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Big data in freight transport
This document discusses big data from the perspective of freight companies. It notes several global trends impacting freight, including demographic shifts, rapid urbanization, and technological breakthroughs. It outlines areas where logistics companies need to develop in relation to e-commerce, including infrastructure, processes, and services. Challenges for the freight industry are also presented, such as low profit margins, fragmented industry, and data silos. The importance of digitalization and real-time decision making using big data is emphasized. Blockchain technology and its potential applications for freight are also briefly discussed.
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Big data in freight transport
- 1. Big data froma 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. Demographic and social change Shift in economic power Rapid urbanisation Technological breakthroughsClimate changeand resource scarcity 5 GLOBAL TRENDS Source: PWC (google: pwc megatrends 2014)
- 3. Things are happeningoutside the freight industry (and have been for some time)
- 4. Things are happeningoutside the freight industry (and have been for some time) 1957
- 5. Things are happeningoutside the freight industry (and have been for some time) Image: Richard Hancock, twitter.com/CanaryWorf 2015
- 7. Stage Coach Wheelby arbyreed on Flickr Development of transportation technology has been fairly linear …for the last 5500 years
- 8. We are inthe middle of a gigantic exponential development curve beginning
- 9. A new globaleco system where new types of, knowledge based, industries compete with traditional ones http://jaysimons.deviantart.com/art/Map-of-the-Internet-1-0-427143215
- 10. 355:365:2015BWH by hermitsmooreson Flickr (CC-BY,NC,SA) Make analogue information digital Digitization:
- 11. MobileWorldCongress2016byKārlisDambrānsonFlickr(CC-BY) Increased use of digitaltechnology Digitalization:
- 12. MobileWorldCongress2016byKārlisDambrānsonFlickr(CC-BY) Increased use of digitaltechnology Digitalization: Make analogue information digital Digitization: Both are important! (and interesting)
- 13. Ominous Windmill byConrad Kuiper on Flickr (CC-BY,NC,SA) Digit(al)ization is not a trend
- 14. Ominous Windmill byConrad Kuiper on Flickr (CC-BY,NC,SA) Digit(al)ization is not a trend It is a force of nature
- 16. Process improvement Service developm entInfrastructure developm ent Customer controls last mile Faster and better returns Better delivery experience Secure identificationon 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
- 17. Process improvement Service developm entInfrastructure developm ent Customer controls last mile Faster and better returns Better delivery experience Secure identificationon 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
- 18. Customer controls last mile Faster and better returns Better delivery experience Secure identification onpickup/ 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.
- 19. Challenges The Challenger byMartín Vinacur on Flickr (CC-BY) Low profit margins Social issues Fragmented industry Data all over the place, but not where most needed Large investments
- 20. Image: Alain Delorme,alaindelorme.com The current model is focused on economy of scale and standardization
- 21. 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)
- 22. 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
- 23. Business processes Infrastructure Paperbased Phone Papers Road signs A nalogue tools R D S M onitorfuel cosnum ption Digitalisationversion 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
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- 28. • Data amountsincrease 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
- 29. Gartners Hype Cyclefor 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
- 30. Gartners Hype Cyclefor Emerging Technologies Source: Gartner July 2015 Could affect transportation and logistics
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- 32. 2011 2013 2015 ”Bigdata 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
- 33. 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
- 34. Jawbone measures sleep interruptionduring earthquake https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
- 35. Not statistics Exhausted byAdrian Sampson on Flickr (CC-BY) just
- 36. Not Business Intelligence Basingstoke Office StaffDesk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA) just
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- 38. Varela Rozdos, Iand 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
- 39. 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
- 41. 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
- 42. 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
- 43. Strategic Tactical OperationalPredictive Time horizons We are approaching this boundary …and we are starting to move past it! Real-time!
- 44. The Action ofNew 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/
- 45. En la cima!by Alejandro Juárez on Flickr (CC-BY) 3 mountaintops to climb…
- 46. En la cima!by Alejandro Juárez on Flickr (CC-BY) 3 data types Mountaintop #1 Collection of data in real-time Fixed Historical Snapshot
- 47. 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…
- 48. 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
- 49. Say hi tothe new sensors http://mobsentech.com
- 50. Mountaintop #2 Processing ofdata 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
- 51. Mountaintop #2 Processing ofdata in real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
- 52. Mountaintop #3 Exploiting datain real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)
- 53. IFTTT.com IF This, ThenThat Connects unrelated services
- 54. Real-time decision making notalways successful…
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- 59. Requirement Transport 1 Transport 2 Noaccess! Full access!
- 60. Requirements. Different. Port areaCity center Freight terminal Bridge
- 61. 7Big Data BestPractice 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”
- 62. 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
- 63. Integration of digitaland physical worlds http://www.sygic.com/gps-navigation/addon/head-up-display
- 64. The sharing economyhits freight transport (again and again…)
- 65. Servitization Move up inthe 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
- 66. smile! by Judyvan der Velden (CC-BY,NC,SA) Anticipatory shipping http://www.scdigest.com/ontarget/ 14-01-21-1.php?cid=7767
- 67. 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)
- 68. Curated services madepossible with data Mindconnect Sendify
- 69.
- 70. Created by OliverO'Brien (UCL Geography/UCL CASA)
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- 77. 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
- 78. 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)
- 79. Big data froma freight company perspective Per Olof Arnäs, PhD Chalmers University of Technology Gothenburg, Sweden about.me/perolofarnas Slides online: slideshare.net/poar Film by Waze