Analysis of Crowdsourcing Logistics in B2C e-commerce: Costs and Environmental perspective
Abstract
Currently, the logistics in e-commerce is one of the big problems, especially in the deliveries between the company and the consumer, as result it generates additional costs that make the online service is not all efficient that it could be. Therefore, the present work focuses on the study of a new practice in the B2C e-commerce logistics, Logistic Crowdsourcing, as well as the creation of a model of costs and emissions about this innovative practice. To carry out the study about this practice has been made a literature review and the study of some projects of companies that nowadays are offering this service. Later, also it is created a model to calculate the costs and the emissions of the Crowdsourcing Logistics. Regarding the results, in the analysed articles there are nothing about Crowdsourcing Logistics, that is, there is a gap, even if there are real projects around this practice currently being carried out. This way, it has been possible to define this concept, how this practice is made and the most important players. About the creation of the model and the related simulations, it can say that the results have allow to extract some interesting conclusions, which allow to understand still better this innovative practice. So, this paper aims to offer an alternative to current problems in the B2C e-commerce logistics with the Crowdsourcing Logistics and study the costs and the emissions of some hypothetic scenarios with the model created for this practice. And this way understand better the concept and really if it is a good and optimal solution, alternative or not.
Full text
POLITECNICO DI MILANO
School o Indus ial and In o ma ion Enginee ing
Mas e o Science P og amme in Managemen Enginee ing
ANALYSIS OF CROWDSOURCING LOGISTICS IN B2C E-COMMERCE:
COSTS AND ENVIRONMENTAL PERSPECTIVE
Supe iso : P o . Ricca do Mangia acina
Co-Supe iso : P o . Angela Tumino
Au ho : Da id Ba dasco San José
ID numbe : 10552166
Academic yea : 2015-2016
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C owdsou cing Logis ics in B2C e-Comme ce
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ABSTRACT
Cu en ly, he logis ics in e-comme ce is one o he big p oblems, especially in
he deli e ies be ween he company and he consume , as esul i gene a es
addi ional cos s ha make he online se ice is no all e icien ha i could be.
The e o e, he p esen wo k ocuses on he s udy o a new p ac ice in he B2C
e-comme ce logis ics, Logis ic C owdsou cing, as well as he c ea ion o a
model o cos s and emissions abou his inno a i e p ac ice.
To ca y ou he s udy abou his p ac ice has been made a li e a u e e iew and
he s udy o some p ojec s o companies ha nowadays a e o e ing his se ice.
La e , also i is c ea ed a model o calcula e he cos s and he emissions o he
C owdsou cing Logis ics.
Rega ding he esul s, in he analysed a icles he e a e no hing abou
C owdsou cing Logis ics, ha is, he e is a gap, e en i he e a e eal p ojec s
a ound his p ac ice cu en ly being ca ied ou . This way, i has been possible o
de ine his concep , how his p ac ice is made and he mos impo an playe s.
Abou he c ea ion o he model and he ela ed simula ions, i can say ha he
esul s ha e allow o ex ac some in e es ing conclusions, which allow o
unde s and s ill be e his inno a i e p ac ice.
So, his pape aims o o e an al e na i e o cu en p oblems in he B2C e-
comme ce logis ics wi h he C owdsou cing Logis ics and s udy he cos s and
he emissions o some hypo he ic scena ios wi h he model c ea ed o his
p ac ice. And his way unde s and be e he concep and eally i i is a good
and op imal solu ion, al e na i e o no .
Key Wo ds: C owdsou cing Logis ics B2C C owdsou cing Logis ics, B2C
Logis ics, C owdsou cing Las Mile, B2C C owdsou cing.
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CONTENTS
ABSTRACT _______________________________________________________ 3
CONTENTS _______________________________________________________ 4
LIST OF FIGURES __________________________________________________ 6
LIST OF TABLES __________________________________________________ 7
INTRODUCTION ___________________________________________________ 8
JUSTIFICATION ...................................................................................................... 9
OBJECTIVE AND SCOPE..................................................................................... 10
1. LITERATURE REVIEW ___________________________________________ 12
1.1. INTRODUCTION AND SCOPE OF THE STUDY ........................................ 12
1.2. METHODOLOGY ......................................................................................... 14
1.3. SUMMARY OF REVIEW AND DISCUSSION ............................................. 16
1.3.1. Main cha ac e is ics o he pape s examined ........................................... 17
1.3.2. Resea ch me hods used .......................................................................... 20
1.3.3. Themes a ising om he e iew ............................................................... 20
1.4. CONCLUSIONS ........................................................................................... 45
2. PROJECTS ___________________________________________________ 47
2.1. IDENTIFY OF THE MAIN PROJECTS ........................................................ 47
2.2. CLASSIFITACION AND SUMMARY OF THE PROJECTS ......................... 48
2.2.1. Basic ........................................................................................................ 49
2.2.2. C owd ....................................................................................................... 52
2.2.3. Deli e ies.................................................................................................. 57
2.2.4. O he Aspec s .......................................................................................... 60
2.3. CONCLUSIONS ........................................................................................... 61
3. MODEL ______________________________________________________ 63
3.1. Objec i e ....................................................................................................... 63
3.2. Con ex ......................................................................................................... 64
3.3. Scope ........................................................................................................... 66
3.4. Me hodology ................................................................................................. 67
3.5. Cos Model ................................................................................................... 68
3.6. En i onmen al Model .................................................................................... 73
4. ANALYSIS OF THE RESULTS ____________________________________ 77
C owdsou cing Logis ics in B2C e-Comme ce
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4.1. P ocedu e ..................................................................................................... 77
4.2. Cos s: Summa y and conclusions ................................................................ 83
4.3. Emissions: Summa y and conclusions ......................................................... 86
5. CONCLUSIONS _______________________________________________ 90
6. BIBLIOGRAPHY _______________________________________________ 92
6.1. REFERENCES ............................................................................................. 92
6.2. WEBS ........................................................................................................... 97
ANNEXES ______________________________________________________ 101
1. Resul s o he simula ion .................................................................................. 101
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LIST OF FIGURES
Page
Figu e 1: Phases o he Me hodology……………………………………………...15
Figu e 2: Diag am o Al e na i es………………………………………..………..36
Figu e 3: How C owdsou cing Logis ics wo ks…………………………………...62
Figu e 4: Main window o he c ea ed Model (Excel) …………………….……...78
Figu e 5: Da um conside ed………………………………………………………..79
Figu e 6: Basic Scena io………………………….………………………………...81
Figu e 7: Cases……………………………………………………………………...82
Figu e 8: Cos s Case 1……………………………………………………………...85
Figu e 9: Cos s Case 25………………………………………………….………...85
Figu e 10: Emissions Case 1…………………………………….………………...88
Figu e 11: Emissions Case 25…………………………………………...………...88
C owdsou cing Logis ics in B2C e-Comme ce
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LIST OF TABLES
Page
Table 1: Basic Classi ica ion………………………………….……………………18
Table 2: A ea Classi ica ion………………………,,……………………………….21
Table 3: Ad an ages and Disad an ages o T adi ional Shopping Model……..23
Table 4: Ad an ages and Disad an ages o Online Shopping Model………….27
Table 5: A ea Classi ica ion (II) ……………………………………………………31
Table 6: Ad an ages and Disad an ages o Al e na i es………………………..44
Table 5: P ojec s…………………………………………………………………….48
Table 8: Basic Classi ica ion o he P ojec s………………………………………51
Table 9: C owd Classi ica ion o he P ojec s……………………………………..54
Table 10: Deli e ies Classi ica ion o he P ojec s………………………………..58
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INTRODUCTION
Cu en ly logis ics in e-comme ce, especially in B2C (Business o Consume )
is one o he main p oblems, i no he main, because his in ol ed o he
companies high cos s and in many cases consume s’ dissa is ac ion. I is
mainly o his eason ha nowadays i is ying o ind, and implemen new
logis ics p ac ices in o de o ind he igh solu ion ha achie es he
sa is ac ion by such e aile s as pa o end cus ome s.
In his p ojec has chosen o ocus and s udy one o hese new logis ic
p ac ices ha a e es ing and implemen ing nowadays, speci ically
C owdsou cing Logis ics. In such a way, o ca y ou his wo k and aking in o
accoun he scope o his Mas e in Managemen Enginee ing, i has decided
o make a e iew li e a u e in o de o see i he e is li e a u e abou his
inno a i e logis ic p ac ice. And hen, o analyse i cu en ly he e a e eal
p ojec s ha a e using his p ac ice, i means companies ha p o ide his
logis ic se ice. I ’s a gi en ha his p ac ice could be he u u e in he logis ics
o e-comme ce. So, i has decided o make a cos and en i onmen al model
wi h he objec i e o s udy be e his new p ac ice. And ob iously, a e wa ds
makes simula ions wi h his and ex ac s conclusions.
In conclusion, i can say ha he main pu pose o his p ojec is o s udy he
p ac ice o C owdsou cing Logis ics h ough he analysis o li e a u e and he
s udy o eal cases, ha is companies ha cu en ly a e ope a ing his logis ic
se ice. On he o he hand, make a cos s and en i onmen al model in o de o
analyse hese wo aspec s abou he p ac ice. Following o analyse he model
c ea ed, ha e been s udied di e en scena ios wi h he inali y o ex ac
conclusions abou his inno a i e se ice. And hen o his, i has seen and
decided i he model is cohe en o no .
C owdsou cing Logis ics in B2C e-Comme ce
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JUSTIFICATION
This p ojec has been made in o de o ca y ou he Mas e ’s Final P ojec
and o ob ain he Mas e in Managemen Enginee ing.
The eason has aken o make he p ojec abou his opic, mainly a ises om
ou easons, which a e:
Fi s , my a ini y a ound he logis ics and di e en aspec s ela ed wi h
his. Also and in ela ion wi h his ha in he u u e I would like o wo k
in a posi ion ela ed wi h his a ea.
The second is he idea o he p ojec o e ed o he p o esso s, which I
conside ha is e y in e es ing and clea ly could be he u u e in he
B2C e-comme ce logis ics. So, his has been a g ea mo i a ion o
ca y ou his p ojec .
The hi d eason is because I am a egula use o e-comme ce. I
means ha he p oblems ha cu en ly he e a e in his business
model, I ha e been able o li e and eel hese in i s pe son, as well as
he consequences o hese.
Finally, I belie e ha he sec o o e-comme ce is a e y powe ul and
has a g ea p esen and u u e. So, o ind a solu ion o one o i s main
p oblems could be i al o he de elopmen and u u e as much he
B2C e-comme ce as he socie y. Mo eo e , he ci ed p ac ice hasn’
s ill been made known in lo s o coun ies and nei he has aken
ad an age o his. So, i means ha hese ac s gi e mo e possibili ies
o o inc ease he use o his p ac ice.
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· Delimi ing he ield: The pape s and he in o ma ion a e limi ed o he
ela ed ela ion wi h he s ipula ed ield ha is especially wi h he
C owdsou cing Logisi ics p ac ise. And he ela ion wi h he li e a u e can
be wi h he logis ic o wi h some o he key wo ds ha hey ha e been
conside ed.
- PHASE 2: Analysis o he selec ed li e a u e
Following in line wi h Ricca do Mangia acina e al. (2015), and keeping he
objec i e o his li e a u e e iew in mind, he selec pape s classi y i on:
1) Thei main cha ac e is ics: This is a basic classi ica ion ( i le o he
jou nal whe e he pape has been published, au ho , yea , i s au ho ’s
coun y) and he iden i ica ion o he esea ch me hod adop ed.
2) The main a ea abou which is abou he pape .
3) Di e en c i e ions abou which he pape s alk in ela ion wi h he main
opic.
- PHASE 3: Resea ch gaps and po en ial a eas
Once ime ha ha e been done he p e ious phases, i could al eady iden i y
gaps whe e li e a u e doesn’ alk and could be u u e a eas o esea ch,
s udy. The e o e , in his sec io will de e mine hose a eas whe e i would be
easible o would be mo e likely o ca y ou an in es iga ion in line wi h he
ocused objec i es and clea ly a ound he de e mined ield.
