Product-Service System (PSS) development prioritizes the technical aspects of implementing Artificial Intelligence (AI), overlooking the strategic rationale behind its adoption. This paper investigates the impact of AI on value co-creation within industrial contexts by analyzing successful AI applications in PSS development across diverse domains like industrial systems engineering and healthcare technology. Through a multiple case study approach, this paper aims to explore the strategic motivations for using AI and its influence on the co-creation process. The analysis reveals several key benefits of AI adoption. Firstly, it fosters collaborative and iterative development, empowering both internal teams and external stakeholders to actively participate in value co-creation. Secondly, AI helps uncover novel value propositions that might remain hidden through traditional methods thus boosting the value co-creation. Finally, AI acts as a catalyst for building dynamic knowledge ecosystems. By facilitating data-driven insights and collaboration, AI enables continuous learning and adoption within the co-creation process. 

The increasing demand for products and services coupled with growing environmental concerns has necessitated a shift towards sustainable product development. Traditional methods often prioritize functionality over environmental impact, leading to resource depletion and waste generation. To address this, as environmental concerns are increasing in importance, innovative solutions are required to integrate sustainability considerations into product lifecycles.

This study investigates the role of Artificial Intelligence (AI) in promoting sustainability within product service systems. A systematic literature review was conducted to identify key AI technologies and methodologies employed across different stages of product development. The analysis focused on the impact of these technologies on environmental sustainability and business performance.

The findings reveal that AI technologies, including machine learning, natural language processing, and virtual prototyping, can significantly enhance sustainability. These tools may optimize product design, reduce material consumption, and minimize environmental impact. Furthermore, AI applications in predictive maintenance, end-of-life management, and energy efficiency contribute to resource optimization and waste reduction.

AI has the potential to transform product service system development by integrating sustainability principles. By optimizing resource utilization, reducing waste, and enhancing decision-making, AI can drive both environmental and economic benefits. While challenges such as data quality and algorithm development exist, the overall positive impact of AI on sustainability is evident.  

Accurate monitoring of draw-in behaviour during sheet metal forming is crucial for understanding material flow, optimizing process parameters, and validating finite element (FE) simulations. This study presents an integrated approach combining high-resolution optical measurement, laser displacement sensors, and numerical simulations to analyse draw-in variations during the first forming operation of an automotive front door inner panel. A dedicated optical system was employed to capture sequential images of the blank edge, which were calibrated and processed using computer vision techniques to extract precise draw-in values at predefined locations. The results demonstrate that optical monitoring provides reliable insights related to the sheet metal forming process, highlighting the influence of real-world process disturbances. Furthermore, the study explores the feasibility of integrating measured draw-in data into an adaptive control framework, applying artificial intelligence techniques to refine process stability. By utilizing experimental data alongside numerical predictions, this methodology enhances process understanding and enables data-driven decision-making in industrial sheet metal forming. The findings contribute to the development of intelligent forming control strategies, bridging the gap between modelling and real-world manufacturing conditions to improve product quality and production efficiency.

With the increased focus on smart manufacturing and Industry 4.0, the use of simulations for the creation of cyber-physical manufacturing systems is increasing. The sheet metal forming manufacturing process, commonly used for production of automotive body components, is one of the processes that currently benefits from the use of simulations without exploiting them in a cyber-physical system setup. This study set out to initially identify the key controllable and uncontrollable parameters of the sheet metal forming manufacturing process for the design of an intelligent quality controller. Subsequently, the study investigates the possibility of using data points from a stochastic numerical analysis as training data for an Artificial Neural Network. The stochastic numerical model used is based on the existing Finite Element simulation standard at Volvo Cars to allow for a seamless integration of the methodology into the standard workflow of CAE departments. Lastly, the study will present a validation of the trained Artificial Neural Network using the Volvo XC90 inner front door component as an industrial demonstrator.

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. 

Within the automotive industry, there is an increasing demand for a paradigmshift in terms of which materials are used for the manufacturing of the automotive body. Globalclimate goals are forcing a rapid adaption of new, advanced, sustainable material grades suchas the fossil free steels and materials containing higher scrap content. With the introduction ofthese new and untested materials, methods for accounting for variation in material propertiesare needed directly in the press lines.The following study will focus on creating an initial virtual shadow of the manufacturing of aVolvo XC90 inner door panel through the application of Artificial Neural Networks (ANN). Thevirtual shadow differs from the concept of the digital twin by only being a virtual representationof the production line, with training data generated exclusively by numerical simulations, andhaving no automated communication with the physical press line control system. The virtualshadow can be used as an assistance to the press line operators to see how different press linesettings and material parameter variations will impact the quality of the stamped component.The study aims to validate the virtual shadow through accurate predictions of the materialdraw-in measured in the physical press line.

Solution providers are transitioning from product-centric models to service-oriented solutions. This shift has led to the rise of Product-Service Systems (PSS), which offer a holistic approach by integrating physical products with associated services. However, the inherent complexity and collaborative nature of PSS development present a significant challenge: information gathering, analysis, and knowledge building. This is further amplified in the early stages of PSS development due to data challenges such as uncertainty, ambiguity, and complexity. This complicates informed decision-making, potentially leading to the risk of sub-optimal outcomes and impacting the success of final offerings.

This research proposes an AI-powered data analysis approach to address these data challenges and augment the decision-making process of PSS development. The focus is on supporting early-stage decision-making, as decisions made at this stage greatly impact the success of final solutions. The research investigates how data can be utilized and visualized to extract actionable insights, ultimately facilitating informed decision-making.

The presented research demonstrates that AI-powered data analysis effectively supports informed decision-making in early-stage PSS development. By extracting actionable insights from complex data, handling data limitations, and enabling informed strategic decisions, knowledge sharing, and collaboration are facilitated among stakeholders. Furthermore, integrating AI with visualization tools fosters knowledge building and a deeper understanding of system behavior, ultimately leading to more successful PSS solutions. The efficacy of AI-powered data analysis for handling diverse data types across application domains is demonstrated, potentially leading to benefits such as a deeper understanding of system behavior and proactive solution strategies. These advancements contribute to developing decision support systems specifically for PSS development.

Overall, this research demonstrates the efficacy of AI-powered data analysis in overcoming data challenges and empowering decision-makers in early-stage PSS development. This translates to more informed choices, leading to the creation of successful and efficient PSS solutions.

This study investigates the challenges of reverse engineering in finite element modelling of sheet metal forming, specifically for the Volvo XC90 front door inner component. Advanced models incorporating anisotropic behaviour of steel and non-linear friction are compared against actual real-world measurements. The methodology involves simplifying complex continuous parameters into more manageable representative data sets and assessing model accuracy under both uniform and varied blank holder force settings, guided by measured contact pressure distributions. Although the results indicate an improvement in accuracy, they underscore the need for additional methodological improvements and more accurate replication of tooling effects to enhance the fidelity and effectiveness of these models.

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.