Safety stock is a fundamental component of inventory management, intended to protect against uncertainty in demand and lead time. Conventional safety stock formulations rely heavily on symmetric measures of variability, most notably the standard deviation of demand. While statistically valid, such measures implicitly assume that deviations above and below the mean contribute equally to service risk¾an assumption that may be economically misleading when demand variability is asymmetric. This study investigates the performance of standard deviation-based safety stock under asymmetric demand conditions using a simulation-based analysis of a continuous-review $(R, Q)$ inventory system. Demand is generated under three controlled scenarios: (i) variability dominated by downside outliers, (ii) variability driven by upside outliers, and (iii) mixed demand variability combined with stochastic lead time. Four inventory policies are evaluated: no safety stock, standard deviation-based safety stock, an average-maximum rule, and a quantile-based reorder point. Performance is assessed using replicated simulations (50 independent runs per scenario and policy) and reported in terms of service-level metrics (cycle service level, fill rate, stockout frequency and volume) and economic outcomes (average on-hand inventory and total cost). The results show that when demand variability is dominated by downside outliers, standard deviation-based safety stock systematically increases inventory levels and total cost without improving service performance. In such settings, the absence of safety stock achieves comparable service levels at significantly lower cost. When demand includes genuine upper-tail risk or when lead time is stochastic, safety stock becomes necessary to maintain service reliability; however, quantile-based reorder points consistently achieve similar service levels with lower inventory investment than symmetric dispersion-based policies. Overall, the findings demonstrate that the limitation lies not in the safety stock concept itself, but in the use of symmetric uncertainty estimators under asymmetric demand conditions. From a managerial perspective, the study highlights the importance of aligning safety stock estimation methods with the structure of demand risk, thereby avoiding unnecessary inventory accumulation while preserving service quality. Received: January 12, 2026Revised: February 2, 2026Accepted: February 10, 2026

Bumdes Mart is a supermarket in Bukit Intan Prosperous Village, Kunto Darussalam Regency, Rokan Hulu Regency, Riau Province. Supermarkets face great challenges in managing product inventory, with frequent shortages or excess inventory due to monthly sales fluctuations. Therefore, there is a need to categorize products and estimate future sales. This study aims to categorize the product groups sold by Bumdes Mart into groups A, B, and C. Then, Monte Carlo simulation is used to predict future product sales quantities, and the ROP method is used to determine the reorder point. ABC analysis, Monte Carlo simulation, and the reorder point (ROP) method are used to develop an effective inventory control strategy and solve the problem of Bumdes Mart in Bukit Intan Prosperous Village. The comes about appear that Course A items incorporate 8 item sorts. Through Monte Carlo recreation, we decided that the anticipated esteem of future deals information isn't much diverse from the real information of the previous year, and the full mistake of each reenactment is less than 5%. The ROP of each item is: 48 boxes, 53 pieces, 133 packs, 456 pieces, 32 boxes, 6 sacks, 32 boxes and 6 packs. Keywords: ABC analysis, Monte Carlo Simulation, ROP, Stock, Stock Control

The intense competition in the printing industry demands companies to improve efficiency, particularly in managing raw material inventories. PT. JKL faces challenges in controlling paper inventory, including excessive stock reaching up to 32,000 kg per year and inefficient ordering and shipping costs due to mismatches between order quantity and truck capacity. These issues lead to increased costs and the risk of stockouts or overstocking. The purpose of this study is to identify the most efficient quantity and schedule for paper ordering while keeping overall inventory costs low using the WWA (Wagner Within Algorithm) method. A quantitative approach is employed, involving demand forecasting, WWA calculation, safety stock, and reorder point analysis. The results indicate that implementing the WWA method can reduce the number of orders from 24 to 12 annually and achieve inventory cost savings of Rp 375,368,025. Additionally, the company can maintain a safety stock level 3,411 kg and a reorder point set of 13,204 kg. Thus, the WWA method is effective in addressing PT. JKL’s inventory control problems and can be adopted as a strategic tool for operational efficiency. Keywords: Demand Forecasting, Inventory Control, Inventory Cost, Wagner Within Algorithm

Inventory control is an important aspect in maintaining stock availability and operational cost efficiency within a company. At Perum BULOG Bukittinggi Branch, the ordering policy for commercial rice has not been fully optimized, which has the potential to increase total inventory costs. This study aims to determine the optimal order quantity, safety stock, reorder point, and total inventory cost using the Economic Order Quantity (EOQ) method. The research employs a quantitative approach with a descriptive research design. The population of this study consists of commercial rice inventory data at Perum BULOG Bukittinggi Branch, while the sample includes annual demand data, ordering costs, holding costs, and the company’s lead time. Data collection techniques were carried out through documentation and literature study, while data analysis was conducted using the EOQ method. The results show that the optimal order quantity is 1,543 units with an ordering frequency of 7–8 times per year. The application of the EOQ method reduces the total inventory cost from Rp33,500,374 to Rp30,199,932, resulting in a cost efficiency of Rp3,300,441 per year. The calculated safety stock is 359 units and the reorder point is 585 units. The implementation of the EOQ method can improve cost efficiency and make the inventory control system more measurable and systematic at Perum BULOG Bukittinggi Branch..

