How are big data and digitalization influencing the Supply Chain in the case of the Last Mile issue ?

1. Last Mile : The Big Issue
How are big data and digitalization influencing the
Supply Chain in the case of the Last Mile issue ?
NEOMA BS Executive MBA
Benoit Lafond, Dejan Vasilijevic, Cédric Gibiat
1. The Last Mile issue
We’ll start by clarifying the Last Mile concept and related problems. Focusing on B2C, which we consider the
core issue, we will then make a short overview of potential solutions implemented over the last decade to
understand the main results and findings. We’ll eventually look ho Big Data can impact and improve existing
solutions to the issue and if it might completely disrupt the Last Mile supply chain.
a. What is the « Last Mile » ?
i. A sweeping concept
Being in the information and telecommunications industry, in the humanitarian emergency rescue or in the
express transport business, the Last Mile is fully a distribution concept. Whatever the comparisons made with
other systems, bloodstream or water distribution for example, the issue always revolves around the delivery of
a « relatively small amount of a resource a short distance to a very large number of physically separated
endpoints » (Wikipedia 2014).
The Last Mile concept is used either when describing the challenges of delivering urban areas with heavy traffic
or when addressing the supply of health products to isolated villages. It is also equally used in transport for
both passenger travel and freight transport functions (Wikipedia 2014).
Of course many differences can be found between moving goods or people, in high or low population density
areas, at the speed of a telecommunications or at human pace (U.S. Agency for International Development
2009) but Last Mile is always referred as an « issue », a « burden », a « problem » or « challenge ».
Let’s see how we can better define this sweeping concept within the supply chain area.
ii. A Logistics or Supply Chain term?
The literature we studied often refers to the «Last Mile logistics » in related areas such as e- or m-commerce,
urban freight or express parcels delivery. It is always the last part of a delivery process but sometimes limited
to the urban area, sometimes including the upstream logistics to the last transit point (J. Wohlrab 2012).
We don’t think as « many logistics veterans [that] we have progressed from transportation to physical
distribution to logistics to supply chain »; we think that the Last Mile is a matter of integration of activities,
processes and information (Dr Marien 2003). We also consider that the Last Mile is a global supply chain
management issue in which information management is as important as physical distribution and where the
collection, analysis and use of relevant information is vital for success (Lovelock 2008).
Starting from The council of Supply Chain Management Professionals’ definition and from Lovelock - reknown
services marketing author - we would like to use the following definition for the Last Mile:

2. Last Mile is the last part of the B2C freight delivery process from the last transit point to the final endpoint of
consumption* in the supply chain ; as all Supply Chain Management functions, it’s including the planning,
implementation and control of the flow of goods, services, and related information.
* Deliveries from distribution centers to supermarkets or retail shops are therefore excluded.
Several authors (Romeo Danielis 2012) acknowledge Last Mile in urban freight distribution as a major supply
chain issue due to the conflicting objectives of all stakeholders in a complex urban environment where
economic, social and environmental performance is almost impossible to achieve. Let’ see…
iii. Why focus on urban freight ?
As a growing part of the world population lives in cities, urban transport must support booming passengers and
freight mobility needs to achieve economic development. The focus of urban transport has been put more on
passengers than freight but trends affecting urban freight will result in major impacts with high environmental
and social costs if not managed efficiently and rapidly.
Among these trends, changes within population structure often come first (the percentage and the aging of
urban population, the growing share of single households… which may turn into a continuous growth of home
deliveries) followed by various economic trends all aiming at reducing working capital and risks through
minimum stock levels and just-in-time small frequent deliveries (Prof. Michael Browne 2007). And since late
90’s, the boom of E-commerce has dramatically increased the frequency of smaller / faster deliveries.
Urban freight problems can be divided between three general categories: local last-mile/first-mile delivery and
pick-up, environmental impacts, and trade node problems (Dablanc 2012) but we think that last mile is the
core problem. Trade nodes issues are specific to cities which are hubs for maritime, air or road trade and
environmental impacts will relate more and more to businesses or homes deliveries in urban areas with the
evolution of retail, e- and m-commerce.
Although « the average share of freight vehicles on urban roads may be generally low […] the impact of these
vehicles on city spaces is significant » (Reisman 2011). This is the paradox of urban freight last mile : relatively
little attention but exponential impacts as most urban freight is carried by trucks which account from two cars
in traffic to five cars at crossroads, which create noise, visual and pollution issues, resulting in congestions and
reduced parking spaces due to loading / unloading activities.
iv. And on B2C ?