1.3. SUMMARY OF REVIEW AND DISCUSSION
In line wi h Na a aja a hinam e al. (2009), i has done he Table I wi h he
objec i e o summa ize he basic in o ma ion o e e y pape and he esea ch
me hod adop ed. And aligned wi h Pe ego e al. (2011), he pape s a e
ch onological o de ed wi h he inali y o show he e olu ion o las mile logis ic
issues ela ed o B2C e-comme ce o e ime.
C owdsou cing Logis ics in B2C e-Comme ce
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1.3.1. Main cha ac e is ics o he pape s examined
Nex i can obse e he main cha ac e is ics o he pape s examined:
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Table 6: Basic Classi ica ion
C owdsou cing Logis ics in B2C e-Comme ce
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The o al numbe o analysed pape s has been o 15, which we e published in
11 di e en in e na ional scien i ic jou nals, wi h a mean alue o 1,4
con ibu ions pe jou nal. The pape s we e published in di e en ypes o
jou nals. Speci ically, he 73% in logis ics and anspo a ion jou nals and he
o he in in o ma ion and communica ion echnologies jou nals.
Abou he egions add essed, he numbe o con ibu ions in which he i s
au ho is om Finland is 3 (20%), he same ha om Uni ed Kingdom ha is 3
oo. Then, Uni ed S a es o Ame ica ollows hem wi h 2 publica ions (13%). The
es o he pape s (47%) we e w i en by esea che s om o he coun ies, which
a e: Hong Kong, The Ne he lands, Jo dan, Belgium, Pakis an, Aus ia and
Ge many (all hese wi h only 1 con ibu ion by au ho o e e y coun y). So,
alking abou he i s au ho ’s coun y i can say ha Eu ope is he con inen was
w i en mo e publica ions (10 pape s), ollowed by Asia (3 pape s) and Ame ica
(2 pape s). Also, i has been obse ed abou he au ho s ha Punaki i, M. is he
mo e p esen au ho s in he publica ions, who is p esen in 3 o he analysed
pape s and ied o o e a new al e na i e o Las Mile Logis ic.
An in ega ds o he yea o publica ion, i can be iden i ied wo clea pe iods.
F om 2001 un il 2007, we en’ published lo s o con ibu ions abou he main
opic, speci ically a 40%. The inc ease o publica ions occu ed om 2008 un il
now (2016), whe e he e a e he es o pape s (60%). I should be no ed ha in
he yea 2014 we e whe e mo e publica ions did, ollowed by he yea 2013,
2008 and 2002 wi h 2 pape s e e y yea . The es o yea s only ca ied ou one
con ibu e pe yea . Too i is impo an o say ha he ime equi ed conduc ing a
esea ch s udy, and o a pape o be w i en, e iewed, and accep ed gene a es
ha he yea o publica ion o he pape s doesn’ ma ch exac ly wi h he si ua ion
o he momen . Bu i can obse e ha in he las yea s he e is an inc ease o
he publica ions, as a esul o he impo an g ow h o he e-comme ce. So, he
inc ease is ascending, because e e y ime his ype o business is mo e used by
he socie y and ob iously i causes news p oblems and solu ions, al e na i es
ha a e e lec ed in all his pape s.
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1.3.2. Resea ch me hods used
The publica ions a e classi ied and e alua ed based on hei esea ch
me hodology. And he main ca ego ies used a e based on a s udy by Meixell
and No bis (2008), who iden i ied se en esea ch me hods, which a e: su ey,
simula ion, in e iews, ma hema ical models, case s udies, concep ual models
and o he s.
The 40% (6 pape s) o he pape s e iewed p esen ma h models; ollow by
su eys (20%), simula ions (13%) and concep ual models (13%) and he es ,
in e iews and o he s, wi h one pape e e yone.
1.3.3. Themes a ising om he e iew
Nex i can obse e he classi ica ion abou he main a eas o he pape s
examined:
C owdsou cing Logis ics in B2C e-Comme ce
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Table 7: A ea Classi ica ion
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1.3.3.1. T adi ional Shopping Model VS Online Shopping Model
The e a e some analysed pape s (3 pape s) ha alk abou he di e ence
be ween he T adi ional Shopping Model and he Online Shopping Model.
Speci ically, in ela ion wi h he main opic o his p ojec which is he logis ics.
Such as say B own and Gui ida (2014), he impo ance o deli e y and he
suppo ing logis ical p ocess is well ecognised in he ope a ions and supply
chain li e a u e.
Nowadays e e y one o hese models has a di e en g ow h and cos s and
en i onmen impac in he socie y. I ’s ob ious ha e e y o his ype o model
has his ad an ages and disad an ages oo. Following i will analyse wha says
he li e a u e abou hese models.
· T adi ional Shopping Model:
The pape s ha iden i y and alk abou he T adi ional Shopping Model a e
h ee. And i should be no ed ha wo o hese a e connec ed wi h he
en i onmen opic (n.9 and n.13).
The T adi ional Shopping Model is an op ion o dis ibu ing he goods and i is
he adi ional sys em wi h supe ma ke s and e ail shops (Aized and S ai,
2015). When he cus ome s buy wi h his model, hey hemsel es pick up he
pu chased i em om he e aile and sel -deli e s he i em o he home using
hei own ehicle (B own and Gui ida, 2014). So, as ell Edwa ds e al. (2010),
he cus ome does mos o he labou in ensi e wo k, because she/he has o
o de picking and anspo he goods a home. And i is ob ious ha in his
case, he pu chase implies o go o he physical loca ion.
Abou he adi ional supply chain, he goods a e deli e ed o s o e and has
been p e iously ci ed, he cus ome picks he i ems be o e aking hem a home
(Edwa ds e al., 2010).
C owdsou cing Logis ics in B2C e-Comme ce
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The e o e, hen o see some cha ac e is ics o he T adi ional Shop Model, i
could say ha he main ad an ages and disad an ages o his a e:
TRADITIONAL SHOPPING MODEL
ADVANTAGES
DISADVANTAGES
· The cus ome s can see wha hey
a e buying and i is necessa y hey
can p o e i .
· Is be e o he company, because
his o m, i hasn’ deli e y cos s.
· The cus ome s can ecei e help
om he shop assis an .
· Is uncom o able o he cus ome s,
because hey need o go o he
physical shop.
· Go o he shop has a cos ( ime and
anspo ).
· Is bad o he en i onmen , because
e e y cus ome goes o he shop and
his gene a es lo s o emissions.
Table 8: Ad an ages and Disad an ages o T adi ional Shopping Model
· Online Shopping Model:
All he analysed pape s alk abou he Online Shopping Model and e e y o
hese con ibu e di e en in e es ing in o ma ion o he opic o his p ojec .
The Online Shopping Model is ano he op ion o dis ibu ing he goods ha is a
sys em wi h di ec o consume deli e ies (Aized and S ai, 2015). Speci ically,
as say B own and Gui ida (2014), in e-comme ce, he i em is deli e ed o he
cus ome by he e ail selle o by an agen con ac ed by he selle o p o ide a
home deli e y se ice. So, he cus ome s can buy wi hou isi ing he physical
loca ion. O in line wi h Edwa ds e al. (2010), he as majo i y o online
pu chases esul in he physical mo emen o a small package (o single i em) o
an indi idual add ess ( ypically a consume ’s home) by pa cel ca ie . In
gene al, hese deli e ies a e dis ibu ed om local pa cel ca ie depo s and
consis o mixed loads in he back o ans. So, in his model he e aile s mus
deli e pe sonalised o de s o highly dispe sed loca ions wi hin ela i ely na ow
ime window.
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In ela ion wi h he en i onmen , he Online Shopping Model has yields
en i onmen al bene i s, because basically i educes pe sonal a el demand
(Edwa ds e al., 2010).
Xu e al. (2008) say ha he abili y o ul il and deli e o de s on ime could
de e mine an e- aile ’s success. Speci ically, one o he no able bene i s o
online shopping is he con enience and ime sa ing when compa ed o
adi ional shopping. I includes o he ac o s such as o ease online paymen ,
home deli e y and e u n p ocedu es ha all combine o make In e ne shopping
mo e con enien han adi ional.
The alue o he home deli e y ma ke has inc eased apidly du ing he las ew
yea s. And he mos common home deli e y model is when he cus ome can
selec a ime window when he i ems a e deli e ed (Kämä äinen and Punaki i,
2004).
· Supply chain:
In line wi h Kull e al. (2007), he cu en g ow h and popula i y o e-comme ce
has a ec ed many consume s’ e e yday li es by p o iding a wide ange o
choices, mo e a ailable in o ma ion and ease o pu chasing. And i should be
no ed ha as companies ex end supply chains ia di ec deli e y o consume s,
supply chain e iciency depends upon he usabili y o he online o de ing sys em.
Abou he supply chain in he Online Shopping Model, Vanelslande e al. (2013)
iden i ies which ypes o supply chains a e he mos commonly be ound in he
online e ailing o g oce y i ems in Wes Eu ope. And based on his in o ma ion,
he e a e h ee ypes o commonly employed supply chains ha a e iden i ied,
which a e:
- Pu e playe : The companies, which use his model, only dedica e o sell
hei p oduc s by e-comme ce. I means hey don’ ha e any physical
shop whe e he cus ome s can go o buy hei p oduc s, because all he
ac ions a e by In e ne . Inside his model, he main deli e y models a e:
C owdsou cing Logis ics in B2C e-Comme ce
25
o Van deli e y:
This ype o supply chain is used by pu e playe e aile s ha ca y
ou he las mile deli e y wi h a dedica ed lee o ehicles. The
picking ope a ions a e pe o med in one o mo e dedica ed
dis ibu ion cen es, as well as in p o iding he dedica ed ehicles
o en places a inancial bu den on he company employing his ype o
supply chain se up. And in his model, he cos o las mile deli e y is
gene ally e y high, because o no ha ing su icien cus ome
densi y, as well as ha ing o deal wi h ime-slo ed deli e ies ha a e
e y common wi h his ype o supply chain o p o ide a high
cus ome se ice.
o Pa cel deli e y:
This model employs a pa cel ca ie ’s (e.g. UPS, DHL, e c.) om
exis ing dis ibu ion ne wo k o deli e he goods o he shoppe . This
enables he e aile o ake ad an age o a dis ibu ion ne wo k ha
con e se a la ge a ea and al eady handles la ge olumes, hus
leading o lowe las mile deli e y cos s. Because his ype o deli e y
o he shoppe is easily accessible and cos -e icien , many pu e play
e-comme ce ac o s choose a pa cel ca ie o b idge he las mile. Bu
he pe son making he deli e y is gene ally no able o p o ide he
shoppe wi h ex a in o ma ion o alue added se ices. So, his leads
o a educed deli e y se ice owa ds he cus ome . And he picking
is one o mo e dedica ed dis ibu ion cen es.
- Click and mo a : The companies, which use his model, dedica e o sell
hei p oduc s by e-comme ce and by physical shop. Inside his model,
he main deli e y model is:
o Van deli e y:
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1.3.3.3. P oblems in Las Mile Logis ic
In line wi h B own and Gui ida (2014), he las mile p oblem comp ises one o
he mos cos ly and highes pollu ing segmen s o he supply chain in which
companies deli e goods o end cos ume s. Bu F aze (2000) al eady iden i ied
ha ime cons ain s, poo quali y o home deli e y se ices, and lack o a ie y
o deli e y op ions o be he in luen ial ac o s ha make home deli e y he
weakes link in he In e ne chain.
Cha an an (2001) epo s ha non-sa is ac o y deli e y schedule opped he lis
(34%) o dissa is ac ion wi h e-comme ce. I means he impo ance o deli e y
o online shopping o he cus ome s. Speci ically, he 40% o hese p e e
easie deli e y o collec ion in addi ion o cheape o e s and lowe deli e y
cha ges o e ed by e- aile s. And a su ey o 100 companies conduc ed by
Consignia (2001) e elled ha 58% o he esponden s anked deli e y a he
ime and place ha is con enien o he consume as he second mos impo an
ac o in luencing he ma ke .
Aized and S ai (2014) say ha he las mile is conside ed one o he mos
expensi e pa s o he supply chain and accoun s o 13% up o 75% o he o al
supply chain cos s. Speci ically, nex o he picking and packing ope a ions,
home deli e y is he majo cos d i e in online g oce y shopping (Mikko
Punaki i and Ka i Tanskanen, 2002). To con i m his a i ma ion, an empi ical
s udy (Ring and Tige , 2001) examining deli e y models adop ed by g oce y
e aile s in he USA, UK and Eu ope ound ha he wo kille cos s acing pu e
In e ne g oce s a e he picking cos s and he deli e y cos s.
