Post on 01-Jun-2018
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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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Forecasting Success Stories
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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Forecasting Success Stories
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 Success Stories
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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Decisions Based on Forecasts
-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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Decisions Based on Forecasts
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
E'aluation o Forecast
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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
E'aluation o Forecast
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-SE 7 7
-easures o Error
-AD 7 7
-A8E 7 7
E'aluation o Forecast
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READING AND EERCISES
*esson 1
eading
Section
E8ercises
"+ pp. 6