Selection of Marketing Staff Using Simple Additive Weight and VIKOR Algorithm - Muhammad Ali; Nurhayati; Ayu Azzahra Batubara (Journal of Computers and Digital Business, 2024)
In today’s rapidly evolving technological landscape, decision-making processes within organizations are increasingly relying on advanced computational methods to enhance efficiency and accuracy. This is particularly relevant in human resource management, where selecting suitable candidates for key positions is critical. Traditional methods of staff recruitment often rely on subjective assessments, which may lead to biases and inconsistencies. To address these challenges, this study proposes the use of the Simple Additive Weighting (SAW) and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) algorithms as multi-criteria decision-making tools for selecting marketing staff. The SAW method offers a straightforward approach by assigning weighted scores to various criteria. In contrast, the VIKOR method provides a ranking system that considers ideal and compromise solutions for candidate selection. Integrating these two algorithms makes the selection process more objective and data-driven, reducing the risk of human error and improving overall decision quality. This paper outlines implementing the combined SAW-VIKOR model in the marketing staff recruitment process, highlighting its potential to optimize candidate evaluation and selection. The results demonstrate that utilizing these algorithms enhances the decision-making process, leading to better alignment of selected staff with organizational goals. This approach is valuable for organizations looking to leverage technology in their recruitment strategies.