All hese high cos s p o ide an oppo uni y o companies o achie e subs an ial
e iciencies h ough op imal planning and p ope execu ion o a deli e y plan
which may in ol e analyses o edesign he o e all dis ibu ion ne wo k,
es ablishing mo e e icien ou ings, changing deli e y zonings, o upg ading o a
mo e uel-e icien anspo a ion lee (B own and Gui ida, 2014).
C owdsou cing Logis ics in B2C e-Comme ce
33
Managing his po ion o he supply chain (las mile) has been a pa icula
p oblem om logis ics in as uc u e s andpoin , mos no ably because o ade-
o s be ween ou ing e iciency and cus ome con enience. And o selec he
me hod which consume s place o de s can ha e a signi ican impac on
ansac ion cos s and cus ome s se ice (Kull e al., 2007). So, one o he mos
di icul p oblems o logis ics managemen is such a ul ilmen p ocess is o
sol e how o plan and implemen a cos e ec i e deli e y ope a ion, in a
dynamic en i onmen , whe e commi men s o cus ome s mus be gi en while
o de s a e being ecei ed (Sla e , 2002). In p ac ice howe e , o many B2C
companies, as sugges ed by New on (2001), he cos sa ings p omised by e-
comme ce a e ea en up by high deli e y expenses.
· P oblems:
Some o he analysed pape s, speci ically 33% (5 pape s), alk abou he main
p oblems in he Las Mile Logis ic and iden i y hem. Basically, hese
publica ions de ine wo ypes o p oblems in he deli e y a home, which a e:
- High deg ee o ailed deli e ies (Wel e eden (2008); Xu e al. (2008);
Edwa ds e al. (2010); Aized and S ai (2014)):
The incidence o i s ime deli e y is one o he mos common p oblems
in he Las Mile Logis ic and i is ob ious ha i causes impo an ex a
cos s o he companies.
Such as say Xu e al. (2008), he mos adi ional deli e y op ion used by
many e aile s o home shopping is using cou ie and pos al se ices.
An he deli e y ime a ied including same-day, nex day and mul i day
deli e y. I should be no ed o ha many pa cels do no i h ough mail o
le e boxes o equi e consignee signa u e, which implies ha cus ome s
need o be a home when he pa cel is deli e ed (Wel e eden, 2008).
The p oblem is ha he mos common o people is no o be a home
du ing he wo king day. So, hey a e no a home when he mos home
34
Da id Ba dasco San José
deli e ies a e made (Edwa ds e al. 2010). Talking abou some da es, he
wo king households inc eased by 22% be ween 1992 and 2006. I
means, he incidence o ailed deli e ies has inc eased and nowadays
ollows he same di ec ion, because e e y ime people use mo e e-
comme ce and i inc eases home deli e ies. A su ey shows ha 34% o
he esponden s indica ed ha he bes deli e y ime slo s om hem
would be be ween 6 pm and 8 pm (Xu e al., 2008). Bu his isn’ a good
op ion, because i c ea es la ge demands o deli e y o a sho busy
pe iod, which esul s in he deli e y lee uns a low capaci y o 80% o
he day, hen a all capaci y o he sen .
And his p oblem is called “No a home” p oblem, because he people is
no a home a home ime deli e y (Xu e al., 2008). And i is conside ed
one o he mos c i ical ac o s o he success o he home deli e y
ope a ions, because i causes highe ope a ing cos s o e aile s and
ca ie s, as esul ha need o be edeli e ed o e u ned he packages o
he sende and hen epea ano he ime all he deli e y p ocess. And
also, incon eniences o cus ome s ha lead o lowe sa is ac ion,
because hey don’ ha e hei pu chase when hey wan . So, he b oken
p omises and unme expec a ions o las mile e-comme ce le bo h
consume s and in es o s dissa is ied. Fo hese easons, i is wo king o
ind al e na i es solu ions o y o esol e his p oblem.
- The e u n o una ended goods (Edwa ds e al. (2010); Aized and S ai
(2014); Hbne e al. (2016)):
The incidence o e u n o una ended goods is o he o he mos
common p oblems in he Las Mile Logis ic and i is ob ious ha i
causes impo an ex a cos s o he companies.
Cus ome s e u n i ems o a numbe o di e en easons. Typically
be ween 25 and 30% o all non- ood goods bough online a e e u ned
compa ed wi h jus 6-10% o goods pu chased by adi ional shopping
me hods (Edwa ds e al., 2010).
C owdsou cing Logis ics in B2C e-Comme ce
35
Acco ding o Hbne e al. (2016), one o he d awbacks o online
shopping is he cus ome ’s inabili y o see and eel he p oduc be o e
pu chasing i . Especially in online g oce y his becomes a common ac o
as consume s ha e gene al ese a ions abou he e aile selec ing and
ouching hei ood and consume abou he quali y.
Abou how esol e his p oblem, i depends o he ype o company. I
means, in g oce y s o es he e aile s can o e o cus ome s a money-
back gua an ee, check and e u n a ecep ion, e u n by CEP deli e y, o
accep ance and e unding (Hbne e al. (2016)). And in o he indus ies,
he ypical e u n channels a e: e u n i ems o a physical s o e o send
i ems back h ough he s anda d pos al se ice. And be ween hese
channels, app oxima ely hal o e u ns a e ia ca ie collec ion and hal
by pos (Edwa ds e al, 2010). I should be no ed o ha some deli e y
companies o e an o he channel, and i consis s o send ans on
sepa a e pick-up uns dedica ed solely o collec ing e u ned i ems.
I is impo an o say ha he e a e some companies ha allow choose
he cus ome s, which will be he e u n channel, ha use. Bu i only
makes mo e complex he si ua ion, because he e aile s ha e o
manage mo e se ices. And i is ob ious ha all hese gene a e
impo an ex a cos s o he e aile s. Fo hese easons, i is wo king o
ind al e na i es solu ions o y o esol e his p oblem.
I should be no ed ha apa o hese ha a e he main p oblems, Aized and S ai
(2014) alk abou wo mo e, which a e: High deg ee o emp y unning o ehicles
and low olume o deli e y goods.
1.3.3.4. Al e na i es Models o Las Mile Logis ic
Then o analysed all he publica ions, some al e na i es models o Las Mile
Logis ic ha e been iden i ied wi h he objec i e o sol e he di e en p oblems in
36
Da id Ba dasco San José
Las Mile and o y o o e mo e possibili ies o he inal cus ome . Basically,
wo al e na i es a e:
· Home deli e y:
This concep is ocused in ha goods a e deli e ed o he s o e and cus ome s
pe o m he picking and inal deli e y o hei home hemsel es. So, home
deli e y concep p o ides addi ional cus ome sa is ac ion.
The di ec concep o e s consume s wo models: an a ended model o
ecep ion and una ended model o ecep ion.
- A ended Deli e y (Wel e eden, 2008; Al-Nawayseh e al., 2013;
Hbne e al., 2016):
The 20% (3 pape s) o analysed pape s alk abou he a ended
deli e y.
Figu e 2: Diag am o Al e na i es
C owdsou cing Logis ics in B2C e-Comme ce
37
Acco ding o Al-Nawayseh e al. (2013), he a ended deli e y is he
a ended home ecep ion whe e cus ome s usually choose he
deli e y place and ime window o ecei e hei deli e y wi hin i . So,
his model implies ha he cus ome has o be a he poin o
ecep ion wi hin he ime window ha she/he has selec ed o accep
he deli e y and i implies ha she/he is wai ing o he /his deli e y.
On he o he hand, one o he e aile s’ objec i es is maximizing
ehicle u iliza ion and minimizes anspo a ion cos s o ge a ce ain
le el o cus ome s’ se ice and he sa is ac ion o hese. And i
equi es dynamically assigning deli e y ime slo s as new o de s
a i e and he dynamically c ea ing and adjus ing deli e y ou es
(Hbne e al., 2016). I means he ehicle ou ing becomes mo e
complex, because i has o due o cus ome s’ ime es ic ions and i
is ob ious ha i gene a es cos s.
Analysing he p oblems abou his deli e y model, basically he e a e
wo big p oblems, which a e:
o The high demand on ce ain windows migh complica e he
se ice, because is possible ha he p o ide canno make all
he deli e ies a he same ime, so i gene a es capaci y
p oblems.
o The p oblem o ailed deli e y: I has been old in he p e ious
pa . I means ha he cus ome is no a ailable o o de he
ecep ion when she/he had said, and as esul he uck
e u ns wi hou ul ilmen and all he p oblems ha i implies.
In spi e o his complexi y ha his model gene a es o all
pa icipan s, he a ended deli e y model is used o home deli e y o
g oce y goods ac oss Eu ope ega dless o ma ke p oli e a ion
38
Da id Ba dasco San José
(Hbne e al., 2016). Speci ically, in mos coun ies, his model
accoun s o he la ges sha e o las mile deli e ies.
One al e na i e p ac ice o a ended deli e y is he Se ice Pos s,
which ollowing is old:
Only one o he analysed pape s (Wel e eden, 2008) alks abou
his al e na i e p ac ice o las mile deli e y.
This al e na i e consis s in ha he pa cels a e deli e ed o a
s o e, pe ol s a ion o pos o ice whe e cus ome s can pay,
collec and e u n hei pa cel. Speci ically, Wel e eden (2008)
de ines i as a shop in shop concep . I should be no ed ha a a
se ice poin , he pe sons who manage he collec ion p ocedu e
a e he shop assis an s.
This p ac ice o e s some oppo uni ies, which can be posi i e o
all he pa s. The Se ice Poin s a e o en loca ed in a s o e and i
means ha is combined he collec ion o he pa cels wi h o he
shopping ac i i ies. So, i can o e e aile s p ospec s o a e enue
inc ease. And i is eally good oo o he deli e y companies,
because his way hey can combine he deli e y o pa cels wi h
he egula supply o he shops.
- Una ended Deli e y (Kämä äinen and Punaki i, 2004; Xu e al.,
2008; Hbne e al., 2016):
The 20% o analysed pape s alk abou he una ended deli e y.
In line wi h Xu e al. (2008), he o iginal concep o una ended
deli e y is simply lea ing an i em on someone’s doo s ep, o in he
ga den shed. So, in his model e aile s can deli e online pu chase o
whe he he cus ome is a home o no , because he shopping baske
C owdsou cing Logis ics in B2C e-Comme ce
39
is place in on o he cus ome ’s home o be collec ed when she/he
a i es.
Kämä äinen and Punaki i (2004) wi h hei simula ions sugges ha
his deli e y model educe home deli e y cos s conside ably,
speci ically by up o 60%. Bu he p oblem is ha his hasn’ been
widely used, because i equi es in es men s and commi men om
he cus ome . Fo he o he hand, Hbne e al. (2016) say ha he
cos o deli e y can be educed by up o 40% compa ed o a end
home deli e y wi h a ecep ion box o una ended home deli e y.
The main p oblem o his is ha i b ings many secu i y conce ns
pa icula ly when i ems a e pe ishable o ha e high alue. Bu i is a
eally good op ion o sol e he p oblems o home deli e ies ail,
because in 50-60% o households no on is a home du ing he no mal
wo kday an a e age o 12% o home deli e ies ail (Hbne e al.,
2016).
This deli e y model has di e en ad an ages. Talking abou he
logis ics, his elimina es igh ime slo s and capaci y p oblems
esul ing om une en demand du ing wo king hou s. I means ha
demand peaks a e e ened ou . And una ended ecep ion sho ens
he wo king hou s o he dis ibu o . So, i elimina es he edeli e y
cos when he cus ome s a e no a home a hei selec ed deli e y
ime slo . I is impo an o say o ha no mally e aile s ha ollow his
model will cha ge addi ional ees i he cus ome is no able o ecei e
hei deli e y in he ag eed ime slo .
The mos common al e na i e solu ions o una ended ecep ion a e:
Deli e y Boxes:
This p ac ice is ocused on he use o insula ed box con aining he
goods is deli e ed o he cus ome and a ached secu ely in a
40
Da id Ba dasco San José
locking de ice bol ed on he building wall. Then he emp y boxes
a e collec ed on he day ollowing deli e y o la e . So, basically
he deli e y boxes a e insula ed boxes wi h a docking mechanism
ha a e e u ned o he e aile . And he e a e 2 ways o pick up
emp y box:
Pick up o he deli e y boxes is done a he sec deli e y.
