demand forecasting (best)

8/17/2019 Demand Forecasting (Best)

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Demand Forecasting

Dr. A.N. Sah



Forecasting

1.“1.“Prediction is very difcult, especially iPrediction is very difcult, especially i

it's about the uture.it's about the uture.” - Nils Bohr” - Nils Bohr

2. “2. “ The successful business manageris a forecaster rst; purchasing,producing, marketing, pricing, and

organizing all follow”-anonymous.



Signifcance• Managers today operate in a ery competitie and !ncertain

enironment. Managers al"ays try to red!ce !ncertainty andma#e $etter estimates o% "hat "ill happen in the %!t!re. &hisgoal is attained $y demand estimation and %orecasting.

• 'hen demand is predicted acc!rately( it can $e met in atimely and e)cient manner. Acc!rate demand estimationand %orecasts help a company aoid lost sales or stoc#-o!tsit!ations( and preent c!stomers %rom going to competitors.

At the $ottom line( the acc!rate %orecast helps in proc!ringra" materials and component parts m!ch more cost-e*ectiely aoiding last min!te p!rchases.

• Do companies estimate demand %or their prod!cts+ Do theyestimate demand elasticity( income and cross elasticity"hich is important %or pricing policy+ ,o" do companiesestimate trend seasonality in sales( prod!ction etc. ,o" docompanies eal!ate e*ectieness o% their adertising+ ,o"do companies fnd relationship $et"een o!tp!t inp!t. ,o"to decide "hether one sho!ld go %or ne" prod!ct or not.

• n many small frms( the entire process is s!$/ectie inolingint!ition and years o% e0perience. lanners and policy ma#ersat all leels need to #no" the %!t!re trends in $!siness

actiity in order to cond!ct $!siness in profta$le mannerirrespectie o% long term or short term planning.



$/ecties

• 3ie the %!ndamental r!les o%%orecasting

• 4alc!late a %orecast !sing a moingaerage( "eighted moing aerage(and e0ponential smoothing

• 4alc!late the acc!racy o% a %orecast



What is forecasting?

Forecasting is a tool used for predicting

future demand based on

past demand information.



Why is forecasting important?

Demand for products and services is usually uncertain.

Forecasting can be used for…

• Strategic planning (long range planning)

• Finance and accounting (budgets and cost controls)

• Mareting (future sales! ne" products)

• #roduction and operations



What is forecasting all about?

$emand for Mercedes % &lass

'imean Feb Mar pr May un ul ug

ctual demand (past sales)

#redicted demand

We try to predict thefuture by looing bac

at the past

Predicted

demand

looking

back sixmonths



'hat5s Forecasting All A$o!t+

From the March 16( 2667 'S89

Ahead o% the scars( an economics pro%essor( at the re:!esto% 'ee#end 8o!rnal( processed data a$o!t this year;s flmsnominated %or $est pict!re thro!gh his statistical model and

predicted "ith <=.>? certainty that @Bro#e$ac# Mo!ntain@"o!ld "in. ops. ast year( the pro%essor t!ned his model!ntil it correctly predicted 1 o% the preio!s 26 $est-pict!rea"ardsC then it predicted that @&he Aiator@ "o!ld "inC @MillionDollar Ba$y@ "on instead.

Sometimes models t!ned to prior res!lts don;t hae greatpredictie po"ers.



Some general characteristics o%%orecasts

• Forecasts are al"ays "rong

• Forecasts are more acc!rate %or gro!ps or%amilies o% items

• Forecasts are more acc!rate %or shorter timeperiods

• ery %orecast sho!ld incl!de an errorestimate

• Forecasts are no s!$stit!te %or calc!lateddemand.



*ey issues in forecasting

+. forecast is only as good as the information included in the

forecast (past data)

,. -istory is not a perfect predictor of the future (i.e. there is

no such thing as a perfect forecast)

/%M%M0%/ Forecasting is based on the assumptionthat the past predicts the future1 When forecasting! thin

carefully "hether or not the past is strongly related to

"hat you e2pect to see in the future…



%2ample Mercedes %3class vs. M3class Sales

Month E-class Sales M-class Sales

Jan 23,345 -

Feb 22,034 -

Mar 21,453 -

pr 24,!"# -

Ma$ 23,5%1 - Jun 22,%!4 -

Jul & &

4uestion &an "e predict the ne" model M3class sales based onthe data in the the table?

ns"er Maybe... We need to consider ho" much the t"o

marets have in common



What should "e consider "hen looing at

past demand data?

• 'rends

• Seasonality

• &yclical elements

• utocorrelation

• /andom variation



Some mportant E!estions

• 'hat is the p!rpose o% the %orecast+

• 'hich systems "ill !se the %orecast+

• ,o" important is the past in estimating the

%!t!re+

Ans"ers "ill help determine time horions(techni:!es( and leel o% detail %or the%orecast.



'ypes of forecasting methods

/ely on data andanalytical techni5ues.

/ely on sub6ectiveopinions from one or

more e2perts.