Abstract This document details a methodology to modernize your ETL (Extract/Transform/Load) pipelines using automated processes, cloud conversion, and performance tracking as appropriate for business goals. This includes a transition to scalable, cloud-native operations with Informatica PowerCenter and Azure Data Factory to replace outdated SSIS packages as well as implementing properly structured ETL testing through CI/CD processes using tools such as Jenkins and GitHub. This paper describes how to recommend the modernization of an ETL (Extraction, Transformation, and Loading) process to include: automating the entire ETL solution (replace the existing configurations with automation), hosting the solution in a Cloud platform, and monitoring ETL performance against business requirements. Examples of how we will accomplish this include using the Cloud-based applications Informatica PowerCenter or Azure Data Factory to replace legacy SSIS packages. Additionally, we will follow a structured test process using the CI/CD (Continuous Integration/Continuous Delivery) tool suites of Jenkins and GitHub. Power BI will be the analytics and reporting tool selection for Agile project management. The paper describes how to benchmark pipeline performance to identify the Key Performance Indicators (KPIs) to evaluate pipeline performance. The document evaluates why the traditional measure of Reorder Point (ROP) is inaccurate and typically not used; the solution proposed is to use a cloud-native architecture such as AWS MSK and Snowflake to support the development of predictive reorder points. Lastly, a case study of an Electronics company will be provided where a 60% savings and 96% accuracy have occurred during peak seasons, and a comprehensive six-month migration plan to augment ROP adjustments through Generational AI presents opportunities for improvement. Keyboards: Informatica PowerCenter, Azure Data Factory, SSIS packages, Reorder Point (ROP), Generational AI

Drug inventory control is a critical component of pharmaceutical management in referral hospitals with high service complexity. This study aimed to evaluate pharmaceutical inventory control performance at the Pharmacy Installation of RSUP Persahabatan and to formulate improvement strategies using an Operational Research approach. A mixed-methods design with a sequential explanatory approach was applied. Quantitative analysis was conducted on 2,291 drug items recorded in the inventory system during October 2025 using ABC-VEN classification to identify priority medicines, followed by calculations of Economic Order Quantity (EOQ) and Reorder Point (ROP). Qualitative data were obtained through in-depth interviews with key informants to strengthen the interpretation of quantitative findings. The results showed that 131 Pareto A medicines had an investment value of IDR 6,788,411,815, indicating a high concentration of inventory investment in a small proportion of items. In addition, stagnant stock was identified in 211 drug items with a total investment value of IDR 444,994,656, while stock out events accounted for 11.25% of total drug items. EOQ and ROP calculations revealed substantial variation across drug items, reflecting differences in demand characteristics, investment value, and procurement lead times, indicating that uniform ordering policies are not appropriate. Scenario-based simulations demonstrated that integrating ABC-VEN, EOQ, and ROP has the potential to improve inventory management efficiency, reduce the risks of stock outs and stagnant stock, and support the continuity of hospital pharmaceutical services.

The lack of a systematic calculation approach makes it difficult for APR Nine to monitor fabric raw material inventory, resulting in raw material purchases still being based on habit and intuition. With a total inventory of 2,001 rolls and raw material usage of 1,803 rolls of fabric, there were seven times of excess stock and five times of shortage during the period of October 2024 to September 2025. The difference between the two was 198 rolls. This study uses the Economic Order Quantity (EOQ) method to analyze fabric raw material inventory control. This study uses a quantitative approach with descriptive analysis using data from the period of October 2024 to September 2025. The results of the study indicate that 1694 orders with an ordering frequency of 10 times, a safety stock of 29 rolls, and a reorder point of 35 rolls are the ideal quantities when using the Economic Order Quantity (EOQ) approach. Compared to the previous APR Nine approach, the application of the Economic Order Quantity (EOQ) method is more effective in reducing overall inventory costs from Rp. 5,011,949.35 to Rp. 4,508,824.67.

