real-time (big) data in freight transport - meeting the global trends
DESCRIPTION
A talk (40-50 mins) on how the freight transport sector needs to face up to the megatrands of this age, and how these can be addressed partly through digital development. Real-time data collection, processing and exploitation are discussed, as well as Big data. It's not business as usual. This is the internet happening to freight transport. There is no "usual" anymore. Get used to it.TRANSCRIPT
![Page 1: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/1.jpg)
!
REAL-TIME (BIG) DATA IN FREIGHT TRANSPORT - MEETING THE GLOBAL TRENDS
Per Olof Arnäs Chalmers University of Technology @Dr_PO [email protected] about.me/perolofarnas Slides: slideshare.net/poar
Image: www.simonstalenhag.se
![Page 2: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/2.jpg)
Northern LEAD Logistics Research Centre
Founded by: Chalmers University of Technology
University of Gothenburg Logistics and Transport Society LTS
![Page 3: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/3.jpg)
Tomorrow’s logistics. We are finding the answers.
Around 70 researchersResearch centre for sustainable logistics
solutionsFive core research
groups
Organises, facilitates, disseminates highly relevant
logistics researchCollaboration between
Chalmers and University of Gothenburg
![Page 4: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/4.jpg)
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
![Page 5: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/5.jpg)
Doing some Sisyphus work by Kalexanderson on Flickr (CC-BY,NC,SA)
5 GLOBAL TRENDS
Source: PWC (google: pwc megatrends 2014)
![Page 6: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/6.jpg)
Crowd by James Cridland on Flickr (CC-BY)
Megatrend #1
Demographic and social
change
Source: PWC (google: pwc megatrends 2014)
![Page 7: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/7.jpg)
Four Storms And A Twister by JD Hancock on Flickr (CC-BY)
Megatrend #2
Shift in economic
powerSource: PWC (google: pwc megatrends 2014)
![Page 8: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/8.jpg)
Boston Downtown at Night by Werner Kunz on Flickr (CC-BY,NC,SA)
Megatrend #3
Rapid urbanisation
Source: PWC (google: pwc megatrends 2014)
![Page 9: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/9.jpg)
¡Rayos! by José Eugenio Gómez Rodríguez on Flickr (CC-BY,NC,SA)
Megatrend #4
Climate change and
resource scarcity
Source: PWC (google: pwc megatrends 2014)
![Page 10: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/10.jpg)
Megatrend #5
Technological breakthroughs
Source: PWC (google: pwc megatrends 2014)
![Page 11: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/11.jpg)
Demographic and social
change
Shift in economic
power
Rapid urbanisation
Technological breakthroughsClimate
change and resource scarcity
5 GLOBAL TRENDS
![Page 12: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/12.jpg)
Stage Coach Wheel by arbyreed on Flickr
Development of transportation technology has been
fairly linear
…for the last 5500 years
![Page 13: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/13.jpg)
We are in the middle of a gigantic exponential development curve
beginning
![Page 14: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/14.jpg)
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
![Page 15: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/15.jpg)
Startups don’t compete with airlines...
by purchasing a bunch of planeshiring 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)
![Page 16: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/16.jpg)
Startups compete with airlines by inventing videoconferencing.
Startups don’t compete with airlines...
by purchasing a bunch of planeshiring 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)
![Page 17: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/17.jpg)
RESOURCE UTILISATION LOW
Source: Kent Lumsden
![Page 18: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/18.jpg)
RESOURCE UTILISATION LOW
Source: Kent Lumsden
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
![Page 19: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/19.jpg)
RESOURCE UTILISATION LOW
Source: Kent Lumsden
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
![Page 20: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/20.jpg)
But the biggest problem in transportation is time.
There is not enough of it. Ever.
In S
ea
rch
Of
Lo
st T
ime
by
bo
ge
nfr
eu
nd
on
Flic
kr
![Page 21: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/21.jpg)
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
![Page 22: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/22.jpg)
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)
![Page 23: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/23.jpg)
Image: Alain Delorme, alaindelorme.com
The current model is focused on economy of scale and standardization
![Page 24: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/24.jpg)
The current paradigm
![Page 25: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/25.jpg)
So…
What are we doing about all
this?
![Page 26: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/26.jpg)
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
![Page 27: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/27.jpg)
Gartners Hype Cycle for Emerging Technologies
Source: Gartner August 2014
![Page 28: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/28.jpg)
Could affect freight transport
Gartners Hype Cycle for Emerging Technologies
![Page 29: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/29.jpg)
Increasing freight transport demand
http://www.eea.europa.eu/data-and-maps/figures/freight-transport-activity-growth-for-eu-25
EU-25
![Page 30: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/30.jpg)
![Page 31: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/31.jpg)
OpportunitiesDigitalisation Increasing goods volumes
New technology
Political
interest
Quad Aces by fitzsean on Flickr
![Page 32: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/32.jpg)
ICT MaturityApp
"Multi-Touch" by DaveLawler on Flickr (CC-BY)
![Page 33: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/33.jpg)
Strategic Tactical Operational Predictive
Time horizons
We are approaching this boundary
…and we are starting to move past it!
