Week 7 - Forecasting

7/24/2019 Week 7 - Forecasting

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COMM341: Operations

ManagementForecasting TechniquesG. Pond


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Outline

Background

Time-Series Models Associative Models

Forecast Accuracy

Forecast Control


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Bacground

Within a business context, orecasting methobe used to orecast!

Consumer demand

"tility #rices

$et%ork ex#ansion &social media'

(abour re)uirements Machine-time demands

Ca#acity gro%th

Market share gro%th*+


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e#endent emand

nternal &e+g+, our scre%s or every cleg'

- .o% can /rms in0uence internal demand

nde#endent emand

2xternal &e+g+ , consumer demand oat Christmas' .o% can /rms in0uence external deman

!emand


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Time"#eries Models

$a3ve Method

Moving Averages Weighted Moving Averages

2x#onential Smoothing

(inear Trend 4ro5ection Trend-Ad5usted 2x#onential Smoo

Cyclical6Seasonal emands


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The $a%&e Method

Three 4ossibilities!

7' Stable Series

Actual 8alue at 4revious Timeste# 9 Forecast 8alue at $ext

:an Feb Mar A#r May :une :uly;

;;

May ?bserved8alue

:une Forecast8alue


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The $a%&e Method


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The $a%&e Method

' Trend

ierence Bet%een T%o PreviousTimeste#s D 8alue o Previou9 Forecast 8alue at NextTimeste#

:an Feb Mar A#r May :une :uly;

;;

E;;>

>;;> G >;; 9

> D 9;


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Mo&ing '&erage

Forecast value is the average o th

#revious n observed values*


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Mo&ing '&erage

!ate 1 ( 3 4 ) * +,Temperature -< < -=

Consider the ollo%ing tem#eratures or the

%eek in March, 7II>, in Jingston!

"se a -#eriod moving average to orecast ttem#erature on ay =*


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Mo&ing '&erage

What value should you use or 1 n this class, the value or %ill b

given in the #roblem+

n #ractice* it de#ends+ The olslides illustrate the eect oincreasing the value o +


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Mo&ing '&erage

The scattergram belo% de#icts the number

students registered inbusiness6commerce6management #rograms#rovince o ?ntario!


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Mo&ing '&erage


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/eighted Mo&ing '&erages

Consider this table o tem#eratures!

Su##ose an e)ui#ment malunction is sus#eon the


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0ponential #moothing

%here!

is the orecast value at timeis the Lsmoothing constant, in the i

&;,7'

is the observed value at time reviousste#'

is the orecast value at the #revious tim

An alternative orm o the same e)uation!


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Small #roblem though*.o% do %e start an ex#onentially smoot

orecast to /nd %hen it de#ends on havin

orecast value at the previoustime ste#

So %e use the $a3ve method to orecast thevalue+


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Keconsider the number o registrants!

evelo# an ex#onentially smoothing orecas9 ;+>

To start, use the na3ve method to obtain aorecast value or @ear *

@ear Actual


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$o% %e can use the ex#onentially smoothedorecasting ormula*

@ear ActualForecast


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* and %e %ould continue the orecast throu

remaining timeste#s available

@ear ActualForecast


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$ote!

Be sure to start the orecast using one o na3ve methods

Smaller values o the smoothing constantmore em#hasis on orecast values and tre&conse)uently, the orecast is more smoo

Ty#ical values or are in the range &;+;,;

"se as much o the data as #ossible to heLtrain your model+

2i T d P i


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2inear Trend Proection

%here!

is the #ro5ected &or estimated' value ais the vertical axis interce#t is the slo#e o the line

This method is based on the e)uation o a stline &that you may kno% as '

Seems easy*+

2i T d P ti


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Finding the values or and can be a bit tedi

2i T d P ti


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0ampleConsider the ollo%ing table o students regin undergraduate business degree #rograms?ntario! @ear Actual


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0amplecont56To develo# a linear trend orecast, add additcolumns, as ollo%s!

