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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 the key factors influencing warehouse inventory performance at Eskom Distribution in the Gauteng Cluster using factor analysis. The aim is to identify critical elements that impact inventory management effectiveness and enhance operational performance. Eskom faces challenges in inventory control, including forecasting inaccuracies, procurement risks, and technology integration limitations, despite advancements in Economic Order Quantity (EOQ) and Reorder Point (ROP) models. This research applies a quantitative approach with a positivist paradigm, using simple random sampling to select 144 respondents from a target population of 230 employees involved in inventory management. Data was collected through structured questionnaires and analyzed using Principal Component Analysis (PCA) to uncover the factors affecting warehouse performance. The study identifies three major components—operational efficiency, inventory accuracy, and record-keeping—that account for the majority of variance in performance outcomes. These findings are aligned with previous studies emphasizing the role of accurate inventory data, efficient operations, and technology in enhancing warehouse functioning. The implications suggest that Eskom and similar organizations should invest in advanced inventory management systems, improve staff training, and adopt real-time tracking solutions. Further research could explore how external disruptions and emerging technologies, such as AI and blockchain, influence inventory performance across various sectors. This study contributes valuable insights to the field of inventory management within the public sector, offering practical recommendations for improving warehouse efficiency and reducing operational delays.  

The study assesses the benefits and challenges of inventory management practices and systems in the South African automotive industry. The study used a qualitative approach in which focus groups and semi-structured interviews were conducted on 18 supply chain professionals drawn from the automotive sector in four provinces in South Africa. ATLAS.ti software was used to analyse data. The study generated two themes: the benefits and challenges of inventory management practices and systems. This study concludes that inventory management practices play a critical role within companies in the South African automotive sector since managing inventory is an indispensable part of the more extensive supply chain management (SCM) practice. Regarding inventory management challenges, SCM professionals find it difficult to retain the resilience required to adapt and react to any disruptive circumstances due to inadequate support from the South African government. Also, it appeared difficult to deal with this challenge as some companies within the South African automotive sector have limited resources, making it extremely difficult to deal with uncertainties when they arise.

This research aims to observe current inventory management applied by one of the medical equipment distributors specializing in eye health devices, and provide recommendations for an optimal inventory management system to achieve cost efficiency afterward. The method used in this research is quantitative, focusing on processing and analyzing numerical data obtained from the company to calculate safety stock and the number of orders. In addition, ABC classification is also used in data processing to group items based on their value. Items belonging to class A (having a value of 80% for the company) will be used in the data processing. The next step is to conduct forecasting simulations for demand forecasts. The results of the forecast will be used for calculating the safety stock and order quantity. The output from the results of data processing and analysis in this study shows 67 items included in class A, which will then be processed using Minitab software for forecasting. Based on comparing the four forecasting methods, the one with the lowest error value is Trend Analysis. If the company can apply the storage method according to the fixed-time period model calculation, there is a potential savings of US $ 717,133 or 63% of the total overstock