Economic Order Quantity Research Articles (Page 1)

In inventory management, business organizations gradually face challenges due to the complexities of managing perishable goods whose value diminishes over time. In such circumstances, interval’s bounds estimated business policy can be adopted to study a non-deterministic inventory model incorporating decay, preservation technology, and financial incentives, viz. advanced payments and fixed discounts. This study explores an interval Economic Order Quantity (EOQ) model incorporating advanced payment with discount options under preservation technology framework in interval environment. In this model, the demand rate is expressed as a convex combination of linear and power patterns of the selling price. The present model is formulated mathematically using interval differential equations and interval mathematics. Then, the corresponding interval-valued average profit of the model is obtained. In order to optimize the corresponding interval optimization problem, C-U optimization technique is developed. Employing the C-U optimization technique, the said interval optimization problem is converted into crisp optimization problems. Then, these problems are solved numerically by Wolfrom MATHEMATICA-11.0 software and validated with the help of two numerical examples. Finally, sensitivity analyses have been performed to study the impact of known inventory parameters on optimal policy.

This study examines inventory control for RON 92 (Pertamax) fuel using the Economic Order Quantity (EOQ) method at Pertashop Ulak Bandung, Muara Enim Regency. The objective of this research is to analyze inventory control by implementing Economic Order Quantity (EOQ), Safety Stock (SS), Re-Order Point (ROP), and Total Inventory Cost (TIC) through a quantitative descriptive approach. Primary data were used, covering a one-year period from January 2024 to December 2024. Data were processed using Microsoft Excel. The results show that the optimal economic order quantity is 6,289.37 liters per order. The required safety stock is 119.05 liters, while the re-order point is determined at 1,719.05 liters. Furthermore, implementing the EOQ method successfully reduced the Total Inventory Cost (TIC) by Rp12,306,739.12 compared to the existing company policy.

Inventory management is a critical component of procurement efficiency, ensuring maximum resource usage, reduced operational costs, and timely delivery of products and services. Efficient inventory management procedures are crucial for public utility firms like Nakuru Water and Sanitation Company (NAWASSCO) to maintain uninterrupted service delivery and meet consumer demands. However, inefficiencies in inventory planning, management, record accuracy, and stock level monitoring frequently result in delayed procurement procedures, stock outs, and excessive inventory expenses. The purpose of this study was to determine the impact of inventory management practices on procurement performance at NAWASSCO. The study specifically looked at the impact of inventory planning, inventory control techniques, record accuracy, and stock level monitoring on procurement performance. The Economic Order Quantity Model, Just-in-Time Theory, and Inventory Control Theory served as the study's framework. The target population comprised 63 (sixty-three) NAWASSCO personnel from areas such as procurement, finance, technical operations, ICT, Audit, stores, and department heads. The study adopted a census technique to include all 63personnel. A pilot study was conducted in Nakuru Rural Water and Sanitation Company to ascertain the instrument of data collection reliability and validity. Questionnaires were preferred in data collection and offered convenience to respondents. The data was analyzed using descriptive and inferential statistical techniques. With the aid of the Statistical Package for Social Sciences (SPSS). Descriptive analysis used frequencies, percentages, means, and standard deviations, while inferential statistical analysis involved the use of Pearson's product-moment correlation coefficient. The relationship between inventory management practices and procurement performance at Nakuru Water and Sanitation Company (NAWASSCO) was determined through regression analysis using R², beta coefficients, standard errors, and p-values. The descriptive and inferential data indicated that inventory management strategies have a substantial impact on procurement performance. The results showed that inventory planning (β = 0.355, p =.000 < 0.05), inventory control techniques (β = 0.425, p =.000 < 0.05), and inventory record accuracy (β = 0.438, p =.000 < 0.05) significantly improved procurement performance. This indicates that excellent inventory planning, proper management mechanisms, and correct inventory records all contribute to increased procurement efficiency. The report recommends that NAWASSCO incorporate efficient inventory planning into its procurement framework to achieve accurate forecasts and timely material replenishment. Furthermore, the company should develop its inventory control systems and update inventory records on a regular basis to improve decision-making and reduce discrepancies. Furthermore, it should combine its stock level monitoring technologies with other inventory management components, such as planning, control, and record accuracy, to ensure timely reordering, avoid stock outs or overstocking. Implementing these principles can improve openness, shorten procurement delays, and boost overall organizational performance.

