## 5 Developing the Approach for the Assessment of Innovation Projects in Manufacturing
### Phase II: Approach Development for Holistic Innovation Assessment in Manufacturing  
A three-step approach was chosen to develop an appropriate MADM method addressing the gaps in the state of the art. Figure 7 provides an overview of the derivation of the assessment method. In the first step, a structured literature review (see Section 3) was used to identify the most relevant methodological approaches and the used assessment methods. The table shown in Section 3 (Table 2) gives an overview of the identified approaches. Subsequently, the requirements were derived based on the research gap and an evaluation method was developed by combining the identified methods in a meaningful way based on their advantages and disadvantages. In the second step, the approach was presented to two academic experts from the innovation and manufacturing fields, using semi-structured expert interviews to discuss the overall concept and the criteria selected for the assessment. Lastly, a prototype of the approach was implemented using a MATLAB script to initialize the initial validation of the approach. Among the plethora of MADM approaches presented in the literature, AHP and TOP-SIS stand out as the most suitable methods because of their comprehensible, methodical, and individually adaptable procedure. Moreover, [PERSON] et al. [25] proposed a methodology for implementing Fuzzy sets in MADM methods according to the characteristics of the data to be parsed by the model and included the possibility of managing group decision scenarios by discussing several aggregation methods for judgments. Figure 8 provides a schematic overview of the elaborated approach for holistic innovation assessment in manufacturing. Within an industrial scenario, a finite set of alternatives will be compared by decision makers aiming to select the best initiative to undertake according to the attributes and criteria identified in Section 5.1. This kind of problem is well represented by the definition of a MADM method. Based on the state of the art, the approach requires applicability to human-, technological-, and organizational innovation, and demands a holistic assessment, taking potentials, efforts, and risks into account. Further, the approach needs to be industrial-applicable within the early phases of the innovation process. The development of a decision support system of this kind involves the implementation of the criteria identified in Section 5.1 with evaluation scales and a suitable mathematical method allowing to synthesize the assessment activities' resulting in a ranking of the alternatives [50]. Figure 7: Overview of the methodological derivation of the approach for the holistic assessment of innovation in manufacturing. The model presented exploits the strengths of AHP to transform the decision-making problem into a hierarchic structure of criteria to be selected among the attributes discussed in Section 5.1. The relative weights of the attributes were obtained using pairwise comparisons performed with linguistic judgments translated in Fuzzy triangular numbers utilizing a variation of the fundamental scale proposed by [PERSON] [18], defined according to the principles discussed by [PERSON] et al. [25]. The comparisons were performed at each level of the hierarchical structure, as proposed by [PERSON] and [PERSON] [80]. Afterwards, the weights of the attributes were extracted by each pairwise comparison matrix through the eigenvalue method, linking the eigenvector associated with the maximum eigenvalue of each pairwise comparison matrix to an expression of the relative dominance of the criteria. The weights of the lowest-level attributes of the structure were multiplied by the relative weights of their parent clusters. Aggregation of the matrices corresponding to different decision-makers was achieved through the geometric mean. The de-fuzzification of the matrices using the centroid method, consistent with [PERSON] [81], is the preliminary step for the consistency check and the extraction of the weights [82].