Pick up o he boxes is done sepa a ely on he day a e
deli e y.
Rega ding o Punaki i e al. (2001), his solu ion enables a as e
g ow h a and highe lexibili y o he in es men s, because o a
smalle in es men equi ed pe cus ome . The d awback is he
addi ional cos o collec ing he emp y boxes.
Recep ion boxes (Locke Poin ):
This solu ion consis s in a collec ion o locke s. Speci ically, he
pa cels a e deli e ed o he locke s poin whe e cus ome s can
pay, collec and i necessa y, e u n hei pa cel. I should be no ed
ha he locke poin s employ baggage locke s echnology and use
PIN codes o con ol he deli e y by he ca ie and he collec ion
o he pa cel by he cus ome (Wel e eden, 2008). And usually
hese boxes a e ins alled in he consume ’s home ya d o ga age.
In line wi h Kämä äinen and Punaki i (2004) and om poin o
iew in B2C en i onmen , he ecep ion boxes ha e some
ad an ages, which a e: ime sa ings, lexibili y, independence o
he supplie ime able, no need o ca y pu chases, dec easing o
impulse buying and sys ema ic pu chasing. Bu he e a e also
some disad an ages, which basically a e: high in es men and
limi ed access o deli e o he boxes in B2C. Speci ically, high
in es men is conside ed he majo obs acle, because he box can
C owdsou cing Logis ics in B2C e-Comme ce
41
cos be ween 1000 and 2000 eu os depending on he ype o box
(Kämä äinen and Punaki i, 2004).
I is impo an say ha he ecep ion box is widely seen as a
po en ial home appliance o he u u e and he e a e al eady many
manu ac u e s in he ma ke . Howe e , u iliza ion a e o he
ecep ion box is usually poo (Kämä äinen and Punaki i, 2004).
Sha ed ecep ion boxes:
This solu ion is ocused in usage o sha ed ecep ion boxes also
known as CDP. Abou he cha ac e is ics o he sha ed ecep ion
box uni es, hese ha e a ious amoun s o sepa a e locke , and
he sepa a e locke s ha e elec onic looks wi h a changing
opening code o enable sha ed usage o he locke s using a
mobile phone (Kämä äinen and Punaki i, 2004). As p e iously i
said, his concep is also known as he au oma ed collec ion and
Deli e y Poin (CDP). And a he CDP he o de ed goods can be
s o ed un il he cus ome is able o collec he deli e y.
In line wi h Mikko Punaki i and Ka i Tanskanen (2002), using his
concep , he u ilisa ion a e o he acili ies would be highe han in
he case o cus ome -speci ic concep s.
The main p oblem is he high in es men in ol ed in una ended
ecep ion acili ies could be sol e by sha ing he esponsibili y. Bu
he e a e o he s ac o s ha a ec home deli e y anspo a ion
cos s in his model, which a e (Mikko Punaki i and Ka i
Tanskanen, 2002):
o The capaci y o he sha ed ecep ion box uni , ha is,
he numbe o sepa a e locke s.
o The numbe o sepa a e ecep ion box uni s.
o The u iliza ion a e o he sha ed ecep ion box uni s.
48
Da id Ba dasco San José
he own web o he p ojec and o he sou ces o in o ma ion o In e ne . All wi h
he objec i e o o check and o claim ha he iden i ied cases know sui ably he
de e mined equi emen s. The p ojec s ha ha e been ound a e de ailed below:
No
TITLE
1
AMAZON FLEX
2
DELIV
3
DOORDASH
4
INSTACART
5
MyWays
6
POSTMATES
7
RICKSHAW
8
SIDECAR DELIVERIES
9
Ube EATS
10
Ube RUSH
11
ZIPMENTS
Table 10: P ojec s
2.2. CLASSIFITACION AND SUMMARY OF THE PROJECTS
One ime ha he p ojec s ha e been iden i ied, i has p oceeded o analyse
hem. In o de o analyse hem o he bes possible way and o s udy hem
sui ably, i has ca ied h ough di e en classi ica ions. This o m, i can see
clea ly di e en key aspec s abou he p ojec s and can d aw con enien
conclusions. The classi ica ions a e:
1) Basic: I is ocused in o de e mine he basic in o ma ion o e e y p ojec
ha mainly i is ocused in wo aspec s: he p ojec and he company ha
ca y h ough he p ojec .
2) O he C owd: I is based on o de ine he main cha ac e is ics o he
C owd ha mus ha e o wo k in he pe inen p ojec .
C owdsou cing Logis ics in B2C e-Comme ce
49
3) Deli e ies: I is ocused in he key aspec s in he deli e ies which
basically a e: he pick up, he deli e y a eas and wha he deli e y is.
Is impo an o say ha he analysis is ca ied ou o a o al o ele en p ojec s.
2.2.1. Basic
Be o e o p ocess o do he basic classi ica ion is e y impo an o know wha
he companies hink abou hei p ojec s abou C owdsou cing Logis ic. So, nex
i can see how he companies de ine hei p ojec s:
- Amazon de ines Amazon Flex, he p ojec abou he las mile deli e y
as collabo a i e economy in he logis ic whe e he independen d i e s
deli e pa cels.
- Deli de ines i s p ojec as a same day deli e y se ice e olu ionizing
how online and in s o e cus ome s ge hei goods.
- Doo Dash de ines i s p ojec as a echnology pla o m ha connec s
local business o people. They aim o make e e y ci y smalle by b inging
he ood o people- as e , eshe , and om a he away.
- Ins aca de ines i s p ojec as a g oce y deli e y se ice ha deli e s in
as li le as an hou .
- Deu sche Pos DHL de ines MyWays as a comple ely new deli e y
se ice o people who need pa cels deli e ed whe e hey wan , when
hey wan .
- Pos ma es de ines i s p ojec as a e olu iona y u ban logis ic and on-
demand deli e y pla o m ha connec s cus ome s wi h local cou ie s.
- Rickshaw de ines i s p ojec as a same day deli e y pla o m ha allows
any business o schedule deli e ies o hei cus ome s wi hou he hassle
o managing a lee o ca s and d i e s.
50
Da id Ba dasco San José
- Sideca Deli e ies de ines i s p ojec as a same day se ice o local
business whe eby goods, ood and lowe s we e o be deli e ed o local
consume s using i s exis ing pool o d i e s.
- Ube de ines Ube Ea s as deli e s he bes meals om a ou i e local
es au an s in 10 minu es o less. I b ings cus ome s a meal on demand
wi h none o he hassle.
- Ube de ines Ube Rush as a connec ion be ween cus ome s and
cou ie s o make a deli e y. Cus ome s can use i o powe as e
deli e ies and e u ns.
- Deli de ines Zipmen s as a communi y base logis ics pla o m
p o iding business and indi iduals wi h he as es , mos a o dable same
day deli e y se ice a ailable.
I is impo an o say ha ac ually he e a e wo p ojec s ha a en’ wo king
which a e MyWays and Sideca deli e ies.
Nex i can obse e he basic classi ica ion o he s udied p ojec s:
C owdsou cing Logis ics in B2C e-Comme ce
51
Table 8: Basic Classi ica ion o he P ojec s
52
Da id Ba dasco San José
Rega ding o he basic da um o he p ojec s, o emphasize ha he o igin o
hese p ojec s has place be ween he yea 2011 and 2015. Being he yea 2014
when ewe p ojec s ca ied ou , speci ically one. Howe e , du ing he yea s
2013 and 2015 we e when mo e p ojec s did (3 e e y yea ).
As ega ds a he companies which come om he p ojec , hey ha e been
analysed by he ype o company ha hey a e in his momen . Speci ically, ou
o hese ha is a 36% a e mul ina ionals companies and he es a e s a -ups.
And in ela ion wi h his aspec , i can obse e ha he p ojec name and he
company name is he same in he case o s a -ups. Too is impo an o say ha
as well as much Deli as Ube has wo p ojec s, bu Deli bough one o hese o
a s a -up, speci ically o Zipmen s which nowadays s ill is called o he same
name al hough i is p ope y o Deli .
Rela ed o he o igin coun y o he companies, en o hese (91%) a e om
Ame ica, speci ically om Uni ed S a es o Ame ica and only one (9%) is
Eu opean, speci ically om Ge many. Being mo e speci ic, inside in Ame ican
companies, he 64% has o igin in San F ancisco, Cali o nia.
On o he hand, i has iden i ied i he ac i i y o he p ojec is a se ice o a
ne wo k. I means ha i has conside ed a ne wo k i he p ojec ake pa o he
logis ic ne wo k in he own company ( he company has he p oduc s, he
wa ehouses, e c.). And i has conside ed a se ice i he p ojec isn’ in ol ed in
a ne wo k, so he ac i i y is only o sa is y a cus ome s’ necessi y and is he
main and unique ac i i y o he company. Then o o analysed his aspec , i can
see ha mo e o less he 80% o he p ojec s a e se ice and he es (20%) a e
ne wo ks ollowing he conside a ion.
2.2.2. C owd
Then o o iden i y, analyse and classi y he C owd abou he di e en selec ed
p ojec s, i can obse e ha he e a e se ies o equi emen s o can ake pa o
his. These equi emen s is ocused mainly in:
- Be a leas he minimum age.
C owdsou cing Logis ics in B2C e-Comme ce
53
- Ha e he ype o equi ed and pe mi ed ehicle o ca y ou he pick
up deli e y and he deli e ies.
- Ha e a phone wi h de e mined cha ac e is ics.
- Ha e a se ies o documen s wi h he eques ed condi ions.
- Be able o ha e he physical capaci y o li packages o a ce ain
quan i y o weigh .
I should be no ed ha o ca y h ough he logis ic job (pick up and deli e he
cus ome s’ pu chase) he membe o he C owd ecei es a ewa d.
Nex i can obse e he c owd classi ica ion o he s udied p ojec s:
54
Da id Ba dasco San José
Table 9: C owd Classi ica ion o he P ojec s
C owdsou cing Logis ics in B2C e-Comme ce
55
Fi s o all o emphasize ha he ange o minimum age o can be membe o he
C owd and o make he eques ed jobs is be ween 18 and 21 yea s old.
Speci ically o he analysed p ojec s, he 45% demand o be a leas 18 yea s
old, he 18% demand o be a leas 19 yea s old and he es demand o be 21
yea s old.
As ega ds how he pickups and he deli e ies a e ca ied ou o he cus ome s
basically i can dis inguish be ween mo o ehicles (ca , an, mo o cycle), bike
o on oo . The 45% o he p ojec s only allow making hei job by mo o
ehicles, whe e he ca is wi h di e ence he mos eques ed. I is impo an o
say ha he 55% o he analysed p ojec s allow he possibili y o no o use a
mo o ehicle, i means ha hei o e he al e na i e o make he pickups and
deli e ies by bike o walking. Speci ically o his 55%, he 50% o he p ojec s
o e he al e na i e o on oo o go by bike and he o he 50% only o e he
al e na i e o go by bike. Anyway he e a e some p ojec s as o example
Amazon Flex ha wan s o o e oppo uni ies o deli e ia bike o on oo in he
u u e.
I should be no ed ha he ehicle mus be p ope y o he membe o he
C owd. The e a e some p ojec s ha eques some ex a equi emen s as o
example, he p ojec o Deli eques o hei membe s o he C owd ha hei
ca s mus be o yea 2000 o newe and wi h ai condi ioning.
To es ablish communica ion be ween he company and he membe o he
C owd ha is o he membe knows whe e mus go o pick up he deli e y and
whe e he mus deli e i , all he p ojec s use he same medium o ecei e he
o de s, he phone. The 91% o he p ojec s allow ha e an ope a i e sys em
And oid o iPhone, anyone o hese is alid. Howe e , he unique p ojec ha
only allows one ope a i e sys em (And oid) is Amazon Flex. Wi h espec o an
o he in e es ing da um abou his aspec , i should be poin ed ou ha some
p ojec s, as o example Ins aca demand o he membe s o he C owd ha
ha e a ecen sma phone, i means as minimum a iPhone 4 o And oid 4.
