Real-time (Big) Data in Freight Transport - Meeting the global trends

REAL-TIME (BIG) DATA IN FREIGHT TRANSPORT -
MEETING THE GLOBAL TRENDS
Per Olof Arnäs
Chalmers University of Technology
@Dr_PO
per-olof.arnas@chalmers.se
about.me/perolofarnas
Slides: slideshare.net/poar
Image: www.simonstalenhag.se

Northern LEAD  
Logistics Research Centre
Founded by:
Chalmers University of Technology
University of Gothenburg
Logistics and Transport Society LTS

Tomorrow’s logistics. We are finding the answers.
Around 70 researchers
Research centre for
sustainable logistics
solutions
Five core research
groups
Organises, facilitates,
disseminates highly relevant
logistics research
Collaboration between
Chalmers and University
of Gothenburg

Professors 10
Visiting Professors 6
Associate Professors 5
Post docs 6
Faculty 14
PhD students 32
Total 73
Five core
research groups
Physical
Distribution
Production
Logistics
Industrial marketing
& purchasing
Logistics &
Transport
Optimization

Thought
leadership
through
high quality
research
and
innovation
Challenges
and trends
Outcomes for
policy and business
Volatility and risk
Demographic
changes
Complexity and
structural
flexibility
Resource
limitations
Manufacturing
and retail
revolution
Environmental
impact
Focused areas
Urban Transport
Long Distance Transport
Purchasing of
transport
Logistics and Production Networks
Information,
planning and
control
Measurements
and measuring
Service
development in
networks
Resource
utilisation
Intermodality

Doing some Sisyphus work by Kalexanderson on Flickr (CC-BY,NC,SA)
5
GLOBAL
TRENDS
Source: PWC (google: pwc megatrends 2014)

Crowd by James Cridland on Flickr (CC-BY)
Megatrend #1
Demographic
and social
change

Four Storms And A Twister by JD Hancock on Flickr (CC-BY)
Megatrend #2
Shift in
economic
power

Boston Downtown at Night by Werner Kunz on Flickr (CC-BY,NC,SA)
Megatrend #3
Rapid
urbanisation

¡Rayos! by José Eugenio Gómez Rodríguez on Flickr (CC-BY,NC,SA)
Megatrend #4
Climate
change and
resource
scarcity

Megatrend #5
Technological
breakthroughsSource: PWC (google: pwc megatrends 2014)

Demographic
and social
change
Shift in
economic
power
Rapid
urbanisation
Technological
breakthroughsClimate
change and
resource
scarcity
5
GLOBAL
TRENDS

Things are happening outside the
freight industry
(and have been for some time)

Stage Coach Wheel by arbyreed on Flickr
Development of transportation
technology has been
fairly linear
…for the last 5500 years

We are in the middle of a gigantic
exponential development curve
beginning

A new global eco system
where new types of,
knowledge based,
industries compete with
traditional ones
http://jaysimons.deviantart.com/art/Map-of-the-Internet-1-0-427143215

Startups don’t compete with airlines...
by purchasing a bunch of planes
hiring a bunch of pilots
and locking up a bunch of terminals at airports.
Quote: bryce.vc/post/18404303850/the-problem-with-innovation
Image: Connecting the community, my Twitter strategy, and American Airlines at DFW by Trey Ratcliff on Flickr (CC-BY,NC,SA)

Startups compete with airlines by
inventing videoconferencing.
Startups don’t compete with airlines...
by purchasing a bunch of planes
hiring a bunch of pilots
and locking up a bunch of terminals at airports.
Quote: bryce.vc/post/18404303850/the-problem-with-innovation
Image: Connecting the community, my Twitter strategy, and American Airlines at DFW by Trey Ratcliff on Flickr (CC-BY,NC,SA)

ICT Maturity
App
"Multi-Touch" by DaveLawler on Flickr (CC-BY)

Source: European Commission, EU Transport in Figures, Statistical Pocketbook 2012
Demand for
transport is
coupled with
economic
development

