Abstract

The literature is abundant with research studies that have been developed to provide an efficient model which has never been studied for multicriteria inventory classification MCIC problems. Therefore, this study proposes two hybrid inventory classification systems. Recently, the TOPSIS model has been widely developed, initially examined, and found very efficient to the (MCIC) due to its advantages and simplicity of use. In contrast, to the best of our knowledge, the PROMETHEE II and COPRAS models for determining (criteria weights) parameter values and classification, resulting in low/or competitive inventory cost and high service level. The proposed models utilize two sets of weights of CRITIC and Spearman’s rho (SR) tools for learning and optimizing PROMETHEE II and COPARS. In addition, a performance analysis is validated using a real-world dataset composed of 63 stock-keeping units (SKUs). The performance is compared to existing six MCIC classification models. The results reflect performances of the proposed models. Additionally, the comparative analysis indicates that the COPRAS model is the most preferred. Finally, the performance of the proposed models can be a great support for the overall supply chain system and decisions.