The manufacturing sector is witnessing a paradigm shift toward servitization, where companies are transitioning from selling products to offering product-service systems. This shift creates additional challenges, where the providers must ensure the expected value throughout the operational phase of the solution. Especially when dealing with a system-of-systems (SoS), evaluating performance across diverse contexts and business models while understanding the interconnectedness between systems becomes critical. To address these challenges during the design phase, this article presents a novel integrated simulation framework that supports the development team in exploring value from a SoS perspective. This framework utilizes agent-based simulation and offers three key features: multifidelity, modular and multidisciplinary. The applicability of the proposed framework is further demonstrated in a quarry industry case.

The early design phase of complex, capital-intensive systems is critical for shaping their architecture and value proposition. However, such systems face numerous challenges from technological, economic, market, and regulatory domains. In addition, considering system-of-systems introduces new hurdles as the focus shifts from measuring performance to assessing overall effectiveness. Together with the growing trend of servitization, where traditional products are combined with value-added services to deliver functions, a lot of uncertainty is introduced during design decision-making. To handle these uncertainties, systems engineering literature advocates for incorporating lifecycle properties into the system that enable the system to deal with these uncertainties once deployed. Systems that consistently meet evolving stakeholder expectations, despite the changing contexts, are called value-robust systems. Changeability is one such property that allows the system to achieve value robustness by changing internally in response to changes externally. During the design stages, the goal is to identify and integrate options that would enable the system to exercise change and sustain value under all conditions.

In this light, this thesis aims to support the integration of changeability in complex systems by facilitating its assessment during the early design stages. To achieve this goal, it first identifies the existing methods and challenges in changeability assessment for achieving value robustness. To address these challenges, it proposes the Changeability Assessment in Systems during Early Design (CASED) method, which supports development teams in creating value-robust systems in the face of uncertainty. CASED is one of the core contributions of this work, allowing a holistic consideration of identification, quantification, and valuation of changeability during early design stages. It maps the expected mean value and expected standard deviation for each design as a function of changeability level, which serves as a guide for decisions concerning changeability. Additionally, this thesis explores the use of Extended Reality technologies to address perceptual complexity by visualizing operational scenarios and proposes designing for changeability as a mechanism for creating value-robust circular 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.

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.

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 ongoing servitization journey of the manufacturing industries instills a through-life perspective of value, where a combination of products and services is delivered to meet expectations. Often described as a product-service system (PSS), these systems are poised with many complexity aspects, introducing uncertainties during the design phase. Incorporating changeability is one of the known strategies to deal with such uncertainties, where the system changes in the face of uncertainty to sustain value, thereby achieving value robustness. While the theme of dealing with multiple uncertainties has been discussed since the inception of PSS, changeability is still poorly addressed. To bridge this gap, an integrative literature review is performed to outline various complexities aspects and their link to uncertainty from a PSS perspective. Also, the state-of-the-art approach to achieving value robustness is presented via changeability incorporation. Subsequently, a reference framework is proposed to guide decision-makers in changeability incorporation in PSS, especially during the early design stages.

Innovation plays a vital role in ensuring sustainable mining operations. Electrification and autonomy are two significant trends, but their implementation brings complexity at vehicle and site levels. Therefore, it is crucial to understand how these technologies impact the overall site value creation. This paper suggests a hybrid approach that combines Agent-Based Simulation and vehicle dynamics modeling to explore site configurations. By regarding a mining site as a System-of-Systems, designers can concurrently test different designs to find the optimal combination for a specific scenario.

The paper outlines a vision for how XR technologies can be utilized to support PSS design decisions concerning changeability with an emphasis on perceptual limitations. An XR-enabled PSS changeability incorporation method is proposed based on the extension of the Responsive Systems Comparison method by integrating XR technologies to leverage the potential of information visualization for decision-making. The proposed method is theoretically described and applied to a demonstrative case in the construction machinery industry facing the problem of designing the next-generation autonomous and electrical vehicles for quarrying and mining. © 2023, IFIP International Federation for Information Processing.