Master Thesis Disturbance production data management and analysis

Background

Volvo GTO in Skövde produces components and heavy-duty diesel engines for several applications (Trucks, construction equipment, industrial engines, marine engines, etc.). The main processes in Skövde are Foundry, Machining, and Assembly.

We are now facing a significant journey of transformation in terms of new products and digitalization of our operations. We hope you want to be part of and contribute to that journey.

Inefficiencies in data collection due to the inclusion of faulty data can compromise the accuracy of simulation models and, therefore, the production system analysis. Existing studies often rely on neatly structured data, overlooking the challenges posed by real-world data sources in manufacturing. A crucial aspect for us in Skövde is the machine stoppage analysis, which is essential for calculating the disturbances in production and critical input data to simulation models. The quality of data that represents the disturbances in resources could significantly impact the output of a simulation study.

Hence, this thesis aims to tackle these data-related challenges in industrial production systems, emphasizing the need for well-structured data collection and exploring automated analysis techniques to categorize machine-stop data accurately.

Purpose

The purpose of this master thesis is to address disturbance data management and analysis. The objective is to create a tool that filters the data correctly, disregards faulty data points, categorizes it accurately to represent reality better, and is ready to be included in simulation models.

Goals

• Investigate the inefficiencies in data collection and management from previous final-year projects performed at Volvo and within the available research literature.
• Based on previous work, extend and develop a tool that automatically filters and categorizes machine stop data in a production line for use in simulation models.
• Study how different data aggregation and categorization ways (e.g., several clusters) according to different criteria (percentage of downtime, length of failure, etc.) can affect simulation results. Propose an accurate procedure when representing disturbances.
• Validate and verify the tool and proposed procedure through a case study.

Project details

For this project, we preferably want a master's student with a suitable background in statistics, data science, and/or simulation. The work will start after an agreement to be finished before the summer of 2024.

Start date: Jan 2024

Number of students: 1-2

Application is made via the Volvo website: http://www.volvogroup.com

Contacts: Carlos Alberto Barrera Diaz [email protected]