Analisis pengendalian persediaan suku cadang sealant untuk maintenance pesawat udara pada PT XYZ = Analysis of spare parts sealant inventory control for aircraft maintenance at PT XYZ

Rivaldo, Rivaldo (2024) Analisis pengendalian persediaan suku cadang sealant untuk maintenance pesawat udara pada PT XYZ = Analysis of spare parts sealant inventory control for aircraft maintenance at PT XYZ. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Sealant merupakan salah satu material yang digunakan pada segala jenis kegiatan maintenance pesawat udara. Beberapa tantangan yang dihadapi oleh perusahaan terkait sealant yakni, umur simpan yang terbatas (shelf-life) dan lead time yang panjang, sehingga memerlukan perencanaan persediaan yang efektif dan efisien untuk mencegah terjadinya waste akibat kelebihan stok dan menjadi kedaluwarsa. Untuk mengatasi permasalahan yang ada maka, dilakukan penelitian terhadap pengendalian persediaan material sealant dengan cara melakukan forecasing dengan menggunakan 4 metode usulan yakni: Croston’s, Syntetos-Boylan Approximation (SBA), Shale-Boylan & Johnston (SBJ), dan multiplicative decomposition (seasonal) untuk kemudian membandingkannya dengan metode forecasting yang digunakan oleh perusahaan yakni, 3-month moving average, dengan nilai kesalahan peramalan yang ditunjukkan oleh mean absolute deviations (MAD) dan mean square error (MSE). Serta, melakukan perhitungan dengan menggunakan 3 model manajemen persediaan usulan yakni: economic order quantity (EOQ), periodic order quantity (POQ), dan continuous review untuk kemudian membandingkannya dengan model yang saat ini digunakan oleh perusahaan yakni, min-max. Berdasarkan hasil penelitian yang telah dilakukan, didapatkan bahwa, terdapat variasi terhadap nilai kesalahan peramalan, 3 dari 4 metode usulan yakni Croston’s, Syntetos-Boylan Approximation (SBA), Shale-Boylan & Johnston (SBJ), memiliki nilai kesalahan peramalan yang ditunjukkan oleh MAD lebih kecil dibanding metode forecasting yang saat ini digunakan oleh perusahaan yakni, 3-month moving average. Sedangkan, metode forecasting multiplicative decomposition (seasonal) dapat menghasilkan kuantitas permintaan ramalan untuk 14 bulan kedepan. Dan hasil selanjutnya, model economic order quantity (EOQ) memberikan total cost yang lebih efisien dibanding model lainnya bahkan model yang saat ini digunakan oleh perusahaan, yakni min-max dengan selisih antara model min-max sebesar US$ 3.386,36. / Sealant is a material widely used in various types of aircraft maintenance activities. Several challenges that company facing related to sealant, such as its limited shelf life and long lead times, so its necessitating another effective and efficient inventory planning method to prevent waste due to overstocking and expiration. To address those issues, this research was conducted on sealant inventory control by forecasting that using four proposed methods: Croston’s, Syntetos-Boylan Approximation (SBA), Shale-Boylan & Johnston (SBJ), and multiplicative decomposition (seasonal). These methods were then compared with the company's current forecasting method, that is 3-month moving average, then evaluating the forecast errors indicated by mean absolute deviations (MAD) and mean square error (MSE). The next, calculations were performed by using three proposed inventory management models: economic order quantity (EOQ), periodic order quantity (POQ), and continuous review. These were compared with the company's current model, min-max. The research findings indicated that there were variations in forecasting errors, three out of the four proposed methods: Croston’s, Syntetos-Boylan Approximation (SBA), and Shale-Boylan & Johnston (SBJ), had lower forecasting errors, as shown by MAD, compared to the company's current 3-month moving average method. The multiplicative decomposition (seasonal) method was able to generate forecasted demand quantities for the next 14 months. Furthermore, the economic order quantity (EOQ) model provided a more cost-efficient total cost compared to other models, including the company's current min-max model, with a cost difference of US$ 3,386.36.

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