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ITS and freight transport - an urban perspective

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When it comes to urban freight, we face large challenges regarding ITS. This talk (45 minutes) presents a number of issues where the freight transport industry needs to upgrade/enter the 21st century and embrace developments like Big data.

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ITS and freight transport - an urban perspective

  1. 1. ITS AND FREIGHT TRANSPORT - AN URBAN PERSPECTIVE Per Olof Arnäs Chalmers University of Technology @Dr_PO per-olof.arnas@chalmers.se about.me/perolofarnas Slides: slideshare.net/poar Stora Hamnkanalen by Mikael Tigerström on Flickr (CC-BY)
  2. 2. Northern LEAD Logistics Research Centre Founded by: Chalmers University of Technology University of Gothenburg Logistics and Transport Society LTS
  3. 3. Five core research groups Professors 10 Visiting Professors 6 Associate Professors 5 Post docs 6 Faculty 14 PhD students 32 Total 73 Industrial marketing & purchasing Physical Distribution Production Logistics Logistics & Transport Optimization
  4. 4. Demographic and social change Shift in economic power Rapid urbanisation Technological 5 GLOBAL TRENDS Climate breakthroughs change and resource scarcity
  5. 5. Things are happening outside the freight industry (and have been for some time)
  6. 6. beginning We are in the middle of a gigantic exponential development curve
  7. 7. 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
  8. 8. Startups don’t compete with airlines... by purchasing a bunch of planes hiring a bunch of pilots and locking up a bunch of terminals at airports. Quote: bryce.vc/post/18404303850/the-problem-with-innovation Image: Connecting the community, my Twitter strategy, and American Airlines at DFW by Trey Ratcliff on Flickr (CC-BY,NC,SA)
  9. 9. Startups don’t compete with airlines... by purchasing a bunch of planes hiring a bunch of pilots and locking up a bunch of terminals at airports. Startups compete with airlines by inventing videoconferencing. Quote: bryce.vc/post/18404303850/the-problem-with-innovation Image: Connecting the community, my Twitter strategy, and American Airlines at DFW by Trey Ratcliff on Flickr (CC-BY,NC,SA)
  10. 10. App ICT Maturity "Multi-Touch" by DaveLawler on Flickr (CC-BY)
  11. 11. Intelligent Transport Systems (ITS) are advanced applications which without embodying intelligence as such aim to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated and ‘smarter’ use of transport networks. ITS DIRECTIVE 2010/40/EU
  12. 12. In other words: We use computers to make transportation better. (That doesn’t sound so hard, does it?)
  13. 13. RESOURCE UTILISATION LOW Safety imbalance Variation in resource demand Source: Kent Lumsden Chain imbalance Caused by the chain Technological imbalance E.g. mismatch in equipment Operational imbalance Goods and resource flow not compatible Structural imbalance Uneven transport demand
  14. 14. RESOURCE UTILISATION LOW Safety imbalance Variation in resource demand Source: Kent Lumsden Chain imbalance Caused by the chain Technological imbalance E.g. mismatch in equipment Several of these imbalances can be Operational imbalance Goods and resource flow not compatible reduced by reducing uncertainties Structural imbalance Uneven transport demand
  15. 15. But the biggest problem in transportation is time. There is not enough of it. Ever. In Search Of Lost Time by bogenfreund on Flickr
  16. 16. 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
  17. 17. 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)
  18. 18. Image: Alain Delorme, alaindelorme.com The current model is focused on economy of scale and standardization
  19. 19. The current paradigm
  20. 20. Process improvement Service development Infrastructure development Faster and better returns Customer controls last mile 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
  21. 21. Process improvement Service development Infrastructure development Faster and better returns Customer controls last mile 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
  22. 22. 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 friendly 3PL targeted at E-commerce Faster, more reliable and secure deliveries in Europe Better security Source: Svensk Digital Handel 2014 Bo Zetterqvist 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.
  23. 23. 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 friendly 3PL targeted at E-commerce Faster, more reliable and secure deliveries in Europe Better security The freight industry has work to do… Source: Svensk Digital Handel 2014 Bo Zetterqvist 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.
  24. 24. So… What are we doing about all this?
  25. 25. 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
  26. 26. Gartners Hype Cycle for Emerging Technologies Source: Gartner August 2014
  27. 27. Gartners Hype Cycle for Emerging Technologies Could affect freight transport
  28. 28. Functions Goods Vehicle Software Computers Paper based Open interface Advanced order handling Simple order handling Monitor fuel cosnumption Phone Papers Barcodes Electronically documents generated freight Web based UI Platform based systems Hardware-oriented Data collection (proprietary) systems Based access Performance Based access Digitalisation 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 RFID-tags Communication with vehicles E-invoice Web based booking Business processes Infrastructure 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 2014-10-15