Wi h he objec i e o gua an ee a igh unning and o o e secu i y o hei
cus ome s, all he analysed p ojec s eques a se ies o documen s o he
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Da id Ba dasco San José
membe s o he C owd which basically hey a e: ha e a alid d i e ’s license, an
insu ance o he ehicle and pass a backg ound check. Some p ojec s eques
mo e equi emen s abou his aspec as o example he p ojec o Doo Dash
which eques s a leas 2 yea s o d i ing expe ience o hei membe s o
Ube Rush and Ube EATS which eques a leas 1 yea .
Some analysed p ojec s eques ha he membe s o he C owd ha e some
physical abili ies which hey a e linked o be able o li weigh . All his wi h he
objec i e o o mo e co ec ly he cus ome s’ deli e y. Speci ically, he 45% o
he p ojec s don’ eques a minimum (o don’ hink ha i is a key aspec ) and
he es (55%) conside ha i is impo an . O he p ojec s ha hink ha his
abili y is impo an which a e: Deli , Ins aca , Rickshaw, Ube EATS, Ube Rush and
Zipmen s, hey conside basically ha he cou ie s ha deli e by bike o on oo mus
be able o li 30 pounds and he cou ie s ha deli e by mo o ehicle mus be able o
li 50 pounds.
As ega ds o he a ailabili y o he membe s o he C owd is ob ious ha i
always goes in unc ion o he deli e y olume ha could be and he a ailabili y.
Bu all he p ojec s o e big lexibili y o hei membe s o choose hei wo k
hou s. Some o he p ojec s o e blocks o ime o wo k, as o example Amazon
Flex which allow choose any a ailable 2,4 and 8 hou s block o ime o wo k he
same day. O he p ojec o Rickshaw ha allow choose any a ailable 4 and 8
hou s block o ime o wo k he same day.
In some o he analysed p ojec s, hey eques o ha e a cus ome se ice skills
o hei membe s o he C owd. Speci ically hese p ojec s a e: Deli , Ins aca ,
Rickshaw, Sideca Deli e ies and Zipmen s.
Also i is impo an o emphasise ha some o he selec ion p ocess o be
membe o he C owd a e mo e demanding han o he s. I means ha in some
o hese hey only eques o ul il he main cha ac e is ics ha a e eques ed.
Howe e , in o he s as o example in he Deli p ojec , i ca ies ou an ex ensi e
il e ing p ocess which includes in e iews by ideo. O o example, Pos ma es
ha does o pass a deli e ies es and a pe sonal in e iew.
C owdsou cing Logis ics in B2C e-Comme ce
57
Finally, wi h espec o he ewa d o he comple ed jobs, all he analysed
p ojec s de e mine he p ice o he membe o he C owd pe hou and no o
pa cel. The compensa ion ange is be ween 18$ and 30$ pe hou . Bu he mos
common is ha company pays up o 25$ pe hou (55%). Only he e a e wo
p ojec s (Ube EATS and Ube Rush) whe e he company could pays mo e (up o
30$ pe hou ) and hey a e o he same company, Ube . I is impo an o
emphasise he case o MyWays which allows a he cus ome o decide how
much will pay o he se ice. Then he company ake a pa o his quan i y,
speci ically a 10% and he es is o he membe o he C owd. O he aspec
impo an abou MyWays in di e ence wi h he o he p ojec s is ha pays in
K onas, because i ca ies ou in S ockholm (Sweden) and he o he p ojec s
pays wi h dolla s.
2.2.3. Deli e ies
Nex i can obse e he deli e ies classi ica ion o he s udied p ojec s:
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Da id Ba dasco San José
om he Local Shop o e e y cus ome and o he weigh o he deli e y,
because e e y anspo has a capaci y.
·Cos : The model says ha is he o al cos o he logis ical se ice
(C owdsou cing Logis ics) o he company in unc ion o he
cha ac e is ics o he Membe o he C owd. And basically i shows he
main cos s o his, which a e: Uni Cos o Acquisi ion (Pick up Cos and
Deli e ies Cos ), In o ma ion and Con ol Cos , Cos o Launch o he
se ice and B eakage Cos .
· Emissions: The model says ha is he o al emissions o he logis ical
se ice (C owdsou cing Logis ics) wi h he chosen anspo and he
de e mined ou es.
3.2. Con ex
I should be no ed ha o ca y ou he c ea ion o he model is necessa y o
answe and ix some aspec s ha a e basic in he p e iously ci ed p ac ice.
These a e:
· Who buy he se ice?
Then o analyse di e en p ojec s abou C owdsou cing Logis ics, i has
been obse ed ha almos cases he se ice was bough by Local
Shops. So, in he p oposed Model, he Local Shops will buy he se ice,
because nowadays he p ojec s a e ocused in his sys em.
Abou he Local Shops and in ela ion wi h he opic o his p ojec
(C owdsou cing Logis ics in B2C), i has been conside ed in he model
ha he Local Shops used o sell he adi ional me hod and e-comme ce.
I should be no ed ha wi h he e-comme ce and wi h his new logis ic
se ice he inal cus ome could ecei e his pu chases a home wi hou
ha e o mo e o his house.
· Who is he se ice p o ide ?
C owdsou cing Logis ics in B2C e-Comme ce
65
Then o analyse di e en p ojec s abou C owdsou cing Logis ics, i has
been obse ed ha almos cases he se ice p o ide a e s a -ups.
Speci ically, he s a -ups a e bo n o sa is y he cus ome s’ logis ic
necessi ies. So, in he p oposed Model, he se ice p o ide is a s a -up,
because nowadays he mos p ojec s come om his ype o company.
· Who makes he deli e y?
The deli e y will make by he C owd. The C owd is a numbe o people
ha wan o do his logis ic se ice, o e hei skills o his and ha e he
cha ac e is ics and he equi emen s ha a e necessa y o e e y
company.
Then o analyse di e en p ojec s abou C owdsou cing Logis ics, i has
decided ha in he p oposed Model he Membe s o he C owd ha e he
mos common cha ac e is ics and skills o all he s udied p ojec s.
Speci ically, hese a e:
· Type o anspo o make he deli e ies: Ca , Mo o bike, Bicycle
o on Foo .
· I he Membe s o he C owd make he deli e ies wi h some
ehicle, hey ha e o ha e hei own ehicle.
· Be a leas 18 yea s old.
· Ha e a phone. I could be And oid o iPhone.
· Ha e a alid d i e ’s license, insu ance and pass a backg ound
check.
· P o essionalism and good communica ion skills when dealing
wi h clien s.
· Abili y o li packages ha ange om 10-25 kg in and ou o
ehicles, up and down ligh s o s ai s.
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Da id Ba dasco San José
3.3. Scope
The scope o he model is delimi ed basically by he hypo hesis es ablishes,
which a e:
1) I will be assigned one Local Shop, which will ha e di e en deli e ies o
make o i s cus ome s, a one Membe o he C owd. Speci ically, i will
be assigned he nea es a he Membe 's o he C owd loca ion.
2) I is conside ed ha he Membe o he C owd mus comple e all he
deli e ies o he Local Shop ha has been assigned.
3) The e isn' a p io i y o de in he deli e ies in he Local Shop. Speci ically,
he p io i y o de will go in unc ion o he op imal ou e. To calcula e he
op imal ou e has been used Cla ke's and W igh 's algo i hm which is
adi ional o VRP (Vehicle ou ing p oblem) and is used o plan he
ou es. I has conside ed con enien o use his algo i hm, because in his
ype o se ice he done dis ance is undamen al, basic ac o . Fo his
eason, i is e y impo an o selec he bes ou e o op imize cos s and
emissions.
4) I is conside ed ha he demand o he se ice by he Local Shops is
p oduced all a he same ime (in he same hou ). Fo his eason, e e y
Membe o he C owd has o go o one speci ic Local Shop, because he
i s deli e y o e e y Local Shop is mo e o less a he same ime.
5) The demand ax o he se ice o he Shops mus be less o equal han
he numbe o he Membe s o he C owd, because i no i won' be
possible o p o ide all he Shops ha need he se ices.
6) The Local Shops and he inal cus ome s a e in he u ban ci y.
7) The dis ance is conside ed in Euclidean dis ance wi h he coo dina es in
Km.
C owdsou cing Logis ics in B2C e-Comme ce
67
8) The Membe o he C owd can deli e by ca , by mo o bike, by bicycle o
on oo .
3.4. Me hodology
The me hodology applied o his model is he nex :
- Fi s o all, he equi ed da a a e en e ed.
- Then, he op imal ou e is calcula ed wi h he a ached condi ions. And he
algo i hm use o his is he Vehicle Rou ing P oblem, speci ically Cla ke’s and
W igh ’s algo i hm. The esolu ion o his algo i hm (pa allel e sion) is:
Fi s S ep: I is calcula ed all sa ings aij wi h espec o he deposi o all
he cus ome s’ pai s “I” and “j”.
Second S ep: To o de all he cus ome s' pai s o he bigges sa ings o
he smalle sa ings.
Thi d S ep: While he e a e cus ome s o assign:
o Selec he pai (i, j) ha isn' assigned wi h mino o equal demand
a he highes capaci y o he ou e and wi h he bigges sa ings.
Cases:
I) I i and j a en' assigned: To open a new ou e wi h he ex emes i and
j.
II) I i is assigned wi h he ex eme o an open ou e R wi h a bigge o
equal capaci y han he demand o j: To assign j a R.
III) I j is assigned wi h he ex eme o an open ou e R wi h a bigge o
equal capaci y han he demand o i: To assign i a R.
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Da id Ba dasco San José
IV) I i is he ex eme o an open ou e R and j is he ex eme o an open
ou e R', and he sum o he loads o R and R' don’ exceed he
bigges capaci y o he ou e, me ge R and R'.
- One ime ha he ou e is calcula ed, nex is applied he cos s and
en i onmen al model wi h he o mulas, he da um and a iables, and he
es ic ions co esponding (in he nex sec ion a e old all hese concep s).
3.5. Cos Model
Nex he cos model is old wi h his o mulas, da um and a iables, and he
es ic ions.
3.5.1. Fo mulas
The cos s ha ha e been conside ed basically a e:
· Uni Cos o Acquisi ion (Cu): This is he cos o he se ice o one
membe o he C owd o one local shop wi h all hei deli e ies ha he
shop has in ha momen .
· Cos o Launch o he se ice (CL): This is he cos ha implies o
launch he se ice e e y ime ha one local shop needs, equi es he
se ice o C owdsou cing Logis ics (managemen , pe sonal, e c.)
· B eakage Cos (CB): This is he cos ha implies no o ul il wi h he
demand o he Local Shops, because he e a en' enough Membe s o
he C owd.
So, he o mula o calcula e he deli e y cos is:
Uni s: (€/deli e y)
· Uni Cos o Acquisi ion (Cu): The o mula is he nex :
C owdsou cing Logis ics in B2C e-Comme ce
69
Uni s: €/deli e y
- Cpick_up: This is he cos ha implies he Membe o he C owd goes
om whe e she/he is o he nea es Local Shop.
o Cpick_up:
Cpick_up_T anspo : This is he cos ha implies he anspo o
he Membe o he C owd o he nea es Local Shop.
Cpick_up_Pe sonal: This is he cos ha implies he equi ed,
used ime o he Membe o he C owd.
ES: This is he numbe o deli e ies ha has o make he
local shop ha has been selec ed.
- Cdeli e ies: This is he cos ha implies he Membe o he C owd goes
om he Local Shop o make all he deli e ies ha he shop has. Also
i includes he ime ha he Membe o he C owd needs o pick up
he deli e ies in he shop and he ime ha she/he needs o deli e
e e y deli e y o he cus ome wi h he co ec se ice.
o Cdeli e ies:
Cdeli e y_T anspo : This is he cos ha implies he anspo
om he Local Shop o all he deli e ies ha he shop has
o do.
Cdeli e y_Pe sonal: This is he cos ha implies he equi ed,
used ime o he Membe o he C owd.
ES: This is he numbe o deli e ies ha has o make he
local shop which has been selec ed.
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Da id Ba dasco San José
- CFailed_deli e ies: This is he cos ha implies o make ailed deli e ies. I
means ha he inal cus ome isn' a home, so he MC has o come
back o he Local Shop o lea e he deli e y.
o CFailed_deli e ies:
CFailed_deli e y_T anspo : This is he cos ha implies he
anspo om he Local Shop o all he ailed deli e ies
ha he shop has o do.
