Akademik Veri Yönetim Sistemi
International Journal of Computational Intelligence Systems, cilt.19, sa.1, 2026 (SCI-Expanded, Scopus)
Evaluating inventory management systems is a critical decision-making problem for businesses in almost every industry, as it significantly affects their business models and operational processes. Nevertheless, essential gaps in the relevant literature still need to be addressed. To fill the gaps, this paper introduces an interval-valued spherical fuzzy (IVSF) decision tool based on the preference selection index (PSI) and operational competitiveness ratings analysis (OCRA) methods for evaluating inventory management system platforms. Accordingly, the primary objective of this study is to propose a novel hybrid interval-valued spherical fuzzy decision-making framework (IVSF-PSI-OCRA) capable of systematically addressing complex and uncertainty-laden inventory management system selection problems. Accordingly, the study is structured around clearly defined research questions focusing on framework construction, robustness under uncertainty, and operational alignment. One of the primary novelties of the work is the development of IVSF-PSI and IVSF-OCRA models, thereby proposing the IVSF-PSI-OCRA methodology for the first time in the literature to address challenging decision-making problems. A case study evaluating an inventory management platform demonstrates the applicability of the suggested approach. IVSF-PSI is used to determine the criterion weights, whereas IVSF-OCRA is used to determine platform rankings. The analysis results highlight that the weights of the 24 evaluation criteria are close. The first alternative is the best among the six options, followed by the third and second. The research findings particularly emphasize the complexity of inventory management systems and the multidimensional nature of evaluation criteria. Furthermore, the conclusions of an extensive robustness check confirm the validity and reliability of the suggested model. Developing IVSF-PSI, IVSF-OCRA, and IVSF-PSI-OCRA methodologies is the essential contribution of the study. Another significant contribution is applying the proposed model in evaluating the inventory management system. The research provides an innovative and multidimensional evaluation approach to selecting inventory management systems, making valuable contributions to the research community members focusing on the relevant literature and to industry decision-makers. This study introduces an inventory selection framework based on Interval-Valued Spherical Fuzzy (IVSF) sets, which constitutes a significant methodological advancement over traditional fuzzy and intuitionistic fuzzy approaches. Unlike conventional models that represent uncertainty using a single degree of membership, the proposed IVSF structure simultaneously captures membership, non-membership, and hesitancy parameters within interval boundaries, allowing a more flexible and realistic representation of expert hesitation and ambiguity. By explicitly modeling neutral membership and interval-based uncertainty, the proposed methodology overcomes key limitations of previous inventory selection studies, which often rely on crisp, single-valued fuzzy assumptions and thus fail to reflect the complexity of real-world decision environments adequately. Consequently, the proposed model enhances the robustness, interpretability, and decision reliability of inventory selection under uncertainty. The proposed methodology enhances economic decision-making in supply chains by facilitating cost-efficient selection of inventory management platforms, enabling businesses to improve operational performance, minimize procurement and warehousing costs, and better align their supply and demand processes.