually "ig" or lo, t"eforecast "as a bias errorforecast "as a bias error

Monitoring and ;ontrollingMonitoring and ;ontrolling

ForecastsForecastsracking .ignalracking .ignal


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2008 Prentice Hall, Inc. 4 **

Monitoring and ;ontrollingMonitoring and ;ontrolling

ForecastsForecasts

rackingracking

signalsignal

RSFERSFE

;A


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racking .ignalracking .ignal

racking signalracking signal

88

++M!DsM!Ds

%%

-pper control limit-pper control limit

7oer control limit7oer control limit

imeime

.ignal e0ceeding limit.ignal e0ceeding limit

!cceptable!cceptablerangerange


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2008 Prentice Hall, Inc. 4 1+1

racking .ignal /0ampleracking .ignal /0ample

;umulati$e;umulati$e!bsolute!bsolute !bsolute!bsolute

!ctual!ctual ForecastForecast ForecastForecast ForecastForecasttrtr DemandDemand DemandDemand /rror/rror #.F/#.F/ /rror/rror /rror/rror M!DM!D

11 *+*+ 1++1++ #1+#1+ #1+#1+ 1+1+ 1+1+ 1+-+1+-+

22 *&*& 1++1++ #& #1& && 1&1& (-&(-&%% 11&11& 1++1++ 1&1& ++ 1&1& %+%+ 1+-+1+-+

44 1++1++ 11+11+ #1+#1+ #1+#1+ 1+1+ 4+4+ 1+-+1+-+

&& 12&12& 11+11+ 1&1& && 1&1& &&&& 11-+11-+

'' 14+14+ 11+11+ %+%+ %&%& %+%+ )&)& 14-214-2


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2008 Prentice Hall, Inc. 4 1+2

;umulati$e;umulati$e!bsolute!bsolute !bsolute!bsolute

!ctual!ctual ForecastForecast ForecastForecast ForecastForecasttrtr DemandDemand DemandDemand /rror/rror #.F/#.F/ /rror/rror /rror/rror M!DM!D

11 *+*+ 1++1++ #1+#1+ #1+#1+ 1+1+ 1+1+ 1+-+1+-+

22 *&*& 1++1++ #& #1& && 1&1& (-&(-&%% 11&11& 1++1++ 1&1& ++ 1&1& %+%+ 1+-+1+-+

44 1++1++ 11+11+ #1+#1+ #1+#1+ 1+1+ 4+4+ 1+-+1+-+

&& 12&12& 11+11+ 1&1& && 1&1& &&&& 11-+11-+

'' 14+14+ 11+11+ %+%+ %&%& %+%+ )&)& 14-214-2

racking .ignal /0ampleracking .ignal /0ample

racking.ignal

C#.F/5M!D

#1+1+ 5 #1

#1&(-& 5 #2+1+ 5 +#1+1+ 5 #1

&11 5 +-&%&14-2 5 2-&

"e $ariation of t"e tracking signal"e $ariation of t"e tracking signalbeteenbeteen #2-+#2-+andand 2-&2-&is it"in acceptableis it"in acceptablelimitslimits


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2008 Prentice Hall, Inc. 4 1+&

Forecasting in t"e .er$iceForecasting in t"e .er$ice

.ector.ector Presents unusual c"allengesPresents unusual c"allenges

.pecial need for s"ort term records.pecial need for s"ort term records

9eeds differ greatly as function of9eeds differ greatly as function ofindustry and productindustry and product

Holidays and ot"er calendar e$entsHolidays and ot"er calendar e$ents

-nusual e$ents-nusual e$ents

Fast Food #estaurantFast Food #estaurant


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Fast Food #estaurantFast Food #estaurantForecastForecast

*+*+ %

3'3' %

3+3+ %

'' %

33(3*33(3* 3(*3(* 1(J1(J '(?'(? 2(2( (3+(3+3*(33*(3 *(1*(1 J('J(' ?(2?(2 (( 3+(333+(33

C7unc"timeC7unc"time CDinnertimeCDinnertime

Hour of dayHour of day

Percenta

geofsales

Percenta

geofsales



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Fed/0 ;all ;enter ForecastFed/0 ;all ;enter Forecast

3*3* %

3+3+ %

%

?? %

JJ %

** %

++ %

!BMB!BMB PBMBPBMB

** JJ ?? 3+3+ 3*3* ** JJ ?? 3+3+ 3*3*

Forecasting in OM (Lecture#2)

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Transcript of Forecasting in OM (Lecture#2)