4ualitative methods 4uantitative methods



4ualitative forecasting methods

Grass Roots deriving future demand by asing the personclosest to the customer.

Market Research trying to identify customer habits7 ne"

product ideas.

Panel Consensus deriving future estimations from the

synergy of a panel of e2perts in the area.

Historical Analogy identifying another similar maret.

Delphi Method similar to the panel consensus but "ith

concealed identities.



4uantitative forecasting methods

Time Series models that predict future demand based

on past history trends

Causal Relationship models that use statisticaltechni5ues to establish relationships bet"een various

items and demand

Simulation models that can incorporate some

randomness and non3linear effects



-o" should "e pic our forecasting model?

+. $ata availability

,. 'ime hori8on for the forecast

9. /e5uired accuracy

:. /e5uired /esources



'ime Series Moving average

• 'he moving average model uses the last t periods in order to

predict demand in period t'+.

• 'here can be t"o types of moving average models simple

moving average and "eighted moving average

• 'he moving average model assumption is that the most

accurate prediction of future demand is a simple (linear)

combination of past demand.



'ime series simple moving average

;n the simple moving average models the forecast value is

F t'1 ( t ' t-1 ' ) ' t-n

n

t is the current period.

F t'1 is the forecast for ne2t period

n is the forecasting hori8on (ho" far bac "e loo)!

is the actual sales figure from each period.



%2ample forecasting sales at *roger

*roger sells (among other stuff) bottled spring "ater

Month Bottles

Jan 1,325 Feb 1,353

Mar 1,305

pr 1,2#5

Ma$ 1,210 Jun 1,1"5

Jul &

What will

the sales

be for

July?



What if "e use a 93month simple moving average?

F Jul ( Jun ' Ma$ ' pr

3( 1,22#

What if "e use a <3month simple moving average?

F Jul ( Jun ' Ma$ ' pr ' Mar ' Feb

5( 1,2%!



What do "e observe?

93month

M forecast

<3month

M forecast

<3month average smoothes data more7

93month average more responsive



Sta$ility ers!s responsieness inmoing aerages



'ime series "eighted moving average

We may "ant to give more importance to some of the data…

F t'1 ( *t t ' *t-1 t-1 ' ) ' *t-n t-n

*t ' *t-1 ' ) ' *t-n ( +

t is the current period.

F t'1 is the forecast for ne2t period

n is the forecasting hori8on (ho" far bac "e loo)!

is the actual sales figure from each period.

* is the importance ("eight) "e give to each period



Why do "e need the WM models?

0ecause of the ability to give more importance to "hathappened recently! "ithout losing the impact of the past.

$emand for Mercedes %3class

'imean Feb Mar pr May un ul ug

ctual demand (past sales)

#rediction "hen using =3month SM

#rediction "hen using =3months WM

For a =3month

SM! attributinge5ual "eights to all

past data "e miss

the do"n"ard trend



%2ample *roger sales of bottled "ater

Month Bottles

Jan 1,325

Feb 1,353

Mar 1,305

pr 1,2#5

Ma$ 1,210

Jun 1,1"5

Jul &

What will

be the

sales for

July?



=3month simple moving average…

;n other "ords! because "e used e5ual "eights! a slight do"n"ard

trend that actually e2ists is not observed…

F Jul ( Jun ' Ma$ ' pr ' Mar ' Feb ' Jan

% ( 1,2##



What if "e use a "eighted moving average?

Mae the "eights for the last three months more than the first

three months…

=3month

SM

WM

:> @ =>

WM

9> @ A>

WM

,> @ B>

ulyForecast

1,2## 1,2%# 1,25# 1,24#

'he higher the importance "e give to recent data! the more "e

pic up the declining trend in our forecast.



-o" do "e choose "eights?

+. $epending on the importance that "e feel past data has

,. $epending on no"n seasonality ("eights of past data

can also be 8ero).

WM is better than SM

because of the ability to

!ary the weights"



'ime Series %2ponential Smoothing (%S)

Main idea 'he prediction of the future depends mostly on themost recent observation! and on the error for the latest forecast.

Smoothi

ng

constant

al#ha α

$enotes the importance

of the past error



Why use e2ponential smoothing?

+. Cses less storage space for data

,. %2tremely accurate

9. %asy to understand

:. Dittle calculation comple2ity

<. 'here are simple accuracy tests



%2ponential smoothing the method

ssume that "e are currently in period t . We calculated the

forecast for the last period ( F t-1) and "e no" the actual demand

last period ( t-1) …

)( +++ −−− −+=

t t t t F F F α

'he smoothing constant + e2presses ho" much our forecast "ill

react to observed differences…

;f + is lo" there is little reaction to differences.

;f + is high there is a lot of reaction to differences.