Passenger cars dominate modal split
Air transport is the fastest
growing mode (until 2007) Road and sea transport are
the fastest growing modes in
freight transport
Source: European
Commission, EU
Transport in Figures,
Statistical
Pocketbook 2012

Increasing freight transport demand
http://www.eea.europa.eu/data-and-maps/figures/freight-transport-activity-growth-for-eu-25
EU-25

Final energy consumption, EU-28, 2012
(% of total, based on tonnes of oil equivalent)
Source: Eurostat

Environm
ental
perform
ance
Social
perform
ance
Economic
performance
Sustainability
The triple bottom line
Craig R. Carter Dale S. Rogers, (2008),"A framework of sustainable supply chain management: movngtoward new theory”,
International Journal of Physical Distribution & Logistics Management, Vol. 38 Iss 5 pp. 360 - 387

Environm
ental
perform
ance
Social
perform
ance
Economic
performance
Sustainability
Focus on transportation!

RESOURCE UTILISATION LOW
Source: Kent Lumsden

RESOURCE UTILISATION
LOW
Safety imbalance
Variation in resource demand
Chain imbalance
Caused by the chain
Technological imbalance
E.g. mismatch in equipment
Operational imbalance
Goods and resource flow not compatible
Structural imbalance
Uneven transport demand

RESOURCE UTILISATION
LOW
Safety imbalance
Variation in resource demand
Chain imbalance
Caused by the chain
Technological imbalance
E.g. mismatch in equipment
Operational imbalance
Goods and resource flow not compatible
Structural imbalance
Uneven transport demand
Several of these
imbalances can be
reduced by
reducing
uncertainties

But the biggest problem in
transportation is time.
There is not enough of it.
Ever.
InSearchOfLostTimebybogenfreundonFlickr

The transport
industry does not like
real-time decisions.
At all.
Batch-handling
Zip codes Zones
Time-tables
DSC_9073.jpg by James England on Flickr (CC-BY)

Strategic Tactical Operational Predictive
Time horizons
Freight industry
Most (preferably all)
decisions in the
transportation industry are
made here. At the latest.
Uninformed,
ad-hoc, and
probably non
optimal,
decisions
Science
fiction

January February March April May June July August September October November December
10%
Nov 25
When do we start generating profit?
Profit margin =
Sales - Cost
Sales

10%
Nov 25
2%
Dec 25
Profit margin =
Sales - Cost
Sales

10%
Nov 25
1%
Dec 29
2%
Dec 25
Profit margin =
Sales - Cost
Sales

Basic
data

From/To/What/When

Owner:
the
customer
Resource
alloca9on

What
vehicle? 
Owner:
Traﬃc
controller
Cargo
informa9on

Loaded
amount

Owner:
Supplier
Proof
of
Delivery

Time/date/signature

Owner:
Driver
Pricing

Owner:
Financial
dept.
Enough
informa9on
to
create
invoice
All
of
these
steps
cost
money!
From
order
to
invoice 
(transporta9on
company)

An
increase
in
the
number
of

orders
lead
to
increased
cost
Goal:
De-‐couple
the

dependecy
between

administa9ve
cost
and

order
volume
Number
of
orders
per
year
Total
cost
for

administra9on
of
all
orders
Handle
excep9ons
–
not

transac9ons!

Image: Alain Delorme, alaindelorme.com
The current
model is focused
on economy of
scale and
standardization

Process
improvement
Service
developm
entInfrastructure
developm
ent
Customer
controls last
mile
Faster and
better
returns
Better
delivery
experience
Secure
identification on
pickup/delivery
Distribution
of food
Home
delivery
Support
companies that
want to add E-
commerce to
their business
Collect-in-store
Local
same-day
delivery
Improved
delivery note
Delivery and
pickup during
weekends
Marketing of
the E-channel
Sustainable and
climate friendly
3PL targeted at E-
commerce
Faster, more reliable
and secure
deliveries in Europe
Better
infrastructure on
consumer side
Better
security
Source: Svensk Digital Handel 2014 Bo Zetterqvist
Areas of development
for logistics
companies in relation
to e-commerce

