Congratulations Omsri Kumar Aeddula, Licentiate in Mechanical Engineering!
Omsri Kumar Aeddula presented his licentiate thesis entitled “Data-Driven Decision Support Systems for Product Development – A Data Exploration Study Using Machine Learning” in front of an hybrid audience of online listeners and physically present people. In all a crowd of some 50 participants.
Omsri presented the context and motivation for his research and then took the audience through research methodology and findings (thesis consists of a summary part and three individual research publications) and then landed in a summary of his findings. Omsri has been working crossdisciplinary research between mechanical engineering and also applied health technology, something that made the licentiate especially interesting.
Opponent for the day was the researcher Mehdi Tarkian of Linköping University. Mehdi went went deeper into details to press Omsri and enrich the knowledge around the material and in order to get a sense of how Omsri had approached the research and also on how conclusion where made, leading up to a good discussion.
At the end also the audience had the chance to ask questions on the research.
Examiner Professor Tobias Larsson then concluded by declaring that Omsri had passed the exam and achieved the licentiate degree.
Besides the examiner and the present supervisors of Omsri, also Professor Peter Anderberg has been a supervisor.
Modern product development is a complex chain of events and decisions. The ongoing digital transformation of society, increasing demands in innovative solutions puts pressure on organizations to maintain, or increase competitiveness. As a consequence, a major challenge in the product development is the search for information, analysis, and the build of knowledge. This is even more challenging when the design element comprises complex structural hierarchy and limited data generation capabilities. This challenge is even more pronounced in the conceptual stage of product development where information is scarce, vague, and potentially conflicting. The ability to conduct exploration of high-level useful information using a machine learning approach in the conceptual design stage would hence enhance be of importance to support the design decision-makers, where the decisions made at this stage impact the success of overall product development process.
The thesis aims to investigate the conceptual stage of product development, proposing methods and tools in order to support the decision-making process by the building of data-driven decision support systems. The study highlights how the data can be utilized and visualized to extract useful information in design exploration studies at the conceptual stage of product development. The ability to build data-driven decision support systems in the early phases facilitates more informed decisions.
The thesis presents initial descriptive study findings from the empirical studies, showing the capabilities of the machine learning approaches in extracting useful information, and building data-driven decision support systems. The thesis initially describes how the linear regression model and artificial neural networks extract useful information in design exploration, providing support for the decision-makers to understand the consequences of the design choices through cause-and-effect relationships on a detailed level. Furthermore, the presented approach also provides input to a novel visualization construct intended to enhance comprehensibility within cross-functional design teams. The thesis further studies how the data can be augmented and analyzed to extract the necessary information from an existing design element to support the decision-making process in an oral healthcare context.
Keywords: Product Development, Data-driven DSS, Machine Learning, Conceptual Stage, Data Analytics