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.

Increased stakeholder expectations and tougher global competition push the manufacturing industries to develop more complex solutions, introducing numerous uncertainties in design decision-making. Incorporating changeability is one way to systematically deal with uncertainty, which requires dealing with the identification, valuation, and quantification of changeability during the early design stages. Besides, when considering a system-of-systems (SoS) problem, it necessitates optimizing the portfolio of options within the SoS while considering the transition “in” and “of” the system combinatorially. In this light, this paper presents the Changeability Assessment in Systems during Early Design (CASED) method for development teams to create value-robust systems in the face of uncertainty. The CASED method utilizes epochs and eras to represent uncertainty and analyzes the impact of changing system configurations and control policies on overall system value. Considering transitions “in” and “of” the system involves solving a linear assignment problem to minimize the switch costs. A dynamic programming procedure for an era finds the avenues when change shall be exercised. Iterating through several eras, a map of the expected mean value and expected standard deviation is generated for all designs, which serves as a guide for decisions concerning changeability. The applicability of the method is further confirmed through a fleet of haulers operating in a quarry.

The paper presents a replicable simulation architecture to assess the economic, environmental, and resilience performance of commercial electric and automated marine passenger transport systems. Built on Discrete Event Simulation (DES), the architecture balances simulation complexity and detail in early system development. It comprises key components such as the modelling framework, data flow, data integration and user interface. A case study in Karlskrona, Sweden, demonstrates its application, showing that DES enhances transparency, facilitates stakeholder communication, and supports iterative solution refinement. The results highlight the architecture’s potential to support sustainable urban waterborne transport and decision-making under uncertainty.

Increasing sustainability expectations requires support for the design of systems that are reactive in minimizing potential negative impact and proactive in guiding engineering decision-making toward more value-robust long-term decisions. This article identifies a gap in the methodological support for the design of circular systems, building on the hypothesis that computer-based simulation models will drive the development of more value-robust systems designed to behave positively in a changeable operational environment during the whole lifecycle. The article presents a framework for value-robust circular systems design, complementing the current approaches for circular design aimed at increasing decision-makers' awareness about the complexity of circular systems to be designed. The framework is theoretically described and demonstrated through its applications in four case studies in the field of construction machinery investigating new circular solutions for the future of mining, quarrying and road construction. The framework supports the development of more resilient and sustainable systems, strengthening the feedback loop between exploring new technologies, proposing innovative concepts and evaluating system performance.

Recent academic contributions explore the integration of Digital Twins (DTs) within smart Product-Service System (sPSS). This integration aims to innovate business propositions, hardware and services. However, gaps persist in developing DT environments to support early-stage collaborative innovation for sPSS, and limited studies explore how real-time synchronized digital replicas enhance value co-creation in this area. This paper addresses this gap by presenting a framework and practical example of integrating value-driven decision support into early sPSS conceptual design. A case study on the development of the Smart Electric Vehicle (SEV) conducted with a global automotive Original Equipment Manufacturer (OEM) demonstrates the framework’s efficacy. Through qualitative data analyses based on experimental validation in a case company, the DT proves effective in aiding decision makers in selecting value-adding configurations within specific scenarios. Furthermore, the DT serves as a visual decision-making tool, fostering collaboration across diverse teams within the automotive company. This collaboration facilitates value creation across practitioners with varied backgrounds, emphasizing the DT’s role in enhancing early-stage innovation and co-creation processes in the sPSS domain.

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 construction machinery industry faces many uncertainties stemming from environmental, operational, and market-related factors. To mitigate future risks, development teams often favor more broadly applicable solutions compared to localized performance gains in specific scenarios. This situation highlights the necessity of incorporating changeability in these solutions for developing value-robust systems that can manage future uncertainty. Changeability assessment relies on an effective tradespace exploration that provides a unified view of different system configurations and control policies. To support the design teams in exploring such tradespaces, this paper presents an approach combining Dynamic Programming (DP) and Reinforcement Learning (RL) for evaluating optimal control policies, illustrated through a wheel loader application. The underlying basis is that the overall task can be decomposed into several sub-tasks to be solved by DP or RL selectively. A control policy combined from these sub-tasks is presented along with an illustrative tradespace mapping system attributes. The results show that by combining the strengths of DP and RL, the proposed approach can be beneficial when exploring a wide range of solutions. It allows direct comparisons between configuration and control policy changes, which is crucial for effective changeability assessment. However, several limitations have been acknowledged and will be addressed in future studies.

The rapid development of new technologies such as electrification, autonomy, and other contextual factors pose significant challenges to development teams in balancing competing aspects while developing value-robust solutions. One approach for achieving value robustness is designing for changeability. This paper presents a tradespace exploration from a Systems-of-Systems perspective to facilitate changeability assessment during early design stages. The approach is further demonstrated on a fleet of haulers operating in a mining site. © 2024 Proceedings of the Design Society. All rights reserved.

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.

The use of data-driven techniques can support the trade-off between different PSS design concepts by allowing dynamic indexing and retrieval of information to detect correlations and emerging trends. The chapter presents a data-driven approach exploiting historical PSS operational data and Computer Aided Design (CAD) models to populate value models for alternative design configurations inside the feasible design space. The approach encompasses the use of the Design of Experiment techniques and machine learning (ML) to create surrogate models of value for each potential PSS design configuration. The application of the approach is described through two case studies run in collaboration with a tier-one aero-engine sub-system manufacturer and an original equipment manufacturer in the construction equipment field.