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Big data in freight transport

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Big data from a freight company perspective.

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Big data in freight transport

  1. 1. Big data from a freight company perspective Per Olof Arnäs, PhD Chalmers University of Technology Gothenburg, Sweden about.me/perolofarnas Slides online: slideshare.net/poar Film by Waze
  2. 2. Demographic and social change Shift in economic power Rapid urbanisation Technological breakthroughsClimate change and resource scarcity 5 GLOBAL TRENDS Source: PWC (google: pwc megatrends 2014)
  3. 3. Things are happening outside the freight industry (and have been for some time)
  4. 4. Things are happening outside the freight industry (and have been for some time) 1957
  5. 5. Things are happening outside the freight industry (and have been for some time) Image: Richard Hancock, twitter.com/CanaryWorf 2015
  6. 6. Stage Coach Wheel by arbyreed on Flickr Development of transportation technology has been fairly linear …for the last 5500 years
  7. 7. We are in the middle of a gigantic exponential development curve beginning
  8. 8. A new global eco system where new types of, knowledge based, industries compete with traditional ones http://jaysimons.deviantart.com/art/Map-of-the-Internet-1-0-427143215
  9. 9. 355:365:2015BWH by hermitsmoores on Flickr (CC-BY,NC,SA) Make analogue information digital Digitization:
  10. 10. MobileWorldCongress2016byKārlisDambrānsonFlickr(CC-BY) Increased use of digital technology Digitalization:
  11. 11. MobileWorldCongress2016byKārlisDambrānsonFlickr(CC-BY) Increased use of digital technology Digitalization: Make analogue information digital Digitization: Both are important! (and interesting)
  12. 12. Ominous Windmill by Conrad Kuiper on Flickr (CC-BY,NC,SA) Digit(al)ization is not a trend
  13. 13. Ominous Windmill by Conrad Kuiper on Flickr (CC-BY,NC,SA) Digit(al)ization is not a trend It is a force of nature
  14. 14. Process improvement Service developm entInfrastructure developm ent 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
  15. 15. Process improvement Service developm entInfrastructure developm ent 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
  16. 16. 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 friendly 3PL 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.
  17. 17. Challenges The Challenger by Martín Vinacur on Flickr (CC-BY) Low profit margins Social issues Fragmented industry Data all over the place, but not where most needed Large investments
  18. 18. Image: Alain Delorme, alaindelorme.com The current model is focused on economy of scale and standardization
  19. 19. 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)
  20. 20. 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
  21. 21. Business processes Infrastructure Paperbased Phone
 Papers Road signs A nalogue tools R D S M onitorfuel cosnum ption Digitalisation version 0 0.5 1.0 1.5 2.0 E-mail Fax TMS- systems Excel Route planning G PS fornavigation Electronically generated freightdocum ents Barcodes RFID-tags Simple order handling Advanced order handling Openinterface W eb based UI Platform based system s Hardware- oriented Datacollection systems (proprietary) Communicationwith vehicles E-invoice W eb based booking Route optimisation Thesocialweb Openconnectivity Integrated prognosis Data collection systems (open) Tolling system s Webservices with traffic data Dynamic routing systems Performance BasedaccessPerformanceBasedaccess Mashups
 Multipledata sources Probedata Individual routing inform ation Platooning Platooning Exceptions handling Smartgoods Manual Computers Software Functions Distributed decision making G oods as bi- directional hyperlink Paperbased CC-BY Per Olof Arnäs, Chalmers Goods Vehicle 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 m aintenance 2014-10-15
  22. 22. Smart access/guidance control
  23. 23. Smart access/guidance control
  24. 24. Smart access/guidance control
  25. 25. Smart access/guidance control
  26. 26. • Data amounts increase greatly • There are data gaps/silos preventing development • Lack of standards • Personal data privacy is a long-term threat • Lack of talent/capacity to handle foreseen need https://ts.catapult.org.uk/documents/10631/169582/The+Transport+Data +Revolution/99e9d52f-08a7-402d-b726-90c4622bf09d
  27. 27. Gartners Hype Cycle for Emerging Technologies Augmenting humans with technology Machines replacing humans Humans and machines working alongside each other Machines better understanding humans and the environment Humans better understanding machines Machines and humans becoming smarter
  28. 28. Gartners Hype Cycle for Emerging Technologies Source: Gartner July 2015 Could affect transportation and logistics
  29. 29. http://www.dhl.com/en/about_us/logistics_insights/dhl_trend_research/trendradar.html
  30. 30. 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
  31. 31. 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
  32. 32. Jawbone measures sleep interruption during earthquake https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
  33. 33. Not statistics Exhausted by Adrian Sampson on Flickr (CC-BY) just
