Analisis Komparasi Metode SAW Dan TOPSIS Dalam Pemilihan Distributor Barang Gudang


Authors

  • Fahreza Dandy Sihmawanto Universitas Semarang, Semarang, Indonesia
  • Ganef Tri Wijayatno Universitas Semarang, Semarang, Indonesia
  • Prind Triajeng Pungkasanti Universitas Semarang, Semarang, Indonesia

DOI:

https://doi.org/10.47065/bulletincsr.v6i1.925

Keywords:

SAW; TOPSIS; Decision Making; Distributor; MCDM; Warehouse Management

Abstract

Distributor selection is a strategic decision in supply chain management that affects product availability, operational costs, and customer satisfaction. This study aims to compare and evaluate distributor selection decision-making using two Multi-Criteria Decision Making (MCDM) methods, namely Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Primary data was obtained through structured interviews with three expert practitioners at a distribution company. Five evaluation criteria were used: product quality (weight 0.30), product price (0.20), delivery accuracy (0.20), service response (0.15), and stock availability (0.15). Ten distributor alternatives were assessed using a 1–10 scale and processed independently using SAW normalization and TOPSIS vector normalization to generate rankings from each method. The results show that PT. Mitra Listrik Nusantara ranked first in both methods with a SAW score of 0.9414 and a TOPSIS score of 0.8057. Ranking consistency comparison through Spearman correlation analysis yielded a value of ? = 0.9515, indicating very high agreement between the two methods. This study provides practical contributions to warehouse management in adopting a data-driven approach for measurable and accountable distributor selection.

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Published: 2025-12-31

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How to Cite

Sihmawanto, F. D., Wijayatno, G. T., & Pungkasanti, P. T. (2025). Analisis Komparasi Metode SAW Dan TOPSIS Dalam Pemilihan Distributor Barang Gudang. Bulletin of Computer Science Research, 6(1), 521-530. https://doi.org/10.47065/bulletincsr.v6i1.925

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