In the manufacturing industry, optimal raw material inventory management is crucial for smooth production and cost efficiency. PT SSI faces problems in managing insert frames, which causes high ordering and storage costs. This study analyzes the application of the Economic Order Quantity (EOQ) method to determine the optimal order quantity and reduce total inventory costs. With a descriptive quantitative approach, data were collected through interviews, historical analysis of ordering and inventory costs, and calculations of EOQ and Total Inventory Cost (TIC). The results showed that the EOQ method reduced the frequency of orders from 48 to 24 times per year with an optimal order quantity of 63,155 units. Total inventory costs were also reduced from Rp 279,412,534 to Rp 230,256,690 per year, proving that the EOQ method is more efficient. The novelty of this research is the combination of EOQ with specific variables of the semiconductor industry. Further research is recommended to explore demand uncertainty and the integration of EOQ with artificial intelligence and ERP systems to improve inventory management accuracy. Keywords: Economic Order Quantity (EOQ), Inventory Control, Operational Efficiency, Optimal Order Quantity, Total Inventory Cost

In the cigarette paper industry, pallet packaging plays a crucial role in the supply chain. It helps protect cigarette paper from physical damage and environmental factors during transportation and storage. PT BM faces several challenges, one of which is inefficiency in inventory management, leading to unnecessary costs in production and distribution processes. This study applies the Economic Order Quantity (EOQ) method to determine the optimal order quantity of raw materials, ensuring a smooth production process. The calculation results show that implementing the EOQ method leads to more efficient total inventory costs compared to the company's previous ordering policy. For local pallets, the optimal order quantity changed from 3,600 sets ordered 12 times per year (costing Rp 43,200,000.00) to 514,208 sets ordered only 3 times per year (costing Rp 10,800,000.00). A similar improvement was observed for export pallets, where the order quantity changed from 3,600 sets ordered 24 times per year (costing Rp 52,200,000.00) to 338,327 sets ordered only 3 times per year (costing Rp 6,525,000.00). Additionally, the total inventory cost using the EOQ method was recorded at Rp 38,333,333.33 for local pallets and Rp 36,912,500.00 for export pallets. Keywords: EOQ, Inventory, Pallet Packaging

Purpose: The aim of this study was to find out the influence of modern inventory control techniques on supply chain performance of retail stores in Kisii Town. Methodology: The study adopted a cross sectional study design. The unit of analysis for this study was 83 stores managers of the retail stores. Purposive sampling was be used to select the samples for the study. The study made use of questionnaires to collect primary data. A pilot test was conducted to test the reliability and validity of the data collection instruments. SPSS software program was used to facilitate data processing and analysis. Descriptive and inferential statistics were used to analyze quantitative data. Findings: The findings indicated that economic order quantity inventory control technique, re-order point, fixed stock levels and FSN analysis techniques positively influenced supply chain performance of retail stores in Kisii Town. Unique Contribution to Theory Practice and Policy: The study recommended that management of retail stores should use the inventory control techniques to only store what was being required in the production process but to enhance the overall store performance.

PT.Y is a national automotive component company, where one of its products is Headlining Assy Roof which is routinely distributed to automotive manufacturing companies in Indonesia. The problem which occurs is high frequent orders cause high ordering costs and high storage costs, not meeting the company's targets. Therefore, this study aims to determine the optimal and economical number of raw material orders, reorder time or reorder point, safety stock, and the amount of total inventory cost; so that raw material inventory control gets optimal results to support smooth production. This research is a descriptive study with a quantitative approach, data collection techniques based on the company's historical data related to ordering and storage costs. The analysis method uses the Economic Order Quantity (EOQ) by applying mathematical formulas. With the EOQ formula, the company can identify the most cost-effective order quantity, Headlining Assy Roof. The results of the study show that with the Economic Order Quantity (EOQ) method, the optimal order quantity is 679,248 pcs, with an order frequency of 8 times/ year, an ordering cost of Rp 5,125,167/ year, a storage cost of Rp 5,124,773/ year, and a total inventory cost (TIC) of Rp 10,249,940/ year. With the EOQ method, the total savings in TIC inventory costs that can be obtained by PT. Y is Rp 132,870,060/ year compared to the company's previous method.

This study explores the interdisciplinary application of inventory management principles in English literature, focusing on how techniques such as ABC analysis, Economic Order Quantity (EOQ), and demand forecasting can optimize literary resource organization and thematic analysis. Drawing from both logistics and digital humanities, the paper examines how these quantitative models can enhance library management, improve access to high-demand texts, and support data-driven literary studies. Using a mixed-methods approach, the research analyzes quantitative data on library usage patterns and qualitative insights from scholars and librarians to illustrate the transformative potential of inventory systems in academic contexts. Findings suggest that inventory-based methods not only streamline resource allocation but also foster thematic discovery across literary corpora. This interdisciplinary synthesis highlights how operational strategies from management sciences can inform and enrich humanities research in the digital age.

