A talk on possibilities and issues regarding Big data in freight transport.

1. Big data in freight
transport
!
Per Olof Arnäs
Chalmers
@Dr_PO
per-olof.arnas@chalmers.se
!
Slides on slideshare.net/poar
Film by Foursquare. Google: checkins foursquare

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

3. Gartners Hype Cycle for Emerging Technologies
Source: Gartner August 2014

4. Gartners Hype Cycle for Emerging Technologies
Could affect freight transport

5. Gartners Hype Cycle for Emerging Technologies
”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

6. So…
What is
Big data?

7. 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

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

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

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

11. Google flights
https://www.google.se/flights/

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

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

14. Time horizons
Strategic Tactical Operational Predictive
Real-time!
But with technology,
we are approaching
this boundary
…and we are
starting to
move past it!
Freight industry

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

16. 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)

17. Goods Vehicle
Business
processes
Infra-structure
Functions
Software
Computers
Paper based
Open
interface
Advanced order
handling
Monitor fuel
cosnumption
Phone
Papers
Barcodes
(proprietary)
Based access
Performance
Based access
Digitization version 0 0.5 1.0 1.5 2.0
Road signs
Analogue
tools
RDS
E-mail
Fax
TMS-systems
Excel
Route
planning
GPS for navigation
Electronically documents
generated
freight RFID-tags
Simple order handling
Web
based UI
Platform based
systems
Hardware-oriented
Data collection
systems
Communication with
vehicles E-invoice
Web based
booking
Route
optimisation
The social web Open connectivity
Integrated
prognosis
Data collection
systems (open)
Tolling
systems
Webservices with
traffic data
Dynamic
routing
systems
Performance
Mashups
Multiple data
sources
Probe data
Individual
routing
information
Platooning
Platooning
Exceptions
handling
Smart goods
Manual
Distributed
decision
making
Goods as bi-directional
hyperlink
Paper based
CC-BY Per Olof Arnäs, Chalmers
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
maintenance
2014-08-26

18. 3 mountaintops to climb…
En la cima! by Alejandro Juárez on Flickr (CC-BY)

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

20. Mountaintop #1
Collection of data in real-time
5 data domains
Vehicle Driver Cargo Company
En la cima! by Alejandro Juárez on Flickr (CC-BY)
Infrastructure/
facility
at le a s t…

21. Fixed Historical Snapshot
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
Vehicle
Cargo
Driver
Company
Infrastructure
/facility
Map
+ fixed data layers Traffic history
Current traffic
Queue
Availability
DATA MATRIX

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

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

24. 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)
En la cima! by Alejandro Juárez on Flickr (CC-BY)
Fulham-Manchester United
24-02-2007 by vuhlser on Flickr (CC-BY)

25. Mountaintop #3
Exploiting data in real-time
Boeing-KC-97 Stratotanker by x-ray delta one on Flickr (CC-BY)
En la cima! by Alejandro Juárez on Flickr (CC-BY)

26. CASES
(MANY)

27. CASES
(MANY MORE)

28. Examples of applications of Big data in freight
Human resources
Reduction in driver
turnover, driver
assignment, using
sentiment data
analysis
(Waller and Fawcett, 2013)
Inventory
management
Real-time capacity
availability
Transportation
management
Optimal routing, taking
into account weather,
traffic congestion, and
driver characteristics
Forecasting
Time of delivery,
factoring in weather,
driver characteristics,
time of day and date
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

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

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

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

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

33. Predict future events

34. Avoid unpleasant surprises

35. 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

36. Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Cross-disciplinary
Cross-industries
Cross-borders

37. Big data in freight
transport
!
Per Olof Arnäs
Chalmers
@Dr_PO
per-olof.arnas@chalmers.se
!
Slides on slideshare.net/poar
Film by Foursquare. Google: checkins foursquare