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.

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 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 summarizes the experiences of working for a decade to promote the transition of courses toward the CDIO framework. It presents a list of lessons learned when applying the CDIO principles in master-level classes in the field of Mechanical Engineering. The paper aims to formalize operational guidelines, serving as a platform for further discussion and as a set of practical recommendations for teachers and educators initially redesigning their courses toward a more CDIO-oriented structure. The paper initially presents the rationale formoving toward CDIO and describes where and how data were collected. Further, it lists several lessons learned for teachers on (1) how to support students in maximizing their learning opportunities in a CDIO context, (2) how to manage the relationship and engagement with company partners, dealing with expectations, trust, flexibility, and visibility.

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.

The paper presents an approach where the iterative replication of Discrete Event Simulations on future operational scenarios is used to derive data-driven design merit functions. The presented contribution proposes an extension of the EVOKE (Early Value Oriented Design Exploration with Knowledge Maturity) approach determining when and how the experience-based judgment about maximization, minimization, optimization, and avoidance functions, correlating value drivers and quantified objectives, can be substituted by data-driven mathematical functions obtained by scenarios simulations. The approach is described through a simplified case concerning the development of autonomous electric vehicles to complement the public transport system in the city of Karlskrona in Sweden. The consideration of value drivers and quantified objectives presented is meant to support a preliminary screening of potential design configurations to support the definition of high-level product and system-related functional requirements, to be run before a more detailed conceptual design analysis. © The Author(s), 2023. Published by Cambridge University Press.

The design of Product-Service Systems (PSS) is challenging due to the inherent complexities and the associated uncertainties. This challenge aggravates when the PSS being considered has a longer lifespan, is expected to encounter a dynamic context, and integrates many novel technologies. From systems engineering literature, one of the measures for mitigating the risks associated with the uncertainties is incorporating means in the system to change internally as a response to change externally. Such systems are referred to as value-robust systems, and their development largely relies on Tradespace exploration and synthesis. Tradespace exploration and synthesis can be challenging and a time-consuming task due to dimensionality. In this light, this paper aims to present an approach that enables the population of the Tradespace and then, supports the synthesis of such a Tradespace using a clustering algorithm for support changeability quantification in PSS. The proposed method is also implemented on a demonstrative case from the construction machinery industry.