This study represent the exploration of an economic order quantity (EOQ) model where it is perceived that inventory cost at starting time and estimated inventory cost are not necessarily the same. The reason is assumed as delay in time interval between cost estimation and actual starting time of the inventory. Inflation of money, promotional strategies and debilitation rate of decaying items are included in the model. Instability in political ground or uncertain situation of a country as well as the whole world create more instability in the economic system. Thus, inflation of money is considered. The model includes deterioration rate in fuzzy sense, fuzzy inflation and fuzzy promotional index. Therefore, the model is solved by method of signed distance, triangular fuzzy and trapezoidal fuzzy for maximization of profit. Carbon emissions due to transportation is calculated for adding sustainability concept to the model. Optimal order quantity, optimal shortage quantity, optimal selling period and optimal shortage period are found to make the model distinctive. The formulated model is thoroughly explained for both crisp, triangular fuzzy and trapezoidal fuzzy environment and it is concluded that trapezoidal fuzzy number is better in its applicability for decision maker. Because, under the trapezoidal fuzzy environment, the profit value is increased by 10.51% as compared to crisp environment and 3.89% with respect to triangular fuzzy environment. One more finding is that, trapezoidal fuzzy number is better with its simple arithmetic operation as well as easy 1 2 and intuitive interpretation approach. The finding is supported in respect to profitability from the proposed model. A practical example from a fertilizer company is considered in the model to add a real touch. Sensitivity analysis is performed supported by graphical representation. Comparative analysis is derived to show the uniqueness of the model. The model ends with conclusion and direction to possible future study.

This study analyzes the impact of raw material price fluctuations on production costs and selling prices, while evaluating the effectiveness of inventory management strategies using the Economic Order Quantity (EOQ) method in the case of the UMKM Sambal Valir. Employing a qualitative intrinsic case study approach, data were collected through in-depth interviews, observation, and documentation involving the business owner, consumers, and two competitors (Dapur Sabil and Auko Lauk). The findings reveal that the volatility of key raw materials, particularly chili and cooking oil, has a direct effect on price stability and production efficiency. The application of EOQ proved effective in reducing ordering and holding costs, minimizing the risk of overstocking or stockouts, and supporting more adaptive production planning. Furthermore, the study emphasizes the importance of understanding consumer behavior and demand patterns in determining optimal order quantities and frequencies. These results highlight that an EOQ-based inventory strategy not only enhances cost efficiency but also strengthens the competitiveness of small and medium enterprises in navigating raw material market volatility

PT Bumi Mandiri Resources is a manufacturing company engaged in producing wooden pallets with a make-to-order production system or production after an order is received. Wooden pallets are transport aids processed from solid wood materials to facilitate logistics operations. The demand for wooden pallets in Indonesia is influenced by the level of manufacturing activity and export-import conditions that cause fluctuations in demand for pallets that are not fixed. PT BMR faces challenges in raw material inventory because it still uses conventional and unsystematic methods. This study aims to design a more efficient raw material inventory planning for one year using the Single Exponential Smoothing (SES) forecasting method and the Material Requirement Planning (MRP) planning method. The MRP method calculation will be carried out using the Economic Order Quantity (EOQ) and Lot for Lot (LFL) techniques to compare the results of the best raw material planning design. The calculation results show that the SES method with the parameter α (0.09) provides the lowest error rate. This indicates that the MRP method calculation using the EOQ technique provides higher efficiency with cost savings in one year period of 49.54% compared to the LFL technique.

This study was conducted to evaluate the raw material inventory management system at Belikopi Semolowaru using the Economic Order Quantity (EOQ) method. Raw material inventory is a vital aspect in maintaining smooth operational processes, but if managed inefficiently, it can lead to cost wastage and hinder production. The EOQ method was chosen because it can determine the most optimal order quantity, thereby reducing storage and ordering costs. Research data were collected through field observations, interviews with business owners, and review of raw material purchasing documents. The analysis included calculations of EOQ, safety stock, reorder point (ROP), and total inventory costs. The results showed that the implementation of EOQ at Belikopi Semolowaru was more effective than conventional methods, because it was able to reduce storage costs and reduce ordering frequency. The cost savings obtained were also more significant, thus not only increasing operational efficiency but also positively impacting the company's profitability. With more optimal inventory management, Belikopi Semolowaru was able to consistently meet customer demand without experiencing stock shortages or excess raw materials.

