Viðburðir eftir árum

PhD defense - Ívar Örn Arnarsson

Systematic Analysis of Engineering Change Request Data

  • 29.5.2020, 11:00 - 13:00

The PhD defense of Ívar Örn Arnarsson will be held on Friday the 29th of May at 11:00 in room M104 and online (Zoom link)

Title of thesis: Systematic Analysis of Engineering Change Request Data – Applying Data Mining Tools to Gain New Fact-Based Insights
Candidate: Ívar Örn Arnarsson
Date and time: 29th of May at 11:00
Room: M104
Opponent: Christopher McMahon, Technical University of Denmark
Examiner: Johan Malquist, IMS, Chalmers+

Engineering changes are common in industry as they are opportunities to improve, enhance, or adapt a product. They driver for a change can be e.g. related to quality, safety, changes in external circumstances or regulation. These engineering changes often referred as Engineering Change Requests (ECRs) are largely generated through product development projects and are often stored in database while worked and later for some form of knowledge management purpose.
Despite ECR being captured and stored it is often cumbersome for product developers to identify historical ECRs due to the vast amount of them. Historical ECRs might contain valuable knowledge relevant to a current design and it is often wondered if the ECR content might be analyzed in a new way insightful way. The content of ECR data must contain information permitting identification of the types of errors and changes made, including part title, part name, part number, problem description, root cause, solution and test results.
This thesis primarily focuses on ECR data in combination with three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge about ECR information needs, and applying mathematical tools for solution design and testing.
Results show a list of engineering information needs related to ECRs, examples of visualizations based on unstructured data, industrial case study where complex product development processes are modeled using the Markov chain Design Structure Matrix, and studies that investigate how advanced searches based on natural language processing techniques and clustering within engineering databases.

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