30 credits master thesis - smart factory lab   Automation utilizing Computer Vision

- Finding best practice method of deployment

Background
Scania has recently standardized its approach to computer vision In production and logistics by providing a global software tool. This supports the company’s goals of increasing automation, building in-house knowledge, and advancing digital transformation. There is an active request from a Logistic station to use this technology to improve their work process.

Assignment
This thesis focuses on developing a computer vision solution for logistics that automatically reads labels on incoming material and boxes. The system registers the information and guides operators on where to place each item, streamlining material handling and reducing errors. The solution is built using Scania’s standardized computer vision tool, ensuring scalability and integration.
The goal is to explore best practices for using the tool, deliver a working solution at a logistics station, and gather operator feedback.

Key questions (Inspiration)

•    How can computer vision be integrated with existing logistics systems to reduce waste?
•    What setup of cameras, lighting, and positioning provides the most robust label reading?
•    What is the most effective way to guide operators based on the registered information?
•    How can the standardized tool be applied, adapted and integrated to ensure scalability across different logistics sites?
•    What feedback and improvements do operators suggest after testing the solution?

Education and time plan
Education: Master's program in machine engineering, production engineering, management, computer science, data science, or related fields.
Number of students: 2
Start date: January 2026
Estimated time needed: 20 weeks

Application

Your application should include a CV, cover letter and transcripts.

A background check may be conducted for this position. We conduct interviews on an ongoing basis and may close recruitment earlier than the stated date.