Optimasi Persediaan Produk Pipa dengan Karakter Permintaan Lumpy Intermiten di Industri Manufaktur
DOI:
https://doi.org/10.31004/jutin.v9i2.56390Keywords:
inventory control, intermittent demand, reorder point, manufacturing industry, safety stockAbstract
This study develops an optimized inventory control policy for pipe products characterized by intermittent demand in a manufacturing company in Surabaya. Monthly demand data from January to December 2024 were analyzed using the Average Demand Interval (ADI) and the Coefficient of Variation Squared (CV²) to classify demand behavior. The results indicate an ADI of 2.6 and a CV² of 0.82, confirming a lumpy intermittent demand pattern. Based on this classification, inventory parameters were determined for a 15-day lead time. The proposed policy yields a safety stock of 18 units and a reorder point of 25 units at a 95% service level. Implementation of the integrated forecasting–inventory approach reduces annual inventory costs by 21.7% and improves material availability from 89% to 95%. Findings demonstrate that demand classification-based inventory decisions provide measurable operational and economic improvements for project-oriented manufacturing environments.References
Helo, P. (2023). Deep learning-based approach for forecasting intermittent online sales. Discover Artificial Intelligence, 3(1), 45. https://doi.org/10.1007/s44163-023-00085-1
Papadopoulos, C. T. (2021). Demand forecasting methods for spare parts logistics for aviation: a real-world implementation of the Bootstrap method. Procedia Manufacturing, 55, 500–506. https://doi.org/https://doi.org/10.1016/j.promfg.2021.10.068
Macusi, E. D. (2022). Typology of smallholder and commercial shrimp (Penaeus vannamei) farms, including threats and challenges in Davao region, Philippines. Sustainability, 14(9), 5713.
Das, A. (2025). An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical Information System (GIS) and Machine Learning (ML) models in Baitarani Riv. Desalination and Water Treatment, 321, 101039. https://doi.org/https://doi.org/10.1016/j.dwt.2025.101039
Ioannou, G. (2022). Inventory – forecasting: Mind the gap. European Journal of Operational Research, 299(2), 397–419. https://doi.org/https://doi.org/10.1016/j.ejor.2021.07.040
Jumali, M. A. (2025). ANALISIS PENJADWALAN PROYEK PEMINDAHAN PENATAAN SELECTIVE RACKING DI GUDANG LOGISTIK MENGGUNAKAN CPM-PERT. Jurnal Bisnis, Logistik Dan Supply Chain (BLOGCHAIN), 5(2 SE-Articles), 44–50. https://doi.org/10.55122/blogchain.v5i2.1848
Siswoyo, Q. A. R. (2023). IMPLEMENTASI SAFETY STOCK DALAM PENGENDALIAN PERSEDIAAN MINYAK GORENG. KAIZEN: Management Systems & Industrial Engineering Journal, 6(2), 29–34.
Utomo, Y. (2019). PERENCANAAN KEBUTUHAN KAPASITAS WAKTU PRODUKSI PRODUK SPON ALAS TIDUR (Studi Kasus: Perusahaan Alas Tempat Tidur Di Sidoarjo). Jurnal Teknik WAKTU, 17(01), 45–49. Retrieved from http://jurnal.unipasby.ac.id/index.php/waktu/article/view/1867/1681
Meissner, J. (2021). Intermittent demand forecasting for spare parts: A Critical review. Omega, 105, 102513. https://doi.org/https://doi.org/10.1016/j.omega.2021.102513
Mladenić, D. (2022). Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand. Sustainability, Vol. 14. https://doi.org/10.3390/su14159295
Hussain, S. (2025). Enhancing Supply Chain Management: A Comparative Study of Machine Learning Techniques with Cost–Accuracy and ESG-Based Evaluation for Forecasting and Risk Mitigation. Sustainability, Vol. 17. https://doi.org/10.3390/su17135772
Chen, A. (2022). A combined forecasting method for intermittent demand using the automotive aftermarket data. Data Science and Management, 5(2), 43–56. https://doi.org/https://doi.org/10.1016/j.dsm.2022.04.001
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