Integrated Inventory Control Analysis through Demand Forecasting and Probabilistic Stock Modelling - Sesar Husen Santosa; Agung Prayudha Hidayat (Journal of Applied Science, Technology & Humanities | JASTH, 2025)
This study addresses the problem of excess chicken egg inventory at agent warehouses caused by the mismatch between fluctuating demand and incoming supply, which frequently leads to product damage and financial losses. The research aims to design an inventory control policy based on demand forecasting and probabilistic inventory modelling to determine an ideal stock level. A quantitative approach is employed by first applying the two‑period Moving Average method, which produces an MSE of 6.17 and a MAPE of 0.10 with a forecast of 65 crates for the ninth period, and then integrating these forecasts into a probabilistic inventory model at a significance level of  to obtain a reorder point of 214 crates and a safety stock of 60 crates. The results show that this integrated approach enables the agent to trigger replenishment precisely when on‑hand inventory reaches the reorder point, thereby maintaining an adequate service level while reducing overstock and the risk of spoilage. The combined use of a quantitative forecasting model and a probabilistic inventory framework constitutes the main contribution of this study, offering an integrated decision support scheme for determining both the optimal order timing and the appropriate buffer stock level in a perishable‑goods context.