Fuzzy C-Means Clustering with MAIRCA -MCDM Method in Classifying Feasible Logistic Suppliers of Electrical Products

The sustainability of the electrical industries and persistent production runs are dependent on their suppliers. Logistic supplier selection is an indispensable one for electrical products manufacturing concerns. The identification of feasible logistic suppliers is essential and very significant before employing the ranking methods of determining the optimal suppliers. This paper proposes a hybrid decision-making approach that integrates the Fuzzy c-means clustering (FCM) algorithm and the multi-criteria decision-making method of MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis). The hybrid model is two phases in which the interface of the machine learning algorithm performs the task of classifying the logistic suppliers of electrical products based on their feasibility in the first phase. The MAIRCA method is applied in the second phase of ranking the suppliers of electrical products. The efficacy of the hybrid method is tested by comparing the ranking outcomes of the alternatives of logistic suppliers with and without the interference of fuzzy c-means clustering, it results that the integrated MCDM method with fuzzy c-means clustering seems to be more time and cost-efficient. The results of the proposed hybrid method are more convincing and the efficacy of the method is measured in terms of time and cost efficiency.