CFailed_deli e y_Pe sonal: This is he cos ha implies he
equi ed, used ime o he Membe o he C owd.
ES: This is he numbe o deli e ies ha has o make
he local shop ha has been selec ed.
YFail:
· Cos o Launch o he se ice (CL): The o mula is he nex :
Uni s: €/deli e y
- CLaunch: I is he cos o launch he C owdsou cing Logis ics
(managemen , eam wo k, communica ion, da a ans e , e c.).
- ST_Deli e ies: I is he numbe o shops ha a e pa ne s o he company
and need o make deli e ies.
· B eakage Cos (CB): The o mula is he nex :
C owdsou cing Logis ics in B2C e-Comme ce
71
Uni s: €/deli e y
- ST_Deli e ies: I is he numbe o shops ha a e pa ne s o he company
and need o make deli e ies.
- NMC: I is he numbe o he membe s o he C owd.
- CB eak: I is he cos o launch he C owdsou cing Logis ics
(managemen , eam wo k, communica ion, da a ans e , e c.).
- YB:
3.5.2. Explana ion o da um and a iables
The da um and a iables ha ha e been conside e in he p e ious o mulas
a e:
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Da id Ba dasco San José
3.5.3. Res ic ions
The es ic ions and condi ions ha ha e been conside e in hese o mulas a e:
1) E e y Membe o he C owd only can make he deli e s wi h one ype o
anspo . I means by ca , by mo o bike, by bicycle o on oo . So:
2) Abou he uel, i is only necessa y in mo o ehicles. Speci ically, he ca
is he unique anspo ha can wo k wi h pe ol o gasoil, bu only wi h
one o his. And he mo o bike only wo ks wi h pe ol. So:
C owdsou cing Logis ics in B2C e-Comme ce
73
3) Abou he amo iza ion and use, i he Membe o he C owd makes he
deli e y on Foo , i hasn' any cos o amo iza ion and use, because
she/he doesn' use any machine. So:
4) In he cases ha ha e been necessa y o calcula e he anspo ime, i
has used his o mula:
3.6. En i onmen al Model
Nex he en i onmen al model is old wi h his o mulas, da um and a iables,
and he es ic ions.
3.6.1. Fo mulas
The emissions ha ha e been conside e basically a e:
· Pick Up Emissions (EPick_Up): These a e he emissions ha a e
gene a ed, because he Membe o he C owd goes om whe e she/he is
o he nea es Local Shop wi h he /his ehicle.
· Deli e ies Emissions (EDeli e ies): These a e he emissions ha a e
gene a ed, because he Membe o he C owd goes om he Local Shop
o he di e en places whe e a e he deli e ies wi h he /his ehicle.
· Failed Deli e ies Emissions (EFailed_Deli e ies): These a e he emissions
ha a e gene a ed, because he Membe o he C owd can' make he
deli e y ( he cus ome wasn' a home).
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Da id Ba dasco San José
To ca y ou he ela ed simula ions has se ou a baseline scena io, which i has
been changing a iables in o de o achie e he g ea es possible numbe o
da a wi h he di e en possibili ies and can o d aw be e conclusions.
Speci ically, o he abo e scena io he ollowing cha ac e is ics has been
conside ed:
- The se anges o he main a iables ha e been:
o Numbe o Membe s o he C owd: 2,4,6,8 and 10.
o Numbe o shops wi h deli e ies o make: 2,4,6,8 and 10.
o Numbe o cus ome s o he mos nea ly shop om he
selec ed membe o he C owd: 1,2,3,4 and 5.
- The block wo k o each membe o he C owd is app oxima ely 2-4
hou s.
- Simula ions ha e been made o anspo 4 conside ed, which a e:
ca , mo o bike, bicycle and on oo . And gi en ha he ca has he
a ian : diesel o pe ol.
- To ix he coo dina es o each a iable ha equi e loca ion, i s o all
has been conside ed a deli e y a ea o 5 x 5 km, i.e. 25 km2. All wi h
he objec i e ha he ob ainmen o da um was he mos ealis ic and
cohe en as would be possible. Also, i is impo an o say ha he
coo dina es ha e been gi en a ac o o 0.9713, because i has been
conside ed ha using Euclidean coo dina es was necessa y o use a
ac o , because he s ee s do no ollow ha way and some imes
he e ha de ia e om he pa h.
- I has been conside ed coo dina es o he loca ion o he Membe o
he C owd, o he loca ion o he shops and o he loca ion o he
cus ome s. And all hese has been made comple ely andom and
h ough o unc ion o Excel. And he numbe o ailed deli e ies oo.
C owdsou cing Logis ics in B2C e-Comme ce
81
- I has been conside ed ha each anspo could b ing a ce ain
amoun o packages. Speci ically, he amoun s se has been as
ollows:
o Ca (whe he o no diesel o pe ol): 5 deli e ies.
o Mo o cycle: 3 deli e ies.
o Bicycle: 2 deli e ies.
o Walk: 2 deli e ies.
Ob iously, his ac has signi ican ly a ec ed he dis ances and he ou e
has made he membe o he C owd, because i he anspo canno
co e all he deli e ies, i means ha has o come back o he shop and
hen go o he o he cus ome s.
The basic scena io da um, which has made h ough Excel, has been he
nex :
Figu e 6: Basic Scena io
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Da id Ba dasco San José
And om he e, i has aised he ollowing changes o a iables o s udy be e
he model and hus make a mo e p o ound and u h ul analysis. Follow, i can
see an abs ac o all he cases simula ed wi h he di e en a iables:
In each case hose shown has se a numbe o membe s o he C owd, a
numbe o shops ha had deli e ies and a numbe o cus ome s in a pa icula
s o e, which was he one ha was close ega ding he loca ion o he membe
o he c owd. I is impo an o say ha only a e analysed deli e ies pe o m a
single s o e by a single membe o he C owd, bu i is necessa y o conside all
he ac s, because his a ec s and ha e an e ec on he inal p ice and
emissions o each deli e y.
Figu e 7: Cases
C owdsou cing Logis ics in B2C e-Comme ce
83
4.2. Cos s: Summa y and conclusions
Following he simula ion p ocedu e ci ed in he p eceding pa ag aph and wi h
he co esponding model, he e ha e been a o al o 25 simula ions in ela ion
wi h he cos s ha gene a e e e y deli e y (wi h ixed da a and hypo hesis
conside ed). Each o hem in u n, wi h hei espec i e a ian s (ca wo ks wi h
pe ol, ca wo ks wi h diesel, mo o cycle, bicycle and walking).
In he sec ion Annexes (Resul s o he simula ion), i can see all he esul s
ob ained in each case a ound cos s.
A e analysing he esul s, hen a e emphasised hose aspec s a ound cos s
ha a e conside ed mos impo an and ele an o hem in o accoun , as well as
o d aw conclusions abou he p ac ice s udied (C owdsou cing Logis ics) and
he p oposed model. And aking in o accoun he cha ac e is ics and ixed
assump ions, hypo hesis.
Cos s mainly depend on he dis ance a elled and he ime spen by he
membe o he C owd as well as he ype o anspo .
Analysing he esul s, i can be seen ha by pe o ming deli e ies by bicycle o
on oo sa es on consump ion and amo iza ion, bu ins ead in e ms o ime
cos s inc ease. So ha he mos balanced op ion is ha he membe o he
C owd makes he deli e ies by mo o cycle, since i is he mos economical and
he e o e he mos in e es ing op ion o businesses and o he ealiza ion o
C owdsou cing Logis ics.
The cos o deli e y is in a ange be ween 2,43 and 38,62 Eu os pe deli e y.
Emphasize ha much o he cos is in he pa o cus ome ’s deli e y o his
pu chase, as well as he b eaks in se ice when he e a e hese. As ega ds he
o he cos s in ol ed in he p oposed model, hey a e p ac ically negligible.
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Da id Ba dasco San José
No e ha om he pe spec i e o cos s, he b eak o se ice, i.e. no ha ing
su icien esou ces (membe s o he C owd) gene a es signi ican implica ions
in cos s and dimensions lo hese. In he model c ea ed, i has sough o
highligh his aspec , o his eason i has been gi en conside able cos o he
ac ha has b eakages, because i gene a es cus ome dissa is ac ion and
because i is one o he main p oblems ha he p ac ice ci ed oo. This p oblem
ci ed basically ocuses on ha i he numbe o membe s o he C owd is less
han he numbe o s o es ha ha e o make deli e ies, i can no ul il all he
demand and he e o e, i doesn’ allow o se e o cus ome s, which ob iously i
is nega i e. So, om he pe spec i e o cos s bo h he numbe o membe s o
he C owd as he numbe o s o es ha ha e deli e ies o make, a e e y
impo an , because hey a e di ec ly in ol ed wi h he cos o b eakage, which
can inc ease lo deli e y cos s.
Also, i mus say ha he bicycle op ion is a e y emp ing op ion and which
h ough simula ions has been seen ha can play a e y impo an ole in his
p ac ice, since he cos s a e e y close o he mo o cycle, and aking in o
accoun which is a clean anspo , i.e. no con amina ed ( he emissions a e
analysed in he nex sec ion). The e o e, i can be a e y good al e na i e o
o e C owdsou cing Logis ics wi h anspo by bicycle o he membe s o he
C owd. Bu ins ead i equi es mo e ime and he capaci y in some cases is a
p oblem, because bicycle can no b ing he same deli e ies han he ca . So in
his aspec , is a o his.
Nex i can obse e he case 1 a he le el o cos s, whe e i can see he op ion
o walking in his p ac ice s ill a away, since his ype o deli e y is e y
expensi e due o he ime equi ed. In his g aph can also app ecia e he s ong
h ea o bicycles as a clea compe i o o he mo o cycle, lea ing in hi d place
he ca , which loses s eng h. The ca is no he mos sui able anspo o his
se ice, because in he ci y is ha d o go by ca . Anyway i emains an impo an
al e na i e, because i allows lo s o deli e ies on he same ou e, since he ca
is he anspo o mo e capaci y ha has been se o his p ac ice. In he g aph
i can see ha inc easing he numbe o cus ome s, he deli e y cos s d op,
C owdsou cing Logis ics in B2C e-Comme ce
85
which gene a es ha ha e o squeeze he maximum. All his wi h he inali y o
o e u n he ewes imes possible o he s o e.
Wi h espec o he g aph o he case 25, no e ha ollows he same line as
abo e, bu wi h some mino di e ences, which a e linked o he dis ance.
In conclusion, i can say ha C owdsou cing Logis ics om he pe spec i e o
cos s is ocused on he dis ance a elled, in he ime equi ed and he anspo .
No o ge ing, b eaking se ice ha apa om gene a ing cus ome dissa is ac ion
can gene a e e y signi ican inc eases in cos s. The e o e, a e pe o ming
simula ions, i can say ha om his pe spec i e, he model is consis en , since
Figu e 8: Cos s Case 1
Figu e 9: Cos s Case 25
86
Da id Ba dasco San José
he esul s a e e y simila o he eali y (wi h he limi a ions o he model). And
in u n, i can open he doo s o he iabili y o C owdsou cing Logis ics, because
deli e y cos is qui e simila o he cu en main compe i o s and main
al e na i es.
4.3. Emissions: Summa y and conclusions
Following he simula ion p ocedu e ci ed in he p eceding pa ag aph and wi h
he co esponding model, he e ha e been a o al o 25 simula ions in ela ion
wi h he emissions ha gene a e e e y deli e y (wi h ixed da a and hypo hesis
conside ed). Each o hem in u n, wi h hei espec i e a ian s (ca wo ks wi h
pe ol, ca wo ks wi h diesel, mo o cycle, bicycle and walking).
In he sec ion Annexes (Resul s o he simula ion), i can see all he esul s
ob ained in each case a ound emissions.
A e analysing he esul s, hen a e emphasised hose aspec s a ound
emissions ha a e conside ed mos impo an and ele an o hem in o accoun ,
as well as o d aw conclusions abou he p ac ice s udied (C owdsou cing
Logis ics) and he p oposed model. And aking in o accoun he cha ac e is ics
and ixed assump ions, hypo hesis.
Emissions depend mainly on he dis ance a elled and o he anspo used.
