Congratulations Omsri Aeddula, Doctor of Philosophy!
- Post by: Tobias Larsson
- 14th June 2024
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Omsri Aeddula successfully defended his PhD thesis “Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development” in front of a full house of 50 people in the room and online. Omsri made a popular presentation of his research and took the audience through his findings and application cases within mechanical engineering and applied health technology, and then landed in a summary of his findings. Omsri has been working within several applied projects at the department; MD3S, VPSL, FELD, TRUST-SOS, and ASPECT.
Faculty opponent was Prof. Shaun West (Lucerne University of Applied Sciences and Arts, Switzerland) and the grading committee consisted of Prof. Anna Syberfeldt (University of Skövde), Prof. Glenn Johansson (Lund University), and Asc. Prof. Christopher Jouannet (Linköping University and SAAB).
Professor West invited Omsri into a deep conversation around the work he had performed and by this creating a learning experience for Omsri and the audience. Taking a contextual approach and discussing a lot on the “why” around the thesis, and not just the more technical “how”, Shaun and Omsri invited the audience to a pedagogical dance where we got to understand more on the complexity of modern product development, and what possibilities and limitations there are with AI assisted approaches.
After the opponent, grading committee, and audience have had the chance to bring up their questions, the grading committee left the room for their discussion and eventually returned with the verdict; a pass!
We gratulate our newest PhD on such an important milestone in his career!
PhD thesis crew; Dr Johan Wall (head of department / supervisor), Professor Glenn Johansson (Grading committee), Omsri Aeddula, Proessor Anna Syberfeldt (Grading committee), Asc Professor Chrstopher Jouannet (Grading committee) Professor Peter Anderberg (Supervisor), opponent Professor Shaun West, Professor Tobias Larsson (supervisor / examiner).
Abstract
Solution providers are transitioning from product-centric models to service-oriented solutions. This shift has led to the rise of Product-Service Systems (PSS), which offer a holistic approach by integrating physical products with associated services. However, the inherent complexity and collaborative nature of PSS development present a significant challenge: information gathering, analysis, and knowledge building. This is further amplified in the early stages of PSS development due to data challenges such as uncertainty, ambiguity, and complexity. This complicates informed decision-making, potentially leading to the risk of sub-optimal outcomes and impacting the success of final offerings.
This research proposes an AI-powered data analysis approach to address these data challenges and augment the decision-making process of PSS development. The focus is on supporting early-stage decision-making, as decisions made at this stage greatly impact the success of final solutions. The research investigates how data can be utilized and visualized to extract actionable insights, ultimately facilitating informed decision-making.
The presented research demonstrates that AI-powered data analysis effectively supports informed decision-making in early-stage PSS development. By extracting actionable insights from complex data, handling data limitations, and enabling informed strategic decisions, knowledge sharing, and collaboration are facilitated among stakeholders. Furthermore, integrating AI with visualization tools fosters knowledge building and a deeper understanding of system behavior, ultimately leading to more successful PSS solutions. The efficacy of AI-powered data analysis for handling diverse data types across application domains is demonstrated, potentially leading to benefits such as a deeper understanding of system behavior and proactive solution strategies. These advancements contribute to developing decision support systems specifically for PSS development.
Overall, this research demonstrates the efficacy of AI-powered data analysis in overcoming data challenges and empowering decision-makers in early-stage PSS development. This translates to more informed choices, leading to the creation of successful and efficient PSS solutions.
Download full thesis here: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-26162
Find the presentation from the session below.
More information
- BTH press: “Att navigera i datautmaningar: AI-drivet beslutsstöd för utveckling av produkt- och tjänstesystem” https://www.bth.se/om-oss/det-har-ar-bth/kontakt/press/