Pythagorean fuzzy comprehensive distance-based ranking approach for assessing industry 4.0 adoption strategies in the automotive manufacturing sector

Abstract

The automotive sector is experiencing a robust boom, driven by technological advancements, increased customers’ demand, and a growing focus on sustainable development goals. Industry 4.0 (I4.0) adoption in this sector leads to the development of data-driven solutions, manufacturing innovations, higher demand for newer services, and improved operational efficiency. For the successful adoption of I4.0, their strategies should be evaluated with respect to certain criteria. To this aim, this study introduces an integrated Pythagorean fuzzy Comprehensive Distance-Based Ranking (COBRA) approach to evaluate and prioritize the adoption strategies in the automotive manufacturing sector. The proposed framework is divided into four phases. In the first phase, the decision experts’ (DEs) weights are computed with the use of the score function and rank sum model (RSM). In the next phase, an aggregated Pythagorean fuzzy decision matrix is created through a fairly power-weighted operator. For this purpose, the Pythagorean fuzzy fairly power-weighted aggregation operators are introduced to combine individual Pythagorean Fuzzy Information (PFI). In the third phase, the criteria weights are obtained through a combined weighting procedure involving the objective weight by standard deviation (SD)-based method and the subjective weight via Stepwise Weight Assessment Ratio Analysis (SWARA) tool. Based on these phases, a novel Pythagorean fuzzy COBRA approach is developed to deal with the I4.0 adoption strategies evaluation problem. A novel distance measure is also offered to describe the degree of dissimilarity between Pythagorean fuzzy sets (PFSs). Moreover, a comparison with existing distances is discussed to demonstrate the efficiency of the developed distance measure. The suggested methodology is then applied to a case study of the I4.0 adoption strategy selection problem within the context of PFI. Finally, sensitivity and comparative investigations are made to assess the rationality of obtained results.