OPTIMALISASI JUMLAH PRODUKSI TEH BOTOL SOSRO DAN FRUIT TEA MENGGUNAKAN METODE FUZZY INFERENCE SYSTEM TSUKAMOTO

(STUDI KASUS : PT. SINAR SOSRO PALEMBANG)

Authors

  • Dudi Hendra Fachrudin Universitas Logistik dan Bisnis Internasional
  • Nurlaela Kumala Dewi Universitas Logistik dan Bisnis Internasional
  • Muhammad Rafif Novanil Universitas Logistik dan Bisnis Internasional

DOI:

https://doi.org/10.572349/scientica.v1i3.362

Abstract

PT Sinar Sosro Palembang, a manufacturing company of packaged ready-to-drink beverages, is experiencing constraints in the amount of production that is not optimal, causing excess inventory stock and potential losses. Demand fluctuations are the main factor affecting production instability. To overcome this problem, researchers used the Tsukamoto Fuzzy Inference System method in this study. This method utilizes fuzzy sets to represent rules in real-world problems. With input variables of demand and inventory, this method generates output variables of production. The fuzzy set on each variable is applied with a defuzzification process using a centered average calculation. The results show that there are still production periods that are not optimal, as seen in the production data of Sosro Bottled Tea and Fruit Tea bottles. With 8 optimal calculations from 12 total data for Sosro Bottled Tea and 6 optimal calculations from 12 total data for Fruit Tea bottles, the Tsukamoto Fuzzy Inference System method is considered feasible as a consideration for obtaining the optimal amount of production for PT Sinar Sosro Palembang. The implementation of this method is expected to improve production efficiency and reduce excess inventory stock, resulting in more optimal production performance.

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Published

2023-12-13

How to Cite

Fachrudin, D. H., Dewi, N. K., & Novanil, M. R. (2023). OPTIMALISASI JUMLAH PRODUKSI TEH BOTOL SOSRO DAN FRUIT TEA MENGGUNAKAN METODE FUZZY INFERENCE SYSTEM TSUKAMOTO : (STUDI KASUS : PT. SINAR SOSRO PALEMBANG) . Scientica: Jurnal Ilmiah Sains Dan Teknologi, 1(3), 56–68. https://doi.org/10.572349/scientica.v1i3.362