Model product service system dengan collaborative planning forecasting replenishment pada industri suku cadang peralatan olahraga
Info Katalog
File Repositori
Pembimbing 3 : Britdey Banne Tandirerung
Subyek : Inventory control - Spare parts;Product service system
Penerbit : FTI - Usakti
Kota Terbit : Jakarta
Tahun Terbit : 2025
Pembimbing 1 : Docki Saraswati
Pembimbing 2 : Iveline Anne Marie
Kata Kunci : spare parts, product service system, collaborative planning forecasting and replenishment, Monte Car
Status Posting : Published
Status : Lengkap
File Repositori
| No. | Nama File | Hal. | Link |
|---|---|---|---|
| 1. | 2025_TS_MTI_163012200002_Halaman-Judul.pdf | 12 | |
| 2. | 2025_TS_MTI_163012200002_Surat-Pernyataan-Revisi-Terakhir.pdf | 1 | |
| 3. | 2025_TS_MTI_163012200002_Surat-Hasil-Similaritas.pdf | 1 | |
| 4. | 2025_TS_MTI_163012200002_Halaman-Pernyataan-Persetujuan-Publikasi-Tugas-Akhir-untuk-Kepentingan-Akademis.pdf | 1 | |
| 5. | 2025_TS_MTI_163012200002_Lembar-Pengesahan.pdf | 1 | |
| 6. | 2025_TS_MTI_163012200002_Pernyataan-Orisinalitas.pdf | 1 | |
| 7. | 2025_TS_MTI_163012200002_Formulir-Persetujuan-Publikasi-Karya-Ilmiah.pdf | 1 | |
| 8. | 2025_TS_MTI_163012200002_Bab-1.pdf | 8 | |
| 9. | 2025_TS_MTI_163012200002_Bab-2.pdf | 11 |
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| 10. | 2025_TS_MTI_163012200002_Bab-3.pdf |
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| 11. | 2025_TS_MTI_163012200002_Bab-4.pdf |
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| 12. | 2025_TS_MTI_163012200002_Bab-5.pdf | 58 | |
| 13. | 2025_TS_MTI_163012200002_Daftar-Pustaka.pdf | 4 | |
| 14. | 2025_TS_MTI_163012200002_Lampiran.pdf |
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P Penelitian ini mengusulkan model pengendalian persediaan suku cadang yang terintegrasi dengan prinsip product service system (pss) pada pt int. berangkat dari permasalahan operasional yang sering menghadapi stockout dan kerugian, penelitian ini bertujuan untuk meningkatkan efisiensi dan profitabilitas melalui manajemen persediaan yang lebih strategis. tahap perancangan model dimulai dengan klasifikasi persediaan menggunakan pendekatan abc multi-kriteria (berdasarkan usage, criticality, dan availability) untuk memprioritaskan item, di mana dinamo r1 dan r2 teridentifikasi sebagai komponen paling vital. selanjutnya, peramalan permintaan historis dilakukan dengan membandingkan model arima dan artificial neural network (ann), dimana ann terbukti lebih akurat mape r1 22.13% dan r2 19.75%. model ann yang unggul ini kemudian menjadi dasar untuk validasi kebijakan persediaan melalui simulasi monte carlo. hasil validasi menunjukkan kinerja model usulan secara signifikan melampaui kondisi eksisting. model ini berhasil meningkatkan service level dari 82% menjadi 99%, menekan frekuensi stockout dari 4-5 unit menjadi hanya 1 unit per tahun, yang secara langsung mencerminkan keberhasilan model dalam menjamin ketersediaan layanan purna jual dan menunjang strategi uptime produk treadmill. dari sisi finansial terdapat efisiensi biaya yang signifikan total biaya loss setiap kekosongan dinamo r1 menurun dari rp69,7 juta menjadi rp9,7 juta, dan dinamo r2 dari rp59,7 juta menjadi rp11 juta. secara keseluruhan, model ini mengintegrasikan ketiga tahapan klasifikasi, peramalan dan simulasi dalam kerangka kerja yang selaras dengan collaborative planning, forecasting, and replenishment (cpfr), menyediakan solusi yang adaptif dan terukur untuk layanan purna jual. meskipun model memiliki keterbatasan seperti ketergantungan pada kualitas data dan asumsi distribusi permintaan, rekomendasi untuk penelitian selanjutnya meliputi pengembangan model untuk multi-item dengan mempertimbangkan variabilitas lead time serta evaluasi dampak non-finansial dari stockout.
T This study proposes an integrated spare parts inventory control model based on the product service system (pss) principle at pt int. starting from operational problems that often face stockouts and losses, this study aims to improve efficiency and profitability through more strategic inventory management. the model design phase begins with inventory classification using a multi-criteria abc approach (based on usage, criticality, and availability) to prioritize items, where the r1 and r2 dynamos are identified as the most critical components. next, historical demand forecasting was conducted by comparing the arima model and the artificial neural network (ann) model, with ann proving more accurate with mape of 22.13% for r1 and 19.75% for r2. this superior ann model was then used as the basis for validating inventory policies through monte carlo simulation. validation results showed that the proposed model\\\'s performance significantly outperformed existing conditions. the model successfully increased the service level from 82% to 99%, reduced stockout frequency from 4-5 units to just 1 unit per year, directly reflecting the model\\\'s success in ensuring after-sales service availability and supporting the treadmill product uptime strategy. from a financial perspective, there is significant cost efficiency, with total loss costs per r1 dynamo shortage decreasing from rp69.7 million to rp9.7 million, and r2 dynamo from rp59.7 million to rp11 million. overall, this model integrates the three stages of classification, forecasting, and simulation within a framework aligned with collaborative planning, forecasting, and replenishment (cpfr), providing an adaptive and measurable solution for after-sales service. although the model has limitations such as dependence on data quality and assumptions about demand distribution, recommendations for further research include developing a model for multi-item scenarios while considering lead time variability and evaluating the non-financial impacts of stockouts.