Big data from a freight company perspective.

1. Big data from a freight
company perspective
Per Olof Arnäs, PhD
Chalmers University of Technology
Gothenburg, Sweden
about.me/perolofarnas
Slides online: slideshare.net/poar
Film by Waze

2. Demographic
and social
change
Shift in
economic
power
Rapid
urbanisation
Technological
breakthroughsClimate
change and
resource
scarcity
5
GLOBAL
TRENDS
Source: PWC (google: pwc megatrends 2014)

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

4. Things are happening outside the
freight industry
(and have been for some time)
1957

5. Things are happening outside the
freight industry
(and have been for some time)
Image: Richard Hancock, twitter.com/CanaryWorf
2015

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

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

9. 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

10. 355:365:2015BWH by hermitsmoores on Flickr (CC-BY,NC,SA)
Make analogue
information digital
Digitization:

11. MobileWorldCongress2016byKārlisDambrānsonFlickr(CC-BY)
Increased use of
digital technology
Digitalization:

12. MobileWorldCongress2016byKārlisDambrānsonFlickr(CC-BY)
Increased use of
digital technology
Digitalization:
Make analogue
information digital
Digitization:
Both are important! (and interesting)

13. Ominous Windmill by Conrad Kuiper on Flickr (CC-BY,NC,SA)
Digit(al)ization
is not a trend

14. Ominous Windmill by Conrad Kuiper on Flickr (CC-BY,NC,SA)
Digit(al)ization
is not a trend
It is a force of
nature

16. 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

17. 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
Digital
development
needed in
freight
transport

18. 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
Source: Svensk Digital Handel 2014 Bo Zetterqvist
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.

19. Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Low profit margins Social issues
Fragmented
industry
Data all over
the place, but
not where
most needed
Large investments

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

21. 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)

22. 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

23. Business processes Infrastructure
Paperbased
Phone
Papers
Road
signs
A
nalogue
tools
R
D
S
M
onitorfuel
cosnum
ption
Digitalisation 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
2014-10-15

24. Smart access/guidance control

25. Smart access/guidance control

26. Smart access/guidance control

27. Smart access/guidance control

28. • Data amounts increase greatly
• There are data gaps/silos
preventing development
• Lack of standards
• Personal data privacy is a
long-term threat
• Lack of talent/capacity to
handle foreseen need
https://ts.catapult.org.uk/documents/10631/169582/The+Transport+Data
+Revolution/99e9d52f-08a7-402d-b726-90c4622bf09d

29. 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

30. Gartners Hype Cycle for Emerging Technologies
Source: Gartner July 2015
Could affect transportation and logistics

31. http://www.dhl.com/en/about_us/logistics_insights/dhl_trend_research/trendradar.html

32. 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

33. 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

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

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

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

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

38. Varela Rozdos, I and Tjahjono, B, 2014 ”BIG DATA ANALYTICS IN SUPPLY CHAIN MANAGEMENT: TRENDS AND
RELATED RESEARCH”, 6th International Conference on Operations and Supply Chain Management, Bali, 2014

39. Multicolour Jelly Belly beans in Sugar! by MsSaraKelly on Flickr (CC-BY)
Requirements on
Big data specific to
freight transport
Geocoded data
Decentraliseddata
Flows
Goods
Resources
Value
Information
Products
Multiple
perspectives
Strategic
Tactical
Operative Predictive

41. Bitcoin, bitcoin coin, physical bitcoin, bitcoin photo by Antana on Flickr (CC-BY,SA)
Block chain
technology
Records transactions and
data among actors that
do not trust each other
Fully
decentralized

42. https://news.bitcoin.com/nimber-disrupts-logistics-system-blockchain-matters/
http://www.economist.com/news/leaders/21677198-technology-behind-
bitcoin-could-transform-how-economy-works-trust-machine
Bitcoin, bitcoin coin, physical bitcoin, bitcoin photo by Antana on Flickr (CC-BY,SA)
http://www.coindesk.com/how-bitcoins-technology-
could-make-supply-chains-more-transparent/
https://news.bitcoin.com/future-use-cases-blockchain-
technology-parcel-tracking-regardless-courier/
Block chain
technology
Records transactions and
data among actors that
do not trust each other
Fully
decentralized

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

44. The Action of New York City by
Trey Ratcliff on Flickr (CC-BY,NC,SA)
Real-time (data driven)
decision making
Data collection
Data processing
Data exploitation
http://mindconnect.se/
http://waze.com
https://mydrive.tomtom.com/

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

46. En la cima! by Alejandro Juárez on Flickr (CC-BY)
3 data types
Mountaintop #1
Collection of data in real-time
Fixed Historical Snapshot

47. En la cima! by Alejandro Juárez on Flickr (CC-BY)
Mountaintop #1
Collection of data in real-time
5 data domains
Vehicle CargoDriver Company
Infrastructure/
facility
at least…

48. 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

49. Say hi to the new sensors
http://mobsentech.com

50. Mountaintop #2
Processing of data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)
Locals and Tourists #1 (GTWA #2): London by Eric Fischer on Flickr

51. Mountaintop #2
Processing of data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)

52. Mountaintop #3
Exploiting data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)

53. IFTTT.com
IF This, Then That
Connects
unrelated services

54. Real-time decision making
not always successful…

55. CASES
(MANY)

56. CASES
(MANY MORE)

57. Smart access/guidance control

58. Requirement
Transport 1
Transport 2

59. Requirement
Transport 1
Transport 2
No access!
Full access!

60. Requirements. Different.
Port area City center
Freight terminal Bridge

61. 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”

62. 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

63. Integration of digital and physical worlds
http://www.sygic.com/gps-navigation/addon/head-up-display

64. The sharing economy hits freight transport
(again and again…)

65. Servitization
Move up in the
value chain
Upgrade drop points
Consumer services
Expose data
Mall of Scandinavia
http://www.smartcompany.com.au/growth/innovation/41765-online-retailer-offers-
a-courier-that-waits-at-your-door-fashion-advice-not-included.html
https://www.amazon.com/dashbutton
https://www.shyp.com

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

67. http://www.scdigest.com/ontarget/
14-01-21-1.php?cid=7767
Anticipatory
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)

68. Curated services made possible with data
Mindconnect Sendify

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

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

71. Vizualisation/combination

72. Vizualisation/combination

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

74. Manage complex systems

75. Avoid unpleasant surprises

76. Predict future events

77. 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
Create teams

78. 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)

79. Big data from a freight
company perspective
Per Olof Arnäs, PhD
Chalmers University of Technology
Gothenburg, Sweden
about.me/perolofarnas
Slides online: slideshare.net/poar
Film by Waze