# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 3 State of the Art
### PSS Concept Evaluation  
Evaluating different variations of a PSS concept involves considering differences in the structure and behavior of alternatives solution principles of the PSS architecture, e.g., regarding technical systems, services, IT infrastructure, the operational environment, and business-related aspects like contractual agreements, and their impact on important evaluation metrics [19; 32; 33]. Most approaches from the literature on PSS concept evaluation focus on defining these metrics as value models by creating comprehensive metrics to grasp the quality of a PSS concept and its variations [17; 31; 32]. This entails elaborating, prioritizing and combining multi-disciplinary PSS-specific characteristics in a systematic way to define a measure of \"goodness\", guiding the selection process [26; 32]. Besides metrics for the actual process of value creation, there is a strong focus on metrics for social and ecological sustainability aiming to capture the desired contribution of PSSs in this area [17]. Metrics also focus on economic aspects, like profitability, market share, etc., as well as \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Role (Affiliation)** & **Years of Experience** & **Experience in Additive Manufacturing (AM)** \\ \hline AM Production Engineer (Industrial Company) & 4 years & Familiar with the entire AM process and responsible for qualification of new powder specifications \\ \hline Research Associate (Research Institute) & 3 years & Researcher in the domain of powder degradation and its effect on AM build results \\ \hline Research Associate (University) & 2 years & Researcher in the domain of powder degradation and its effect on AM build results \\ \hline AM Designer (Industrial Company) & \textgreater{}5 years & AM design engineer familiar with industrial production processes and required information technology \\ \hline AM Digitization Engineer (Industrial Company) & \textgreater{}5 years & Familiar with entire AM process, as well as digital artifacts and data created and used within the AM process \\ \hline \hline \end{tabular}
\end{table} 
Table 1: AM domain experts involved in conducting the method. operational aspects like upgradability, maintainability, and safety, capturing strategic goals of PSS development [17]. Unifying these metrics in a value model is achieved primarily by the use of MCDM techniques [31; 32]. For example, [PERSON] and [PERSON] [34] employed the Analytical Hierarchy Process (AHP), a widely adopted MCDM method, to facilitate the prioritization of value dimensions in the development of a sustainable PSS within the automotive industry. [PERSON] et al. [35] illustrated the application of AHP for the ranking of value dimensions and drivers, specifically in the evaluation of PSS concepts for aero-engine components [32]. A multitude of approaches is available to defining PSS value models using different MCDM methods based on pair-wise comparisons of evaluation metrics, ranking schemes, etc., some of which also consider ambiguity or uncertainty in assessments by using, e.g., fuzzy techniques [31]. For example, [PERSON] and [PERSON] [36] used fuzzy techniques to determine which product and service aspects are important for PSSs in public transport. However, such techniques are primarily used to grasp vague statements in specific numbers to be used in subsequent MCDM techniques and not to causally assess the impact of these uncertainties. [PERSON] [32] highlighted that this is a major limitation of approaches focusing on value models in the PSS domain, since engineering models and operational data rarely exist due to a lack of predecessor projects. As a result, there is a lack of confidence in value models for PSSs, stemming from subjective assessment of criteria and inherent uncertainties [32]. Hence, decision makers often struggle with identifying the most critical issues underlying PSS concepts impeding progress, making it hard to address early-stage ambiguities in a targeted way [28; 32].