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Big data - Modelling World 2015, London
The document discusses the evolution of transportation technologies, emphasizing the significance of big data and predictive analytics in transforming supply chain management and logistics. It highlights the challenges the freight industry faces, particularly the need for real-time decision-making amidst rapidly changing environments. Furthermore, it outlines the necessity for data collection, processing, and exploitation to enhance operational efficiency and effectiveness in the transport sector.
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Big data - Modelling World 2015, London
- 2. 892 by benmschmidton Flickr (C)19th century shipping visualized through the logs of Matthew Fontaine Maury (1806-1873), US Navy Shipping movements in the 19th century
- 3. Things are happeningoutside the freight industry (and have been for some time)
- 4. Stage Coach Wheelby arbyreed on Flickr <<<<<<<<< Development of transportation technology has been fairly linear …for the last 5500 years
- 5. We are inthe middle of a gigantic exponential development curve beginning
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- 8. Manage complex systems Imagefrom: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center
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- 11. 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
- 13. Human resources Reduction indriver 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
- 14. DHL 2013: ”BigData in Logistics”
- 15. Jawbone measures sleep interruptionduring earthquake https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
- 16. http://www.scdigest.com/ontarget/ 14-01-21-1.php?cid=7767 Package item(s) asa 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
- 20. Image: Alain Delorme,alaindelorme.com The current model is focused on economy of scale and standardization
- 21. But the biggestproblem in transportation is time. There is not enough of it. Ever. InSearchOfLostTimebybogenfreundonFlickr
- 22. The transport industry doesnot like real-time decisions. At all. Batch-handling Zip codes Zones Time-tables DSC_9073.jpg by James England on Flickr (CC-BY)
- 23. Strategic Tactical OperationalPredictive 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
- 24. Multicolour Jelly Bellybeans 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
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- 26. En la cima!by Alejandro Juárez on Flickr (CC-BY) 3 mountaintops to climb…
- 27. En la cima!by Alejandro Juárez on Flickr (CC-BY) 3 data types Mountaintop #1 Collection of data in real-time Fixed Historical Snapshot
- 28. 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…
- 29. Length Weight Width Height Capacity + other PBS-criteria Emissions Fuelconsumption 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
- 30. Say hi tothe new sensors …but they are still not enough
- 31. (Freight) companies wantto 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)
- 32. Mountaintop #2 Processing datain real-time En la cima! by Alejandro Juárez on Flickr (CC-BY) Locals and Tourists #1 (GTWA #2): London by Eric Fischer on Flickr
- 33. Mountaintop #2 Processing datain real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
- 34. Mountaintop #3 Exploiting datain real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
- 35. Real-time decision makingnot always successful…
- 36. Requirement Fixed Historical Snapshot Transport1 Fixed Historical Snapshot Transport 2 Fixed Historical Snapshot
- 37. Requirement Fixed Historical Snapshot Transport1 Fixed Historical Snapshot Transport 2 Fixed Historical Snapshot No access! Full access!
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- 42. 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 areaCity centre Freight terminal Bridge
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- 45. Challenges The Challenger byMartín Vinacur on Flickr (CC-BY) Cross-disciplinary Cross-industries Cross-borders
- 46. It’s not businessas 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)
- 47. It’s not businessas 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)