Unplanned inventory management can lead to excess stock and increased inventory costs. Based on 2024 data, Grosir Benny experienced a condition where incoming goods exceeded outgoing goods, indicating inefficient inventory control. This study aims to analyze inventory management before the implementation of the Economic Order Quantity (EOQ) method and to evaluate the effectiveness of EOQ in determining optimal order quantities. This research employs a quantitative descriptive method with instant noodle inventory as the object of study. The results show that the EOQ method produces an optimal order quantity of 24 cartons per order with 46 orders per year. The calculated safety stock is 42 cartons, and the reorder point is 49 cartons. The implementation of the EOQ method is considered more effective than the previous system, as it aligns order quantities with sales levels, reduces the risk of overstocking, and improves inventory cost efficiency.

This study aims to analyze inventory management by applying the Economic Order Quantity (EOQ), Reorder Point (ROP), and Min–Max Stock methods to determine the optimal order quantity, reorder timing, and annual inventory cost estimation. The data used include an annual demand of 456 units, ordering cost of Rp255,529 per order, carrying cost of Rp110,638 per unit per year, daily usage of 2 units, and a 3-day lead time. The findings indicate that the optimal order quantity is 46 units per order, with a purchasing frequency of 10 times per year. The reorder point is set at 26 units, signaling the inventory level at which a replenishment order must be placed. The Min–Max analysis establishes a minimum inventory level of 26 units and a maximum of 72 units. Comparison of total inventory costs shows that the current company method results in total annual costs of Rp5,168,470, while the EOQ-based method yields a lower cost of Rp5,099,964. Thus, the application of EOQ–ROP–Min–Max provides annual savings of Rp68,506 and leads to a more efficient inventory control system by reducing both stock-out risk and excess inventory.

Purpose: This study examined the influence of inventory control practices on public organisational performance in Tanzania, with specific focus on the Tanzania Immigration Department Kurasini Office, Dar es Salaam. The specific objectives were to examine the influence of inventory tracking on organisational performance, determine the effect of reorder point management on organisational performance, and examine the influence of stock levels maintenance on organisational performance at the Immigration Office in Kurasini, Dar es Salaam. Methodology: The study employed an explanatory research design utilising a quantitative research approach to establish causal relationships between inventory control practices and organisational performance. The target population comprised 208 employees and management staff working at the Immigration Office in Kurasini, Dar es Salaam, including employees directly involved in inventory management, procurement, logistics, and operations, as well as administrative and support staff. A sample size of 67 respondents was determined using Yamane's (1967) formula with a 10% margin of error. Simple random sampling technique was employed to select participants from the target population, ensuring equal probability of selection and minimising selection bias. Primary data were collected through structured questionnaires administered to the sampled respondents. Data were analysed using descriptive statistics and multiple linear regression analysis. Descriptive data usually serve to support inferential analysis. Given that this study focuses on examining influence, the regression results provide clearer evidence. Findings: The findings indicate that inventory tracking (β = 0.394, p = 0.005), stock levels maintenance (β = 0.287, p = 0.006), and re-order point management (β = 0.241, p = 0.027) all exert a positive and statistically significant influence on organizational performance. Therefore, the emphasis is placed on the inferential results rather than descriptive statistics. Unique Contribution to Theory, Practice and Policy: The study extend the resource based view theory by empirical confirming that core inventory control practices, inventory tracking, reorder point management and stock level maintenance significantly influence organization performance. The study recommends the implementation of digital inventory management systems, staff training programmes on modern inventory control practices, establishment of clear reorder point policies, and regular monitoring and evaluation of inventory control procedures.

Raw materials are one of the determining factors in the smooth production process.The obstruction of the production process will affect the output produced and reduce consumer confidence in the company. For this reason, it is necessary to control the inventory of raw materials using Economic Order Quantity (EOQ) method. This study aims to determine how business activities place orders, determine the optimal amount of inventory in placing orders. This analysis is carried out using the Economic Order Quantity, Reorder Point, Safety Stock methods. EOQ aims to determine the amount of raw materials ordered and minimize the total cost of ordering and storage. Reorder point aims to find out when to order back appropriately, and Safety Stock aims to provide security if the order period is not appropriate. Data collection methods using documentation and interviews. The results of research and discussion using EOQ method obtained more effective results. This can be seen from the comparison of company policy with EOQ method. During this time the company made an average purchase of 277.5 kg with a frequency of 24 orders a year. The total inventory cost is Rp 3,804,166. Meanwhile, when using EOQ method, the average purchase amount is 137 kg with an order frequency of 5 times a year. The total cost of inventory is Rp 1,482,639 a year. The safety stock required by the company is 18.5 kg and the company must place a reorder point when the inventory has reached 42.28 kg. From these results, it can be compared if the system currently running in the company incurs higher costs compared to using EOQ method.