Process
improvement
Service
developm
entInfrastructure
developm
ent
Customer
controls
last mile
Faster and
better
returns
Better
delivery
experience
Secure
identification on
pickup/delivery
Distribution
of food
Home
delivery
Support
companies that
want to add E-
commerce to
their business
Collect-in-store
Local
same-day
delivery
Improved
delivery note
Delivery and
pickup during
weekends
Marketing of
the E-channel
Sustainable
and climate
friendly
3PL targeted at
E-commerce
Faster, more
reliable and
secure deliveries
in Europe
Better
infrastructure on
consumer side
Better
security
Areas of development
for logistics
companies in relation
to e-commerce
Digital
development
needed in
freight
transport

Customer
controls
last mile
Faster
and better
returns
Better
delivery
experience
Secure
identification
on pickup/
delivery
Collect-in-
store
Improved
delivery note
Sustainable
and climate
friendly
3PL targeted at
E-commerce
Faster, more
reliable and
secure
deliveries in
Europe
Better
security
Digital development needed in freight transport
Process improvement
Use ICT to make the system more efficient
Real-time decision making, footprinting, better digital interaction between stakeholders
Service development
Use ICT to create new services
Digital information enables new business models
Infrastructure development
Use ICT to interact with infrastructure
Location Based Intelligence etc.

Customer
controls
last mile
Faster
and better
returns
Better
delivery
experience
Secure
identification
on pickup/
delivery
Collect-in-
store
Improved
delivery note
Sustainable
and climate
friendly
3PL targeted at
E-commerce
Faster, more
reliable and
secure
deliveries in
Europe
Better
security
Digital development needed in freight transport
Process improvement
Use ICT to make the system more efficient
Real-time decision making, footprinting, better digital interaction between stakeholders
Service development
Use ICT to create new services
Digital information enables new business models
Infrastructure development
Use ICT to interact with infrastructure
Location Based Intelligence etc.
The freight industry has work to do…

So…
What are we
doing about all
this?

Gartners Hype Cycle for Emerging Technologies
Augmenting
humans with
technology
Machines
replacing
humans
Humans and
machines
working
alongside each
other
Machines
better
understanding
humans and
the
environment
Humans better
understanding
machines
Machines and
humans
becoming
smarter

Source: Gartner August 2014

Could affect freight transport

Strategic Tactical Operational Predictive
Time horizons
We are approaching
this boundary
…and we are
starting to
move past it!
Real-time!

Opportunities
Digitization Increasinggoods volumes
New
technology
Political
interest
Quad Aces by fitzsean on Flickr

Business processes Infrastructure
Paperbased
Phone 
Papers
Road
signs
A
nalogue
tools
R
D
S
M
onitorfuel
cosnum
ption
Digitization version 0 0.5 1.0 1.5 2.0
E-mail
Fax
TMS-
systems
Excel
Route
planning
G
PS
fornavigation
Electronically
generated
freightdocum
ents
Barcodes
RFID-tags
Simple order handling
Advanced order
handling
Openinterface
W
eb
based
UI
Platform
based
system
s
Hardware-
oriented
Datacollection
systems
(proprietary)
Communicationwith
vehicles
E-invoice
W
eb
based
booking
Route
optimisation
Thesocialweb
Openconnectivity
Integrated
prognosis
Data collection
systems (open)
Tolling
system
s
Webservices with
traffic data
Dynamic
routing
systems
Performance
BasedaccessPerformanceBasedaccess
Mashups 
Multipledata
sources
Probedata
Individual
routing
inform
ation
Platooning
Platooning
Exceptions
handling
Smartgoods
Manual
Computers
Software
Functions
Distributed
decision
making
G
oods
as
bi-
directional
hyperlink
Paperbased
CC-BY Per Olof Arnäs, Chalmers
Goods Vehicle
Barcodes

