Early-stage design decisions in complex engineering systems play a critical role in defining the system’s lifecycle performance, cost, and viability. As engineering systems are becoming increasingly interconnected, cyber-physical and multi-disciplinary, traditional decision-making approaches based on historical data and expert judgments often fall short. To navigate the early-stage design decision challenges and support better decision-making, model-driven decision support systems have emerged as promising tools that allow for integration of simulations, optimization, and data-driven models. Yet, in the context of complex engineering systems, the effective implementation of decision support systems remains limited due to socio-technical challenges. 

This thesis systematically investigates and identifies challenges through a literature review and empirical case studies in an industrial setting. The research identified interrelated barriers that hinder effective decision support systems integration and utilization. First, difficulty in integrating heterogeneous simulation models across varying levels of granularity remains technically and methodologically challenging. Second, the uneven maturity level of decision support systems across engineering domains limits their consistent use in collaborative environments. Third, a lack of accessible and intuitive interfaces negatively impacts the usability for non-expert stakeholders. Fourth, the absence of a real-time feedback mechanism limits their function as a boundary object. Fifth, the lack of metrics to gauge and communicate model maturity and reliability creates risks for misinformed decisions. Lastly, the lack of lifecycle management, including evolution, traceability, and reliability of the underlying models, is rarely supported, which undermines long-term sustainability and trust in the decision support systems.

The marine industry is increasingly adopting platform and modular design strategies while facing growing sustainability regulations and emission constraints. This paper proposes an approach that integrates scope 3 upstream CO2 emissions (i.e., procurement) into a Decision Support Environment (DSE) for design space exploration of alternative modular ship design concepts. The DSE, deployed in the conceptual design stage, enables simultaneous testing of various cruise ship configurations regarding CO2 emissions using a bottom-up approach with parametric CO2 models. It leverages data-driven models from existing databases or AI-generated data exemplified in a case study on the hotel system of a cruise ship, illustrating how parametric design variables influence CO2 emissions, demonstrating a preliminary result of a prescriptive study in collaboration with a major international ship manufacturer.

The paper addresses a critical challenge in System-of-Systems (SoS) simulations arising from the different granularity levels in SoS simulations, integrating non-coupled Discrete Element Method results into SoS-level Discrete Event Simulations using surrogate modeling. Illustrated with a wheel loader bucket use-case in mining, it enhances early design decision-making and lays the groundwork for improving SoS simulations in construction equipment design. This paves the way for broader research and application across diverse engineering design domains. © 2024 Proceedings of the Design Society. All rights reserved.

High Variety Low Volume (HVLV) context sets novel requirements for the decision support environment. The coexistence of engineering-to-order or engineering-to-delivery design alongside modular and platform design necessitates the integration of multi-disciplinary models. This paper provides a comprehensive review of existing model-driven and simulation-driven decision support systems, aiming to identify pertinent directions for tailoring decision support systems to meet the needs of practitioners in sales and engineering phases within High Variety Low Volume (HVLV) projects. This study identifies the following development needs: 1) accessibility of data and results to a broader user base, 2) guidelines for interacting with the decision environment without specialized expertise, 3) real-time feedback to decision-making teams, 4) metrics to gauge the maturity level or reliability of the models and 5) change management of the dependency models and calculation models. These are the next steps toward supporting decision-making in complex engineering situations under time pressure. 

The interchangeable usage of the term "meta-model" has caused confusion and ambiguity in research community, blurring their distinctions and applications in the context of collaborative decision-making, with a special focus on complex engineering systems. The definitions of the term found within the targeted application area are mapped and compared. The findings reveal two major interpretations of the term meta-model. One is a framework or blueprint for ensuring consistency and standardization among models, and the other is for approximating complex simulation models, providing computational efficiency. Meta-models, in both of their interpretations, serve different purposes, functions, and the scope of application. The two interpretations have a non-exclusive nature, meaning that both interpretations can be complementary rather than mutually exclusive. The goal of the study is semantic clarification to enhance comprehension and facilitate appropriate utilization of the definition within the realm of collaborative decision-making in complex engineering systems.