<![CDATA[Inventory management in the Fast-Moving Consumer Goods (FMCG) sector is challenged by high demand volatility, limited storage capacity, and product perishability, all pf which substantially increase operational costs when not properly controlled. These challenges become more severe when ordering decisions are made without a structured policy and are influenced by quantity-based discounts that may unintentionally raise inventory levels and expiration risks. This study aims to develop probabilistic multi-item inventory levels and expiration risks. This study aims to develop a probabilistic multi-item inventory model that explicitly incorporates perishability factors to generate optimal replenishment decision under stochastic demand conditions. The methodology includes identifying demand distribution patterns using the Kolmogorov-Smirnov goodness test, forecasting demand through Simple Exponential Smoothing, Holt’s Method, and Linear Trend Models, and integrating the results into a contemporary probabilistic inventory control model that considers storage capacity, discount schemes, and shortage risks. A numerical study involving seven products over a 12-month horizon is conducted to empirically evaluate the model’s performance. The results demonstrate that the proposed model significantly reduces total inventory cost, with notable decreases across purchasing, ordering, holding, shortage, and expiration cost components, achieving cost reductions exceeding 70% compared to the initial condition. The model provides precise value for optimal ordering time (T*), order quantity (Q), and safety stock, offering a robust and practical decision support framework for FMCG companies. These findings reinforce the importance of integrating perishability and capacity constraints into probabilistic inventory models to enhance decision quality and operational efficiency.]]>
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