@ear Actual


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0amplecont56*and /ll in the rest o the table!

@ear Actual


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0amplecont56(astly, /nd the column totals as ollo%s!

@ear Actual


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0amplecont56Substitute into the ormula or !



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0amplecont56Substitute into the ormula or !



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0amplecont56(astly, substitute into the e)uation o a straline!



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? course youNre #robably thinking that %as

ridiculously long and that there has got to beeasier %ay* youNd be right+

4lot the data in 2xcel*


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Kight-click on one o the #oints &in an 2xcel chart'

Sele

Tren



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n the %indo% that o#ens, make the ollo%in

selections!



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%here!

is the smoothed orecast at time , de/neis the smoothed trend at time , de/ned b

Trend"'dusted0 ti l # thi


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As in the case o ex#onential smoothing, %estart the orecast using the na3ve method bucase, %eNll use the na3ve method or trends*

@ear Actual


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@ear Actual


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@ear Actual


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@ear Actual


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@ear Actual


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@ear Actual


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@ear Actual


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7 8

;

;;,;;;

E;;,;;;

7,;;;,;;;

7,;;,;;;

7,E;;,;;;

Month

$umber oomestic4assenger

s

Pearson 'irport 0nplaned 9 !eplan

Passenger olume

#easonal7C8clic !emand


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8

First, %e need to Lde-seasonaliOe the data+ We can consid

month as a Lseason that re#eats yearly and then /nd a 7


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8




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8




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8

!ate 'ctual:an I,;>7

Feb E;,7

:an IE;,I>

7,;EI,E>7,;I=,=


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!ate 'ctual

:an-77 I,;>7

Feb E;,7

:an-7< IE;,I>

Feb I=7,;=>

Mar 7,;;,7=

A#r 7,;E

:un 7,7I,=I>

:ul 7,,7;

1("Period Mo&ing'&erage

7,;EI,E>

7,;I=,=


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!ate 'ctual#easonalelati&e

7

Feb E;,


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#eason

'&erage

#easonalelati&e

:an ;+EI

Feb ;+E7

Mar ;+I;

A#r ;+I

May 7+;=I

:un 7+7;;

:ul 7+


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$o% divide each o the observed &actual' values by its seasrelative -ear !ate 'ctual

#easonalelati&e

!eseasonali;edPassenger 2oad

7 ;+EEIE 7,;E,;E

Feb E;,E,;;

Mar IE;,77 ;+I; 7,;E


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;

;;,;;;

E;;,;;;

7,;;;,;;;

7,;;,;;;

7,E;;,;;;

&x' 9 ;>+Ix D 7;I


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Su##ose %e %ant to orecast #assenger load or ecember&4eriod P>< in my data set',

QB"TQ this /gure re#resents the trend &or de-seasonaliOed load or ecember,


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Associate models are those %here the independent variablsomething other than time+ For exam#le, it may be orecasterm grade &the dependent variable, ' as a unction o the n

#ractice #roblems you com#lete &the independent variable

(inear regression can be done to obtain a trend, 5ust as it %beore+ The only dierence is that %here a##eared in the #e)uations, it is re#laced %ith

%here!

Measuring Forecast 'ccurac8


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With so many dierent orecastingmethods, ho% does one kno% %hibest1

Mean Absolute eviation &MA'

Mean Absolute 4ercentage 2rror&MA42'

Mean S)uared 2rror &MS2'

Correlation coecient &'

Measuring Forecast 'ccurac8


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Correlation Coecient

"sed or linear trends ?$(@ &not ex#onential, logarith

or #o%er ormulas'

The closer is to 7, the better the orecasting modelre#resents the observed data

Correlation Coe


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!ate 1 ( 3 4 ) * +,Tem#erature &' -< < -= -= -< - -7;*

Forecast 8alue &' -7+ -

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Week 7 - Forecasting

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