  29. 29. Increasing freight transport demand http://www.eea.europa.eu/data-and-maps/figures/freight-transport-activity-growth-for-eu-25 EU-25
  30. 30. Focus on transportation!
  31. 31. Digitalisation Increasing goods volumes Opportunities New technology Political interest Quad Aces by fitzsean on Flickr
  32. 32. Time horizons Strategic Tactical Operational Predictive Real-time! We are approaching this boundary …and we are starting to move past it!
  33. 33. Infrastructure Business processes Vehicles Goods Stra-tegic Tac-tical Opera-tional Pre- What happens dictive when access to real-time data increases? not quite clear on the concept by woodleywonderworks on Flickr (CC-BY)
  34. 34. The Action of New York City by Trey Ratcliff on Flickr (CC-BY,NC,SA) Need for speed Data collection Data processing Data exploitation
  35. 35. 3 mountaintops to climb… En la cima! by Alejandro Juárez on Flickr (CC-BY)
  36. 36. Mountaintop #1 Collection of data in real-time 3 data types Fixed Historical Snapshot En la cima! by Alejandro Juárez on Flickr (CC-BY)
  37. 37. 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…
  38. 38. 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
  39. 39. 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)
  40. 40. Mountaintop #2 Processing of data in real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
  41. 41. 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)
  42. 42. Requirement Transport 1 Transport 2
  43. 43. Requirement Transport 1 No access! Transport 2 Full access!
  44. 44. Smart access/guidance control
  45. 45. Smart access/guidance control
  46. 46. Smart access/guidance control
  47. 47. Smart access/guidance control
  48. 48. Requirements. Different. Port area City center Freight terminal Bridge
  49. 49. CASES (MANY)
  50. 50. CASES (MANY MORE)
  51. 51. Big data in freight transport Film by Foursquare. Google: checkins foursquare
  52. 52. ”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 Gartners Hype Cycle for Emerging Technologies
  53. 53. So… What is Big data? 80 by Phil Dragash on Flickr (CC-BY,NC,SA)
  54. 54. 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
  55. 55. Jawbone measures sleep interruption during earthquake https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
  56. 56. Not statistics Exhausted by Adrian Sampson on Flickr (CC-BY) just
  57. 57. Not just Business Intelligence Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA)
  58. 58. http://dashburst.com/infographic/big-data-volume-variety-velocity/
  59. 59. Examples of applications 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
  60. 60. smile! by Judy van der Velden (CC-BY,NC,SA) Speculative shipping http://www.scdigest.com/ontarget/ 14-01-21-1.php?cid=7767
  61. 61. 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)
  62. 62. 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”
  63. 63. Measure real-time system behaviour Emil Johansson - EJOH.SE
  64. 64. Vizualisation http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk
  65. 65. Created by Oliver O'Brien (UCL Geography/UCL CASA)
  66. 66. Vizualisation/combination
  67. 67. Vizualisation/combination
  68. 68. Predict future events
  69. 69. Manage complex systems Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center
  70. 70. Avoid unpleasant surprises
  71. 71. 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
  72. 72. Challenges The Challenger by Martín Vinacur on Flickr (CC-BY)
  73. 73. Challenges The Challenger by Martín Vinacur on Flickr (CC-BY) Company
  74. 74. Challenges The Challenger by Martín Vinacur on Flickr (CC-BY) Supply chain, simple Company
  75. 75. Challenges The Challenger by Martín Vinacur on Flickr (CC-BY) Supply chain, complex Supply chain, simple Company
  76. 76. Challenges The Challenger by Martín Vinacur on Flickr (CC-BY) Eco system Supply chain, complex Supply chain, simple Company
  77. 77. Challenges The Challenger by Martín Vinacur on Flickr (CC-BY) Cross-disciplinary Cross-industries Cross-borders
  78. 78. Not all ideas age with grace The Challenger by Martín Vinacur on Flickr (CC-BY)
  79. 79. Not everyone will want to The Challenger by Martín Vinacur on Flickr (CC-BY) adopt new things…
  80. 80. 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 friendly 3PL targeted at E-commerce Faster, more reliable and secure deliveries in Europe Better security Source: Svensk Digital Handel 2014 Bo Zetterqvist 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.
  81. 81. 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 friendly 3PL targeted at E-commerce Faster, more reliable and secure deliveries in Europe Better security The freight industry has work to do… Source: Svensk Digital Handel 2014 Bo Zetterqvist 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.
  82. 82. Even if it hurts.
  83. 83. 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)
  84. 84. It’s not business as usual. This is the internet happening to freight transport. There is no ’usual’ anymore. Get used to it. Hello Kitty Darth Vader by JD Hancock on Flickr (CC-BY)
  85. 85. ITS AND FREIGHT TRANSPORT - AN URBAN PERSPECTIVE Per Olof Arnäs Chalmers University of Technology @Dr_PO per-olof.arnas@chalmers.se about.me/perolofarnas Slides: slideshare.net/poar Stora Hamnkanalen by Mikael Tigerström on Flickr (CC-BY)

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