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Hållbara transporter 2017 - digitalisering

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Presentation at the conference Hållbara transporter, 2071116

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Hållbara transporter 2017 - digitalisering

  1. 1. Smart access/guidance control
  2. 2. Smart access/guidance control
  3. 3. Smart access/guidance control
  4. 4. Smart access/guidance control
  5. 5. Per Olof Arnäs, PhD Chalmers University of Technology Service Management and Logistics per-olof.arnas@chalmers.se about.me/perolofarnas Slides: slideshare.net/poar @Dr_PO OKIMG_5751 by taymtaym on Flickr (CC-BY) Some observations regarding the digital future of transportation
  6. 6. Demographic and social change Shift in economic power Rapid urbanisation Technological breakthroughsClimate change and resource scarcity 5 GLOBAL TRENDS
  7. 7. Things are happening outside the freight industry (and have been for some time)
  8. 8. Things are happening outside the freight industry (and have been for some time) 1957
  9. 9. Things are happening outside the freight industry (and have been for some time) Image: Richard Hancock, twitter.com/CanaryWorf 2015
  10. 10. MobileWorldCongress2016byKārlisDambrānsonFlickr(CC-BY) Increased use of digital technology Digitalization: Make analogue information digital Digitization: Both are important! (and interesting)
  11. 11. So… Is nothing happening in freight transport then?!
  12. 12. DIGITAL TRANSACTION HANDLING
  13. 13. Basic data From/To/What/When Owner: the customer Resource allocation What vehicle?
 Owner: Traffic controller Cargo information Loaded amount Owner: Supplier Proof of Delivery Time/date/signature Owner: Driver Pricing Owner: Financial dept. Enough information to create invoice All of these steps cost money! From order to invoice
 (transportation company)
  14. 14. An increase in the number of orders lead to increased cost Goal: De-couple the dependecy between administative cost and order volume Number of orders per year Total cost for administration of all orders Handle exceptions – not transactions!
  15. 15. Integration of digital and physical worlds http://www.sygic.com/gps-navigation/addon/head-up-display
  16. 16. Say hi to the new sensors http://mobsentech.com
  17. 17. ”New” modes of transport https://www.youtube.com/watch?v=50sv4M4rgBM https://www.youtube.com/watch?v=HG76DcXySuw
  18. 18. More automation
  19. 19. The sharing economy hits freight transport (again and again…)
  20. 20. Supply (traditional) A few standardised services with high volume Examples: 
 Hotels, retail chains, newspapers, taxi companies
  21. 21. Demand A large variety of preferences Supply (traditional)
  22. 22. Demand A large variety of preferences A long tail of demand is met by a short tail of supply Supply (traditional)
  23. 23. Supply (sharing economy) A large variety of services, each with a small volume A long tail of demand is met by a long tail of supply Demand A large variety of preferences Made possible with technology!
  24. 24. https://www.statista.com/chart/11810/singles-day-vs-cyber-monday-and-black-friday/
  25. 25. 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
  26. 26. Strategic Tactical Operational Predictive Time horizons We are approaching this boundary …and we are starting to move past it! Real-time!
  27. 27. 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/
  28. 28. Artificial intelligence Reasoning Problem solving Learning Creating
  29. 29. Specialised AI General AI
  30. 30. The Economist, April 12, 2017 http://www.economist.com/news/business/21720675-firm-using- algorithm-designed-cern-laboratory-how-germanys-otto-uses Case German e-retailer Otto uses artificial intelligence to make operational decisions
  31. 31. The Solution (cont.) • Use an AI to analyse 3 bn transactions and 200 variables (past sales, searches on site, weather etc.) • The AI foresees future customer orders and makes procurement decisions based on these projections The Economist, April 12, 2017 http://www.economist.com/news/business/21720675-firm- using-algorithm-designed-cern-laboratory-how-germanys- otto-uses
  32. 32. The Results • The AI predicts with 90% accuracy what will be sold within 30 days • The AI procures around 200 000 items per month • Returns are diminished by 2 million items per year • Stock level has been reduced by 20% • More personel were hired (!) The Economist, April 12, 2017 http://www.economist.com/news/business/21720675-firm- using-algorithm-designed-cern-laboratory-how-germanys- otto-uses
  33. 33. Takeaways • AI used to make existing processes better • AI is not only for Amazon and Google • This development has just started… The Economist, April 12, 2017 http://www.economist.com/news/business/21720675-firm- using-algorithm-designed-cern-laboratory-how-germanys- otto-uses
  34. 34. Sep 2015 Mar 2017 ”Blockchain” ”Blockchain supply chain” Just 2 years! Mar 2014 Sep 2015 Mar 2017 Sep 2017 Sep 2017
  35. 35. Bitcoin, bitcoin coin, physical bitcoin, bitcoin photo by Antana on Flickr (CC-BY,SA) Blockchain technology Records transactions and data among actors that do not trust each other Fully decentralized Actors that do not trust each other = your typical supply chain
  36. 36. Can be used to answer questions like: Who owns something? What hassomething beenthrough? Where is something? When did a transaction occur?
  37. 37. Example: You are looking to buy a new sweater from a store. The sweater has a unique QR code attached. You scan the code with your mobile.
  38. 38. Blockchain Asks about this sweater? The QR code is a hyperlink that asks a query of the relevant blockchain(s)
  39. 39. Blockchain Asks about this sweater Product history ? Source of materials Name of all sub-suppliers Carbon footprint Date of production The results are presented in your web browser
  40. 40. Per Olof Arnäs, PhD Chalmers University of Technology Service Management and Logistics per-olof.arnas@chalmers.se about.me/perolofarnas Slides: slideshare.net/poar @Dr_PO OKIMG_5751 by taymtaym on Flickr (CC-BY) Logistikpodden.se Eldsjälar Experter (Föreslå gärna…) Some observations regarding the digital future of transportation

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