Model Pengendalian Persediaan Responsif Berbasis Forecasting dan Probabilistik untuk Material Pada Proyek Preservasi Jalan Tol

Abstract

Pengendalian persediaan agregat pada Unit Asphalt Mixing Plant (AMP) penting untuk menjaga kelancaran produksi hotmix dalam proyek preservasi jalan tol. Permasalahan utama yang ditemukan adalah pemesanan material masih reaktif, belum memiliki batas minimum stok, dan belum berbasis perhitungan kuantitatif. Penelitian ini bertujuan menganalisis kondisi aktual persediaan, mengidentifikasi akar masalah, menghitung parameter pengendalian, serta merumuskan model persediaan yang lebih terstruktur. Metode yang digunakan meliputi observasi, wawancara, 5 Why’s, Fishbone Diagram, AHP, peramalan, stok pengagman, EOQ, dan titik pemesanan ulang. Hasil AHP menunjukkan faktor metode sebagai penyebab dominan dengan bobot 0,45155. Hasil perhitungan menghasilkan nilai EOQ sebesar 475–823 m³ dan ROP tertinggi pada Abu Batu 0–5 mm sebesar 1.107 m³. Model usulan berpotensi menghasilkan efisiensi biaya persediaan sebesar 30%–53%. Dengan demikian, integrasi peramalan, stok pengaman, EOQ, titik pemesanan ulang, dashboard monitoring, dan instruksi kerja dapat menjadi dasar pengendalian persediaan yang lebih terukur dan responsif.

Aggregate inventory control at the Asphalt Mixing Plant (AMP) unit is essential to ensure the continuity of hot mix asphalt production for toll road preservation projects. The main problem identified in this study is that material ordering is still reactive, has no minimum stock limit, and is not based on quantitative calculations. This study aims to analyze the current inventory condition, identify the root causes of the problem, calculate inventory control parameters, and formulate a more structured inventory control model. The methods used include observation, interviews, 5 Why’s analysis, Fishbone Diagram, Analytical Hierarchy Process (AHP), forecasting, safety stock, Economic Order Quantity (EOQ), and Reorder Point (ROP). The AHP results indicate that the methods factor is the dominant cause, with a weight of 0.45155. The calculation results show that the EOQ values range from 475 to 823 m³, while the highest ROP value is found in stone dust 0–5 mm at 1.107 m³. The proposed model has the potential to reduce inventory costs by 30% to 53%. Therefore, the integration of forecasting, safety stock, EOQ, ROP, a monitoring dashboard, and work instructions can serve as the basis for a more measurable and responsive inventory control system.