Ob iously, nei he anspo by bicycle o walk anspo a ion gene a e
emissions. The e o e, his p ac ice has g ea po en ial o be iendly wi h he
en i onmen and does no gene a e emissions, i.e. no o pollu e. So ha i can
be conside ed a g een p ac ice, because when he membe s o he C owd use
hese ypes o anspo , do no pollu e. Howe e , i should be no ed ha
membe s o he C owd mus a el mo e la ge dis ance and ha ing o use hei
own body, i akes longe and a igue by membe s o he C owd. No e ha in he
es o conclusions ha a e d awn a ound emissions has no been alked abou
bicycle and walk, since i has been conside ed e iden ha hese do no
gene a e any emissions and he e o e, his s udy is no necessa y (in his
aspec ), because hey a e he bes op ion ( alking abou emissions).
C owdsou cing Logis ics in B2C e-Comme ce
87
When he membe o he C owd mus make deli e ies o mo e han h ee
cus ome s, he anspo ha gene a es ewe emissions, i.e., he leas pollu ing,
is he ca , speci ically ha wo ks wi h diesel. And his is mainly due o he
capaci y, because he ca can load mo e deli e ies unlike he mo o cycle.
Howe e , i i mus make less han wo deli e ies and he e o e, he membe o
he C owd makes he di ec anspo , i.e. goes om he shop o cus ome s
wi hou ha ing o go back o he shop, i is conside ed ha mo o cycle is he
mos app op ia e anspo (in ela ion wi h emissions), gene a ing ewe
emissions han a ca .
I should be no ed ha he ac ha he numbe o s o es ha ha e deli e ies o
make o he numbe o membe s o he C owd, has been seen ha is no a key
ac o o ha in ol ing changes in he amoun o emissions pe deli e y.
Emissions ange a e om a alue ange o 0 o 1,40 KgC02 / deli e y. No e ha
much o he emissions gene a ed a e in he pa o he deli e y o he pu chase
a he end cus ome , always ha has pe o med mo e han one deli e y,
because i is when he membe o he C owd mus a el mo e dis ance.
When mo e deli e ies a e made, emissions gene a ed by hese deli e ies a e
sp ead and is much mo e signi ican han a single deli e y.
Following is a ached a cha o Case 1, which i can see he emissions
gene a ed by he ca wi h pe ol, diesel and mo o cycle. And i has been
changed he numbe o cus ome s. Also, i can obse e ha when i jus has o
make a single deli e y, ha is, a single cus ome , emissions a e highe han
when ha e o make some deli e ies. The e o e, when i mus ca y ou mo e
deli e ies, emissions sp ead and he emission pe deli e y is educed. Also, i is
impo an o see he end, because hen i is bogged down mo e, and in some
cases inc ease again sligh ly, because he dis ance and he capaci y ma k he
end.
88
Da id Ba dasco San José
Figu e 10: Emissions Case 1
The case 25 ollows he same end as abo e, and despi e o ha e mo e
membe s o he C owd, such as has been said p e iously, in he model aised
his does no a ec . Nei he a g ea e numbe o s o es. The e o e, as in he
p e ious case, wha ma king he end is guided by he dis ance a elled and
he capaci y o he anspo .
Figu e 11: Emissions Case 25
In conclusion, a e analysing he p oposed model, i can be said ha he esul s
ob ained a e consis en and a e mainly go e ned by he dis ance a elled and
by he anspo used, which depending on he emissions ha emi s is wha
C owdsou cing Logis ics in B2C e-Comme ce
89
de e mines he pollu ion deli e ies, and in u n o he p ac ice ci ed. I is
impo an o say he oppo uni y, possibili y o ge make a comple ely clean
anspo , bu on he con a y i implies inc eased a igue by he membe o he
C owd and mo e ime.
96
Da id Ba dasco San José
www.gua dian.co.uk/en i onmen /2007/sep/12/ plas icbags.supe ma ke s
(accessed 6 Decembe 2007)
So, H. W., Gunaseka an, A., & Chung, W. W. (2006). Las Mile ul ilmen
s a egy o compe i i e ad an age. In e na ional Jou nal o Logis ics
Sys ems and Managemen , 2(4), 404-418.
S i as a a, Sami K. 2007. “G een Supply-Chain Managemen : A S a e-o - he-
A Li e a u e Re iew.” In e na ional Jou nal o Managemen Re iews 9(1):
53–80.
Vanelslande , T., Deke ele, L., & Van Ho e, D. (2013). Commonly used e-
comme ce supply chains o as mo ing consume goods: compa ison
and sugges ions o imp o emen . In e na ional Jou nal o Logis ics
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Visse , J.G.S.N & T. Nemo o (2001), E-comme ce and he Consequences o
F eigh T anspo . Resea ch epo , Minis y o Economic A ai s.
Wei, Z. and Zhou, L. (2011), “Case s udy o online e ailing as ashion
indus y”, In e na ional Jou nal o e-Educa ion, e-Business, e-Managemen
and e-Lea ning, Vol. 1 No. 3, pp. 195-200.
Wel e eden, J. W. (2008). B2c e-comme ce logis ics: he ise o collec ion-and-
deli e y poin s in The Ne he lands. In e na ional Jou nal o Re ail &
Dis ibu ion Managemen , 36(8), 638-660.
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una ended deli e y om he consume s and eTaile s' pe spec i es.
In e na ional Jou nal o Elec onic Ma ke ing and Re ailing, 2(1), 20-38.
C owdsou cing Logis ics in B2C e-Comme ce
97
6.2. WEBS
· AMAZON FLEX:
- h ps:// lex.amazon.com/
- h p://www.manu encionyalmacenaje.com/es/no ices/2016/01/amazo
n- lex-ul ima-milla- epa o-de-paque es-al-es ilo-ube -
38753.php#.V HpZT-qzL8
- h p://www.x e naliza.es/amazon- iene-p e is o-la-expansion-del-
p oyec o- lex/
- h p://www. uexpe o.com/2015/09/30/amazon-paga a-a-los-usua ios-
po - epa i -paque es/
- h p://www.logis icay anspo e.es/no icias.php/Amazon-a ianza-su-
nue o-se icio-de-en egas-en-colabo aci%C3%B3n-con-
conduc o es-au %C3%B3nomos.-cl.--amazon-
paque e %C3%ADa/61399
- h p://www.businessinside .com/amazon-wo king-on-sec e i e-
p ojec -called-amazon- lex-2015-8
- h p://www.geekwi e.com/2015/amazon-se - o-launch-new-amazon-
lex-package-pickup-se ice-in-sea le-a ea-wi h-p ime-now/
- h ps://en.wikipedia.o g/wiki/Amazon.com
· DELIV:
- h ps://www.deli .co/
- h ps://www.c unchbase.com/o ganiza ion/deli #/en i y
- h p://www.cne .com/news/speedy-deli e y-s a up-deli -expands- o-
nine-new-ma ke s/
- h ps://en.wikipedia.o g/wiki/Deli
- h p://www.all he ugalladies.com/shopping/become-deli -d i e /
· DOORDASH:
- h ps://www.doo dash.com/
· INSTACART:
98
Da id Ba dasco San José
- h ps://www.ins aca .com
- h p://www. hepennyhoa de .com/don -mind-g oce y-shopping-make-
25-pe -hou -deli e ing- ood-wi h-ins aca /
· MyWays:
- h ps://en.wikipedia.o g/wiki/DHL_Exp ess
- h ps://www.myways.com/
- h p://www.dhl.com/en/p ess/ eleases/ eleases_2013/logis ics/dhl_c
owd_sou ces_deli e ies_in_s ockholm_wi h_myways.h ml#.VwZw
Jj_ zL8
- h p://www.deli e ed.dhl.com/en/a icles/2014/04/wha -s- he-s o y-
m -oom.h ml
· POSTAMATES:
- h ps://pos ma es.com/
- h ps://en.wikipedia.o g/wiki/Pos ma es
· RICKSHAW:
- h ps://go ickshaw.com/
- h ps://angel.co/ ickshaw/ac i i y
· SIDECAR DELIVERIES:
- h ps://www.side.c /
- h ps://en.wikipedia.o g/wiki/Sideca _%28company%29
- h ps://www. hegua dian.com/ echnology/2015/dec/30/ube -
compe i o -sideca -shu -down
- h ps://www.side.c /why-we-sold- o-gm/
· Ube EATS:
- h ps://ube ea s.com
- h p://pos andpa cel.in o/71869/news/ube ea s-se s- o-expand/
· Ube RUSH:
- h p://www.logis icay anspo e.es/no icias.php/Ube -lanza-su-p opia-
emp esa-de-paque e %C3%ADa.-cl.--paque e ia/62627
- h ps:// ush.ube .com/how-i -wo ks
C owdsou cing Logis ics in B2C e-Comme ce
99
- h ps://www.ube .com
- h ps://en.wikipedia.o g/wiki/Ube _%28company%29
- h ps://news oom.ube .com/us-new-yo k/a- eliable- ide- o -you -
deli e ies/
- h p://ly ube newsle e .com/ube - ush/
- h p://www.jobmonkey.com/sha ed-economy/on-demand-
deli e y/ube - ush/
· ZIMPMENTS:
- h ps://zipmen s.com/
- h p://www.xconomy.com/new-yo k/2015/11/10/deli -acqui es-
zipmen s- o -undisclosed-sum/
- h ps://angel.co/zipmen s/ac i i y
h p://www.c owdsou cing.o g/edi o ial/deli e y-ge s-pe sonal-wi h-
zipmen s/27808
100
Da id Ba dasco San José
C owdsou cing Logis ics in B2C e-Comme ce
101
ANNEXES
1. Resul s o he simula ion
RESULTS SCENARIO AND CASES
NAME OF CASE
TRANSPORT
COSTS
EMISSIONS
CASE 1-A
By CAR Pe ol
7,93
1,13
By CAR Diesel
7,36
1,04
By MOTORBIKE
4,69
0,89
By BICYCLE
5,40
0,00
On FOOT
14,16
0,00
CASE 1-B
By CAR Pe ol
4,46
0,58
By CAR Diesel
4,17
0,53
By MOTORBIKE
2,81
0,46
By BICYCLE
3,17
0,00
On FOOT
7,64
0,00
CASE 1-C
By CAR Pe ol
3,80
0,48
By CAR Diesel
3,56
0,44
By MOTORBIKE
2,43
0,38
By BICYCLE
3,32
0,00
On FOOT
8,15
0,00
CASE 1-D
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,17
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,37
0,00
On FOOT
8,29
0,00
CASE 1-E
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,16
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,74
0,00
On FOOT
9,38
0,00
CASE 2-A
By CAR Pe ol
13,98
1,13
By CAR Diesel
13,41
1,04
By MOTORBIKE
10,38
0,89
By BICYCLE
11,45
0,00
On FOOT
20,21
0,00
CASE 2-B
By CAR Pe ol
14,12
1,15
By CAR Diesel
13,54
1,06
By MOTORBIKE
10,81
0,92
By BICYCLE
11,54
0,00
On FOOT
20,47
0,00
CASE 2-C
By CAR Pe ol
9,89
0,48
By CAR Diesel
9,65
0,44
By MOTORBIKE