  34. 34. Not Business Intelligence Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA) just
  35. 35. http://dashburst.com/infographic/big-data-volume-variety-velocity/
  36. 36. Varela Rozdos, I and Tjahjono, B, 2014 ”BIG DATA ANALYTICS IN SUPPLY CHAIN MANAGEMENT: TRENDS AND RELATED RESEARCH”, 6th International Conference on Operations and Supply Chain Management, Bali, 2014
  37. 37. 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
  38. 38. Bitcoin, bitcoin coin, physical bitcoin, bitcoin photo by Antana on Flickr (CC-BY,SA) Block chain technology Records transactions and data among actors that do not trust each other Fully decentralized
  39. 39. https://news.bitcoin.com/nimber-disrupts-logistics-system-blockchain-matters/ http://www.economist.com/news/leaders/21677198-technology-behind- bitcoin-could-transform-how-economy-works-trust-machine Bitcoin, bitcoin coin, physical bitcoin, bitcoin photo by Antana on Flickr (CC-BY,SA) http://www.coindesk.com/how-bitcoins-technology- could-make-supply-chains-more-transparent/ https://news.bitcoin.com/future-use-cases-blockchain- technology-parcel-tracking-regardless-courier/ Block chain technology Records transactions and data among actors that do not trust each other Fully decentralized
  40. 40. Strategic Tactical Operational Predictive Time horizons We are approaching this boundary …and we are starting to move past it! Real-time!
  41. 41. The Action of New York City by Trey Ratcliff on Flickr (CC-BY,NC,SA) Real-time (data driven) decision making Data collection Data processing Data exploitation http://mindconnect.se/ http://waze.com https://mydrive.tomtom.com/
  42. 42. En la cima! by Alejandro Juárez on Flickr (CC-BY) 3 mountaintops to climb…
  43. 43. En la cima! by Alejandro Juárez on Flickr (CC-BY) 3 data types Mountaintop #1 Collection of data in real-time Fixed Historical Snapshot
  44. 44. En la cima! by Alejandro Juárez on Flickr (CC-BY) Mountaintop #1 Collection of data in real-time 5 data domains Vehicle CargoDriver Company Infrastructure/ facility at least…
  45. 45. 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
  46. 46. Say hi to the new sensors http://mobsentech.com
  47. 47. 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
  48. 48. Mountaintop #2 Processing of data in real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
  49. 49. Mountaintop #3 Exploiting data in real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
  50. 50. IFTTT.com IF This, Then That Connects unrelated services
  51. 51. Real-time decision making not always successful…
  52. 52. CASES
 (MANY)
  53. 53. CASES
 (MANY MORE)
  54. 54. Smart access/guidance control
  55. 55. Requirement Transport 1 Transport 2
  56. 56. Requirement Transport 1 Transport 2 No access! Full access!
  57. 57. Requirements. Different. Port area City center Freight terminal Bridge
  58. 58. 7Big Data Best Practice Across Industries Usage of data in order to: Increase Level of Transparency Optimize Resource Consumption Improve Process Quality and Performance Increase customers loyalty and retention Performing precise customer segmentation and targeting Optimize customer interaction and service Expanding revenue streams from existing products Creating new revenue streams from entirely new (data) products Exploit data for: Capitalize on data by: New Business Models 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 New Business ModelsCustomer ExperienceOperational Efficiency Figure 4: Value dimensions for Big Data use cases; Source: DPDHL / Detecon DHL 2013: ”Big Data in Logistics”
  59. 59. 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
  60. 60. Integration of digital and physical worlds http://www.sygic.com/gps-navigation/addon/head-up-display
  61. 61. The sharing economy hits freight transport (again and again…)
  62. 62. Servitization Move up in the value chain Upgrade drop points Consumer services Expose data Mall of Scandinavia http://www.smartcompany.com.au/growth/innovation/41765-online-retailer-offers- a-courier-that-waits-at-your-door-fashion-advice-not-included.html https://www.amazon.com/dashbutton https://www.shyp.com
  63. 63. smile! by Judy van der Velden (CC-BY,NC,SA) Anticipatory shipping http://www.scdigest.com/ontarget/ 14-01-21-1.php?cid=7767
  64. 64. http://www.scdigest.com/ontarget/ 14-01-21-1.php?cid=7767 Anticipatory 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)
  65. 65. Curated services made possible with data Mindconnect Sendify
  66. 66. http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk Vizualisation
  67. 67. Created by Oliver O'Brien (UCL Geography/UCL CASA)
  68. 68. Vizualisation/combination
  69. 69. Vizualisation/combination
  70. 70. Measure real-time system behaviour Emil Johansson - EJOH.SE
  71. 71. Manage complex systems
  72. 72. Avoid unpleasant surprises
  73. 73. Predict future events
  74. 74. 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 Create teams
  75. 75. 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)
  76. 76. Big data from a freight company perspective Per Olof Arnäs, PhD Chalmers University of Technology Gothenburg, Sweden about.me/perolofarnas Slides online: slideshare.net/poar Film by Waze

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