Extracting financial and operations efficiency from “connected” fleet data

Location: Mascot, Sydney NSW

Duration:  5 months

Keywords: Computer Science, Engineering, Mathematics, Statistics

Project Background

A major leading international provider of mobility solutions is looking to the future by investigating the value in stored data across its Asia Pacific region, in order to understand how that impacts the fleet data in costs and potential benefits.

Connected vehicle solutions provide an opportunity to extract cost and customer benefit for large fleet operators. Using IOT technology, fleet operators can gain access to full vehicle diagnostics, driver behavior and significantly improve the customer journey providing easy access to vehicles, 24/7. The data that can be extracted is valuable to improve internal operations, with the capability to save millions of dollars in operational inefficiency. Also, that data would be valuable to other third parties if extracted and formatted properly (ie insurance companies, retailers, cities, etc.)

The intended outcome is to understand full capability of vehicle connectivity, translate this into usable data, create business dashboards that can be used for decision making, and extract unintended cost from the business.

Research to be Conducted

  • Research into global trends initially before commencing to the extract the data and present in a usable format.
  • Validate the data and measure accuracy.
  • Translate the data into usable forms in order to determine the potential financial return.

Skills Required

The skills required include both technical and non-technical skills. As a PHD candidate, the most likely candidates are students of Mathematics and Statistics, Computer Science or Engineering. We need to translate theory to practical application; our ideal candidate will demonstrate intellectual curiosity and business acumen.

Expected Outcomes

The expected outcome from the project is the identification and extraction of data from software provider selected by partner, translate data to actionable formats (alerts) that is useable by local and central operations to improve efficiency, remove waste and/or improve accuracy of data for cost recovery, revenue generation and improve customer satisfaction.

In the final report, the Intern is expected to present what validated data can be extracted, articulate the business benefit/ROI of installing IOT technology and recommend future applications for use of application.

If the project objectives are met, the Industry Partner will fully equip a full country fleet with required hardware and software, extract value and expand application internationally.

Additional Details

The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.

It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner. The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.

The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.

To participate in the APR.Intern program, all applicants must satisfy the following criteria:

  • Be a PhD student currently enrolled at an Australian University.
  • PhD candidature must be confirmed.
  • Applicants must have the written approval of their Principal Supervisor to undertake the internship. This approval must be submitted at the time of application.
  • Internships are also subject to any requirements stipulated by the student’s and the academic mentor’s university.
Applications Close

4 March 2018

INT – 0392