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Internet of Everything - For Real 2014
Internet of Everything - For Real 2014
Internet of Everything - For Real 2014
Internet of Everything - For Real 2014
Internet of Everything - For Real 2014
Internet of Everything - For Real 2014
Internet of Everything - For Real 2014
Internet of Everything - For Real 2014
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Internet of Everything - For Real 2014

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Mats Malmberg, Data Analyst at Springworks talks about: Signed, sealed, delivered with PostNord

Mats Malmberg, Data Analyst at Springworks talks about: Signed, sealed, delivered with PostNord

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  • Presentation, Springworks: we connect vehicles, which is awesome as there are infinitely many possibilities for faschinating innovations.
    My role is to analyze the obtained data, and to create models on how to interpret data in new ways in order to provide new services.

  • We have a “luxury problem”: there are so many interesting things that we can and want to do, but there’s only so much time…
  • So we thought “Is there a single M2M solution that can provide revenue in several different business cases?”
    It might sound like a tedious job to formulate the technical specifications for such a system in order to conform with all possible scenarios, but our solution is quite simple.

    Can we find one solution for everything?
    Account for everything -> Tedious technical specification
    Our solution simple -> We just went for it
  • We just went on ahead and tried.
    Hence we started a vinnova research project where postnord, telia, kth logistics and we our selves cooperate to investigate this matter.
    Springworks – develop infrastructure and analyze data
    Telia – provide connectivity
    Postnord – provide car fleet
    Kth traffic logitics – analyze data
  • The analytical phase of the project has not been running very long, only a few weeks. But already we can see some very promising results, and this is what I’d like to focus on.
    First of all, Kth used to develop models on traffic behaviour based on data that they receive from a large taxi company. The data is irregular and samples are obtained approximately every minute. This introduces a large uncertainty to the results.
    ----- Meeting Notes (6/16/14 08:34) -----

    Installed TEM (show tem)
    Early (few weeks), promising results

  • Before KTH traffic dep. Received data with 1 minute -> large uncertainty
    Today -> no guessing, smaller uncertainty in input

    Postnord delivers mail,
    carfleet necessity, carfleet = cost
    Current plattform -> monitor usage, create reports
  • We can create reports on for instance average velocity, number of stops, idle time, distance and a lot more.
    Furthermore we are currently discussing to develop a pilot project with an ecodrive solution where the driver receives direct feedback on the driving behaviour in order to save fuel. Typically the fuel consumption is decreased by roughly 10% by such feedback systems. This would yield approximately 100milj sek in saved fuel costs, which a figure from postnord themselves.
    ----- Meeting Notes (6/16/14 08:34) -----
    Data from approximately three weeks
    We see -> aproximately 30% idle
    Compare office 1,3 by total time vs number of stops.

    fleet usage - vel, #stop, idletime, dist etc.
    pinpoint areas of improvement

    discussing ecodrive pilot
    direct feedback
    fuel consumption roughly 10% -> 100milj (postnord)

    ----- Meeting Notes (6/16/14 10:01) -----
    peka noggrannare på data
    poängtera att detta är ny kunskap för posten

  • Det här är några av mina slutsatser. Det är inte dom I sig som är det mest spännande, utan att se vart dom tar vägen.

    Presentation, Springworks: we connect vehicles, which is awesome as there are infinitely many possibilities for faschinating innovations.
    My role is to analyze the obtained data, and to create models on how to interpret data in new ways in order to provide new services.

    We have a “luxury problem”: there are so many interesting things that we can and want to do, but there’s only so much time…
  • Transcript

    • 1. Signed Sealed Delivered with Postnord Mats Malmberg, Data Analyst
    • 2. What you can do with telematics if you have a large set of cars • Planning of infrastructure • Identify traffic risks • Traffic information • Map information • Measuring of radio Coverage
    • 3. Can we find one M2M solution that can provide several parallell revenue streams? Problem
    • 4. Let’s try and see what happens! Solution
    • 5. Results
    • 6. Low sample rate introduces a large uncertainty
    • 7. Office 1 Office 2 Office 3 Total number of stops 1076 680 718 Total stop time 40.7 22.4 47.6 Total time 136 119 129.2 Average stop duration 136.1 118.6 2387.1 longest stop 5595 2327 8035 Shortest stop considered 30 30 31 Percentage idle 29.9% 18.8% 36.8%
    • 8. Thank you! Mats Malmberg, Data Analyst

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