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Big data - Modelling World 2015, London

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Presentation from Modelling World 2015 in London (June 4, 2015).

Big data is a game changer for the transportation industry - but the data need to be tamed.

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Big data - Modelling World 2015, London

  1. 1. 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
  2. 2. Things are happening outside the freight industry (and have been for some time)
  3. 3. Stage Coach Wheel by arbyreed on Flickr <<<<<<<<< Development of transportation technology has been fairly linear …for the last 5500 years
  4. 4. We are in the middle of a gigantic exponential development curve beginning
  5. 5. Measure real-time system behaviour Emil Johansson - EJOH.SE
  6. 6. Manage complex systems Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center
  7. 7. Predict future events
  8. 8. Avoid unpleasant surprises
  9. 9. 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
  10. 10. 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
  11. 11. DHL 2013: ”Big Data in Logistics”
  12. 12. Jawbone measures sleep interruption during earthquake https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
  13. 13. http://www.scdigest.com/ontarget/ 14-01-21-1.php?cid=7767 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) Predictive shipping
  14. 14. Image: Alain Delorme, alaindelorme.com The current model is focused on economy of scale and standardization
  15. 15. But the biggest problem in transportation is time. There is not enough of it. Ever. InSearchOfLostTimebybogenfreundonFlickr
  16. 16. 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)
  17. 17. 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
  18. 18. Multicolour Jelly Belly beans in Sugar! by MsSaraKelly on Flickr (CC-BY) Requirements on Big data specific to freight transport Geocoded data Decentraliseddata Flows Goods Resources Value Information Products Multiple perspectives Strategic Tactical Operative Predictive
  19. 19. http://dashburst.com/infographic/big-data-volume-variety-velocity/
  20. 20. En la cima! by Alejandro Juárez on Flickr (CC-BY) 3 mountaintops to climb…
  21. 21. En la cima! by Alejandro Juárez on Flickr (CC-BY) 3 data types Mountaintop #1 Collection of data in real-time Fixed Historical Snapshot
  22. 22. En la cima! by Alejandro Juárez on Flickr (CC-BY) Mountaintop #1 Collecting data in real-time 5 data domains Vehicle CargoDriver Company Infrastructure/ facility at least…
  23. 23. 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 Fixed Historical Snapshot Vehicle Cargo Driver Company Infrastructure /facility Map + fixed data layers Traffic history Current traffic Queue Availability DATA MATRIX
  24. 24. Say hi to the new sensors …but they are still not enough
  25. 25. (Freight) companies want to share as little data as possible, with as little friction as possible, to get the highest utility possible Private Property by Nathan O'Nions on Flickr (CC-BY)
  26. 26. Mountaintop #2 Processing 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
  27. 27. Mountaintop #2 Processing data in real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
  28. 28. Mountaintop #3 Exploiting data in real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
  29. 29. Real-time decision making not always successful…
  30. 30. Requirement Fixed Historical Snapshot Transport 1 Fixed Historical Snapshot Transport 2 Fixed Historical Snapshot
  31. 31. Requirement Fixed Historical Snapshot Transport 1 Fixed Historical Snapshot Transport 2 Fixed Historical Snapshot No access! Full access!
  32. 32. Smart access/guidance control
  33. 33. Smart access/guidance control
  34. 34. Smart access/guidance control
  35. 35. Smart access/guidance control
  36. 36. Fix ed Hist oric al Sna psh ot Fix ed Hist oric al Sna psh ot Requirements. Different. Fix ed Hist oric al Sna psh ot Port area City centre Freight terminal Bridge
  37. 37. CASES (MANY)
  38. 38. CASES (MANY MORE)
  39. 39. Challenges The Challenger by Martín Vinacur on Flickr (CC-BY) Cross-disciplinary Cross-industries Cross-borders
  40. 40. 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)
  41. 41. 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)

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