Faiza, Priyanka Puan Noor (2026) Optimasi Persediaan Bahan Baku Untuk Produksi Timah Kimia Menggunakan Metode Mixed-Integer Linear Programming (MILP). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Produksi timah kimia merupakan proses produksi berkelanjutan (continuous process) yang sangat bergantung pada ketersediaan bahan baku. Ketidakpastian kebutuhan bahan baku dan variasi lead time pengadaan meningkatkan risiko stockout dan overstock. Permasalahan tersebut dihadapi oleh perusahaan yang menggunakan tiga jenis bahan baku utama dalam produksi timah kimia, terdiri atas satu bahan baku lokal dan dua bahan baku impor. Oleh karena itu, diperlukan suatu pendekatan yang mampu menghasilkan kebijakan persediaan bahan baku yang optimal dengan mempertimbangkan berbagai kendala operasional perusahaan. Penelitian ini bertujuan memformulasikan model optimasi persediaan bahan baku menggunakan metode Mixed-Integer Linear Programming (MILP) untuk menentukan kuantitas pemesanan bahan baku yang optimal sehingga meminimalkan total biaya persediaan. Data yang digunakan merupakan data historis kebutuhan dan pengadaan bahan baku perusahaan selama periode Januari 2022 hingga Desember 2025. Model MILP dikembangkan dengan mempertimbangkan kebutuhan bahan baku, lead time, kapasitas gudang, safety stock, kapasitas produksi, dan pemilihan pemasok. Kebutuhan bahan baku periode Januari–Juni 2026 diperkirakan menggunakan metode Holt's Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), dan Simple Moving Average (SMA) sesuai karakteristik masing-masing bahan baku. Hasil peramalan tersebut kemudian digunakan sebagai parameter kebutuhan pada model MILP untuk menyusun rencana pengadaan bahan baku periode 2026. Hasil penelitian menunjukkan bahwa metode Holt's Exponential Smoothing merupakan metode terbaik untuk bahan baku A, ARIMA(0,1,1) untuk bahan baku B, dan Simple Moving Average (SMA) dengan ordo k = 5 untuk bahan baku C. Model MILP menghasilkan solusi feasible yang memenuhi seluruh kendala model. Selain itu, hasil optimasi menunjukkan bahwa total biaya persediaan berhasil ditekan dari USD 56.487.118 menjadi USD 32.976.923, sehingga diperoleh penghematan sebesar 41,62% dibandingkan kebijakan aktual perusahaan. Hasil penelitian ini diharapkan dapat menjadi rekomendasi dalam penyusunan kebijakan pengadaan bahan baku yang lebih efisien serta mendukung pengambilan keputusan dalam pengelolaan persediaan di industri hilirisasi timah.
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The production of chemical tin is a continuous process that highly depends on the availability of raw materials. Uncertainty in raw material requirements and variability in procurement lead times increase the risk of stockouts and overstocking. These challenges are experienced by the company, which utilizes three main raw materials in its chemical tin production process, consisting of one locally sourced material and two imported materials. Therefore, an approach capable of developing an optimal raw material inventory policy while considering the company's operational constraints is required. This study aims to formulate a raw material inventory optimization model using Mixed-Integer Linear Programming (MILP) to determine the optimal order quantities while minimizing total inventory costs. This study uses historical data on raw material requirements and procurement at the company from January 2022 to December 2025. The MILP model was developed by considering raw material requirements, lead times, warehouse capacity, safety stock, production capacity, and supplier selection. Raw material requirements for the January–June 2026 period were forecasted using Holt's Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), and Simple Moving Average (SMA) according to the characteristics of each raw material. The forecasting results were subsequently used as demand parameters in the MILP model to develop the raw material procurement plan for 2026. The results indicate that Holt's Exponential Smoothing was the best forecasting method for raw material A, ARIMA(0,1,1) for raw material B, and Simple Moving Average (SMA) with an order of (k = 5) for raw material C. The MILP model produced a feasible solution that satisfied all model constraints. Furthermore, the optimization results showed that the total inventory cost could be reduced from USD 56,487,118 to USD 32,976,923, resulting in a cost saving of 41.62% compared with the company's existing inventory policy. The findings of this study can serve as a reference for developing more efficient raw material procurement policies and to support decision-making in inventory management within the tin downstream industry.
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