This article explores the role of inventory management in reducing food waste and improving economic performance in selected Slovenian bakeries, contributing to a more efficient, environmentally responsible and sustainable economy. Using semi-structured interviews with key bakery personnel and an in-depth analysis of business documentation, our study applies the Economic Order Quantity (EOQ) model and Newsvendor model to test the following two hypotheses: (H1) improving inventory management at Bakery 1 can reduce total annual procurement costs by more than 15% without causing spoilage or raw material waste, and (H2) minimizingfood waste at Bakery 2 may not necessarily align with maximizing profit. The findings confirm that applying these models can enhance production and procurement planning, demonstrating that while cost reductions and waste minimization are achievable, they may not always be fully aligned. The study underscores the importance of strategic inventory management in balancing financial and environmental objectives in small bakeries.

This study adds originality by looking at the Economic Order Quantity 1 (EOQ) method at Mexshamall operations, a Muslim fashion retail business that deals with difficulties of excessive inventory, ineffective procurement, and cash flow interruptions. The study's goal is to assess the effectiveness of EOQ 8 in managing inventory and boosting buying efficiency in comparison to the firm's conventional methods. The descriptive quantitative methodology used primary data from interviews and observation, as well as secondary data such as purchase reports, ordering costs, holding costs, and demand data. The study compared the company's current procedures to the best order quantity, safety stock, reorder point, and total inventory cost (TIC). The empirical data show that EOQ implementation results in an ideal order size of 103,638 pieces per order, with a frequency of 24 orders annually, safety. TIC declined from IDR 22,620,930 to IDR 18,240,256 per year, saving about IDR 7,661 units, with a reorder threshold of 26,940 units. IDR 4,380,674 and the frequency of purchases exhibited a notable decline, reducing from 48 to 24 instances. In addition, EOQ was shown to increase liquidity, reduce credit risk, and optimize the use of working capital. The implications suggest that EOQ implementation can be rolled out gradually by organizations, fostering data-driven decision-making, improving inventory management, and providing financial rewards. Mexshamall and other small and medium-sized enterprises with comparatively cheap implementation expenses.

This research analyzes raw material inventory control at PT. Adhi Karya (Persero) Tbk AMP Medan Area, considering warehouse capacity limitations and demand fluctuations. The study employs the Economic Order Quantity (EOQ) and Period Order Quantity (POQ) models to determine optimal order quantity, ordering frequency, and total inventory costs. Results show that both models yield substantial cost efficiencies compared to the company’s current policy. The total inventory cost using the EOQ model is Rp630,499,314.50, while the POQ model results in Rp626,289,722.53. In contrast, the company’s current approach incurs a total cost of Rp1,648,260,123.80. Consequently, EOQ and POQ generate savings of Rp1,017,760,809.30 and Rp1,021,970,401.27 respectively. These findings indicate that both models offer effective and economical solutions for inventory control. Additionally, they help avoid overstock and stockouts while adapting to production demands and limited storage space, thus optimizing procurement strategies.

Abstract Accepted by: Phil Scarf Derived for deterministic demand, the Economic Order Quantity (EOQ) formula remains a popular method for stochastic demand, typically in combination with an order level. Textbooks split the inventory cost into the ‘cycle inventory’ cost (the cost of holding half the order quantity) and the cost of safety stock (order level minus expected lead time demand), showing that the EOQ minimizes the total cost of ordering and the cycle-inventory cost. However, under stochastic demand, the EOQ is smaller than the optimal order quantity and often much smaller. A number of authors have suggested exact procedures for determining the optimal order quantity (and order level), but the derivations (and resulting procedures) are complicated, in contrast with the intuitively appealing nature of EOQ and its simplicity. This paper presents an alternative approximation, leading to closed-form order quantity formulas under both a cost and service objective for normally distributed lead-time demand. It splits inventory costs, but (i) uses that the average cycle stock is less than half of the order quantity due to backorders, and (ii) considers inventory left-over at the end of a cycle instead of safety stock. A numerical investigation shows that the approximation is very accurate, with a cost error of <0.02% on average. For the traditional EOQ formula, the cost error is considerable, going up to 6% in some cases, and so it is worthwhile in many real-life situations to use our newly proposed formulas. Moreover, for teaching and training purposes, the adaptations help understand why the EOQ is suboptimal.

