# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 3 State of the Art
### Uncertainty and Risk Assessment in PSS Concept Evaluation  
The actual phenomena of PSS in the concept phase, as the basis for higher-level evaluation metrics, are studied using more engineering-centric approaches [17]. These approaches assess individual situations of PSS concepts using specific modeling and simulation techniques. For example, [PERSON] et al. [37] and [PERSON] et al. [38] each described how respective agent-based simulation can be used to anticipate critical aspects of PSS concepts regarding required capacities, cost, etc., in operations. [PERSON] et al. [39] used discrete-event simulation to predict how different configurations of an asphalt compactor change performance in different road-building scenarios [39]. [PERSON] [40] developed an approach for smart hybrid prototyping in the context of a bicycle-sharing system to evaluate the rental process with high rigor in the concept phase. These approaches can offer detailed insights into phenomena of PSS concepts and their resulting performance and risks but demand high modeling effort, justifying their use only for aspects of a PSS concept already identified as critical and requiring further studies. Approaches dedicated to risk assessments focus on entire systems, considering possible consequences resulting from a causing event to a potentially negative result [13]. Risk assessment involves identifying relevant exposure events and anticipating their likelihood of negative outcomes, such as failing to meet value metrics including safety, sustainability, and performance, which are targets within a PSS concept at various levels of detail [13; 41]. Understanding drivers of risk in a PSS concept is important in defining adequate measures among considered alternatives in a way that increases the chances of success. Concept alternatives that are driven by risks due to epistemic uncertainty (caused by a lack of understanding) can be improved by _discursive measures_, i.e., obtaining more information on the issue, such as through the engineering-centric approaches described above [42]. In the case of aleatoric uncertainty (stochastic nature of phenomena) as the main driver, fundamental changes to the design of a concept have to be made to either reduce exposure to causing events by means of _risk-informed_ measures (e.g., constraining the allowed operational context) or by taking _preventive_ measures, i.e., making the system more robust when causing events take place (e.g., introducing redundancy) [13; 18]. [PERSON] et al. [18] presented a method for PSS concept evaluation focusing on risks from uncertainties in technical and operational areas, as well as company capabilities [42]. They aimed to assist in deciding if a company should assume full responsibility for PSS system availability or implement additional risk reduction measures by asking eight questions structured in a decision tree, leading to the three fundamental strategies of risk reduction [18]. However, they did not focus on the specific concept alternatives or the specific relationships and knowledge gaps that drive risk. [PERSON] et al. [43] provided a method that evaluates PSS concepts under uncertainty for contractual decisions, particularly for the bidding of service contracts for heavy assets like planes and ships. Their approach assesses investment decisions, especially personnel, amid uncertainties in operational costs and system reliability. Scenarios based on historical data calculate expected equipment availability using an Excel-based tool. However, this method is limited to specific industries, focusing on technical risks, and requires historic data, which are lacking in most cases of PSS development. It does not transparently consider complex dependencies and assumptions, with epistemic uncertainty affecting PSS design, since a good understanding of the assets already exists. A widely used method for assessing systems regarding uncertain events and resulting risks is FMEA (Failure Mode Effect Analysis) [44], which [PERSON] et al.