Deep learning and Back Order Solutions
| Title | Deep learning and Back Order Solutions |
|---|---|
| Summary | Deep learning and Back Order Solutions |
| Keywords | |
| TimeFrame | Fall 2018 |
| References | |
| Prerequisites | |
| Author | |
| Supervisor | Sławomir Nowaczyk |
| Level | Master |
| Status | Open |
This is a project together with Volvo Group Truck Operations - Service Market Logistics Apply here: https://xjobs.brassring.com/TGnewUI/Search/Home/Home?partnerid=25079&siteid=5171#jobDetails=663606_5171
Background
Finding solutions to non-availability require a lot of different inputs and judgmental power since the solution that was best yesterday might not be the best today. This makes the process difficult to automize with traditional waterfall logic. We are by that looking into possible applications of using deep learning technologies to solve this problem and we believe that there will be a good added value to collaborate with universities on this matter.
Thesis questions and expected outcome
We would like the students to study of current back order solutions and processes and appropriate technologies in the deep learning area. Data collection, processing and quality review would be relevant during this step of the process as well. The students would review different deep learning technologies that could be applicable and the impact on the process and the feasibility of implementation. As output we would like to have a proof of concept showing the possible impact on current process quality and performance.
Student profile and application
Master students in Logistics and/or Computer Science, or similar fields Application deadline: Nov 5th 2018