8,52
0,38
102
Da id Ba dasco San José
By BICYCLE
9,41
0,00
On FOOT
14,24
0,00
CASE 2-D
By CAR Pe ol
9,46
0,41
By CAR Diesel
9,26
0,38
By MOTORBIKE
8,78
0,44
By BICYCLE
9,46
0,00
On FOOT
14,38
0,00
CASE 2-E
By CAR Pe ol
9,46
0,41
By CAR Diesel
9,25
0,38
By MOTORBIKE
9,05
0,50
By BICYCLE
9,83
0,00
On FOOT
15,47
0,00
CASE 3-A
By CAR Pe ol
20,03
1,13
By CAR Diesel
19,46
1,04
By MOTORBIKE
16,79
0,90
By BICYCLE
17,50
0,00
On FOOT
26,26
0,00
CASE 3-B
By CAR Pe ol
20,17
1,15
By CAR Diesel
19,59
1,06
By MOTORBIKE
16,86
0,92
By BICYCLE
17,59
0,00
On FOOT
26,52
0,00
CASE 3-C
By CAR Pe ol
15,94
0,48
By CAR Diesel
15,70
0,44
By MOTORBIKE
14,57
0,38
By BICYCLE
15,46
0,00
On FOOT
20,29
0,00
CASE 3-D
By CAR Pe ol
15,51
0,41
By CAR Diesel
15,31
0,38
By MOTORBIKE
14,83
0,44
By BICYCLE
15,51
0,00
On FOOT
20,43
0,00
CASE 3-E
By CAR Pe ol
15,51
0,41
By CAR Diesel
15,30
0,38
By MOTORBIKE
15,10
0,50
By BICYCLE
15,88
0,00
On FOOT
21,52
0,00
CASE 4-A
By CAR Pe ol
26,08
1,13
By CAR Diesel
25,51
1,40
By MOTORBIKE
22,84
0,90
By BICYCLE
23,55
0,00
On FOOT
32,31
0,00
CASE 4-B
By CAR Pe ol
26,22
1,15
By CAR Diesel
25,64
1,06
By MOTORBIKE
22,91
0,92
C owdsou cing Logis ics in B2C e-Comme ce
103
By BICYCLE
23,64
0,00
On FOOT
32,57
0,00
CASE 4-C
By CAR Pe ol
21,99
0,48
By CAR Diesel
21,75
0,44
By MOTORBIKE
20,62
0,38
By BICYCLE
21,51
0,00
On FOOT
26,34
0,00
CASE 4-D
By CAR Pe ol
21,56
0,41
By CAR Diesel
21,36
0,38
By MOTORBIKE
20,88
0,44
By BICYCLE
21,56
0,00
On FOOT
26,48
0,00
CASE 4-E
By CAR Pe ol
21,56
0,41
By CAR Diesel
21,35
0,38
By MOTORBIKE
21,15
0,50
By BICYCLE
21,55
0,00
On FOOT
26,47
0,00
CASE 5-A
By CAR Pe ol
32,13
1,13
By CAR Diesel
31,56
1,04
By MOTORBIKE
28,89
0,90
By BICYCLE
29,60
0,00
On FOOT
38,36
0,00
CASE 5-B
By CAR Pe ol
32,27
1,15
By CAR Diesel
31,69
1,06
By MOTORBIKE
28,96
0,92
By BICYCLE
29,69
0,00
On FOOT
38,62
0,00
CASE 5-C
By CAR Pe ol
28,04
0,48
By CAR Diesel
27,80
0,44
By MOTORBIKE
26,67
0,38
By BICYCLE
27,56
0,00
On FOOT
32,39
0,00
CASE 5-D
By CAR Pe ol
27,61
0,41
By CAR Diesel
27,41
0,38
By MOTORBIKE
26,93
0,44
By BICYCLE
27,61
0,00
On FOOT
32,53
0,00
CASE 5-E
By CAR Pe ol
27,61
0,41
By CAR Diesel
27,40
0,38
By MOTORBIKE
27,20
0,50
By BICYCLE
27,60
0,00
On FOOT
32,52
0,00
CASE 6-A
By CAR Pe ol
7,90
1,13
By CAR Diesel
7,34
1,04
By MOTORBIKE
4,66
0,90
104
Da id Ba dasco San José
By BICYCLE
5,37
0,00
On FOOT
14,14
0,00
CASE 6-B
By CAR Pe ol
4,43
0,58
By CAR Diesel
4,15
0,53
By MOTORBIKE
2,78
0,46
By BICYCLE
3,14
0,00
On FOOT
7,61
0,00
CASE 6-C
By CAR Pe ol
3,80
0,48
By CAR Diesel
3,56
0,44
By MOTORBIKE
2,43
0,38
By BICYCLE
3,32
0,00
On FOOT
8,15
0,00
CASE 6-D
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,17
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,37
0,00
On FOOT
8,29
0,00
CASE 6-E
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,16
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,74
0,00
On FOOT
9,38
0,00
CASE 7-A
By CAR Pe ol
7,93
1,13
By CAR Diesel
7,36
1,04
By MOTORBIKE
4,69
0,89
By BICYCLE
5,40
0,00
On FOOT
14,16
0,00
CASE 7-B
By CAR Pe ol
4,43
0,58
By CAR Diesel
4,15
0,53
By MOTORBIKE
2,78
0,46
By BICYCLE
3,14
0,00
On FOOT
7,61
0,00
CASE 7-C
By CAR Pe ol
3,80
0,48
By CAR Diesel
3,56
0,44
By MOTORBIKE
2,43
0,38
By BICYCLE
3,32
0,00
On FOOT
8,15
0,00
CASE 7-D
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,17
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,37
0,00
On FOOT
8,29
0,00
CASE 7-E
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,16
0,38
By MOTORBIKE
2,69
0,44
C owdsou cing Logis ics in B2C e-Comme ce
105
By BICYCLE
3,74
0,00
On FOOT
9,38
0,00
CASE 8-A
By CAR Pe ol
13,95
1,13
By CAR Diesel
13,39
1,04
By MOTORBIKE
10,71
0,90
By BICYCLE
11,42
0,00
On FOOT
20,19
0,00
CASE 8-B
By CAR Pe ol
14,09
1,15
By CAR Diesel
13,52
1,06
By MOTORBIKE
10,79
0,92
By BICYCLE
11,51
0,00
On FOOT
20,45
0,00
CASE 8-C
By CAR Pe ol
9,87
0,48
By CAR Diesel
9,63
0,44
By MOTORBIKE
8,50
0,38
By BICYCLE
9,38
0,00
On FOOT
14,21
0,00
CASE 8-D
By CAR Pe ol
9,44
0,41
By CAR Diesel
9,23
0,38
By MOTORBIKE
8,76
0,44
By BICYCLE
9,43
0,00
On FOOT
14,35
0,00
CASE 8-E
By CAR Pe ol
21,56
0,41
By CAR Diesel
21,35
0,38
By MOTORBIKE
21,15
0,50
By BICYCLE
21,93
0,00
On FOOT
27,57
0,00
CASE 9-A
By CAR Pe ol
19,98
1,13
By CAR Diesel
19,41
1,04
By MOTORBIKE
16,74
0,90
By BICYCLE
17,45
0,00
On FOOT
26,21
0,00
CASE 9-B
By CAR Pe ol
20,12
1,15
By CAR Diesel
19,54
1,06
By MOTORBIKE
16,81
0,92
By BICYCLE
17,54
0,00
On FOOT
26,47
0,00
CASE 9-C
By CAR Pe ol
15,89
0,48
By CAR Diesel
15,65
0,44
By MOTORBIKE
14,52
0,38
By BICYCLE
15,41
0,00
On FOOT
20,24
0,00
CASE 9-D
By CAR Pe ol
15,46
0,41
By CAR Diesel
15,26
0,38
112
Da id Ba dasco San José
By MOTORBIKE
8,49
0,38
By BICYCLE
9,37
0,00
On FOOT
14,20
0,00
CASE 20-D
By CAR Pe ol
9,42
0,41
By CAR Diesel
9,22
0,34
By MOTORBIKE
8,75
0,44
By BICYCLE
9,42
0,00
On FOOT
14,34
0,00
CASE 20-E
By CAR Pe ol
9,42
0,41
By CAR Diesel
9,21
0,38
By MOTORBIKE
9,02
0,50
By BICYCLE
9,79
0,00
On FOOT
15,43
0,00
CASE 21-A
By CAR Pe ol
7,89
1,13
By CAR Diesel
7,32
1,04
By MOTORBIKE
4,65
0,90
By BICYCLE
5,36
0,00
On FOOT
14,12
0,00
CASE 21-B
By CAR Pe ol
4,42
0,58
By CAR Diesel
4,13
0,53
By MOTORBIKE
2,77
0,46
By BICYCLE
3,13
0,00
On FOOT
7,60
0,00
CASE 21-C
By CAR Pe ol
3,80
0,48
By CAR Diesel
3,56
0,44
By MOTORBIKE
2,43
0,38
By BICYCLE
3,32
0,00
On FOOT
8,15
0,00
CASE 21-D
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,17
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,37
0,00
On FOOT
8,29
0,00
CASE 21-E
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,16
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,74
0,00
On FOOT
9,38
0,00
CASE 22-A
By CAR Pe ol
7,89
1,13
By CAR Diesel
7,32
1,04
By MOTORBIKE
4,65
0,90
By BICYCLE
5,36
0,00
On FOOT
14,12
0,00
CASE 22-B
By CAR Pe ol
4,42
0,58
By CAR Diesel
4,13
0,53
C owdsou cing Logis ics in B2C e-Comme ce
113
By MOTORBIKE
2,77
0,46
By BICYCLE
3,13
0,00
On FOOT
7,60
0,00
CASE 22-C
By CAR Pe ol
3,80
0,48
By CAR Diesel
3,56
0,44
By MOTORBIKE
2,43
0,38
By BICYCLE
3,32
0,00
On FOOT
8,15
0,00
CASE 22-D
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,17
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,37
0,00
On FOOT
8,29
0,00
CASE 22-E
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,16
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,74
0,00
On FOOT
9,38
0,00
CASE 23-A
By CAR Pe ol
7,93
1,13
By CAR Diesel
7,36
1,04
By MOTORBIKE
4,69
0,90
By BICYCLE
5,40
0,00
On FOOT
14,16
0,00
CASE 23-B
By CAR Pe ol
8,07
1,15
By CAR Diesel
7,49
1,06
By MOTORBIKE
4,76
0,92
By BICYCLE
5,49
0,00
On FOOT
14,24
0,00
CASE 23-C
By CAR Pe ol
3,80
0,48
By CAR Diesel
3,56
0,44
By MOTORBIKE
2,43
0,38
By BICYCLE
3,32
0,00
On FOOT
8,15
0,00
CASE 23-D
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,17
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,37
0,00
On FOOT
8,29
0,00
CASE 23-E
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,16
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,74
0,00
On FOOT
9,38
0,00
CASE 24-A
By CAR Pe ol
7,93
1,13
By CAR Diesel
7,36
1,04
114
Da id Ba dasco San José
By MOTORBIKE
4,69
0,90
By BICYCLE
5,40
0,00
On FOOT
14,16
0,00
CASE 24-B
By CAR Pe ol
8,07
1,15
By CAR Diesel
7,49
1,06
By MOTORBIKE
4,76
0,92
By BICYCLE
5,49
0,00
On FOOT
14,24
0,00
CASE 24-C
By CAR Pe ol
3,80
0,48
By CAR Diesel
3,56
0,44
By MOTORBIKE
2,43
0,38
By BICYCLE
3,32
0,00
On FOOT
8,15
0,00
CASE 24-D
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,17
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,37
0,00
On FOOT
8,29
0,00
CASE 24-E
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,16
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,74
0,00
On FOOT
9,38
0,00
CASE 25-A
By CAR Pe ol
7,93
1,13
By CAR Diesel
7,36
1,04
By MOTORBIKE
4,69
0,90
By BICYCLE
5,40
0,00
On FOOT
14,16
0,00
CASE 25-B
By CAR Pe ol
8,07
1,15
By CAR Diesel
7,49
1,06
By MOTORBIKE
4,76
0,92
By BICYCLE
5,49
0,00
On FOOT
14,24
0,00
CASE 25-C
By CAR Pe ol
3,80
0,48
By CAR Diesel
3,56
0,44
By MOTORBIKE
2,43
0,38
By BICYCLE
3,32
0,00
On FOOT
8,15
0,00
CASE 25-D
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,17
0,38
By MOTORBIKE
2,69
0,44
By BICYCLE
3,37
0,00
On FOOT
8,29
0,00
CASE 25-E
By CAR Pe ol
3,37
0,41
By CAR Diesel
3,16
0,38
C owdsou cing Logis ics in B2C e-Comme ce
115
By MOTORBIKE
2,69
0,44
By BICYCLE
3,74
0,00
On FOOT
9,38
0,00
· No e:
The o al dis ance conside ed be ween he shop and he cus ome s a e:
- 1 cus ome : 9,72 km.
- 2 cus ome s: 9,94 km.
- 3 cus ome s:
o On oo and by bike: 16,97 km.
o By ca and by mo o bike: 12,70 km.
- 4 cus ome s:
o On oo and by bike: 17,33 km.
o By Mo o bike: 14,96 km.
o By Ca : 10,68 km.
- 4 cus ome s:
o On oo and by bike: 20,05 km.
o By Mo o bike: 17,307 km.
o By Ca : 10,65 km.
The dis ance conside ed be ween he membe o he C owd and he Shop 2 is:
1,37 km.
116
Da id Ba dasco San José