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This study investigates how pharmaceutical supply organizations can balance inventory holding costs and service levels through optimal safety stock policies. Using a case study of a pharmaceutical supply company in Mongolia, the analysis focuses on four high-impact "A-class" products (Vemlidy, Lianhua, Lutasun, and Tabex) and compares three safety stock determination methods: the number-of-days approach, the Theory of Constraints (TOC) replenishment model, and a service-level-based optimization approach. The dataset comprises 12 months of sales data 2024 and projected demand for 2025, covering products that collectively represent approximately 80 percent of total sales revenue. The results indicate that traditional deterministic methods, the number of days and TOC based approaches, systematically overestimate safety stock levels, increasing inventory holding costs unnecessarily. In contrast, the service-level-based method, extended through total cost minimization, aligns safety stock with actual demand variability and risk exposure. The optimal service level of 99% reflects the critical importance of uninterrupted pharmaceutical supply and proves cost-efficient when demand uncertainty and lead-time variability are explicitly modeled. From a practical perspective, this framework supports reduced supply disruptions, improved warehouse utilization, and enhanced operational stability. Theoretically, the research enriches the safety stock literature by providing empirical evidence from an essential medicine supply chain in an import-dependent emerging economy, thereby supporting managerial decision-making in high-risk inventory environments. The study's primary contribution lies in integrating service level-based decision making with total cost minimization, an approach with broad applicability to pharmaceutical supply chains facing structural demand uncertainty.

This study investigates how pharmaceutical supply organizations can balance inventory holding costs and service levels through optimal safety stock policies. Using a case study of a pharmaceutical supply company in Mongolia, the analysis focuses on four high-impact "A-class" products (Vemlidy, Lianhua, Lutasun, and Tabex) and compares three safety stock determination methods: the number-of-days approach, the Theory of Constraints (TOC) replenishment model, and a service-level-based optimization approach. The dataset comprises 12 months of sales data 2024 and projected demand for 2025, covering products that collectively represent approximately 80 percent of total sales revenue. The results indicate that traditional deterministic methods, the number of days and TOC based approaches, systematically overestimate safety stock levels, increasing inventory holding costs unnecessarily. In contrast, the service-level-based method, extended through total cost minimization, aligns safety stock with actual demand variability and risk exposure. The optimal service level of 99% reflects the critical importance of uninterrupted pharmaceutical supply and proves cost-efficient when demand uncertainty and lead-time variability are explicitly modeled. From a practical perspective, this framework supports reduced supply disruptions, improved warehouse utilization, and enhanced operational stability. Theoretically, the research enriches the safety stock literature by providing empirical evidence from an essential medicine supply chain in an import-dependent emerging economy, thereby supporting managerial decision-making in high-risk inventory environments. The study's primary contribution lies in integrating service level-based decision making with total cost minimization, an approach with broad applicability to pharmaceutical supply chains facing structural demand uncertainty.