4. FORECASTING_Quality_Quantitative_Method.ppt

7/25/2019 4. FORECASTING_Quality_Quantitative_Method.ppt

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1

WHICH TO USE?

When using time series data, plotting the

datacan be very hel!ul in ch""sing an

ar"riate !"recasting techni#ue

$"u %ant t" achieve&

'(" attern "r directi"n in !"recast err"r

Err"r ) *At+ Ft ) *-ctual + ."recast

Seen in l"ts "! err"rs "ver time

' Smallest !"recast err"r

/ean s#uare err"r */SE

/ean abs"lute deviati"n */-0

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6

WHICH TO USE?

*Stable time series

1211109876543210

25

20

15

10

5

0

WEEK

SAL

ES

/"ving average %ith large (

Weighted m"ving average *large number "! eri"ds

E7"nential sm""thing %ith cl"se t" 2

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WHICH TO USE?

*Time series %ith changing attern

14131211109876543210

15000

10000

5000

0

PERIOD

P

ATIENTS

/"ving average %ith small (

Weighted m"ving average *small number "! eri"ds

E7"nential sm""thing %ith cl"se t" 1

(a8ve meth"d

-dative ."recasting


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-dative ."recasting

It9s "ssible t" use the c"muter t"c"ntinually m"nit"r !"recast err"r andad:ust the values "! the and c"e!!icients used in e7"nentialsm""thing t" c"ntinually minimi;e!"recast err"r

This techni#ue is called adativesm""thing


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=

WHICH TO USE?

*Time series %ith trend

0"uble e7"nential sm""thing

>inear regressi"n *"nly i! data are linear

Trend r":ecti"n

(a8ve meth"d that %"uld acc"unt !"r the trend

20181614121086420

6000

5500

5000

4500

4000

PERIOD

S

ALES

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WHICH TO USE?

*Time series %ith seas"nality

483624120

90000

70000

50000

30000

10000

MONTH

DE

MAND

/ultilicative m"del

-dditive m"del

(a8ve meth"d that %"uld acc"unt !"r seas"nality-0/ 213 4im 5aber


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@

Time H"ri;"n

The time h"ri;"n !"r a !"recast has a direct bearing

"n the selecti"n "! a !"recasting techni#ues

Sh"rt range techni#ues& they r"duce !"recasts !"r

the ne7t eri"d *eAgA /"ving averages and

e7"nential sm""thing techni#uesA

/edium+range techni#ues& eAgA linear regressi"n,

/ultilicative m"del >"ng+range techni#ues& eAgA #ualitative !"recasting

techni#uesB

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.O4EC-ST E44O4S

Forecast Error:

Et ) -t+ .t

Running Sum of Forecast Errors:

4S.E ) Et

Mean Error:

/E ) *Et D n

4S.E and /E are use!ul !"r measuring the bias in

a !"recastA The bias is the tendency "! a !"recast t"

al%ays be t"" high "r t"" l"%A

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.O4EC-ST E44O4S

Mean Squared Error:

/SE ) *Et6 D n

Standard Deviation of Forecast Errors:

Mean Absolute Deviation of Forecast Errors:

/-0 ) *FEtF D n /SE, and /-0 are measures "! the disersi"n

"! !"recast err"rs !r"m the value ;er"A

/SE)

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/-0 -(0 ST-(0-40 0EGI-TIO(

I! !"recast err"rs Et are n"rmally distributed *err"rs e7hibit "nly

rand"m variati"ns %ith a mean "! ;er" *n" bias/E alm"st e#ual;er" then the !"recast is deemed t" er!"rm ade#uately and&

) /-0 1A6= /-0

6

/-0 2A

Then %e can give a !"recast range and an ass"ciated c"n!idence

level in %hich the actual "bservati"n, -t,%"uld !all

' .t; ' .t !"r A@ c"n!idence level

' .t6 !"r =A


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11

/-0 -(0

(umber"! /-0s(umber"!Js -rea"! ("rmal Kr"bability0istributi"nWithin>imitsH1A2H1A=

H6A2H6A=HA2HA=

H2ACH1A6

H1A?H6A2H6A


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16

EL-/K>E ES .O4CE CO/KOSITE Each salesers"n r":ects their sales

C"mbined at district nati"nal levels

Sales re9s Nn"% cust"mers9 %ants

Tends t" be "verly "timistic a!ter several

eri"ds "! g""d sales

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1

Inv"lves small gr"u "! high+level managers

' Pr"u estimates demand by %"rNing t"gether

C"mbines managerial e7erience %ith statistical

m"dels

4elatively #uicN

Pr"u+thinN9

disadvantage

5U4$ O. ELECUTIGE

OKI(IO(

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6

CO(SU/E4 /-4ET

SU4GE$ -sN cust"mers ab"ut urchasing lans

What c"nsumers say, and %hat they

actually d" are "!ten di!!erent S"metimes di!!icult t" ans%er

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0elhi /eth"d

Iterative gr"ur"cess, c"ntinues

until c"nsensus is

reached tyes "!

articiants

'0ecisi"n maNers

' Sta!!

' 4es"ndents

Staff$Administeringsurveudgments& -0/ 213 4im 5aber


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KOI(TS

/"st ractiti"ners use Err"r ) -ctual + ."recastAThus, i! ."recasts tend t" underestimate -ctuals,

Mias %ill be "sitiveA H"%ever, s"me ractiti"ners

use Err"r ) ."recast + -ctual, %hich %ill yield the

""site sign !"r MiasA (OW $OU4CO/KUTE4 K4OP4-/A

/SE& s"me divide by n+1 *te7tb""N, s"me by nA

-s l"ng as n is n"t t"" small, it maNes littledi!!erenceA

E7"nential sm""thing& icNing the !irst !"recast is

d"ne in a variety "! %aysA-0/ 213 4im 5aber


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=

SO/E TECH(IC-> KOI(TS

0ec"m"siti"n& there are t%" c"mm"n ar"aches&' calculate seas"nals using ra% data and centered m"ving

averages, deseas"nali;e the data, then use regressi"n "n

deseas"nali;ed data t" get trend line

'

calculate seas"nals using ra% data and centered m"vingaverages, then use regressi"n "n centered m"ving averages t"

get trend lineA

There are "ther c"mm"nly used m"dels, m"re

c"mlicated& Winter9s m"del, M"7+5enNins, AAA I! y"ur!"recasts d"n9t cut the mustard, y"u may need t" call

"n the e7erts %h" are !amiliar %ith a variety "! "ther

ar"aches *"r taNe the ."recasting c"urseA

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E>E/E(TS O. - POO0


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E>E/E(TS O. - POO0

.O4EC-ST

Tim!"

A##$%&'R!i&(!

M&)i)*

+$! W%i'')

E&,"

'.$,

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Summary ."recasts are im"rtant !"r "erati"ns managers

and their lanning

H"%ever, it is di!!icult t" redict the !utureA T%"

tyes "! !"recasting&' Rualitative

' Ruantitative

Rualitative !"recasting is the m"st used

meth"d"l"gy, due t" the mathematicalc"mle7ities "! the #uantitative meth"d"l"gy

/"%i*' 2014 P&%)

/&)&& I)#

4. FORECASTING_Quality_Quantitative_Method.ppt

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