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Big data in freight transport

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

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  • Really likes your data matrix view... Think it is critical that the different stakeholders in this transport eco-system starts to focus on what our core parameter to best on supplying instead of compete on all parameters used in the eco-system... Than we are acting as a rational society rather than a hunter-pack...
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Big data in freight transport

  1. 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. 2. beginning We are in the middle of a gigantic exponential development curve
  3. 3. Gartners Hype Cycle for Emerging Technologies Source: Gartner August 2014
  4. 4. Gartners Hype Cycle for Emerging Technologies Could affect freight transport
  5. 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. 6. So… What is Big data?
  7. 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. 8. Not statistics Exhausted by Adrian Sampson on Flickr (CC-BY) just
  9. 9. Not just Business Intelligence Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA)
  10. 10. http://dashburst.com/infographic/big-data-volume-variety-velocity/
  11. 11. Google flights https://www.google.se/flights/
  12. 12. Jawbone measures sleep interruption during earthquake https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
  13. 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. 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. 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. 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. 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. 18. 3 mountaintops to climb… En la cima! by Alejandro Juárez on Flickr (CC-BY)
  19. 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. 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. 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. 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. 23. Mountaintop #2 Processing of data in real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
  24. 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. 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. 26. CASES (MANY)
  27. 27. CASES (MANY MORE)
  28. 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. 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. 30. Manage complex systems Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center
  31. 31. Measure real-time system behaviour Emil Johansson - EJOH.SE
  32. 32. Vizualisation http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk
  33. 33. Predict future events
  34. 34. Avoid unpleasant surprises
  35. 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. 36. Challenges The Challenger by Martín Vinacur on Flickr (CC-BY) Cross-disciplinary Cross-industries Cross-borders
  37. 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

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