forecasting with history · forecasting with history santiago gallino –tuck school of business...
TRANSCRIPT
ForecastingWithHistory
SantiagoGallino– TuckSchoolofBusinessToniMoreno– KelloggSchoolofManagement
July2013– LBS– London,UKJanuary2017
11May2017 2RetailFundamentals
LearningModules
1.Demandforecasting
2.InventoryDecisions
3.AssortmentPlanning
4.PricingDecisions
5.Theomnichannelcustomer
6.Fulfillingomnichanneldemand
7.Omnichanneljourneys
8.Supportinganomnichannel
strategy
11May2017 3RetailFundamentals
M1.2Opening1
• InaccurateforecastcostMoney.• Wetendtothinkthatthiscostonlyoccurwhenweendupwithtoomuchinventorybutitactuallywecanloosemoneybothways.
• Inthisvideowewillstudyhowtounderstandhistoricaldatatocreateforecast.
• Wewilllookat:• Trends• Seasonality• Otherfactors
• Wewilldiscusstheimplicationsofgoodandbadforecastforaretailer.
11May2017 4RetailFundamentals
M1.2Trends2
• Lookingathistoricaldatacanbeusefultoidentifytrendfortheretailerasawhole,forparticularcategoriesorindividualproducts.
• LetslookatthreecategoriesinGermanyovertime
Sales volume of smartphones in Germany 2008-2016
Sales volume of smartphones in Germany from 2008 to 2016 (in million devices)
Source: EITO; IDC; ID 461852
Note: Germany; 2008 to 2015
5 5.7
10.4
15.9
21.622.86
24.4
26.2
0
5
10
15
20
25
30
2008 2009 2010 2011 2012 2013 2014 2015
Sale
s vo
lum
e in
milli
on d
evic
es
Further information regarding this statistic can be found on page 8.
Sales volume of camcorders and digital cameras in Germany 2005-2015
Number of camcorders and digital cameras sold on the consumer market in Germany from 2005 to 2015 (in 1,000 devices)
Source: GfK; gfu; BVT; ID 462742
Note: Germany; 2005 to 2015; private demand
9,320
8,180 8,240 8,250
7,038
5,570
4,0123,401
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
2008 2009 2010 2011 2012 2013 2014 2015
Sale
s vo
lum
e in
thou
sand
dev
ices
Digital cameras Poly. (Digital cameras)
Further information regarding this statistic can be found on page 8.
Sales volume of camcorders and digital cameras in Germany 2005-2015
Number of camcorders and digital cameras sold on the consumer market in Germany from 2005 to 2015 (in 1,000 devices)
Source: GfK; gfu; BVT; ID 462742
Note: Germany; 2005 to 2015; private demand
718
852810
712
644 663 660
772
0
100
200
300
400
500
600
700
800
900
2008 2009 2010 2011 2012 2013 2014 2015
Sale
s vo
lum
e in
thou
sand
dev
ices
Camcorders* Linear (Camcorders*)
Further information regarding this statistic can be found on page 8.
11May2017 8RetailFundamentals
M1.2Trends3
• Lookingathistoricaldatacanbeusefultoidentifytrendfortheretailerasawhole,forparticularcategoriesorindividualproducts.
• LetslookatthreecategoriesinGermanyovertime
• Whyshouldwecareaboutthis?• IfyouareBestBuythisisgoingtobeusefulinformationwhendecidingonwhatproducttocarryinthefuture.
• However,trendsarenottheonlychallengethatretailersfacewhenforecasting.
11May2017 9RetailFundamentals
3
• Seasonalitycanbeabigfactortoo.
M1.2Seasonality
Beer monthly sales in the United Kingdom (UK) 2013-2016
Monthly beer sales volume in the United Kingdom (UK) from May 2013 to May 2016 (in 1,000 hectolitres)
Source: British Beer & Pub Association; ID 308989
Note: United Kingdom; January 2013 to May 2016
Further information regarding this statistic can be found on page 8.
0
1,000
2,000
3,000
4,000
5,000
6,000
May2013
Jun20
13Jul201
3Au
g20
13Sep2013
Oct2013
Nov2013
Dec2013
Jan20
14Feb2014
Mar2014
Apr2
014
May2014
Jun20
14Jul201
4Au
g20
14Sep2014
Oct2014
Nov2014
Dec2014
Jan20
15Feb2015
Mar2015
Apr2
015
May2015
Jun20
15Jul201
5Au
g20
15Sep2015
Oct2015
Nov2015
Dec2015
Jan20
16Feb2016
Mar2016
Apr2
016
May2016
Monthly revenue of the U.S. video game industry 2014-2016, by segment
Total and segment revenue of the U.S. video game industry from November 2014 to November 2016 (in billion U.S. dollars)
Source: NPD Group; AFJV; VentureBeat; ID 201073
Note: United States; November 2014 to November 2016 ; console and portable (excluding PC games); physical and full game digital formats from the PSN and Xbox live platforms
0
0.5
1
1.5
2
2.5
3
3.5
Nov '14
Dec '14
Jan '15
Feb '15
Mar '15
Apr '15
May '15
Jun '15
Jul '15
Aug '15
Sept '15
Oct '15
Nov '15
Dec '15
Jan '16
Feb '16
Mar '16
Apr '16
May '16
Jun '16
Jul '16
Aug '16
Sept '16
Oct '16
Nov '16
Rev
enue
in b
illion
U.S
. dol
lars
Total
Further information regarding this statistic can be found on page 8.
11May2017 12RetailFundamentals
M1.4OtherFactorsandRegression4
• Otherknowndecisionaffectingsales.Promotions,discounts,competition,etc
• Themaintoolusedtoanalyzehistoricaldataandgenerateforecastsbasedonthisdataisregression.
• Let’showaregressioncanhelpusunderstandhistoricaldataandhowwecangenerateagoodforecastbasedonthatdata
11May2017 13RetailFundamentals
4
• Let’slookataparticularstoretotalsalesovertime.50
000
1000
0015
0000
2000
00Sa
les
2011w1 2011w26 2012w1 2012w27 2013w1 2013w26 2014w1Year Week
11May2017 14RetailFundamentals
4
• Let’slookataparticularstoretotalsalesovertime.TrendRemoved-5
0000
050
000
1000
0015
0000
Sale
s
2011w1 2011w26 2012w1 2012w27 2013w1 2013w26 2014w1Year Week
11May2017 15RetailFundamentals
M1.2LevelofaggregationandTrends4
• Let’slookataparticularstoretotalsalesovertime.TrendRemovedandpromoremoved
-500
000
5000
010
0000
Sale
s
2011w1 2011w26 2012w1 2012w27 2013w1 2013w26 2014w1Year Week
11May2017 16RetailFundamentals
M1.2LevelofaggregationandTrends4
• Let’slookataparticularstoretotalsalesovertime.TrendRemovedandmonthremovedandpromoremoved
-400
00-2
0000
020
000
4000
0Sa
les
2011w1 2011w26 2012w1 2012w27 2013w1 2013w26 2014w1Year Week
11May2017 17RetailFundamentals
M1.5Closing5
• Nowwearereadytogenerateaforecastforthisstore.• Whatinformationweneed?Wearegoingtocombinethesecomponentstoobtainourforecast.
• Trend• Seasonality(Holidays)• Promotions
• Thiscanbedoneattheatdifferentaggregationlevels• intermsofchain,store,categoryorproduct• Annual,quarterly,monthly,weeklyordailydata
• Workwiththetoolstounderstandthedifferentfactorsthatcanaffectyourforecastandhowtoimplementit.
11May2017 18RetailFundamentals