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All Journal Jurnal Teknik Industri J@TI (TEKNIK INDUSTRI)
Prastiya, Denny Suci
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering

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OPTIMASI PERENCANAAN PRODUKSI AGREGAT PRODUK TUNGGAL DENGAN MEMPERTIMBANGKAN KAPASITAS PRODUKSI Prastiya, Denny Suci; Wahyuni, Rossi Septy
J@ti Undip: Jurnal Teknik Industri Vol 19, No 2 (2024): Mei 2024
Publisher : Departemen Teknik Industri, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jati.19.2.58-69

Abstract

Pertumbuhan pada industri air minum dalam kemasan mengalami peningkatan secara signifikan. Peningkatan pada industri air minum dalam kemasan dipengaruhi oleh tingginya permintaan akan produk air minum bersih. PT X pada studi kasus ini mengalami fluktuasi permintaan yang diakibatkan tingginya persaingan antara industri sejenis. Oleh karena itu PT X pada studi kasus ini menjalin kerja sama subkontrak dalam memenuhi permintaan. Permasalahan yang dialami oleh PT X adalah meningkatnya harga per unit saat melakukan subkontrak. Tingginya harga unit subkontrak dapat mengakibatkan tingginya biaya perencanaan produksi. Penelitian ini dilakukan untuk meminimasi jumlah unit subkontrak dengan mengoptimalkan produksi internal. Model optimasi dibangun dengan fungsi tujuan linear dan fungsi pembatas non linear sebagai usulan optimasi perencanaan produksi agregat. Di samping itu, berkurangnya jumlah unit subkontrak dapat mengakibatkan tingginya produksi internal perusahaan, sehingga model usulan perlu mempertimbangkan kapasitas produksi. Usulan evaluasi kapasitas dilakukan menggunakan pendekatan rough-cut capacity planning. Berdasarkan hasil optimasi model usulan dapat menghemat biaya perencanaan produksi agregat sebesar 5% dibandingkan dengan kondisi nyata. Abstract[Optimization of Single Product Aggregrate Production Planning Considering Production Capacity] Bottled drinking water industry growth has increased significantly. The increase in the bottled drinking water industry is influenced by the high demand for clean drinking water products. PT X in this case study experienced fluctuations in demand due to high competition between similar industries. Therefore, PT X established sub-contract cooperation to fulfill demand in this case study. The problem experienced by PT X was when carrying out sub-contract cooperation. The high price per unit of sub-contracted products can result in high production planning cost. The research is conducted to minimize the number of sub-contract units while optimizing internal production. The optimization model was built with a linear objective function and a nonlinear constraint function as an optimization proposal for aggregrate production planning. Besides that, the reduced number of sub-contract units can result in high internal production for the company, therefore it is necessary to consider production capacity. The proposed capacity evaluation is conducted using a rough-cut capacity planning approach. Based on the results of the optimization of the proposed model, it can save 5% of aggregrate production planning costs compared to actual conditions.Keywords: integer nonlinear programming; production capacity; aggregrate production planning
Artificial Neural Network Model For Optimization of Forecasting Material Inventory: English Prastiya, Denny Suci; Wahyuni, Rossi Septy
Jurnal Teknik Industri Vol. 25 No. 2 (2024): August
Publisher : Department Industrial Engineering, University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/JTIUMM.Vol25.No2.173-188

Abstract

The increasing competition in the fast-moving consumer goods (FMCG) industry leads to demand fluctuations, negatively impacting the accuracy of demand forecasts and determining optimal lot sizes in material inventory planning. Many companies struggle to adopt appropriate forecasting models, resulting in poor accuracy and higher material costs. This study aims to develop an integrated model for forecasting and material planning using simulation. The artificial neural network (ANN) method is proposed to improve forecasting accuracy, with performance evaluated through mean percentage error (MAPE), mean absolute deviation (MAD), and mean squared error (MSE). The forecast results are then applied to optimize material inventory using the economic order quantity (EOQ) model, considering warehouse capacity constraints. The EOQ model is applied to adjust lot sizes under time-varying demand. The findings highlight the importance of integrating forecasting with inventory planning to provide accurate demand predictions and optimal lot sizing, ultimately minimizing material costs in the FMCG industry. This research contributes to better decision-making in supply chain management by enhancing forecasting accuracy and inventory optimization.
Co-Authors Rossi Septy Wahyuni