Difference between revisions of "Understanding usage of Volvo trucks"
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|Summary=A project in collaboration with Volvo AB on understanding vehicle usage patterns. | |Summary=A project in collaboration with Volvo AB on understanding vehicle usage patterns. | ||
|Keywords=Data Mining. Data representation | |Keywords=Data Mining. Data representation | ||
| + | |TimeFrame=Fall 2018 | ||
|Prerequisites=Good knowledge of machine learning and programming skills for implementing machine learning algorithms | |Prerequisites=Good knowledge of machine learning and programming skills for implementing machine learning algorithms | ||
|Supervisor=Sławomir Nowaczyk, Fredrik Moeschlin (at Volvo) | |Supervisor=Sławomir Nowaczyk, Fredrik Moeschlin (at Volvo) | ||
Revision as of 19:11, 14 October 2018
| Title | Understanding usage of Volvo trucks |
|---|---|
| Summary | A project in collaboration with Volvo AB on understanding vehicle usage patterns. |
| Keywords | Data Mining. Data representation |
| TimeFrame | Fall 2018 |
| References | |
| Prerequisites | Good knowledge of machine learning and programming skills for implementing machine learning algorithms |
| Author | |
| Supervisor | Sławomir Nowaczyk, Fredrik Moeschlin (at Volvo) |
| Level | Master |
| Status | Open |
In discussions different stakeholders pointed out that the current segmentation of Volvo customers is not optimized. There is a large value in correctly understanding and grouping customers' needs in order to optimize offerings and sales. We are currently looking into the segmentation as it is today and it is clear that we can utilize techniques from data mining and machine learning applied on telematics data to better cluster vehicles according to their usage. On a high level we would expect segments such as “City distribution”, “Regional distribution”, … . They are currently described out of their usage, for instance Interregional Haul:
“Long distance transport followed by few clustered deliveries in normally smooth but occasionally also in rougher road conditions. The average distance between delivery and pickup is between 50 km and 250 km. The vehicle often returns to its home base during the day, which means that overnight stays in the vehicle occur in average 1-2 times per week.”
From telematics systems we have detailed information about where users drive, times, distances, locations, different events (stop, login, Power Take-Off (PTO), etc.). If we combine this information from several vehicles and from map data sources (e.g. Open Street Map), we could generate detailed insights of how the vehicle is used. This can lead to more precise segmentation (clustering) with better quantified attributes (road conditions, driving time, actual hub-hub distances etc). We would also be able to generate a more granular segmentation, or sub-groups within the segments. Furthermore we can give an overview of the segments in terms of quantity of customers, regional variation and more. The core value for Volvo is better market understanding to allow for better positioning and offerings.