Real-time!
![Page 34: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/34.jpg)
Business processes Infrastructure
Paper based Phone
Papers
Road signsAnalogue
tools
RDS
Monitor fuel
cosnumption
Digitalisation version 0 0.5 1.0 1.5 2.0
E-m
ail
Fax
TMS
-
systems
Excel
Route planning
GPS for n
avigatio
n
Electro
nically
genera
ted
freig
ht docum
ents
Barcodes
RFI
D-t
ags
Simple order handling
Advanced order handling
Open interface
Web
based UI
Platform based
systems
Hardw
are-
oriented
Data collection
systems
(prop
rietary)
Com
munication w
ith
vehicles
E-invoice
Web based
booking
Route optimisation
Th
e so
cia
l web
Open connectivity
Integrated
prognosis
Data collection
systems (open)
Tolling
systems
Webservices with
traffic data
Dyn
amic
ro
utin
g sy
stem
s
Pe
rform
an
ce
Ba
sed
ac
ce
ss
Pe
rfo
rma
nc
e
Ba
sed
ac
ce
ss
Mas
hups
Mul
tiple
dat
a so
urce
s
Pro
be
dat
a
Individual
routin
g
inform
ation
Platooning
PlatooningExceptions handling
Sm
art g
ood
s
Manual
Computers
Software
Functions
Dis
trib
uted
deci
sion
m
akin
g
Goods as bi-
directio
nal
hyperlink
Paper based
CC-BY Per Olof Arnäs, Chalmers
Goods VehicleBarcodes
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
2014-10-15
![Page 35: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/35.jpg)
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)
![Page 36: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/36.jpg)
The Action of New York City by Trey Ratcliff on Flickr (CC-BY,NC,SA)
Need for speed
Data collection
Data processingData
exploitation
![Page 37: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/37.jpg)
En la cima! by Alejandro Juárez on Flickr (CC-BY)
3 mountaintops to climb…
![Page 38: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/38.jpg)
En la cima! by Alejandro Juárez on Flickr (CC-BY)
3 data types
Mountaintop #1
Collection of data in real-time
Fixed Historical Snapshot
![Page 39: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/39.jpg)
En la cima! by Alejandro Juárez on Flickr (CC-BY)
Mountaintop #1
Collection of data in real-time
5 data domainsVehicle CargoDriver Company
Infrastructure/facility
at leas
t…
![Page 40: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/40.jpg)
Length Weight WidthHeight
Capacity + other PBS-criteria
EmissionsFuel 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
![Page 41: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/41.jpg)
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
![Page 42: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/42.jpg)
Mountaintop #2
Processing of data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)
![Page 43: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/43.jpg)
Mountaintop #3
Exploiting data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)
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)
![Page 44: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/44.jpg)
Mountaintop #3
Exploiting data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)
Boeing-KC-97 Stratotanker by x-ray delta one on Flickr (CC-BY)
![Page 45: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/45.jpg)
smile! by Judy van der Velden (CC-BY,NC,SA)
Speculative shipping
http://www.scdigest.com/ontarget/14-01-21-1.php?cid=7767
![Page 46: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/46.jpg)
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)
![Page 47: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/47.jpg)
CASES (MANY)
![Page 48: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/48.jpg)
CASES (MANY MORE)
![Page 49: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/49.jpg)
Big data in freight transport
!
Film by Foursquare. Google: checkins foursquare
![Page 50: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/50.jpg)
”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
Gartners Hype Cycle for Emerging Technologies
![Page 51: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/51.jpg)
So…
What is Big data?
