big data in freight transport - pdf...business processes infra-!structure per based phone s analogue...
TRANSCRIPT
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Big data in freight transport
!
Per Olof Arnäs Chalmers
@Dr_PO [email protected]
!Slides on slideshare.net/poar
Film by Foursquare. Google: checkins foursquare
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We are in the middle of a gigantic exponential development curve
beginning
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”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
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Gartners Hype Cycle for Emerging Technologies
Source: Gartner August 2014
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Gartners Hype Cycle for Emerging Technologies
Could affect freight transport
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So…
What is Big data?
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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
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Google flights
https://www.google.se/flights/
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Jawbone measures sleep interruption during earthquake
https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
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smile! by Judy van der Velden (CC-BY,NC,SA)
Speculative shipping
http://www.scdigest.com/ontarget/14-01-21-1.php?cid=7767
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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)
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Not statistics
Exhausted by Adrian Sampson on Flickr (CC-BY)
just
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Not Business
Intelligence
Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA)
just
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http://dashburst.com/infographic/big-data-volume-variety-velocity/
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Business processes
Infra- structure
Paper based Phone
Papers
Road signsAnalogue
tools
RDS
Monitor fuel
cosnumption
Digitization 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
2014-08-26
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Strategic Tactical Operational Predictive
Time horizons
We are approaching this boundary
…and we are starting to move past it!
Real-time!
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En la cima! by Alejandro Juárez on Flickr (CC-BY)
3 mountaintops to climb…
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En la cima! by Alejandro Juárez on Flickr (CC-BY)
3 data types
Mountaintop #1
Collection of data in real-time
Fixed Historical Snapshot
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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…
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LengthWeightWidthHeight
Capacity+ other PBS-criteria
EmissionsFuel consumption
Route
PositionSpeed
Direction
WeightOrigin
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
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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
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Mountaintop #2
Processing of data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)
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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)
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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)
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CASES (MANY)
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CASES (MANY MORE)
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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
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Manage complex systems
Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center
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Predict future events
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Avoid unpleasant surprises
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http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk
Vizualisation
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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”
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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
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Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Cross-disciplinary
Cross-industries
Cross-borders
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Big data in freight transport
!
Per Olof Arnäs Chalmers
@Dr_PO [email protected]
!Slides on slideshare.net/poar
Film by Foursquare. Google: checkins foursquare