RFID

Sensors
ERP systems

TMS systems

E-invoices

Cloudbased
services
Order handling

Driver support

Vehicle
economics
RDS-TMC

Road taxes

Active traffic
support
Predictive
m
aintenance
2014-10-14

Goods
Vehicles
Business
processes
Infrastructure
Stra-
tegic
Tac-
tical
Opera-
tional
Pre-
dictiveWhat happens
when access to
real-time data
increases?
not quite clear on the concept by woodleywonderworks on Flickr (CC-BY)

The Action of New York City by
Trey Ratcliff on Flickr (CC-BY,NC,SA)
Need for speed
Data collection
Dataprocessing
Data
exploitation

En la cima! by Alejandro Juárez on Flickr (CC-BY)
3 mountaintops to climb…

3 data types
Mountaintop #1
Collection of data in real-time
Fixed Historical Snapshot

Mountaintop #1
Collection of data in real-time
5 data domains
Vehicle CargoDriver Company
Infrastructure/
facility
at least…

Length 
Weight 
Width 
Height
Capacity 
+ other PBS-criteria
Emissions 
Fuel consumption 
Route
Position 
Speed 
Direction
Weight 
Origin 
Destination
Accepted ETA
Temperature 
+ other state variables
Temperature + other state
variables
Education/training
Speed (ISA) 
Rest/break schedule 
Traffic behaviour
Belt usage 
Alco lock history
Schedule status (time to
next break etc.)
Contracts/
agreements
Previous interactions Backoffice support
Fixed Historical Snapshot
Vehicle
Cargo
Driver
Company
Infrastructure
/facility
Map
+ fixed data layers
Traffic history
Current traffic
Queue
Availability
DATA MATRIX

Mountaintop #2
Processing of data in real-time
Movie by Waze

Mountaintop #2
Processing of data in real-time

Mountaintop #3
Exploiting data in real-time
Connected. 362/365 by AndYaDontStop
on Flickr (CC-BY)
Lisa for I/O Keynote by Max Braun on
Flickr (CC-BY)
Fulham-Manchester United
24-02-2007 by vuhlser on Flickr (CC-
BY)

Requirement
Transport 1
Transport 2

Requirement
Transport 1
Transport 2
No access!
Full access!

Requirements. Different.
Port area City center
Freight terminal Bridge

Big data in freight
transport
Film by Foursquare. Google: checkins foursquare

”Fast Up-and-Coming
Movers Toward the Peak
Are Fueled by Digital
Business and Payments”
”…the market has settled
into a reasonable set of
approaches, and the new
technologies and practices
are additive to existing
solutions”
(regarding the decline of Big data on the curve)
Gartner, August 2014

So…
What is
Big data?
{ biométrique ... } by David Jubert om Flickr (CC-BY,NC,SA)

2011 2013 2015
”Big data is an all-
encompassing term for
any collection of data sets
so large and complex that
it becomes difficult to
process using on-hand
data management tools or
traditional data
processing applications.”
- Wikipedia
2015

892 by benmschmidt on Flickr (C)19th century shipping visualized through the logs of Matthew Fontaine Maury (1806-1873), US Navy
Shipping
movements in
the 19th century

Jawbone measures sleep
interruption during earthquake
https://jawbone.com/blog/napa-earthquake-effect-on-sleep/

Not statistics
Exhausted by Adrian Sampson on Flickr (CC-BY)
just

Not
Business
Intelligence
Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA)
just

http://dashburst.com/infographic/big-data-volume-variety-velocity/

Human resources
Reduction in driver
turnover, driver
assignment, using
sentiment data
analysis
Real-time capacity
availability
Inventory
management
Examples of applications in freight
(Waller and Fawcett, 2013)
Transportation
management
Optimal routing, taking
into account weather,
traffic congestion, and
driver characteristics
Time of delivery,
factoring in weather,
driver characteristics,
time of day and date
Forecasting
Waller, M. A. and Fawcett, S. E. (2013), Data Science, Predictive Analytics, and Big Data: A Revolution That Will
Transform Supply Chain Design and Management. JOURNAL OF BUSINESS LOGISTICS, 34: 77–84

