Opportunities and Barriers for Advancing the API …...Opportunities and Barriers for Advancing the...
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Chair of Software Engineering for Business Information Systems (sebis) Faculty of Informatics Technische Universität München wwwmatthes.in.tum.de Opportunities and Barriers for Advancing the API Economy within the Automotive Industry Fridolin Koch (B.Sc.), 01.10.2018, MA Thesis Kickoff
Transcript of Opportunities and Barriers for Advancing the API …...Opportunities and Barriers for Advancing the...
Chair of Software Engineering for Business Information Systems (sebis) Faculty of InformaticsTechnische Universität Münchenwwwmatthes.in.tum.de
Opportunities and Barriers for Advancing the API Economy within the Automotive IndustryFridolin Koch (B.Sc.), 01.10.2018, MA Thesis Kickoff
Motivation
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Communication Payment Search Shipping
What do this companies have in common?
à Their core product is API based
Motivation
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BUT: What about the Automotive Industry?
Speed Location
Temperature
Tire Pressure
Fuel Consumption
Engine RPMRain Sensors
Accelerometer
APIs providing access and enabling new innovative business models
Research Questions
RQ1 What data is generated by modern vehicles?
RQ2Which use cases and scenarios can be derived by providing
large scale access to vehicle generated data through APIs?
RQ3What are barriers for use-cases and scenarios not being
implemented?
RQ4How could the advancing of API economy within the
automotive industry be accelerated?
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Literature Review
Interview & Coding
Comparison to other Industries
Research Approach
RQ1(Vehicle Data)
RQ2(Use Cases)
RQ3(Barriers)
RQ4(Guidelines)
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Design Science
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Environment IS Research Knowledge BaseRelevance Rigor
Related Work:• Vehicle Sensor Data• Bus-Systems (CAN,
OBD, OBD-II)• IoT APIs & Data• API Economy
evolution in other Industries
• Vehicular ad hoc network (VANET)
Methodology:• Literature Review
Business:• Opportunities for new
revenue streams through a enabling API economy
• Shifting social values (Sharing economy) requires the reevaluation of business models within the automotive industry
Technology:• Autonomous vehicles• APIs
Business Needs
Applicable Theory & Methods
Implementation of Design Artifact in Environment Additions to the Knowledge Base
RefineAssess
Conduction of Expert Interviews to derive
guidelines for tackling current adoption
barriers
Current adoption barriers and
recommendations for enabling API Economy
Adapted from Hevner et. al. 2014
Vehicle Generated Data
• Average number of sensors per vehicle is increasing• 24 in 2002• 40 in 2007 (Estimate)• 70 in 2013 (Prediction)
• Current (2007) luxury cars have over 100 sensor per vehicle [Fleming2008]
Different classification approaches
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Adapted from Abdelhamid et al. 2014
In-Vehicle Sensor
Safety Sensors
Diagnostics Sensors
Convenience Sensors
Environment Monitoring Sensors
Use-Case Scenarios
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Generating revenues
Networked parking service
Predictive maintenance
Usage-based tolling and taxation
Reducing costs
Driving style suggestions
Usage-based insurance
Warranty costs reduction
Increasing safety and
security
Improved road/infrastructure maintenance and
design
Driver's condition monitoring
Road laws monitoring and enforcement
Adapted from McKinsey&Company 2016
Barriers
Data Privacy / GDPR
Data Quality
Redundant Data (Waste of communication and computation resources)
Dynamic availability of resources
No brand-independent data format
Incentives for offering resources / data (Car Owners)
...
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Derive Guidelines for Advancing API Economy
VDA 2016Abdelhamd 2015AutoMat Project
Timeline
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u
u
15. Aug. 14. Sep. 15. Okt. 15. Nov. 16. Dez. 15. Jan. 15. Feb.
Registration
Literature Review
Interview Design
Interview Conduction
Interview Evaluation
Writting
Submission
Technische Universität MünchenFaculty of InformaticsChair of Software Engineering for Business Information Systems
Boltzmannstraße 385748 Garching bei München
Tel +49.89.289.Fax +49.89.289.17136
wwwmatthes.in.tum.de
Fridolin KochB.Sc.
17132
References
Hevner, A., March, S., Park, J., Ram, S.: Design Science Research in Information Systems. MIS Quarterly (28: 1); (2004).
Abdelhamid, S., Hassanein, H.S., Takahara, G.: Vehicle as a mobile sensor. Procedia Computer Science, Volume 34; (2014)
Abdelhamid, S., Hassanein, H.S., Takahara, G.: Vehicle as a Resource (Vaar). IEEE Network (29: 1), pp. 12-17; (2015)
McKinsey&Company: Monetizing car data; (2016)
Verband der Automobilindustrie e. V.: Zugang zum Fahrzeug und zu im Fahrzeug generierten Daten; (2016)
Automat Project, http://www.automat-project.eu/ (Accessed: 28.09.2018)
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Backup
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Vehicle Generated Data I
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Different classification approaches
Vehicle
Powertrain(vehicle energy use,
drivability, performance)
Chassis(vehicle handling,
safety)
Body(occupant needs)
Fleming 2001
Vehicle Generated Data II
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Different classification approaches
In-Vehicle Sensor
Safety Sensors
Distance Sensors
Night Vision Sensors
Speed Sensors
...
Diagnostics Sensors
Powertrain Diagnostics
Chassis Diagnostics
Body Diagnostics
Convenience Sensors
In-Cabin Convenience
Driving Convenience
Environment Monitoring Sensors
Temperature Sensors
Barometric Pressure
...
Adapted from Abdelhamid et al. 2014
