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.

Lately the construction industry has become more interested in designing and constructing environmentally friendly buildings (e.g. sustainable buildings) that can provide both high performance and monetary savings. Analyzing various parameters during sustainable design such as Life Cycle Assessment (LCA) and energy consumption, lighting simulation, green building rating system criteria and associated cost of building components at the conceptual design stage is very useful for designers needing to make decisions related to the selection of optimum design alternatives. Building Information Modeling (BIM) offers designers the ability to assess different design options and to select vital energy strategies and systems at the conceptual stage of proposed buildings. This thesis describes a methodology to implement sustainable design for proposed buildings at their conceptual stage. The proposed methodology is to be implemented through the design and development of a model that simplifies the process of designing sustainable buildings, evaluating their Environmental Impacts (EI), assessing their operational and embodied energy and listing their potential accumulated certification points in an integrated environment. Therefore, a Decision Support System (DSS) is developed by using Multiple Criteria Decision Making (MCDM) techniques to help design team decides and selects the best type of sustainable building components and design families for proposed projects based on three main criteria (i.e. Environmental, Economical factor «cost efficiency » and Social wellbeing) in an attempt to identify the influence of design variations on the sustainable performance of the whole building. The DSS outcomes are incorporated in an integrated model capable of guiding users when performing sustainable design for building projects. The proposed methodology contains five modules: 1) Database Management System (DBMS), 2) Energy and lighting analysis, 3) Life Cycle Assessment (LCA), 4) LEED and 5) Life Cycle Cost (LCC). To improve the workability of the proposed model, a use case of abovementioned modules are going to be created as plug-ins in BIM tool. The successful implementation of such a methodology represents a significant advancement in the ability to attain sustainable design of a building during the early stages, to evaluate its EI, and to list its potentially earned certification points and associated soft costs.