meeting the future - big data in freight transport
TRANSCRIPT
Meeting the future Big data in freight transport
SJÖLOG 2015 Per Olof Arnäs
Chalmers @Dr_PO
Slides on slideshare.net/poar
Film by Foursquare. Google: checkins foursquare
Things are happening outside the freight industry
(and have been for some time)
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
Process improvement
Servic
e
developm
entInfrastructure
development
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
Servic
e
developm
entInfrastructure
development
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
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
friendly3PL 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.
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
friendly3PL 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.
The freight industry has work to do…
We are in the middle of a gigantic exponential development curve
beginning
Jawbone measures sleep interruption during earthquake
https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
So…
What IS Big data?
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
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/
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
Strategic Tactical Operational Predictive
But with technology, we are approaching
this boundary
…and we are starting to move past it!
Real-time!
Time horizons Freight industry
Goods Vehicle
Business process
Infra- structure
Barcodes RFID
Sensors
ERP systems TMS systems
E-invoices Cloudbased services
Order handling Driver support Vehicle economics
RDS-TMC Road taxes Active traffic support
Digitization fronts
En la cima! by Alejandro Juárez on Flickr (CC-BY)
3 mountaintops to climb…
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
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)
Multicolour Jelly Belly beans in Sugar! by MsSaraKelly on Flickr (CC-BY)
Requirements on Big data specific to
freight transport
Geocoded data
Decentralised data
FlowsGoods
Resources
Value
Information
Products
Multiple perspectives
StrategicTactical
Operative Predictive
Human resources
Reduction in driver turnover, driver
assignment, using sentiment data
analysis
Real-time capacity availability
Inventory management
Examples of applications of Big data 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
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”
Measure real-time
system behaviour
Emil Johansson - EJOH.SE
Predict future events
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)
Vizualisation
Locals and Tourists #1 (GTWA #2): London by Eric Fischer on Flickr
Vizualisation
Vizualisation/combination
Vizualisation/combination
Manage complex systems
Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center
Avoid unpleasant surprises
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
Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Cross-disciplinary
Cross-industries
Cross-borders
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
friendly3PL 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.
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
friendly3PL 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.
The freight industry has work to do…
Even if it hurts.
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)
Meeting the future Big data in freight transport
SJÖLOG 2015 Per Olof Arnäs
Chalmers @Dr_PO
Slides on slideshare.net/poar
Film by Foursquare. Google: checkins foursquare