The management of packing material inventory in flexible packaging often faces high investment value, inaccurate demand planning, and inefficient ordering, creating a research gap in selecting an integrated optimization method. This study aims to determine material requirements and optimal order quantities to minimize total inventory cost. The methodology integrates ABC Analysis for material classification, forecasting techniques to predict demand, and Economic Order Quantity (EOQ) to determine the most economical order size. The results show that Plywood is classified as category A due to its dominant investment value. Forecasting evaluation using MAPE and Tracking Signal indicates that the 3-period Weighted Moving Average provides the highest accuracy with the lowest error. The EOQ calculation produces an optimal order quantity of 3,304 units for Plywood Multi 0280x0280x09 dia 77, significantly reducing inventory cost. The integration of ABC, forecasting, and EOQ improves inventory efficiency and supports more reliable production planning

Interval differential equation approach for inventory systems with green and price-dependent nonlinear demand

Efficient drug inventory control is essential for pharmacies to maintain service quality, prevent stockouts, and reduce financial losses caused by excessive inventory. This study develops an integrative inventory optimization model combining ABC analysis, Weighted Moving Average (WMA) forecasting, and the Economic Order Quantity (EOQ) Continuous Review approach. ABC analysis identifies high-priority drugs requiring strict control, WMA forecasts demand for Category A items, and the EOQ model determines optimal order quantity, safety stock, and reorder point. Results show that the integration of forecasting and continuous review improves accuracy in estimating demand fluctuations and reduces total inventory costs compared with existing ordering practices. The originality of this work lies in formalizing the integration of WMA forecasting into EOQ Continuous Review, specifically for pharmaceutical inventory systems. Study limitations include the use of a single-pharmacy dataset, fixed lead-time assumptions, and reliance on only one forecasting method. This integrated approach provides a novel and more responsive solution for pharmaceutical inventory management, as the use of WMA enhances forecast accuracy by emphasizing recent demand shifts, while the EOQ Continuous Review model ensures optimal ordering decisions in real time. Together, these methods create a more adaptive framework that reduces uncertainty, improves stock availability, and minimizes overall inventory costs.

Perishable special economic order quantity with price increase at known specific time

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..

Micro, Small, and Medium Enterprises (MSMEs) in the culinary sector play a vital role in supporting the national economy, yet they still face various operational challenges, particularly in inventory control. Unplanned inventory management and the absence of systematic record-keeping can potentially lead to excess stock, raw material shortages, and operational cost inefficiencies, all of which can impact service quality and business competitiveness. This study aims to analyze the inventory control system of merchandise at the Nelo Ayam Geprek UMKM, identify problems that occur in stock management, and evaluate its contribution to increasing business competitiveness.This research employed a qualitative approach with a case study approach. The population comprised all parties involved in inventory management, with a sample of four informants consisting of the owner and three employees. Data collection techniques included observation, in-depth interviews, and documentation. Data sources included primary and secondary data. Data analysis was conducted through data reduction, data presentation, and conclusion drawing, with validity testing using source and technique triangulation.The results of the study indicate that the inventory control system is not yet structured, there is no consistent stock recording, and the calculation of the economic order quantity has not been implemented. Simulation of the Economic Order method. Implementing a planned inventory control system and optimal ordering calculations can improve operational efficiency, maintain production stability, and strengthen the competitiveness of MSMEs in a sustainable manner.

A sustainable EOQ model with trade credit for defective and deteriorating items, incorporating preservation technology under monsoon fuzzy demand

This study presents a hybrid fuzzy–optimization framework for economic order quantity (EOQ) systems in which demand is influenced by selling price. Traditional EOQ models typically assume deterministic and price-independent demand, limiting their applicability in real-world environments where uncertainty and price sensitivity are significant. To address this gap, the proposed framework integrates fuzzy set theory with mathematical optimization to simultaneously capture uncertainty in key inventory parameters and optimize pricing and replenishment decisions. Demand is modeled as a price-dependent fuzzy function, allowing for flexible representation of vague or imprecise market information. The resulting hybrid model yields optimal order quantities and selling prices that minimize total inventory cost while accounting for uncertainty in demand, holding cost, and ordering cost. Numerical experiments demonstrate the robustness and managerial relevance of the approach, showing that it offers improved decision quality compared with classical EOQ formulations. This framework provides practitioners with a powerful tool for inventory management under uncertain, price-responsive demand conditions. Keywords: EOQ model, fuzzy optimization, price-dependent demand, inventory management, uncertainty modeling, hybrid framework.

Drug inventory management plays a critical role in supporting pharmacy operations, as it directly affects the quality of healthcare services, patient satisfaction, and cost efficiency. Inadequate inventory control often leads to stock imbalances in the form of drug shortages and excess inventory, resulting in higher holding costs, increased risk of drug expiration, and potential revenue loss. This condition was also observed in a pharmacy located in Padang City that had not yet implemented an integrated and systematic drug inventory planning system. This study aims to develop a more effective drug inventory control approach by classifying drug items based on their investment value using ABC analysis, prioritizing drug management according to clinical importance through the VEN classification, and determining the most economical order quantities using the Economic Order Quantity (EOQ) method to minimize total inventory costs. The study utilizes historical sales and inventory data collected over a specific observation period. The results of the ABC analysis indicate that approximately 20% of drug items fall into category A and account for 70% of the total inventory investment value. Based on the VEN classification, 14 drug items (5%) are identified as vital drugs. The integration of ABC and VEN analyses results in 67 priority drug items that are subsequently controlled using the EOQ method. The application of EOQ significantly reduces total inventory costs from IDR 3,166,552 to IDR 1,569,436. These findings demonstrate that the integrated implementation of ABC, VEN, and EOQ methods effectively improves drug inventory control performance, reduces inventory costs, and ensures optimal drug availability without compromising the quality of pharmacy services.

