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All Journal Jurnal InTent: Jurnal Industri dan Teknologi Terpadu
Helda Febiola Aritonang
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Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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STRATEGI PENENTUAN WAKTU PEMBELIAN ALUMINA BERDASARKAN SIMULASI MONTE CARLO TERHADAP PERGERAKAN HARGA PASAR INTERNASIONAL Helda Febiola Aritonang; Resista Vikaliana; Fika Aryani; Heru Satria Rukmana; Ade Suryana
Jurnal Intent: Jurnal Industri dan Teknologi Terpadu Vol. 9 No. 1 (2026): INDUSTRIAL
Publisher : Prodi Teknik Industri, Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/intent.v9i1.5034

Abstract

PT X, as the only integrated aluminum smelter in Indonesia, is highly dependent on imported alumina raw materials from Australia and China. This dependence exposes the company to fluctuations in alumina prices in the volatile global market, which are influenced by geopolitical dynamics, exchange rates, and international economic conditions. This price uncertainty poses risks in procurement planning and production costs. This study aims to predict alumina price movements using the Monte Carlo Simulation method as a basis for determining the optimal purchase timing strategy. Historical alumina price data for the period June 2020–June 2025 from the London Metal Exchange was used to determine the probability distribution, test normality through the Chi-Square test, and generate price simulations through the generation of normally distributed random numbers. The test results show that the data follows a normal distribution, making it suitable for use as a basis for simulation. Alumina price predictions over 61 periods show an average accuracy rate of 93.4%, meaning that the model is considered highly representative of actual conditions. The simulation produces a price series that can be used to identify periods with low price risk. These findings provide strategic benefits for PT X in developing adaptive and risk-based procurement policies, while enriching the literature on the application of Monte Carlo simulation in commodity price forecasting in the extractive industry.  
Co-Authors Ade Suryana Fika Aryani Heru Satria Rukmana Resista Vikaliana