<![CDATA[The rapid expansion of digital commerce has increased the need for efficient and fair product distribution systems, particularly in multi-level sales platforms where customer orders must be assigned to appropriate agents. Determining the best agent is a complex task because the decision should simultaneously consider delivery efficiency, stock availability, and balanced transaction opportunities among agents. This study proposes and evaluates the use of the Simple Additive Weighting (SAW) method to optimize product order distribution on the SR12forLife platform. The proposed model assesses candidate agents using four criteria: distance between customer and agent, available stock, number of previous received orders, and agent activity status. The study employed an operational test dataset consisting of 20 active agents located in several areas of West Sumatra and 105 customer transactions. Implementation and testing were conducted through a web-based system supported by Jupyter Notebook analysis. The experimental results confirmed that the proposed model performed effectively in allocating customer orders. All 105 transactions were successfully assigned to eligible agents, resulting in a 100% allocation success rate without stock shortages. Furthermore, all 20 active agents participated in the allocation process, indicating balanced workload distribution across the sales network. The model also achieved 92.38% regional suitability, where most customer orders were assigned to agents located in the same city or nearest area. These findings demonstrate that the SAW method is effective in producing objective, efficient, and fair order allocation decisions. These results indicate that the proposed model can improve both allocation fairness and operational efficiency in multi-level sales systems. Therefore, the proposed model can be considered an effective and scalable solution for optimizing order distribution in web-based multi-level sales platforms.]]>
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