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Journal : JURNAL VISIONIDA

Integrasi Model Inventori EOQ dan Time Series Forecasting Untuk Sistem Inventori Optimal Britania, Rizka; Tjolleng, Amir; Ardiana Putri, Salma
Jurnal Visionida Vol. 10 No. 1 (2024): Juni
Publisher : Fakultas Ekonomi Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/jvs.v10i1.13310

Abstract

This research aims to evaluate the existing inventory system and determine the optimal inventory system for the future at PT XYZ. The methods used are Economic Order Quantity (EOQ) integrated with time series forecasting. The selection of products studied is based on inbound and outbound volumes in the warehouse. The application of the EOQ method to the five study products resulted in an optimal order quantity for the existing conditions of 925 drums, 737 drums, 612 drums, 705 drums, and 729 drums for products A, B, C, D, and E. There is a potential saving in total inventory costs of 24% generated by the EOQ model compared to the current inventory system in the company. Historical demand data shows a seasonal stationary pattern. Forecast for the coming year was conducted and used in calculating the optimal order quantity for the coming year, which are 775 drums, 891 drums, 611 drums, and 728 drums for products A, B, C, and E. This research can enrich the literature related to EOQ and provide input for companies regarding the potential savings that can be made, and information on inventory system needs for the future.    ABSTRAK Penelitian ini bertujuan mengevaluasi sistem inventori eksisting dan menentukan sistem inventori optimal untuk masa mendatang pada PT XYZ. Metode yang digunakan adalah Economic Order Quantity (EOQ) yang diintegrasikan dengan time series forecasting. Pemilihan produk yang dikaji didasarkan pada volume inbound dan outbound di gudang. Penerapan metode EOQ pada lima produk kajian memberikan hasil kuantitas pemesanan optimal untuk kondisi eksisting sejumlah 925 drum, 737 drum, 612 drum, 705 drum, dan 729 drum untuk produk A, B, C, D, dan E. Terdapat potensi penghematan total ongkos inventori sebesar 24% yang dihasilkan model EOQ dibandingkan dengan sistem inventori yang saat ini berlangsung di perusahaan. Data historis demand menunjukkan adanya pola seasonal stationer. Forecast untuk satu tahun mendatang dilakukan dan digunakan dalam menghitung kuantitas order optimal untuk tahun mendatang, yaitu sebesar 775 drum, 891 drum, 611 drum, dan 728 drum untuk produk A, B, C, dan E. Penelitian ini dapat memperkaya literatur terkait EOQ, dan memberikan masukan bagi perusahaan terkait potensi penghematan yang dapat dilakukan, dan informasi kebutuhan sistem inventori untuk masa mendatang.
PERBANDINGAN MODEL PREDIKSI SARIMA, LSTM, DAN PROPHET DALAM PREDIKSI VOLUME BONGKAR-MUAT KAPAL Britania, Rizka
Jurnal Visionida Vol. 11 No. 2 (2025): Desember
Publisher : Fakultas Ekonomi Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/jvs.v11i2.22161

Abstract

Accurately predicting ship loading and unloading volumes at ports is essential for optimizing operations and strategic planning. This study compares three time series forecasting models: SARIMA, LSTM, and Prophet, to predict ship loading and unloading volumes at Tanjung Priok, Indonesia's busiest port. The dataset covers the period from January 2017 to August 2025 in monthly increments. Hyperparameter tuning was performed on each model to determine the optimal hyperparameter values. Model performance was evaluated on the testing dataset using the Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and accuracy metrics. Based on these four metrics, SARIMA had the best performance with an accuracy rate of 87.97%, followed by the LSTM model at 85.38%, and the Prophet model at 69.40%. SARIMA can capture seasonal patterns and trends in loading and unloading data, making it useful for decision-making related to ship scheduling, crane allocation, and dock management. This study emphasizes the importance of selecting a model based on the characteristics of the data and demonstrates that traditional statistical models, such as SARIMA, are competitive with deep learning models for time series with strong seasonality.