Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through...

Area of Science:

• Operations Research
• Decision Science
• Financial Engineering

Background:

• Multi-Criteria Decision-Making (MCDM) methods are crucial for selecting optimal alternatives with varying criteria importance.
• Over 200 MCDM variations exist, but algorithmic differences lead to inconsistent results and a lack of objective evaluation frameworks.
• Existing evaluations often overlook crucial output dimensions like real-life applicability and rank reversal (RR) performance.

Purpose of the Study:

• To propose and validate a novel, dual-criterion framework for objectively evaluating MCDM methods.
• To assess the financial performance (FP) and rank reversal (RR) stability of nine distinct MCDM methods.
• To identify the most suitable MCDM methods based on empirical data analytics and statistical validation.

Main Methods:

• Calculated financial performance (FP) for 140 listed manufacturing companies using nine MCDM methods integrated with Step-wise Weight Assessment Ratio Analysis (SWARA).
• Compared MCDM-based FP results with simultaneous stock market returns to establish statistical relationships.
• Introduced a novel statistical procedure to evaluate the rank reversal (RR) performance of the MCDM methods.

Main Results:

• Empirical data analytics revealed significant differences in the financial performance and RR stability across the evaluated MCDM methods.
• Faire Un Choix Adéquat (FUCA) and Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) exhibited strong performance.
• The proposed dual-criterion framework confirmed FUCA and CRADIS as the most appropriate MCDM methods based on joint agreement.

Conclusions:

• The study successfully established a robust, data-driven framework for evaluating MCDM methods, addressing the existing research gap.
• FUCA and CRADIS emerge as highly reliable MCDM techniques, demonstrating superior financial predictive power and rank reversal resistance.
• Findings provide valuable insights for practitioners and researchers seeking to select appropriate MCDM tools for financial decision-making.