80 by Phil Dragash on Flickr (CC-BY,NC,SA)
![Page 52: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/52.jpg)
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
![Page 53: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/53.jpg)
Google flights
https://www.google.se/flights/
![Page 54: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/54.jpg)
Jawbone measures sleep interruption during earthquake
https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
![Page 55: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/55.jpg)
Not statistics
Exhausted by Adrian Sampson on Flickr (CC-BY)
just
![Page 56: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/56.jpg)
Not Business
Intelligence
Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA)
just
![Page 57: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/57.jpg)
http://dashburst.com/infographic/big-data-volume-variety-velocity/
![Page 58: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/58.jpg)
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
![Page 59: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/59.jpg)
Manage complex systems
Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center
![Page 60: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/60.jpg)
Predict future events
![Page 61: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/61.jpg)
Avoid unpleasant surprises
![Page 62: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/62.jpg)
http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk
Vizualisation
![Page 63: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/63.jpg)
7Big Data Best Practice Across Industries
Usage of data in order to:Increase Level of TransparencyOptimize ResourceConsumption Improve Process Qualityand Performance
Increase customersloyalty and retentionPerforming precisecustomer segmentationand targetingOptimize customerinteraction and service
Expanding revenuestreams from existingproductsCreating new revenuestreams from entirelynew (data) products
Exploit data for: Capitalize on data by:
New Business Models
Customer Experience
OperationalEfficiency
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
2.1 Operational Efficiency
For metropolitan police departments, the task of tracking down criminals to preserve public safety can sometimes be tedious. With many siloed information repositories, casework often involves making manual connection of many data points. This takes times and dramatically slows case resolution. Moreover, road policing resources are deployed reactively, making it very difficult to catch criminals in the act. In most cases, it is not possible to resolve these challenges by increasing police staffing, as government budgets are limited.
One authority that is leveraging its various data sources is the New York Police Department (NYPD). By capturing and connecting pieces of crime-related information, it hopes to stay one step ahead of the perpetrators of crime.6 Long before the term Big Data was coined, the NYPD made an effort to break up the compartmentalization of its data ingests (e.g., data from 911 calls, investigation reports, and more). With a single view of all the informa-
tion related to one particular crime, officers achieve a more coherent, real-time picture of their cases. This shift has significantly sped up retrospective analysis and allows the NYPD to take action earlier in tracking down individual criminals.
The steadily decreasing rates of violent crime in New York7 have been attributed not only to this more effective streamlining of the many data items required to perform casework but also to a fundamental change in policing practice.8 By introducing statistical analysis and georaphical mapping of crime spots, the NYPD has been able to create a “bigger picture” to guide resource deployment and patrol practice.
Now the department can recognize crime patterns using computational analysis, and this delivers insights enabling each commanding officer to proactively identify hot spots of criminal activity.
6 “NYPD changes the crime control equation by the way it uses information”, IBM; cf. https://www-01.ibm.com/software/success/cssdb.nsf/CS/JSTS-6PFJAZ7 “Index Crimes By Region”, New York State Division of Criminal Justice Services, May 2013, cf. http://www.criminaljustice.ny.gov/crimnet/ojsa/stats.htm8 “Compstat and Organizational Change in the Lowell Police Department”, Willis et. al., Police Foundation, 2004; cf. http://www.policefoundation.org/
content/compstat-and-organizational-change-lowell-police-department
2.1.1 Utilizing data to predict crime hotspots
DHL 2013: ”Big Data in Logistics”
![Page 64: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/64.jpg)
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
![Page 65: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/65.jpg)
It’s not business as usual.
Hello Kitty Darth Vader by JD Hancock on Flickr (CC-BY)
![Page 66: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/66.jpg)
It’s not business as usual.
This is the internet happening to freight
transport.
Hello Kitty Darth Vader by JD Hancock on Flickr (CC-BY)
![Page 67: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/67.jpg)
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)
![Page 68: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/68.jpg)
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)
![Page 69: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/69.jpg)
Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
![Page 70: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/70.jpg)
Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Company
![Page 71: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/71.jpg)
Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Company
Supply chain, simple
![Page 72: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/72.jpg)
Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Company
Supply chain, simple
Supply chain, complex
![Page 73: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/73.jpg)
Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Company
Supply chain, simple
Supply chain, complex
Eco system
![Page 74: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/74.jpg)
Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Cross-disciplinary
Cross-industries
Cross-borders
![Page 75: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/75.jpg)
The Challenger by Martín Vinacur on Flickr (CC-BY)
Not all ideas age with grace
![Page 76: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/76.jpg)
Someone must do the work
The Challenger by Martín Vinacur on Flickr (CC-BY)
![Page 77: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/77.jpg)
The Challenger by Martín Vinacur on Flickr (CC-BY)
Not everyone will want to adopt new things…
![Page 78: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/78.jpg)
![Page 79: Real-time (Big) Data in Freight Transport - Meeting the global trends](https://reader031.vdocuments.us/reader031/viewer/2022013003/546b81e3af79599d7d8b6daa/html5/thumbnails/79.jpg)
!
REAL-TIME (BIG) DATA IN FREIGHT TRANSPORT - MEETING THE GLOBAL TRENDS
Per Olof Arnäs Chalmers University of Technology @Dr_PO [email protected] about.me/perolofarnas Slides: slideshare.net/poar
Image: www.simonstalenhag.se