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.

Today, Manufacturing companies are adopting a servitization strategy and Product-Service System model to enhance value and remain competitive. Often, this transition also means to embrace a System-of-Systems (SoS) perspective. Concurrently, companies face challenges with volatile, uncertain, complex, and ambiguous (VUCA) environments. One way to tackle VUCA is to utilize simulation modeling. However, developing SoS simulations can be complex and cumbersome. This paper extracts lessons learned from six case studies to identify effective and ineffective practices in developing simulation models. The analysis has led to nine design principles for more effective simulation modeling. Furthermore, the paper explores simulation techniques for modeling SoS and discusses effective VUCA management. Finally, the paper proposes four future research directions to advance SoS simulation research.

This paper presents an approach that utilizes artificial intelligence techniques to identify autonomous machine behavior patterns. The context for investigation involves a fleet of prototype autonomous haulers as part of a Product Service System solution under development in the construction and mining industry. The approach involves using deep learning-based object detection and computer vision to understand how prototype machines operate in different situations. The trained model accurately predicts and tracks the loaded and unloaded machines and helps to identify the data patterns such as course deviations, machine failures, unexpected slowdowns, battery life, machine activity, number of cycles per charge, and speed. PSS solutions hinge on efficiently allocating resources to meet the required site-level output. Solution providers can make more informed decisions at the earlier stages of development by using the AI techniques outlined in the paper, considering asset management and reallocation of resources to account for unplanned stoppages or unexpected slowdowns. Understanding machine behavioral aspects in early-stage PSS development could enable more efficient and customized PSS solutions.

The paper presents an Artificial Intelligence-driven approach to predictive maintenance for Product-Service System (PSS) development. This study focuses on time-based and condition-based maintenance, leveraging variational autoencoders to identify both predicted and unpredicted maintenance issues in autonomous haulers. By analyzing data patterns and forecasting future values, this approach enables proactive maintenance and informed decision-making in the early stages of PSS development. 

The inclusion of interaction terms enhances the model’s ability to capture the interdependencies among system components, addressing hidden failure modes. Comprehensive evaluations demonstrate the effectiveness and robustness of the developed models, showcasing resilience to noise and variations in operational data. 

The integration of predictive maintenance with PSS development offers a strategic advantage, providing insights into vehicle performance early in the development phases. This empowers decision-makers for efficient resource allocation and proactive maintenance planning. The research highlights the limitations and potential areas of improvement while also emphasizing the practical applicability and significance of the developed models in enhancing PSS development. 

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.