Although we couldn’t find statistics to evaluate the shares of B2B and B2C Last Mile traffic as well as the
distribution of delivery versus pick-up Last Mile traffic (express parcels carriers who hold a great share of the
market don’t communicate easily on these figures), we decided to focus on B2C deliveries.
B2C Last Mile deliveries differ from B2B with characteristics such as narrow time windows, more stakeholders,
final customers asking for convenient, free, guaranteed on-time home delivery and e-commerce players more
and more considering delivery as a full part of the « customer’s experience ». With large and homogenous
high-density shipments, few stops and delivery failures, low frequency and time sensitivity, traditional B2B
deliveries vary a lot from B2C home deliveries (Aranko 2013) ; with businesses relocating more and more in
dedicated sub-urban areas, the need for B2B players to reinvent « their » Last Mile is less stringent.
Considering the growth and the specific attributes of e-commerce deliveries, we think that the situation of Last
Mile B2C deliveries will not be viable in the long run. It is therefore vital to invent new strategies to achieve the
private commercial objectives at a sustainable public social and environmental price.
b. Characterizing Last Mile in B2C
In order to better tackle the problem, we will now try to characterize the Last Mile B2C deliveries by looking at
the involved stakeholders, its attributes and possible classifications to figure out the best possible solutions.

3. i. The Last-Mile stakeholders: the « usual suspects »?
Several authors identify only three categories – end-consumers, transportation operators and public
administration - as key stakeholders involved in urban freight transport (J. Wohlrab 2012). Although freight
carriers, manufacturers, wholesalers and retailers can be considered as a single category (transportation
operators in this particular case) as they share the same objective of meeting customers’ needs at the lowest
possible cost, we think that it is worthwhile to separate shippers from freight carriers. Express parcel carriers,
being very concentrated and powerful in some countries, can indeed pursue their own strategy to improve
their profitability and they have the financial power to conduct large scale experiments. We also think that they
will soon be competing directly with their current biggest customers, the major e-commerce players looking to
increase the value they deliver through differentiated delivery services.
Stakeholders are the « usual suspects » but the Last Mile in B2C is putting them in a real schizophrenic position:
o Consumers who require same day delivery are also residents demanding minimum disturbances
o Companies seeking for the highest service quality and tightest control of the Last Mile costs
o Public authorities trying to balance all functions of the city at an affordable cost
ii. The B2C Last-Mile delivery main attributes
Unlike B2B, B2C home deliveries attributes are all leading to increased costs so the cost issue became central
when addressing the Last Mile issue.
Attributes
SMALL Shipment size, Vehicle size, number of loads
HIGH Delivery frequency, Time sensitivity, Costs
MANY Number of delivery stops, Shipment types, Vehicle required
Adapted from Nicholls & Watson, 2005
Beside visible physical characteristics, some authors looked at those having significant impacts on the efficiency
and costs of the process, among which they found five which drive innovation in the Last-Mile (Roel Gevaers,
Characteristics of innovation in the Last Mile logistics 2009) :
o Service level requested by customers with narrower time windows,
o Type of delivery (attended and unattended) resulting in very high failure rate,
o Geographical area impacting routes efficiency,
o Fleet and technology
o The environment as the shorter the lead time, the more polluting the delivery becomes.
Important differences can also be found when looking at the different places and types of reception of the
goods (Roel Gevaers, Cost modelling and simulation of B2C last-mile 2013) :
Adapted from Gevaers, Van de Voorde, 2009 - 2013
Type of delivery
Place of delivery
Starting
Point
Pick Up at
Distribution
Center
Clustering
Delivery
Boxes
Collection
Points /
Shops
Home
Deliveries
Unattended Attended

4. c. What is the issue?
The B2C Last Mile issue can be stated like this:
The Last Mile of the freight B2C supply chain in urban areas is expensive, inefficient and polluting.
i. Are costs the real issue?
Many authors refer to some common beliefs such as the cost of Last Mile ranging from 13% up to 75% of the
total supply chain costs but, as far as we could see, limited data exist on the real cost of this last part of the
supply chain. We will later in the paper compare the various costs of potential solutions, but we lack
information to precisely assess the real cost drivers of B2C Last Mile deliveries.
