# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 6 Discussion and Outlook  
This section synthesizes the theoretical contributions, practical implications, generalizability, and limitations of the proposed PSS abstraction framework and GEAR method, considering the findings in the context of the initial research questions raised in Section 2. It aims to integrate our results with both theoretical and practical perspectives, outline the contribution to existing literature, and highlight future research opportunities. This study makes several theoretical contributions to the field of product-service system concept evaluation. Primarily, we present a novel framework that facilitates the abstraction of PSS concepts and their variations using a graph-based reasoning approach. This approach enhances transparency and reduces biases by systematically structuring and synthesizing uncertain a priori knowledge for considered PSS concept variations during the early concept phase. By explicitly differentiating between aleatoric and epistemic uncertainties, our model offers a more fine-grained understanding of the uncertainties and knowledge gaps  
Figure 20: Aggregated influence of knowledge gaps as the red area on overall expected benefit per PSS concept variation in blue. that often hinder effective PSS concept detailing. The research questions are addressed as follows:  
* **RQ 1:** How can product service system (PSS) concepts, in the context of their anticipated life cycle, be abstracted semantically and syntactically represented as a graph-based model in order to effectively reason about heterogeneous and uncertain aspects? The PSS abstraction framework is detailed in Section 4.2. It describes the required semantic perspective on a PSS concept under evaluation in terms of its working principles and the way they are connected causally. This understanding is structured according to the CVL, EVL, and KGL in order to capture the causal chains influenced by different concept variations and vague and uncertain knowledge, allowing for consistent and transparent reasoning. * **RQ 2:** What necessary steps are required to analyze PSS concepts represented as graph-based models concerning their anticipated performance levels, aiming to better understand and manage uncertainties? The GEAR method is described in Section 4.6 and details required inputs and outputs for the overall approach. Recommended activities are described, assisting in applying the principles of detailing while abstracting the PSS concept according to the PSS abstraction framework as a probabilistic graph. * **RQ 3:** What are the benefits and limitations of employing a graph-based reasoning approach in evaluating PSS concepts in an industrial context? The approach is applicable to industrial contexts and can provide a systematic approach to evaluating PSS concepts and their variations in light of uncertainties and knowledge gaps. During the process of knowledge elicitation, different viewpoints of domain experts had to be exchanged and reconciled so that a better mutual understanding was created through the process. This is the basis for a transparent presentation of the thinking process of people developing a PSS concept, allowing decision makers to understand the situation more transparently and assess the impacts of knowledge gaps. The approach requires substantial cognitive effort and large amounts of modeling labor, as various dependencies have to be elicited and formalized. This provides the practical implication that a person guiding and facilitating the method is required, who has to be familiar with the approach and the steps of the GEAR method, as well as the use of IT tools for probabilistic modeling. The complexity of the PSS concept to be evaluated in terms of level of detail, as well as scale and scope (number of concept variations), also has a great impact on modeling effort, putting a natural limit on the complexity to be considered. Another limitation is the validity of the model, which can only be checked for plausibility, as with most models in the concept phase.