This study aims to enhance the efficiency of inventory control for uPVC AW 3/4inch pipe products at PT XYZ by applying time series forecasting methods using historical sales data. Three forecasting approaches were tested, Double Exponential Smoothing, Holt Winters Multiplicative, and Seasonal Autoregressive Integrated Moving Average (SARIMA). The accuracy of each model was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Error (MSE) to determine the most reliable forecasting result. The analysis showed that SARIMA model produced the smallest error value, indicating the best level of accuracy. The forecasting output from this model was then used as a basis for calculating safety stock, reorder point, and maximum stock levels to improve inventory control decisions. The finding indicate that databased forecasting helps PT XYZ align production quantities with actual market demand, reducing overstock conditions and optimizing warehouse capacity utilization. This research highlights the importance of applying quantitative forecasting methods to strengthen data driven decision making in inventory management and provides practical insights for manufacturing companies seeking to improve operational efficiency.

Effective inventory management primarily relies on precise demand forecasting, an essential yet challenging aspect for companies pursuing operational excellence. This paper outlines the design and implementation of a forecasting interface integrated with a sales dashboard to enhance demand prediction accuracy and inventory decision-making. The interface incorporates four established forecasting techniques—Naïve, Moving Average, Weighted Moving Average, and Exponential Smoothing—to systematically address demand fluctuations. Created in Excel and automated with VBA, it provides reorder points, safety stock levels, and forecasted demand, along with other distinctly user-friendly inputs and outputs. In addition, a dynamic sales dashboard has been developed with visual features representing historical and projected demand, sales distribution by products and regions which further facilitate detailed analysis and informed inventory management decisions. This study outlines the interface and dashboard development process along with important codes. It also highlights the practical implications of integrating technical forecasting methods with intuitive visualization tools to enhance inventory management substantially. The forecasting interface and sales dashboard were further verified and validated through different scenarios including: high demand in season peaks, managing with variation in lead time issues, and forecasting in case of launching new product.

This study aims to: (1) Determine how to analyze the optimization of raw material inventory in the tofu agroindustry in Mataram City; (2) Determine the impact on sustainability in optimizing raw material inventory in the tofu agroindustry in Mataram City. The method used in this study is descriptive. The unit of analysis in this study is tofu entrepreneurs in Mataram City. This study was conducted in Mataram City, with 13 sub-districts; 2 were selected: Sekarbela Sub-district in Kekalik Jaya Village and Sandubaya Sub-district in Abiantubuh Baru Village, using quota sampling. Determination of sample areas using proportional random sampling, namely, 15 people. The number of samples was allocated to sub-districts using proportional random sampling, with Kekalik Jaya Village receiving 6 and Abiantubuh Baru Village receiving 8. The data used in this study are quantitative and qualitative. Data sources used include primary data and secondary data. Data collection was conducted through direct interviews with respondents. The results of this study are: (1) the analysis of soybean raw material inventory optimization in the tofu agroindustry in Mataram City is still not optimal based on the EOQ method analysis, the optimal order quantity is 34,403 kg per order, with a frequency of 20 times per year or around 1-2 times per month. This is much more efficient than the actual practice of 6,259 kg per order with a frequency of 109 times per year. The required safety stock is 7,114 kg to account for demand fluctuations and delivery delays during the 10-day lead time. The reorder point is 26,000 kg, with a total annual inventory cost of IDR 3,247,757, consisting of an ordering cost of IDR 1,623,867 and a storage cost of IDR 1,623,890. (2) The application of inventory optimization with the EOQ method has a significant positive impact on the sustainability of the tofu agroindustry in Mataram City. There was a very significant inventory cost saving of IDR 5,973,140, or 64.78% of the total actual inventory cost of IDR 9,220,897, mainly from a 81.81% reduction in ordering costs through reduced order frequency.

Effective blood inventory management is necessary for sustaining appropriate supply levels and reducing costs, particularly given the blood products perishable nature alongside the demand uncertainty. This study employs the (r, Q) replenishment policy as a robust and easily implementable method for hospitals intern blood banks. Therefore, we formulate a modular simulation approach that engenders replenishment orders predicated on reorder point r and order quantity Q, while taking into consideration the variable shelf lives and demand variability. Thus, numerical experiments reveal that the (r, Q) policy standardizes ordering decisions by shifting away from purely ad-hoc demands, minimizes both stockout and spoilage costs compared to “order-on-demand” approaches, and adjustable to hospital specific metrics and comprehensible to medical staff with limited supply chain expertise. These insights emphasize the practical significance of the (r, Q) approach for perishable blood products and propose strategic assistance for its application in healthcare logistics.

