The design of a circular PSS solution goes beyond the traditional perspective of circular product design encompassing multiple complexity dimensions that need to be considered and addressed in the early stages of design. The paper provides a literature-based outlook on the levels of complexity to be faced when making decisions in early PSS design by positioning the concept of changeability inherited from the systems engineering literature into the context of the design of circular PSS. The paper ultimately stresses the need to consider the changeability of PSS as a relevant dimension in the assessment of its circularity potential. It does so by proposing a framework for the design of circular PSS solutions, summarizing the main design strategies and approaches currently described in the literature to mitigate the uncertainties generated by PSS complexity.

The paper presents an approach, based on the development of digital twins, to support the transition toward electromobility and autonomy in the mining industry, by supporting the design space exploration of future operational scenarios based on different construction equipment and mining site configurations. With such an intent, the paper presents an approach combining systems and systems-of-systems simulations to run trade-off analysis based on different product-service systems (PSS) configurations. Additionally, the paper integrates the "operational context"variables in PSS design simulations to create a digital twin of a "mining operational scenario"customizable for the specific configurations of each mine. The paper exemplifies the proposed approach by contextualizing in a reference mining site describing how the multi-dimensional simulations have enabled PSS trade-off analysis and PSS sensitivity analyses, and how operational context variables are integrated into the digital twin of the operational scenario. © 2022 The Authors.

Tough competition and volatile global markets have pushed the manufacturing industries to develop solutions more customer-centric with optimal utilization of resources. One of the key reasons behind developing a customer-centric solution is the increased customer value that imparts a competitive edge to the manufacturing industries, eventually leading them to sustain their businesses. Over the years, this has led the transition of manufacturing industries towards offering “functions” instead of pure products. Academic literature often describes this change as the transition towards offering a Product-Service System (PSS), where the functions are typically delivered as a mix of products and services.

Developing PSS is a highly challenging task as value entails a multi-dimensional viewpoint based on different stakeholders and many novel technologies integrated along uncertain lifecycles. An optimal PSS for a specific occasion becomes situational as this occasion is bound to change due to underlying future operational uncertainties. This view accentuates the need for inculcating mechanisms in the PSS to sustain value under operational uncertainties, thus attaining value robustness. Literature in systems engineering elaborates on changeability as one of the cores for developing a value-robust system. A changeable system is a system that can change internally as a response to the changes externally to maintain the value expectation over time. With this frame of reference, it is argued that the notion of changeability can be a good supplement for developing a value-robust PSS. From a design perspective, changeability needs to be quantified to strike a balance between the total change-related cost and the benefits.

In this light, this thesis is directed toward the quantification of changeability for supporting early design decisions concerning value-robust PSS. To achieve this goal, this thesis first highlights the challenges concerning changeability quantification for a value-robust PSS design. Building on these challenges, it delves into established techniques of design optimization, dynamic programming, and discrete-event simulation to propose a framework that can exemplify the relationship between system configuration, system control, and contextual variables to gain insights about a suitable combination of configuration and control of the system to maintain its value in uncertain operational scenarios. To enhance the proposed framework with operational data, an outline of the state-of-the-art in the collection and utilization of operational data to support design decision-making is presented. Finally, the thesis concludes by highlighting the strength and weaknesses of the proposed framework along with some industrial implications. Broadly, two challenges are emphasized in the proposed framework, computational complexity and lack of contextual knowledge, and addressing them has been left for future studies.

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.

The digitalization era has brought about unprecedented challenges for the manufacturing industries, pushing them to deliver solutions that encompass both product and service-related dimensions, known as Product-service Systems. This paper presents a number of lessons learned in the process of integrating the analysis of operational data as decision support in engineering design based on the empirical studies from two Swedish manufacturing companies operating in the construction machinery sector. The paper highlights the need to consider a five-dimensional perspective when collecting and analyzing data, encompassing data from the product, the service, the environment, the infrastructure, and the humans involved. Finally, a conceptual framework for data-driven design automation of Product-service Systems is proposed by leveraging the use of these data, introducing the concept of a Product-Service System Configurator as an enabler of design automation. The implementation of the proposed framework on multiple case studies in different industrial contexts shall be considered as the next step of the research.

When a Product-Service System (PSS) has a longer lifecycle, it is subjected to several internal and external changes along its path that may deteriorate its value. A PSS capable of delivering value despite the circumstances is called a value robust PSS, and one way of achieving value robustness is by incorporating changeability. A lot of uncertainties prevail in decision-making concerning changeability as change is a phenomenon not clearly understood in PSS design. To bridge this gap, this paper presents a reference framework to support the definition and quantification of changeability in PSS design to enable the delivery of a value robust PSS. The paper is mainly focused mainly on the realization of the concept of changeability in PSS design, crossing the boundary between rich literature in the field of PSS and Systems Engineering.

The paper presents a framework for the integration of the system’s design variables, state variables, control strategies, and contextual variables into a design optimization problem to assist early-stage design decisions. The framework is based on a global optimizer incorporating Dynamic Programming, and its applicability is demonstrated by the conceptual design of an electrical hauler. Pareto front of optimal design solutions, in terms of time and cost, together with optimal velocity profiles and battery state-of-charge is visualized for the given mining scenario.

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.

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 emergence of IoT has propelled the traditionally known Product-Service System (PSS) to be characterized by smarter technologies, enabling them to collect and process data from the operational stage and facilitate communication between the customer and the provider. Commonly referred to as Smart Product-Service Systems (Smart PSS), these systems promise to create value at a personal level by collecting and effectively utilizing the operational data. However, one of the fundamental challenges is the lack of awareness as to what kind of data can be collected from the operational stage and what can be achieved from this data. This paper systematically reviews scientific literature to underline the kind of data being collected from the operational stage, the purposes being achieved from that data, and how they lead to value creation. The systematic review of 60 representative studies enabled the definition of the operational scenario that comprises 4 dimensions of data and 10 classes of data within these dimensions to generically identify what kind of data is being collected. The intend presented by various authors led to the generalization of 5 themes that target different purposes of collecting data. Further, the papers were classified with regards to functional or non-functional requirements to see how data in different approaches are leveraged for value creation. Finally, the discussion highlights the current gaps in the literature and raises several opportunities for future contributions.

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 International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies. © The Author(s), 2023. Published by Cambridge University Press.

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.

With the advances of telemetries and computational power, the question for firms is no longer what can be collected but what shall be collected. It is easy to add sensors and gather a vast amount of data but without knowing their purpose, the gains are marginal. In the field of Value-Driven Design, value is quantified and measured to ensure an overall higher output, something crucial for complex and ambiguous domains such as Product-Service Systems. This paper aims at finding the connection between PSS value and operational data from an industrial perspective. By using Participatory Action Research in a manufacturing firm, a value-data framework and method were developed that succeed in connecting these two extremities as well as visualize them with the support of network graph theory.

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.