Big data in freight transport

Transcription

1 Big data in freight transport Per Olof Arnäs Slides on slideshare.net/poar! Film by Foursquare. Google: checkins foursquare

2 beginning We are in the middle of a gigantic exponential development curve

3 Gartners Hype Cycle for Emerging Technologies 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

4 Gartners Hype Cycle for Emerging Technologies Source: Gartner August 2014

5 Gartners Hype Cycle for Emerging Technologies Could affect freight transport

6 So What is Big data?

7 Big data is an allencompassing 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

8 Google flights

9 Jawbone measures sleep interruption during earthquake

10 Speculative shipping smile! by Judy van der Velden (CC-BY,NC,SA) php?cid=7767

11 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 Determine package location Convey delivery address, package ID to delivery location Assign delivery address to package Yes Orders satisfied by item(s) received? No Package redirected? Yes No Convey indication of new destination geographic area and package ID to current location Deliver package to delivery address smile! by Judy van der Velden (CC-BY,NC,SA) php?cid=7767

12 just Not statistics Exhausted by Adrian Sampson on Flickr (CC-BY)

13 Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA) Not just Business Intelligence

14

15 Barcodes RFID Sensors Goods Smart goods Distributed decision making RFID-tags Goods as bidirectional hyperlink Barcodes Electronically generated freight documents Paper based Paper based Functions Software Computers Manual Phone Papers Monitor fuel cosnumption Open interface Advanced order handling Simple order handling Data collection systems (open) Platform based systems Hardwareoriented Predictive maintenance Data collection systems (proprietary) Web based UI Platooning Performance Based access Vehicle Order handling Driver support Vehicle economics Digitization version ERP systems TMS systems E-invoices Cloudbased services The social web Business Open connectivity processes Communication with vehicles E-invoice Integrated prognosis TMSsystems Web based booking Exceptions handling Route planning Excel Analogue tools Fax Route optimisation Road signs RDS Webservices with traffic data GPS for navigation Platooning Tolling systems Dynamic routing systems Individual routing information Probe data Mashups Multiple data sources Performance Based access Infra- structure RDS-TMC Road taxes Active traffic support CC-BY Per Olof Arnäs, Chalmers

16 Time horizons Strategic Tactical Operational Predictive Real-time! We are approaching this boundary and we are starting to move past it!

17 3 mountaintops to climb En la cima! by Alejandro Juárez on Flickr (CC-BY)

18 Mountaintop #1 Collection of data in real-time 3 data types Fixed Historical Snapshot En la cima! by Alejandro Juárez on Flickr (CC-BY)

19 Mountaintop #1 Collection of data in real-time 5 data domains at least Infrastructure/ Vehicle Driver Cargo Company facility En la cima! by Alejandro Juárez on Flickr (CC-BY)

20 Fixed Historical Snapshot DATA MATRIX Vehicle Length Weight Width Height Capacity + other PBS-criteria Emissions Fuel consumption Route Position Speed Direction Cargo Weight Origin Destination Accepted ETA Temperature + other state variables Temperature + other state variables Driver Education/training Speed (ISA) Rest/break schedule Traffic behaviour Belt usage Alco lock history Schedule status (time to next break etc.) Company Contracts/ agreements Previous interactions Backoffice support Infrastructure /facility Map + fixed data layers Traffic history Current traffic Queue Availability

21 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)

22 Mountaintop #2 Processing of data in real-time En la cima! by Alejandro Juárez on Flickr (CC-BY)

23 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) Fulham-Manchester United by vuhlser on Flickr (CC- BY) En la cima! by Alejandro Juárez on Flickr (CC-BY)

24 Mountaintop #3 Exploiting data in real-time Boeing-KC-97 Stratotanker by x-ray delta one on Flickr (CC-BY) En la cima! by Alejandro Juárez on Flickr (CC-BY)

25 CASES (MANY)

26 CASES (MANY MORE)

27 Examples of applications in freight (Waller and Fawcett, 2013) Inventory Human resources management Reduction in driver turnover, driver Real-time capacity availability assignment, using sentiment data Forecasting Time of delivery, factoring in weather, driver characteristics, time of day and date Transportation management Optimal routing, taking into account weather, traffic congestion, and driver characteristics analysis 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

28 Manage complex systems Image from:

29 Predict future events

30 Avoid unpleasant surprises

31 Vizualisation

32 Big Data Best Practice Across Industries 7 Operational Efficiency Operational Efficiency Customer Experience Customer Experience New Business Models New Business Models Use data to: Increase level of Usage of data in order to: Increase Level of Transparency transparency Optimize Resource Consumption Improve consumption Process Quality and Performance Optimize resource Improve process quality and performance Exploit data for: to: Increase customers loyalty and retention Performing precise Perform customer precise segmentation customer segmentation and targeting and targeting Optimize customer Optimize interaction customer and service interaction and service Capitalize on data by: by: Expanding revenue streams from streams existing from existing products products Creating new revenue streams from entirely new new (data) products (data) products Figure 4: Value dimensions for Big Data use cases; Source: DPDHL / Detecon DHL 2013: Big Data in Logistics

33 Data scientists - the new superstars Domain knowledge critical! "Data Science Venn Diagram" by Drew Conway - Own work. Licensed under Creative Commons Attribution- Share Alike 3.0 via Wikimedia Commons - File:Data_Science_Venn_Diagram.png#mediaviewer/File:Data_Science_Venn_Diagram.png 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

34 Challenges Cross-disciplinary Cross-borders Cross-industries The Challenger by Martín Vinacur on Flickr (CC-BY)

35 Big data in freight transport Per Olof Arnäs Slides on slideshare.net/poar! Film by Foursquare. Google: checkins foursquare