Manage complex systems
Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center

Measure
real-time
system
behaviour
Emil Johansson - EJOH.SE

http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk
Vizualisation

Created by Oliver O'Brien (UCL Geography/UCL CASA)

7Big Data Best Practice Across Industries
Usage of data in order to:
Increase Level of
Transparency
Optimize Resource
Consumption
Improve Process Quality
and Performance
Increase customers
loyalty and retention
Performing precise
customer segmentation
and targeting
Optimize customer
interaction and service
Expanding revenue
streams from existing
products
Creating new revenue
streams from entirely
new (data) products
Exploit data for: Capitalize on data by:
New
Business Models
Customer
Experience
Operational
Efficiency
Use data to:
• Increase level of
transparency
• Optimize resource
consumption
• Improve process quality
and performance
Exploit data to:
• Increase customer
loyalty and retention
• Perform precise customer
segmentation and targeting
• Optimize customer interaction
and service
Capitalize on data by:
• Expanding revenue streams
from existing products
• Creating new revenue
streams from entirely new
(data) products
New Business ModelsCustomer ExperienceOperational Efficiency
Figure 4: Value dimensions for Big Data use cases; Source: DPDHL / Detecon
DHL 2013: ”Big Data in Logistics”

Domain
knowledge
critical!
See for instance: Waller, M. A. and Fawcett, S. E. (2013), Data
Science, Predictive Analytics, and Big Data: A Revolution
That Will Transform Supply Chain Design and Management.
JOURNAL OF BUSINESS LOGISTICS, 34: 77–84
Data scientists -
the new superstars
"Data Science Venn Diagram" by Drew Conway - Own work. Licensed under Creative Commons Attribution-
Share Alike 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/
File:Data_Science_Venn_Diagram.png#mediaviewer/File:Data_Science_Venn_Diagram.png

smile! by Judy van der Velden (CC-BY,NC,SA)
Speculative
shipping
http://www.scdigest.com/ontarget/
14-01-21-1.php?cid=7767

http://www.scdigest.com/ontarget/
14-01-21-1.php?cid=7767
Speculative
shipping Package item(s) as a package for
eventual shipment to a delivery address
Associate unique ID with package
Select destination geographic area for
package
Ship package to selected distribution
geographic area without completely
specifying delivery address
Orders
satisfied by item(s)
received?
Package
redirected?
Determine package location
Convey delivery address, package ID to
delivery location
Assign delivery address to package
Deliver package to delivery address
Convey indication of new destination
geographic area and package ID to
current location
Yes
Yes
No
No
smile! by Judy van der Velden (CC-BY,NC,SA)

Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)

Challenges
Company

Challenges
Company
Supply chain,
simple

Challenges
Company
Supply chain,
simple
Supply chain,
complex

Challenges
Company
Supply chain,
simple
Supply chain,
complex
Eco system

Challenges
Cross-disciplinary
Cross-industries
Cross-borders

Private Property by Nathan O'Nions on Flickr (CC-BY)
(Freight) companies want to share
as little data as possible,
with as little friction as possible,
to get the highest utility possible

Real-time decision making not
always successful…

Not all ideas age with grace

Not everyone will want to
adopt new things…

Even if it hurts. Although…
Indiana Jones

It’s not business as usual.
This is the internet
happening to freight
transport.
There is no ’usual’
anymore.
Hello Kitty Darth Vader by JD Hancock on Flickr (CC-BY)

It’s not business as usual.
Get used to it.
This is the internet
happening to freight
transport.
There is no ’usual’
anymore.
Hello Kitty Darth Vader by JD Hancock on Flickr (CC-BY)

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