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Jam

Enhancing fuel efficiency and reducing vehicle maintenance and downtime costs, using real-time data from vehicle sensors (IoT) and a machine learning algorithm for big data analysis.

Total Cost €

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EC-Contrib. €

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Partnership

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 Jam project word cloud

Explore the words cloud of the Jam project. It provides you a very rough idea of what is the project "Jam" about.

uk    32    ecu    2019    efficient    interpretation    portugal    regulations    vor    operators    compares    117    industry    predicting    subscribers    events    advantages    single    jam    optimizing    heavy    industrial    thousands    innovative    constant    troubleshooting    mining    off    agnostic    fuel    frost    algorithm    operation    markets    hardware    interfaces    differences    brings    france    eco    passenger    vehicle    protocols    light    time    fleet    business    market    details    amount    maintenance    2020    compliance    billion    parts    hundreds    sullivan    biggest    constitute    learning    prevention    sensor    fleets    save    designed    historical    solutions    ghg    money    starting    dollar    critical    obd    competitive    hours    efficiency    repairs    euros    total    spain    duty    day    put    iot    software    driving    italy    scheduled    solution    labour    global    companies    reducing    surpassed    trucks    road    million    environmental    germany    operate    emissions    physical    businesses    medium    machine    poland    barrier    agriculture    represented    vehicles    data    2011   

Project "Jam" data sheet

The following table provides information about the project.

Coordinator
STRA, SA 

Organization address
address: INSTITUTO PEDRO NUNES, RUA PEDRO NUNES QUINTA DA NORA, ED.D
city: COIMBRA
postcode: 3030 199
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Portugal [PT]
 Project website http://stratio.pt/jam/
 Total cost 71˙429 €
 EC max contribution 50˙000 € (70%)
 Programme 1. H2020-EU.3.4. (SOCIETAL CHALLENGES - Smart, Green And Integrated Transport)
2. H2020-EU.2.3.1. (Mainstreaming SME support, especially through a dedicated instrument)
 Code Call H2020-SMEINST-1-2015
 Funding Scheme SME-1
 Starting year 2016
 Duration (year-month-day) from 2016-02-01   to  2016-05-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    STRA, SA PT (COIMBRA) coordinator 50˙000.00

Map

Leaflet | Map data © OpenStreetMap contributors, CC-BY-SA, Imagery © Mapbox

 Project objective

Jam is an IoT solution (hardware & software), aiming to improve the efficiency of businesses operating medium/heavy-duty vehicle (e.g. passenger and distribution fleets) and industrial fleets (e.g. mining trucks and agriculture vehicles), targeting key needs: - Fuel efficiency: fuel represented, in 2011, 32% of the total cost of fleet operation - Compliance with environmental regulations and reduction of GHG emissions - Vehicle off-road (VOR) time and maintenance costs: Many operators put the cost of having a single VOR as being in the hundreds, if not thousands, euros/day

According to Frost & Sullivan, the OBD market is expected to reach 117.8 million subscribers in 2019 and to become a billion-dollar industry by 2020. Although there are already solutions on the market for ECU interpretation on light-duty vehicles, the amount of new protocols, physical interfaces and differences in details in the way industrial vehicles operate constitute a barrier, not yet surpassed. Business and industrial fleets need an agnostic solution that allows optimizing the fleet management by reducing the maintenance costs and VOR time. Jam brings an innovative approach and focus on prevention and constant analysis of real-time vehicle data through a machine-learning algorithm allowing companies to save money on fuel costs, vehicle parts and hours of labour due to a more efficient management of fleet resources.

Competitive advantages: - Designed for medium/heavy-duty and industrial vehicles - Agnostic system - Machine-learning algorithm that compares the ECU sensor data with historical data, predicting critical events - New eco-driving approach - Troubleshooting and scheduled repairs approach focused on prevention and constant analysis of real-time vehicle data The solution will be implemented at a global scale, starting in European markets: Portugal for early market uptake and testing; and then the biggest EU markets (Germany, UK, France, Poland, Italy and Spain).

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The information about "JAM" are provided by the European Opendata Portal: CORDIS opendata.

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