dual combination weighting method

The dual combination weighting method is a hybrid multi-criteria decision-making (MCDM) model that integrates subjective and objective weighting to overcome the inherent limitations of a single approach. Its core concept involves linearly combining 'subjective weights,' which reflect decision-makers' experience and preferences (e.g., obtained via Analytic Hierarchy Process, AHP), with 'objective weights,' derived purely from the variability of raw data (e.g., calculated using the Entropy Weight Method, EWM), to generate a more comprehensive and robust final weight. Within risk management systems, this method is positioned in the 'risk evaluation' phase of the ISO 31000 standard. It is particularly suitable for complex decisions requiring a trade-off between multiple quantitative and qualitative criteria, such as supply chain partner selection or new technology investment appraisal. It differs from using AHP alone by introducing data-driven objectivity to calibrate expert judgment, and from using EWM alone by incorporating expert insights on non-quantifiable factors like goodwill or innovation culture.

In enterprise risk management, particularly for selecting supply chain or joint venture partners, the dual combination weighting method systematically reduces the risk of poor choices. Step 1: Establish an evaluation indicator system and standardize data. Define a comprehensive set of criteria based on business goals (e.g., technical capability, financial stability, compliance record), collect relevant data for all potential partners, and normalize it. Step 2: Calculate subjective and objective weights separately. Use the Analytic Hierarchy Process (AHP) with a panel of experts to determine subjective weights. Concurrently, use the Entropy Weight Method (EWM) on the standardized data to calculate objective weights. Step 3: Combine weights and conduct a comprehensive scoring. Using a preference coefficient α, the final weight is calculated as W_combined = α * W_subjective + (1-α) * W_objective. This combined weight is then used to score and rank partners. Implementing this method can reduce operational disruption risks from partner failure by an estimated 15-20% and improve overall supply chain compliance and resilience.

Taiwanese enterprises face three main challenges when implementing the dual combination weighting method. Challenge 1: Difficulty in obtaining expert knowledge. Finding unbiased experts for the subjective weighting phase can be difficult, especially in niche industries. Solution: Form a cross-departmental evaluation team and use the Delphi Method for multiple rounds of anonymous feedback to reach a consensus before applying AHP. Challenge 2: Inconsistent objective data quality. Many SMEs lack systematic data collection, leading to incomplete or inaccurate data for objective weighting. Solution: Initiate a data governance project to define key metrics and collection standards. Initially, assign a higher preference (α) to subjective weights and adjust as data quality improves. Challenge 3: Methodological complexity. The mathematical calculations can be a barrier for employees without a data science background. Solution: Develop standardized Excel templates or use specialized decision-making software. The priority is to build an indicator library and data collection templates, achievable within three months.

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