Express carriers do communicate that it « accounts for about 35% of the operational cost for a parcel delivery
company » (Poppelaars 2014) while UPS estimates that when they save one mile per driver per day, they save
them $50 million a year.
The cost issue can be approached indirectly when looking at the cost increase resulting of a smaller delivery
window: it «ranges from 0% to 30% as the delivery window narrows from 24 hours down to 1 hour » (Alberto
Grando 2005) or at the cost reduction from 5 to 8% resulting from a 10% increase in customer density.
ii. Efficiency under pressure
With failed (“not-at-home”) deliveries up to 30% and return rates in e-commerce between 20 and 30% (Lenz
2004), the overall efficiency of the B2C Last Mile process is considered very low. It was quite surprising for us as
Express Carriers, the « Masters of the Last Mile », are seen as high performance logistics companies with
efficient last-mile operations and high standardization of their processes.
But as seen when looking at stakeholders, new and increasing customers requirements are putting already low
efficiency at stake, forcing participants to look for new solutions to overcome the most common challenges :
low visibility and lack of control at the point of delivery, complexity of local delivery planning with more and
more criteria to consider (delivery time window, expected time of arrival, volume & weight, delivery capacity,
shipment density and route profitability) and increasing local delivery constraints from authorities.
If efficiency is the issue, let’s have a closer look at what undermines B2C Last Mile efficiency.
All authors agree that the high degree of failed “not-at-home” deliveries is the major problem (Roel Gevaers,
Characteristics of innovation in the Last Mile logistics 2009) that makes the routing process very inefficient and
costly. The high degree of empty running also adds to the « attended home » raising costs, pollution and
congestion when the level of consumer density is too low or in case of returns management.
Traffic restricted to certain routes or time periods by local authorities are often cited as adding additional
constraints on routing and scheduling but they should be considered as part of the remediation strategies to
fight against environmental consequences rather than part of the original issue.
iii. Conclusion
The B2C Last Mile issue is about the conflict between shippers and carrier companies looking both for
increased efficiency, and the resulting economic, social and environmental costs for consumers and citizens
on the other hand.
d. Today’s Last Mile B2C strategies
i. Possible strategies for reducing Last-Mile impacts
Strategies for reducing Last Mile impacts can be classified based on their primary objective (Reisman 2011) :
1) regulate freight traffic to decrease the negative impacts
2) increase operational efficiency

5. 3) increase cooperation with consumers (and other stakeholders)
4) implement new distribution modes and/or new delivery options
ii. Traffic / vehicles regulations and urban access policies
Urban freight transport policies use specific combinations (Romeo Danielis 2012) of time-access regulations,
vehicle restrictions, loadingunloading and fiscal policies or land use planning to lower impacts of freight
transport on urban areas.
We will not look deeply into these policies as they are not directly intended at increasing efficiency of the Last
Mile process. One can also wonder if all the policies that increase the costs are worth it as the overall transport
costs account for a very small proportion of the final price of goods.
iii. Operational efficiency oriented strategies
Among possible strategies to increase operational efficiency, many focus on the concentration of freight
activity in specialized urban distribution centers to consolidate small shipmentsdistribution. « A review of the
transport activity in 17 relatively small European distribution centers found that the facilities reduced freight
vehicle trips by 30 to 80 percent, distance traveled by 30 to 45 percent, and vehicle emission from 25 to 60
percent » (Reisman 2011). Yet, some authors claim that consolidation centers are not feasible without public
subsidies and that many have closed a few years after creation (Dablanc 2012).
Within the possible strategies, off-peak or night deliveries give very good results when shippers get help to
purchase adequate vehicles.
Albert Heijn’s – a well-known retailer in the Netherlands - night time distribution PIEK project (Senter Novem
2008) shows how night deliveries can help lower congestions. Albert Heijn’s trucks were previously entering
the city of Den Haag in the morning during rush hours due to restricted access, resulting in more jam in peak-
hours and increased costs. The experiment was conducted for 10 shops on 1000 deliveries for 2 months with
special trucks which all components are under 60 dd(A) when a « standard » reversing beep is 110 db(A) and
showed excellent results :
- Less congestion due to lower average delivery time down from 1.30 to 0.30
- Dramatically reduced emissions
- Up to 30% costs savings through better usage of capacity, longer vehicles, less waiting hours and km
iv. Customers oriented strategies
Customer oriented strategies can leverage a user-centric approach where « the end-user is seen mainly as an
information source. » or a « user-driven approach, in which a close co-operation will result in new business
opportunities » (Aranko 2013). The use of such concepts in B2C Last Mile logistics helps carriers increase the
convenience of their service for consumers who now expect to be able to re-route parcels, determine delivery
costs, and even break-up their orders to multiple addresses (Aranko 2013).
