Lecture 4 Forecasting f04 331

8/9/2019 Lecture 4 Forecasting f04 331

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Outline

Forecasting Success Stories

Decisions Based on Forecasts

Characteristics of Forecasts

Components of demand

Evaluation of forecasts

LESSON 4: FORECASTING


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Sharing forecasting informationalong the supply chain is not verycommon. The result is forecast erroras much as 60 percent of actualdemand. S economy alone can

save !"#$ %illion in inventoryinvestment &ith a coordinatedforecasting. 'al()art has initiatedsuch a process &ith 'arner(

*am%ert+ a manufacturer of*isterine.

,e&lett(-acard uses forecastingmethod for ne& product

development.


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Taco Bell developed a forecastingmethod that gives customer demandfor every "/(minute interval. Theforecast is used to determine thenum%er of employees reuired. Taco

Bell achieved la%or savings of morethan !10 million from "$$2 to "$$6.

Compa delayed the announcementof several ne& -entium(%ased

models in "$$1. The decision &as%ased on a forecasting method andcontrary to the company %elief.


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Forecasting is necessary to determine the num%er ofreservations an airline should accept for a particularflight ( over%ooing+ traffic management+ discountallocation+ etc.


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-roduction

34ggregate planning+inventory control+scheduling

)areting3 5e& product

introduction+ sales(force allocation+promotions

Finance3 -lanteuipment

investment+ %udgetaryplanning

-ersonnel

3 'orforce planning+hiring+ layoff


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The decisions should not %e

segregated %y functional area+as they influence each otherand are %est %est made 7ointly.For e8ample+ Coca(cola

considers the demand forecastover the coming uarter anddecides on the timing of variouspromotions. The promotioninformation is then used to

update the demand forecast.Based on this forecast+ Coca(Cola &ill decide on aproduction plan for the uarter.

This plan may reuireadditional investment+ hiring+or perhaps su%contracting ofproduction. Coe &ill maethese decisions %ased on the

production plan and e8istingcapacity+ and it must maethem all in advance of actualproduction.


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Characteristics o Forecasts

Forecasts are al&ays&rong9 so consider %othe8pected value and ameasure of forecast error.

*ong(term forecasts areless accurate than short(term forecasts. Fore8ample+ #(Eleven :apan

has a replenishment processthat ena%les it to respond toan order &ithin hours. ;f astore manager places an

order %y "0 am+ the order isdelivered %y # pm the sameday. The store manager thushas to forecast &hat &ill sellthat night less than "< hours

%efore the actual sale.


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Characteristics o Forecasts

Some time series =called aggregate series> are o%tained%y summing up more than one time series =calleddisaggregate series> . For e8ample+ annual sales areo%tained %y adding "< monthly sales. The annual salesis an aggregate series and monthly sales is adisaggregate series. 4ggregate forecasts are moreaccurate than disaggregate forecasts. ?ariation in @D-of a country is much less than the annual earnings of a

company. Conseuently+ it is easy to forecast the @D-of a country &ith less than


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Co!"onents o De!and

4verage demand Trend

3 @radual shift in average demand

Seasonal pattern

3 -eriodic oscillation in demand &hich repeats Cycle

3 Similar to seasonal patterns+ length and magnitudeof the cycle may vary

andom movements

4uto(correlation


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#antit$

Ti!e

%a& A'erage: Data cluster a(out a hori)ontal line*

Co!"onents o De!and


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#uanti

t$

Ti!e

%(& Linear trend: Data consistentl$ increase or decrease*

Co!"onents o De!and


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#uanti

t$

+ + + + + + + + + + + +, F - A - , , A S O N D

-onths

%c& Seasonal inluence: Data consistentl$ sho.

"ea/s and 'alle$s*

0ear 1

Co!"onents o De!and


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#uanti

t$

+ + + + + + + + + + + +, F - A - , , A S O N D

-onths

%c& Seasonal inluence: Data consistentl$ sho.

"ea/s and 'alle$s*

0ear 1

0ear 2

Co!"onents o De!and


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Co!"onents o De!and


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#uanti

t$

+ + + + + +1 2 3 4 5

0ears

%c& C$clical !o'e!ents: Gradual changes o'er

e6tended "eriods o ti!e*

Co!"onents o De!and


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Co!"onents o De!and

Suppose that a companyhas institute a salesincentive system thatprovides a %onus for theemployee &ith the %est

improvement in %ooingsfrom one month to the ne8t.'ith such an incentive inplace+ a month of poor sales

is often follo&ed %y a monthof good sales. Similarly+ amonth of good sales &ouldusually %e follo&ed %y a lull.


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Co!"onents o De!and

This means that sales inconsecutive months tend to%e negatively correlated.This information can %eused to improve sales

forecasts. 4utocorrelation isthe correlation amongvalues of o%served dataseparated %y a fi8ed num%er

of periods. ;n the e8amplea%ove+ &e &ould say thatthe series has a negativeautocorrelation of order one.


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Demand

Time

Trend

andom

movement

Dem

and

Time

Trend &ithseasonal pattern

Co!"onents o De!and


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Snow Skiing

Seasonal

Long term growth trend

Demand for skiing products increasedsharply after the Nagano Olympics


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E'aluation o Forecast

There are many forecasting techniues and soft&are. Theperformance of a forecasting techniue can %e measured %ythe error produced over time.

'e shall no& discuss various measures used to evaluate

forecasting techniues. *et+

Error-ercentage4%solute)ean)4-E

Deviation4%solute)ean)4D

ErrorSuared)ean)SE

errorsforecastofdeviationStandard

periodinerrorForecast

periodinForecast

periodindata4ctual

=

=

=

=

==

=

=

ttt

t

t

DFtE

tF

tD


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+Et+

nEt

2

n

-AD 7-SE 7

-A8E 7

7 -SE

+Et + %199&Dtn

-easures o Forecast Error

Et7 F

t; D

t



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A(solute

Error A(solute 8ercent

-onth< De!and< Forecast< Error< S=uared< Error< Error

4 2?9 2@9

239 29

5 259 249

? 219 29

> 2? 249;

Total



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-SE 7 7

-easures o Error

-AD 7 7

-A8E 7 7



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READING AND EERCISES

*esson 1

eading

Section

E8ercises

"+ pp. 6

Lecture 4 Forecasting f04 331

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Transcript of Lecture 4 Forecasting f04 331