Honey is a key raw material in the traditional medicine industry, processed through a supply chain that involves various stakeholders, such as suppliers, producers, and distributors. However, the management of the honey supply chain faces several challenges, particularly related to waste, including overproduction, excess inventory of raw materials, and defective products. This study aims to develop a framework for reducing waste in the honey supply chain using a Seven Wastes approach to identify problems, conduct root cause analysis through a fishbone diagram, and map the roles and classifications of stakeholders in implementing solutions. The results indicate that waste is caused by various factors, and the roles of stakeholders are classified based on supply chain drivers. This classification includes the management of machine capacity and maintenance at facilities, the implementation of Economic Order Quantity (EOQ), Just-in-Time (JIT), safety stock systems, as well as improvements in raw material quality in inventory management and procurement, and information classification using a forecasting system based on historical data. The implementation of these solutions is expected to optimize supply chain management and reduce waste.

Abstract: Inventory forecasting practices are pivotal for FMCG manufacturing firms in Kenya’s competitive market in order to improve their operational performance. The fast-moving consumer manufacturing firms have been experiencing performance challenges that affect their daily operations through increased production costs, increased lead-time, poor customer satisfaction. The study sought to determine the relationship between inventory forecasting practices and performance of fast-moving consumer manufacturing firms in Kilifi County, Kenya. The study targeted 11 Fast-moving consumer manufacturing firms in Kilifi County, Kenya as unit of analysis while procurement officers, logistic officers, transport officers, distribution officers and warehouse officers were the unit of observation. The target population was 2922 and therefore the sample size of 351 respondents was determined using Yamane 1967 sampling formula. The study adopted stratified random sampling. The research was anchored on two theories Systems Theory and the Theory of Economic Order quantity (EOQ). The study used descriptive research design, the study targeted 351 respondents comprising of procurement officers, logistic officers, transport officers, ware-housing officers. Primary data was collected through structured questionnaires. Data was analysed using SPSS version 31 where descriptive statistics such as means, standard deviation, frequencies and percentages were used. Correlation analysis was done to test the strength and direction of linear relationship between variables. Multiple regression analysis was conducted to determine the relationship between independent and dependent variables. A pilot test was conducted with 10% of the sample size and it showed strong reliability (Cronbach’s alpha = 0.795) The pilot test results revealed that the data collection instruments used in the study were both valid and reliable. Inventory forecasting demonstrated a strong positive relationship with performance. The study concluded that effective inventory forecasting plays a key role in boosting the performance of FMCG manufacturing firms in Kilifi County.

This study aims to analyze the effectiveness of raw material inventory control at the Kediri Wet Noodle business by comparing the traditional method with the Economic Order Quantity (EOQ) method. A descriptive quantitative approach was applied, utilizing EOQ, Safety Stock (SS), Re-order Point (ROP), and total inventory cost analysis. The results indicate that the traditional method leads to 24 purchase cycles per year with an inventory cost of IDR 4,320,000, without considering safety stock or re-order points. In contrast, the EOQ method shows an optimal purchase quantity of 1,421 sacks (35,525 Kg) with five purchase cycles per year, an inventory cost of IDR 574,455, a Safety Stock of 67 sacks (1,675 Kg), and a Re-order Point of 107 sacks (2,675 Kg). Therefore, the EOQ method reduces inventory costs by up to 86% compared to the traditional method. Nevertheless, its limitation lies in the exclusion of raw material shelf life and storage condition factors.

UD. X is one of the companies engaged in the field of furniture that produces tables, chairs, cupboards, and others. At this time at UD. X often experiences uncertainty in providing raw materials, resulting in delays in the production process. From these problems, there is a delay in providing raw materials. The purpose of this study is to find the most optimal method for companies with the calculation of Material Requirement Planning (MRP). From the results of the forecast accuracy calculation, the most appropriate method for forecasting table demand is Exponential Smoothing with α = 0.5, which produces a Mean Absolute Deviation (MAD) value of 0.60, Mean Square Error (MSE) of 0.42, and Mean Absolute Percentage Error (MAPE) of 49.25%. For chair forecasting, the right method is Exponential Smoothing with α = 0.9, which produces a MAD value of 0.04, MSE of 0.00, and MAPE of 1.42%. The most efficient aggregate planning for UD. Mebel Masya is to use Level Strategy because it produces the lowest total cost of Rp.249,600,000. In comparison, Chase Strategy has a total cost of Rp.296,400,000, and Mixed Strategy also requires a cost of Rp.296,400,000. The most economical raw material inventory system is the Economic Order Quantity method because the costs incurred are lower than the Lot For Lot and Least Unit Cost methods. By using the MRP method, UD. Mebel Masya gets the lowest inventory costs for wood raw materials at Rp.61,320,000, the glue of Rp.215,000, and for polish of Rp.136,000.