A study at DHL Finland (Moberg 2013) showed that only 15 % of the customers calling DHL customer service
after a failed delivery chose the self pick-up option while 60 % requested second delivery to the same address
and 25 % a delivery to a new address. Although new pick-up options are flourishing, customers always prefer
the home delivery (Lenz 2004) so carriers must look how they can better collaborate with the final customer.
A recent relatively small scale experiment in UK supported by a packaging consolidation center reached 99.99%
of packages delivered first time thanks to a consumer choice portal enabling the consumer to make informed
decisions regarding the mode of delivery, the timing, reliability and eco-friendly transportation mode.
Giving back to customer the possibility to define how (express or not, attended or unattended, with what type
of vehicle), when (off peak hours, with pre-delivery alert…) and what to do if delivery fails leads to enhanced
performance of the Last Mile supply chain through low failed delivery rate. It is very promising but requires IT
integration among different partners which make it difficult to achieve at a very large scale.

6. This strategy to be successful must also give the consumers clear information on the related costs. A research
team in Netherlands came up with a Last Mile costs per product formula (Roel Gevaers, Cost modelling and
simulation of B2C last-mile 2013) but also with a costs comparison between different delivery options.
While the reference scenario delivery cost per unit with at home delivery but without any time window or lead-
time agreed is 3.87 euros, it more than doubles with a one hour time window. On the other hand, office drop
instead of home delivery can reduce B2C Last Mile costs by 30%.
v. Alternative strategies
Since early years of e-commerce, express carriers and major e-retailers have experienced alternative Last Mile
strategies. They first tried to shift traffic from trucks to more efficient and cleaner modes by using alternative
transportation vehicles. We will not deepen the use of alternative fuels or electric trucks as they don’t really
represent a new distribution strategy. Truck-free freight in Venice or delivery tunnels in Helsinki are sometimes
cited in the literature but they are too much dependent on local situations to be valuable large scale solutions.
On the contrary, we see the experiments done by many local companies, such as Boston Metro Pedal Power,
who use bicycles with trailers attached to haul up local freight as real new business models. Although it is the
most eco-friendly, it is too slow, too much labor-intensive and with too low capacity to replace the trucks.
However, we think that this solution will grow in the future because it is the cheapest option for Last Mile
distribution : compared to our reference scenario, researchers showed that cost reduction can reach up to 45%
using cargo bikes (Roel Gevaers, Cost modelling and simulation of B2C last-mile 2013).
Some new business models are also experienced by big players or startups. Walmart started brainstorming
how it could use its capacity to collect and/or drop off parcels and fully utilize the free capacity of the trucks
delivering their stores (KEWILL 2014). Startups such as, Stuff2Send (www.stuff2send.com) adopted a Peer-to-
Peer (P2P) approach where anyone can get paid to deliver : users simply post a request, carriers bid to deliver
the items, senders select best offers and pay for the task once it has been completed. Another startup, Shutl,
who’s just been bought over by Ebay worked with e-commerce companies, determining the nearest store
where the item can be found, delivering it to online buyers within minutes directly from the store.
Different companies also implemented new delivery options from private delivery box to shared collection
lockers in urban area. Unfortunately, all these new options designed to lower the failed deliveries did not
succeed apart from the DHL « pack station » in the Netherlands only because of the very high market
penetration of DHL.
The most alternative successful strategy by far has been the creation of collection points networks in shops.
The reference European brand Kiala, created in 2001, reached more than 4 600 collection points throughout
Europe and a 50% annual growth rate between 2001 and 2011 and was eventually taken over by UPS. From a
cost perspective, when considering an average of 2 parcels to be collected per collection point, the cost per
unit is about half of the standard home-delivery cost (Roel Gevaers, Cost modelling and simulation of B2C last-
mile 2013). Of course, this solution is very much dependent of the distance to the collection point which needs
to be under a few hundreds meters so only suitable in dense city areas.
2. Big Data: the Last Mile’s final destination?
a. Big Data: Hype, hope or real opportunity?