This community service activity was conducted at Grind Now Coffee Bar, an MSME in Batam City’s food and beverage sector, to address challenges in managing raw material inventory, which often led to inaccurate stock records, shortages, and overstocking, affecting daily operations and efficiency. The program aimed to improve inventory management by applying the Economic Order Quantity (EOQ) and Reorder Point (ROP) models, providing a systematic and measurable approach to procurement. Activities included detailed observation of operational practices, interviews with staff to understand current challenges, collection of data on raw material usage and purchasing patterns, and the implementation of a website-based inventory control system to streamline record-keeping and monitoring. The results showed that EOQ and ROP models helped determine optimal order quantities and appropriate reorder timing, while the inventory system improved organization, reduced errors, and enabled real-time stock monitoring. Overall, this intervention enhanced decision-making, promoted operational efficiency, and supported the sustainability and competitiveness of Grind Now Coffee Bar.

Grouper is a high-value fishery commodity with increasing demand in both domestic and international markets, necessitating a stable raw material supply to support sustainable processing activities. However, feasibility assessments for developing grouper processing industries in Indonesia rarely prioritize raw material availability, despite this factor being essential for maintaining consistent production. This study evaluates the feasibility of establishing a grouper processing industry in Java by integrating cross-regional supply mapping with an inventory control analysis based on the Safety Stock (SS) and Reorder Point (ROP) approaches. A mixed-methods design was used, combining semi-structured interviews, field observations at ten major supply points along the northern coast of Java, and analysis of twelve months of supply and demand data. Findings indicate that the total monthly grouper supply reaches approximately 254 tons, while industrial requirements are around 50 tons. Cold chain capacity, logistics conditions, and the distance of distribution influence price differences across regions. Seasonal fluctuations can be managed by relying on both capture fisheries and floating net cage aquaculture, which help maintain a consistent supply throughout the year. A simulation using an SS value of 602 kilograms and an ROP value of 2,402 kilograms demonstrated that no stock outs occurred during a ten-day evaluation. Overall, the study confirms that the supply of grouper raw materials in Java is stable, feasible, and capable of supporting the growth of processing industries, providing a practical basis for future supply chain planning. These findings offer guidance for policymakers and industry stakeholders nationwide. Keywords: grouper supply chain, raw-material feasibility, inventory control, safety stock, reorder point

Inventory is a crucial factor in retail operations. Currently, the inventory information system at Toko Husein is still conducted manually using logbooks, leading to stock inaccuracies, data loss risks, and reporting delays. This study aims to design a web-based inventory information system using the Min-Max method to determine minimum, maximum, and safety stock levels to ensure product availability. The research methods included observation, interviews, and literature review, with system development using PHP programming language and MySQL database. System design was modeled using Unified Modeling Language (UML). The result of this research is a system capable of recording incoming and outgoing goods transactions, presenting real-time inventory reports, and providing reorder point notifications based on Min-Max calculations. Black Box testing indicates that the system functions validly. This system is expected to improve stock management efficiency and data accuracy at Toko Husein.

One of the problems faced by business actors of PT Tanabe Food (Frozen Food) is the imbalance of good and proper raw material inventory. This study aims to analysis the raw material inventory process be adapted to the market's needs to minimize production costs and raw material inventory. This research uses the Economic Order Quantity (EOQ) method. Techniques in collecting data during this research are interviews, observation, and documentation. The results obtained in the research at PT Tanabe Food in using the EOQ method are the use of raw materials per period of 56,952.5 kg, the frequency of purchases is 12-13 times, the safety stock is 430.3 kg, the reorder point is 860.6 kg, and the total cost of raw material inventory is Rp. 5,356,168, while in the calculation, according to company policy is the use of raw materials per period of 61,964 kg, the frequency of purchases is 96 times, there is no safety stock, there are uncertain goods at the reorder point, and the total cost of raw material inventory is Rp. 87,281,408. The results show that the calculation of the EOQ method is more profitable than the company's existing calculations.

This study aims to implement a web-based inventory control system at OTW29 Nasi Padang Vegetarian to improve stock management efficiency. Previously, inventory records were managed manually, leading to frequent inaccuracies and disruptions in operational processes. The developed system integrates the Economic Order Quantity (EOQ) and Reorder Point (ROP) methods to determine optimal order quantities and reorder levels. The implementation results show that the system is capable of providing real-time stock information, increasing recording accuracy, and supporting faster and more precise raw material ordering decisions. The system has proven effective in reducing the risk of stockouts or overstocking and enhancing the overall operational efficiency of the MSME.