# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 1 Introduction  
Product service systems (PSS) are integrated bundles of technical systems and digital and physical services providing comprehensive customer solutions that a product alone would not be capable of [1; 2]. In this way, the life cycle of products can be better tracked and controlled in the field, which promises new opportunities for value creation but also requires fundamentally new capabilities to develop and operate such offerings [3; 4]. Although the economic, environmental, and social benefits of PSSs have been emphasized in academia for decades [5; 6; 7] and strong efforts are being made by companies in industry to become a service providers in order to differentiate from the competition through new solutions and business models [8; 9], PSSs are still not the standard business model due to numerous engineering challenges [10]. Arriving at a stage where a PSS concept can be signed-off on for implementation requires a preceding process of iterative improvement in which variations of solution elements are iteratively evaluated and selected in review meetings for further refinement until a sufficient level of confidence in the concept is reached [11; 12]. The overall goal of companies is to select PSS concept variations for detailing in an optimal trade-off between the level of expected goal achievement and associated risk, which is ultimately a company-specific decision depending on risk averseness and strategy [13]. However, in order to enable decision makers in that regard, the level of goal achievement and associated risk for each PSS concept variation must be determined. Evaluating a PSS concept and its variations is a challenging task, since it involves anticipating how a still vaguely defined PSS will behave over long time periods in complex environments involving intangible service elements that make it hard to precisely predict behavior [14; 15; 16; 17]. In order to improve discussions about targeted measures for concept refinement, not only is a general assessment of risk for each variation required; instead,transparency is required regarding where uncertainty and knowledge gaps exist and what possible knock-on effects might be hindering precise assessments [18]. Product development companies often have little experience with the development of PSSs and do not have meaningful models, data, and empirical knowledge in the concept phase [19] or struggle to make this information usable for detailed analysis [20]. Therefore, a lot of the evaluation is based on expert judgment, which tends to be opaque in reasoning and impaired by cognitive biases [21, 22]. Therefore, evaluating PSS concept variations in uncertain conditions requires a transparent understanding of where major uncertainties for each variation arise and how they influence the overall level of expected performance of the PSS concept in order to make more informed decisions on how to systematically reduce risk by obtaining additional information on critical influences within the PSS concept [18]. To support PSS concept evaluation, the aim is to develop a new approach that allows uncertain knowledge used in the reasoning underlying PSS concept evaluation to be structured in such a way as to allow for understanding of how different variations of the PSS concept and associated knowledge gaps affect the assessment. Therefore, a novel approach for the systematic evaluation of PSS concept variations based on probabilistic graph-based reasoning is proposed. Graph-based reasoning enables probabilistic inference based on the combination of an uncertain a priori knowledge base through the use of algorithms in order to arrive at consistent conclusions [23, 24]. This way, transparency is increased and biases are decreased regarding assumptions and the underlying relationships evaluated in order to arrive at a PSS concept assessment.