The question is legitimate as sizing data volumes and observing information explosion date back to the early
70’s. From R. Mashey, who first presented a paper « Big Data… and the next wave of infrastress » in 1998
(Mashey 1998) to Doug Laney, an analyst with the Meta Group, who published in 2001 what became ten years
later the widely accepted three dimensions defining Big Data - the three Vs, Volume, Velocity and Variety
(Laney 2001) - many IT professionals and observers soon realized that the traditional data management
techniques and tools would not be able to cope with the new so-called fourth production factor : information.

7. We found dozens of definitions for Big Data, but the three following are best describing all the aspects of Big
Data we tried to sum-up in our title:
• “Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective,
innovative forms of information processing for enhanced insight and decision making” (GARTNER, IT
glossary, 2014) because it is clearly oriented towards decision making in a cost-effective way.
• Big Data is « the widespread belief that large data sets offer a higher form of intelligence and knowledge
that can generate insights that were previously impossible, with the aura of truth, objectivity, and
accuracy » (Danah Boyd 2012) because Big Data is sometimes looking as a magical concept
• « Big Data is like teenage sex: everyone talks about it, nobody really knows to do it, everyone thinks
everyone else is doing it, so everyone claims they are doing it… (Dan Ariely on Facebook, 2014). No
comment!
Coming from automatic sensors, smart connected devices, social networks… « Data, data everywhere» as a
special report from the Economist claimed. It is hard to say who came first : the data or the new concepts and
technologies to manipulate them… but there seems to be a consensus about technology as a necessary enabler
for data which couldn’t be handled with traditional technologies a decade ago.
Besides the size of datasets, big shall be understood as big complexity rather than big volume ; Big Data is the
new way we can combine many independent, traditional and digital, structured and unstructured, internal and
external data sources one with another to generate valuable insights and information.
There is no doubt that Big Data is creating value. We now witness examples of successful implementation in
marketing, retail, operations, supply chain, and the development of new business models.
Before looking at how this applies to the Last Mile issue, we tried to list how Big Data can be a value driver:
• through easier access : instant and free access to public or « open » data sources
• through synchronous monitoring : real-time processes and operations, real-time real-life events
monitoring allow real-time optimization and improvements
• through enhanced segmentation : when the individual becomes a segment
• through new business models
• through new value given to personal data
b. Big Data in the Supply Chain
The use of synchronized huge sets of data is common in the Supply Chain domain. Retail has been using point
of sales data since late 70’s to manage inventories, optimize production planning and sales management. Big
players such as Walmart implemented in the 90’s vendor managed inventories concepts and tools built on high
velocity of information exchange with its suppliers and an increased transparency along the supply chain.
But to date, the use of data has been limited to the internal IT system of a company, sometimes opened to its
suppliers or other business partners. Yet it has been unable to solve huge issues regarding efficiency, cost,
customer’s satisfaction, and ecological concerns today’s supply-chains are facing, especially in urban areas.
84% of supply chain executives think Big data will positively impact their traditional concerns within the supply
chain (Eyefort Transport, Supply Chain Big Data Report, 2013). Of course, as they all put efficiency
improvements and costs reduction in the top 3 reasons to invest in Big Data, inventory management projects
often come first.

8. By analyzing SKU level sales history and seasonal data, by mixing internal with external data sets such as
weather conditions and by using predictive algorithms they can reduce inventory levels and stock-outs.
The supply chain is a natural target for Big Data but it is odd to see that 75% of supply chain specialists think
that internal data are more important than external data. They miss the value brought by connecting the
company with external sources; they miss the opportunity to benefit from all the data they can collect along
their supply chain with their wholesalers, retailers…
Focusing a little more on the Last Mile, we’ll now have a look at two applications of Big Data that we think will
have major impacts on all B2C Last Mile strategies: RFID and location data management.
c. Big Data and the Last Mile
When looking back at all possible B2C Last Mile strategies, it’s quite obvious that Big Data can apply to almost
all foreseen strategies from the design of the distribution network to the new distribution options for Last Mile.
We’ll now look at how Big Data can impact each Last Mile strategy from to increase operational efficiency,
customer interaction and service quality and eventually help build new business models.
i. Big Data for increased operational efficiency
Global Express carriers UPS and DHL compete to turn Big Data into a real competitive advantage trying to
optimize demand forecasting and route planning to lower Last Mile failed deliveries.
Effective resources and demand planning
At a strategic level, the configuration of their logistic network of warehouses, distribution hub or local offices
demands accurate forecasts where land use and traffic public data can help make better decisions. To an even
more strategic level, DHL just announced a new “DHL Resilience 360” solution using a comprehensive data pool
for the early detection of potential risks in the supply chains of its customers.
