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 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 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 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 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 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.

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.