Application of risk-based fuzzy decision support systems in new product development: An R-VIKOR approach

Innovative manufacturing firms strive to sustain and enhance their competitive advantages by running a range of new product development (NPD) projects in a consistent manner. The capital and time required to execute the NPD projects have substantially increased over the past years. This magnified the risk-aversion behavior of R & D managers and has increased their sensitivity towards the underlying risk of NPD projects. In particular, the R & D departments have recently started to proactively assess the accuracy of ambiguous information that is extensively used in preliminary market study and customer requirements analysis. Thanks to its high performance in dynamic environments, the R numbers method can be employed to capture and analyze the risk of fuzzy numbers in a variety of decision making models. To tackle the complexity of such analysis, this paper proposes a novel risk based fuzzy VIKOR (R-VIKOR) methodology. Using the interpretive structural modeling, the risk factors are first classified to identify and rank the existing critical risk factors of NPD projects. The ultimate goal of this study is to develop a practical yet simple decision support system tool that enables the R & D managers to effectively examine the riskiness of fuzzy information and assess the relevant risk factors. A real-world case study is presented to test and examine the accuracy and effectiveness of the proposed risk management method. (c) 2021 Elsevier B.V. All rights reserved. <comment>Superscript/Subscript Available</comment