At an operational level, day to day activity forecasts can be improved using historical data to make a better use
of people and vehicles. The Parcel Volume Prediction system, created by DHL, analyses huge data sets from
different sources to better balance load and transport capacity. Historical data about capacity used and
shipments volume are connected to the seasonality of transport, the forecasts of activity growth by region or
sector, the recent changes in raw materials prices… to understand the influence of external factors on future
demand. These forecasts are then distributed to warehouses and distribution centers to adjust the planning of
required resources (DHL, Press Release, 02/2014).
Routes optimizing for parcel deliveries companies
Since the formulation of "Traveling Salesman Problem" more than 80 years ago, logisticians have tried to find
the best possible route. Thanks to Big Data, UPS developed an in-house solution called ORION (On-Road
Integrated Optimization and navigation)
which determines the best possible route
for each driver for its B2B/B2C express
parcels delivery service. Based on detailed
road information for more than 250 million
addresses, enhanced with personalized
optimization rules (rush hours for each area,
the famous « only right turns » rule …) and
traffic related information, UPS can now
adapt to change in customers requests in
real time. Implemented early 2012, ORION
shows encouraging results with a decrease
of 85 million miles driven per yearn resulting
in million of ineffective minutes saved, less
fuel used and less CO2 emitted.

9. ii. Improved customers interactions to reduce failed deliveries
UPS also developed the service "My Choice ", which allows its customers to modify directly on their
smartphone the hour and the place of delivery until the last minute. Big Data can indeed help companies
optimize customer interaction and service and therefore reduce “not at home” syndrome in B2C Last Mile
deliveries.
UPS is also experimenting an innovative approach that allows customers to make informed choices regarding
their transport mode, time-window and even environmental impacts of their choices. What’s the need to get a
parcel the next day if I’m out of the office for one week? Do I need this product I’ll be using in three months
tomorrow? Big Data will allow B2C carriers to use yield management techniques in real-time based on current
traffic information to dynamically adapt the cost for the consumer.
By closing the gap between goods and information flows, RFID has also a great potential for optimizing
customer interactions. The introduction of RFID technology started on the retail’s side with passive UHF tags
requested by big retail chains such as Wal-Mart and Tesco. It is likely that tag prices will soon come down and
be integrated into B2C parcels.
With new RFID active tags, the real-time information will help close the Last Mile information gap where the
customer didn’t know the location of its parcel after the last shipping point. Combined with planning
information and a customer portal, people will be able to collaborate with carriers. One can even imagine that,
once approved by the user, a mobile app is directly collaborating with an intelligent system based on RFID
location information to ensure that the delivery is made when the customer is at home.
iii. From new delivery options to brand new distribution models
New delivery options
Big European retailers as Auchan also developed the new "Drive" concept in an attempt to have the customer
drive the Last Mile. Big Data will allow them to refine location, assortment, opening hours to lower their
operations cost and increase efficiency.
New delivery options, such as delivery box will benefit from Big (open) Data to define the best possible location
for collective pick-up installations based on passengers routes, housing and traffic data… Personal delivery
services will be able to predict best replenishment time and set-up subscription based replenishment that will
allow unattended deliveries to solve the not at home issue.
Crowd based Delivery System
Some companies tried a completely new approach to failed deliveries, by leveraging personal location, social
behaviors to outsource the last mile delivery to private individuals.
The idea behind the collective approach applied to the logistics is simple: the taxi drivers, the students or even
people who make regular routes can be paid to take care of the last mile delivery, on routes which they would
realize anyway. The MyWays service from DHL, who pioneered in the field of Crowd-Delivery, allows individuals
to share their geographic position through a mobile app and accept selected deliveries. The success of such
initiatives will depend upon the ability to reach a critical mass of deliverers, which is not the case yet.
Out of the box business models
In search for the magic recipe, express carriers such as DHL even create new services out of their core business.
DHL is experimenting an environmental intelligence service derived from data collected by its fleet of vehicles
equipped with all kind of air and noise sensors that besides air quality will be able to inform us on the density
of the traffic, the use of parking spaces…
Amazon Dash that enables you to shop from home without effort just by scanning or talking (!) to a little device
may well solve the Last Mile issue by just preventing people to go shopping, just waiting home for their next
delivery.