%2ample bottled "ater at *roger

Month ctual Forecasted

Jan +!9,< +!9A>

Feb +!9<9 +!9=+

Mar +!9>< +!9<E

pr +!,A< +!9:E

Ma$ +!,+> +!99:

Jun ? +!9>E

α >.,




α >.BMonth ctual Forecasted

Jan +!9,< +!9A>

Feb +!9<9 +!99:

Mar +!9>< +!9:E

pr +!,A< +!9+: Ma$ +!,+> +!,B9

Jun ? +!,,<



mpact o% the smoothing constant



&rend..

'hat do yo! thin# "ill happen to amoing aerage or e0ponentialsmoothing model "hen there is a

trend in the data+



;mpact of trend

Sales

Month

/egular e2ponential

smoothing "ill al"ayslag behind the trend.

&an "e include trend

analysis in e2ponential

smoothing?

ctual

$ata

Forecast



%2ponential smoothing "ith trend

t t t , F F-, +=

. F-, +/ F-, F t t t t +++ −−− −+=

. F-, 0/F , , t t t t ++ −− −+=

F;' Forecast including trendG 'rend smoothing constant

'he idea is that the t"o effects are decoupled!

( F is the forecast "ithout trend and is the trend component)




At F t T t FT t

Jan 1325 13!0 -10 13#0

$eb 1353 1334 -2! 130%

Mar 1305 1344 -" 1334

#r 12#5 1311 -21 12"0

May 1210 12#! -2# 1251

Jun 121! -43 11#5

H >.B

G >.<



0ponential Smoothing "ith &rend



Dinear regression in forecasting

Dinear regression is based on

+. Fitting a straight line to data

,. %2plaining the change in one variable through changes in

other variables.

0y using linear regression! "e are trying to e2plore "hich

independent variables affect the dependent variable

dependent ariable ( a ' b × /independent ariable



%2ample do people drin more "hen itIs cold?

lcohol Sales

verage Monthly

'emperature

Which line best

fits the data?



Deast S5uares Method of Dinear /egression

'he goal of DSM is to minimi8e the sum of s5uared errors…

∑,

Min iε



Deast S5uares Method of Dinear /egression

'hen the line is defined by

3b $a −=

,, n

$ n $b

−

−=

∑∑

bJaK +=



-o" can "e compare across forecasting models?

We need a metric that provides estimation of accuracy

$orecast Error

Forecast error $ifference bet"een actual and forecasted value

(also no"n as residual )

%rrors can be

+. biased (consistent)

,. random



Measuring ccuracy MF%

MF% Mean Forecast %rror (0ias)

;t is the average error in the observations

n

F

n

i

t t ∑= −= +

MF%

+. more positive or negative MF% implies "orse

performance7 the forecast is biased.



Measuring ccuracy M$

M$ Mean bsolute $eviation

;t is the average absolute error in the observations

n

F

n

i

t t ∑=

−= +

M)$

+. -igher M$ implies "orse performance.

,. ;f errors are normally distributed! then L+.,<M$



MF MAD9A Dart$oard Analogy

Do" MF% N M$

'he forecast errors

are small Nunbiased



An Analogy Gcont5dH

Do" MF% but highM$

On average! thearro"s hit the

bullseye (so much

for averages1)



MF MAD9An Analogy

-igh MF% N M$

'he forecasts

are inaccurate N biased



Iey oint

Forecast m!st $e meas!red %or acc!racyJ

&he most common means o% doing so is$y meas!ring the either the meana$sol!te deiation or the standard

deiation o% the %orecast error



Measuring ccuracy 'racing signal

'he tracing signal is a measure of ho" often our estimations

have been above or belo" the actual value. ;t is used to decide"hen to re3evaluate using a model.

M)$

/SF%'S =∑

=

−=n

i

t t . F /

+

/SF%

#ositive tracing signal most of the time actual values are

above our forecasted values

Pegative tracing signal most of the time actual values are

belo" our forecasted values

&f 'S ( ) or * -)+ in!estigate"




Month ctual $orecast

an 1,325 13#0

Feb 1,353 130%

Mar 1,305 1334

pr 1,2#5 12"0

May 1,210 1251

un 1,1"5 11#5

Month ctual $orecast

Jan 1,325 1,3#0

Feb 1,353 1,3%1

Mar 1,305 1,35"

pr 1,2#5 1,34"

Ma$ 1,210 1,334

Jun 1,1"5 1,30"

%2ponential Smoothing

(α >.,)

Forecasting "ith trend

(α >.B)(δ >.<)

4uestion Which one is better?



M$ 'S

%2ponentialSmoothing

A> 3 =.>

Forecast;ncluding 'rend

99 3 ,.>

0ottled "ater at *roger compare M$ and 'S

We observe that F;' performs a lot better than %S

&onclusion #robably there is trend in the data "hich

%2ponential smoothing cannot capture



'hich Forecasting Method

Sho!ld Ko! Lse• 3ather the historical data o% "hat yo!

"ant to %orecast

• Diide data into initiation set andeal!ation set

• Lse the frst set to deelop the models

•

Lse the second set to eal!ate• 4ompare the MADs and MFs o% each

model

demand forecasting (best)

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