Demand estimation in EOQ models using second-order difference equations with full backlogging

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.

This study focuses on the problem of deteriorating EOQ models for decaying things under three different scenarios. Partial backlogs and shortages are allowed under the proposed paradigm. We also show the time-convexity of the total cost function. The model is demonstrated with numerical examples. Finally, a sensitivity analysis is used to validate the proposed model. Mathematica software is used to find numerical solutions.

Despite the widely acknowledged benefits of strategic inventory management in enhancing organizational efficiency and profitability many firms, including those in the Nigerian brewing industry are still plagued with sub-optimal inventory levels and their associated financial implications. The main objective of this study therefore, was to examine the effect of inventory management practices on financial performance of Champion Breweries, Uyo. The independent variable being strategic inventory management practices was proxied by Just-in-Time (JIT) strategy, Economic Order Quantity (EOQ) strategy, Lean inventory strategy, Material requirement planning (MRP) strategy and Vendor Managed Inventory (VMI) strategy, while the dependent variable, financial performance, was proxied by gross profit margin. The research design adopted for this study was survey design and primary data used were derived using the researcher self-constructed 5-point Likert questionnaire. The population of the study was 195 staff of Champion Breweries, Uyo. Purposive sampling technique was adopted to select a sample of 85 staff members. Data were analysed using Ordinary Least Square (OLS) regression analysis and the statistical package employed was SPSS version 21. The findings of the study revealed that Just-in-time strategy {0.502(0.000)} has a significant positive effect on gross profit margin; Economic order quantity {0.444(0.000)} has a significant positive effect on gross profit margin; lean inventory strategy {–0.094(0.318)} has an insignificant negative effect on gross profit margin; material requirement planning {0.315(0.021)} has a significant positive effect on gross profit margin and vendor managed inventory {0.206(0.083)} has an insignificant positive effect on gross profit margin of Champion Breweries Plc, Uyo. Among others, it was concluded that effective inventory management strategies play a critical role in improving the gross profit margin of Champion Breweries. Therefore, it was recommended among others, that the management of Champion Breweries, Plc, Uyo, the company should strengthen and maintain its Just-In-Time (JIT) practices. This can be achieved by enhancing supplier relationships to ensure timely delivery of raw materials while and reducing lead times, and this will minimize inventory holding costs and increase profitability.

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.

<p><strong>Effective inventory management is essential to maintain stock availability and control inventory costs in distribution companies. PT Panasonic Gobel Indonesia, Medan Branch, still faces challenges due to non-integrated inventory recording, resulting in data inconsistencies, reporting delays, and difficulties in determining optimal order quantities. These issues increase the risk of stock shortages and excess inventory, highlighting the need for an integrated inventory management information system. This study aims to design and develop a web-based inventory management system using the Safety Stock and Economic Order Quantity (EOQ) methods. The research applies a Research and Development (R&D) approach with the Waterfall model, covering requirement analysis, system design, implementation, and testing stages. Data were collected through observation, interviews, and literature review. The system was developed using PHP with the Laravel framework, a MySQL database, and UML. Model validation was conducted by comparing system calculation results with manual theoretical calculations, while verification was performed through functional and user acceptance testing. The results show that the system can accurately record inventory data, monitor stock levels in real time, and automatically calculate EOQ and safety stock, thereby supporting efficient and optimal inventory control.</strong></p><p><strong><em>Keywords</em></strong> - <em>Economic Order Quantity (EOQ); Inventory Control; Inventory Management Information System; Safety Stock; Web-Based System</em><em></em></p>

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.

This study aimed to analyse the mineral water inventory management practices and improve production cost efficiency at PT. Pintar Sumber Mineral. The research utilized a quantitative descriptive method, with data collected through interviews, documentation reviews, and literature studies. The findings demonstrated that the company had successfully and effectively implemented the Economic Order Quantity (EOQ) model. This effective implementation was evidenced by: The increase in the EOQ value corresponding with the growth in annual demand. The implementation of specific inventory management strategies that supported cost efficiency. While significant demand growth led to an increase in ordering and holding costs, the strategy of adjusting the EOQ model was able to optimally mitigate the impact of these rising costs. This study concluded that the application of the EOQ model had made a positive contribution to the company's operational efficiency.

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 research aims to optimize Green Supply Chain Management (GSCM) implementation in production waste management at Batik Royyan Tuban by integrating the Economic Order Quantity (EOQ) method based on POM-QM for Windows software. The research method used is a case study with a mixed approach: descriptive and quantitative analysis. Data were collected through observations, interviews, and primary and secondary documentation. The results show that the application of EOQ with an annual demand of 19,800 units results in an optimal order quantity of 890 units per order with a total annual inventory cost of Rp 2,224,860. This strategy reduces ordering frequency, optimizes storage space, reduces raw material waste, and supports energy efficiency and carbon emission reduction. The integration of EOQ and GSCM has significantly impacted operational efficiency and environmental sustainability in the batik industry sector.