Hybrid Model with MPSI Weighting and DEPART Ranking in Best-Selling Product Selection in E-Commerce Platforms
Keywords:
Decision Support System, Best-Selling Product, MPSI Weighting, DEPART Method, E-Commerce PlatformAbstract
The increasingly intense competition on e-commerce platforms, along with the high volume and dynamic nature of sales data, has created challenges in objectively determining the best-selling products, especially due to subjectivity in weighting criteria and the ranking methods' insensitivity to relatively small differences in values between products. This study aims to develop a decision support system model for determining best-selling products using a Hybrid Model approach that integrates MPSI weighting and the DEPART ranking method. The MPSI method is used to objectively generate criterion weights based on the level of variation and the contribution of data information, while DEPART is applied to produce more stable rankings through performance deviation analysis among alternatives. The study results show that the iPhone 15 Pro Max 128GB ranks first with a final score of 0.167, followed by the Samsung 32" LED TV in second place with a score of 0.158, and Wireless Noise-Canceling Headphones in third place with a score of 0.141. These findings indicate that the proposed hybrid model is capable of producing a clearer, more consistent, and representative ranking structure relative to actual data conditions. Thus, the MPSI–DEPART integration has been proven effective in enhancing the accuracy and objectivity of the best-selling product selection process and holds potential for broad application in the development of decision support systems in a dynamic and competitive e-commerce environment.
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