10. 3. Conclusion
Whatever the strategy it applies to, Big Data’s « secret » is about making a better faster stronger use of more
information from more sources under more formats to better coordinate all stakeholders of the Last Mile. As
often in the « extended » supply chain problems, it is a cooperation issue and results will depend upon the
ability to find beyond direct competition or conflicting objectives common interests and ways of collaboration.
By synchronizing the flows of information and goods in real-time, Big Data should help decrease failed B2C
deliveries by a fair amount. By making an extensive use of location data and social networks, it should co-
create new business models where consumers / customers act as potential deliverers leveraging the existing
physical infrastructures.
We could also imagine going a step further with a complete dematerialization by converting physical goods into
digital flows of information where your purchased item is instantly delivered home to your personal teleport
3D factory.
Will quantum teleportation be the next Last Mile issue ?
Bibliography
Alberto Grando, Marco Gosso. «Electronic Commerce and Logistics : The Last Mile Dilemma Reference
Framework and Simulation.» Red de Revistas Científicas de América Latina, el Caribe, España y Portugal,
Universidade de São Paulo Brasil, 2005.
Aranko, Jenni. Developing the last mile of a parcel delivery service concept for consumers. Degree Programme
in Service Innovation and Design, Laurea University of Applied Sciences, Laurea Leppävaara, 2013.
Dablanc, Giuliano, Holliday, O’Brien. «Best Practices in Urban Freight Management : Lessons from an
International Survey.» IFSTTAR, French Institute of Science and Technology for Transport,
Development and Networks, University Paris-Est, 2012.
Danah Boyd, Kate Crawford. Critical Questions for Big Data. 2012.
Dr Marien, Edward J. «SCM & Logistics: What's the Difference ?» Inbound Logistics. 02 2003.
www.inboundlogistics.com (accès le 04 27, 2014).
J. Wohlrab, T.S. Harrington, J.S. Srai. «Last Mile Logistics Evaluation.» POMS Annual Conference. Chicago, 2012.
KEWILL. «Urban logistics. Urban myth, or sustainable supply chain strategy ?» www.kewill.com. 2014.
KYATHI TECHNOLOGIES. 2014. www.kyathitechnologies.com.
Laney, Doug. «3D Data Management: Controlling Data Volume, Velocity, and Variety.» Édité par METAGROUP.
METAGROUP, 2001.
Lenz, Prof. Dr. Barbara. «The Last Mile – Old problem, New Options ?» COST Action 355. DLR – Institute of
Transport Research, 2004.
Lovelock, Christopher. Services Marketing. 6E. Pearson, 2008.
Mashey, John R. «Big Data ... and the Next Wave of InfraStress.» Big Data ... and the Next Wave of InfraStress.
SGI, 1998.

11. Poppelaars, John. «Analytics vital in logistics processes.» Logistics Manager of the Year. March 2014.
www.logistiekmanagervanhetjaar.nl (accès le April 2014).
Prof. Michael Browne, Marzena Piotrowska, Dr Allan Woodburn, Julian Allen. «Literature Review WM9 Part I
Urban Freight Transport.» Transport Studies Group, University of Westminster, 2007.
Reisman, Alexandra. «Strategies for Reducing the Impacts of Last-Mile Freight in Urban Business Districts.»
2011.
Roel Gevaers, Eddy Van de Voorde, Thierry Vanelslander. «Characteristics of innovation in the Last Mile
logistics.» Department of Transport and Regional Economics, University of Antwerp, 2009.
—. «City Logistics Bali Indonesia June 2013.» Édité par Department of Transport and Regional Economics
Faculty of Applied Economics University of Antwerp. Cost modelling and simulation of B2C last-mile
characteristics with implications on urban areas and cities. 2013.
Romeo Danielis, Elena Maggi, Lucia Rotaris, Eva Valeri. «Urban supply chains and transportation policies.»
University of Trieste, Italy, University of Molise, Italy, 2012.
Senter Novem. «Albert Heijn & PIEK project.» Senter Novem, 2008.
U.S. Agency for International Development. «DISTRIBUTION ALONG THE NORTH SHORE OF LAKE KWANIA.»
USAID Deliver Project, U.S. Agency for International Development, 2009, 63.
Wikipedia. «Last mile (transport).» Wikipedia. 2014. en.wikipedia.org/wiki/Last_mile_(transport) (accès le 04
25, 2014).
—. «Last mile.» 2014.