Order Quantity Research Articles

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.

Background: Over the past decade, the growth of ecommerce and omnichannel order fulfillment has led to a spike in last-mile delivery services. Last-mile delivery being the most expensive portion of the supply chain has resulted in process improvement initiatives by industry and academia targeting lower operational costs. Methods: In this study, we use simulation to account for the daily randomness regarding order quantities with missed deliveries being rolled over to the next period and attrition of the capacities used to meet the demand for each period. Further, to alleviate the impact on operations due to attrition, we consider the use of automation as a replacement for permanent capacity. Results: From the simulation results, we observe that the negative operational impact of employee turnover can be overcome with a combination of delivery robots and crowdsourcing with a payback period as short as 1.55 years. Conclusions: Optimal resource allocation is further refined by the use of simulation. The use of advanced automation such as robots seems to be a viable option for businesses to lower operational costs for some scenarios.

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 paper addresses green supply chain financing decisions based on inter-chain competition and implicit equity considerations. First, considering the different situations of green supply chain members when facing green investment costs, it separately establishes green supply chain revenue Cournot competition models under the three models of no-financing, manufacturer financing, and retailer financing, as well as two implicit equity holding models. Second, it calculates the optimal order quantity and product greenness of the green supply chain under different scenarios. Finally, mathematical derivation and numerical simulation explore the effects of consumers’ green preference, capital opportunity costs, and other factors on product greenness, optimal order quantity, and supply chain members’ revenue. The results of this paper show that regardless of the financing model or who owns the implicit equity, there are optimal order quantities and optimal product greenness in the green supply chain. Furthermore, under inter-chain competition and implicit equity considerations, the manufacturer in the green supply chain always has financing motivations, but it prefers to be financed by the well-funded retailer. However, the retailer will consider financing only when consumer preference for green products in the green supply chain is below a specific threshold. Moreover, compared to the manufacturer financing model, the product greenness under the retailer financing model is high. It performs better in terms of environmental protection, which contributes to improving the entire supply chain and environmental performance.

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.

Supply-chain scheduling in modern production systems faces significant challenges due to the complexity of balancing cost efficiency, delivery lead times, and order variability—especially under stochastic demand and uncertain transportation conditions. Conventional metaheuristic approaches, such as the standard Non-dominated Sorting Genetic Algorithm II (NSGA-II), often struggle with premature convergence, limited population diversity, and rigid parameter settings that hinder adaptability in dynamic environments. To address these limitations, this study proposes a novel Hybrid Adaptive NSGA-II (HA-NSGA-II) framework, enhanced by two key innovations: (1) a Global-Intensity Mutation Operator (GIMO) that strengthens population diversity by dynamically adjusting mutation intensity across the entire solution space; and (2) an Adaptive Parameter Self-Tuning (APST) module based on reinforcement learning, which intelligently regulates crossover and mutation rates in response to evolutionary progress and environmental changes. A tri-objective optimization model is formulated to simultaneously minimize total supply chain cost, order quantity variance (mitigating the bullwhip effect), and customer delivery delays. The proposed HA-NSGA-II demonstrates superior convergence and diversity performance compared to traditional NSGA-II, offering a more robust and adaptive solution for complex, real-world supply chain scheduling under uncertainty. The model is tested on a simulated supply chain with 5 factories, 15 warehouses, and 50 customers facing random demand and Gaussian-distributed lead times. The new HA-NSGA-II is compared with traditional NSGA-II, SPEA2, and MOEA/D based on Hypervolume (HV), Inverted Generational Distance (IGD), Spread (Δ), Total Inventory Cost, Bullwhip Index (BI), and Robustness Index (RI). Results of 50 simulation runs demonstrate that HA-NSGA-II surpasses all the baseline algorithms with 12.9% enhancement in HV, 41.1% improvement in IGD, and 13.7% reduction in convergence time. In addition, it delivers a 14.6% supply chain cost reduction, a 20.4% bullwhip effect suppression, and 25% accelerated times of fulfillment. The research concludes that the HA-NSGA-II provides a scalable and effective scheduling approach applicable for real-time Industry 4.0 supply chains. Work in the future involves expanding the framework towards multi-period dynamic environments using IoT and blockchain-based visibility.

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.

Navigating channel conflicts: dynamics of sales effort in manufacturer encroachment

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.

Driven by China’s “dual carbon” strategy, concerns about channel fairness and green investment have become key frontier issues in supply chain management. This study focuses on a two-tier supply chain under a low-carbon background and innovatively incorporates both fairness concerns and green investment perspectives. It systematically explores the impact mechanisms of fairness concern coefficients and green investment levels on channel pricing and profit distribution across four scenarios: information symmetry vs. asymmetry and the presence vs. absence of channel encroachment. The simulation results reveal the following: (1) Under information symmetry and without channel encroachment, an increase in the retailer’s fairness concern significantly enhances its bargaining power and profit margin, while the supplier actively adjusts the wholesale price to maintain cooperation stability. (2) Channel encroachment and changes in information structure intensify the nonlinearity and complexity of profit distribution. The marginal benefit of green investment for supply chain members shows a diminishing return, indicating the existence of an optimal investment range. (3) The green premium is predominantly captured by the supplier, while the retailer’s profit margin tends to be compressed, and order quantity exhibits rigidity in response to green investment. (4) The synergy between fairness concerns and green investment drives dynamic adjustments in channel strategies and the overall profit structure of the supply chain. This study not only reveals new equilibrium patterns under the interaction of multidimensional behavioral factors but also provides theoretical support for achieving both economic efficiency and sustainable development goals in supply chains. Based on these findings, it is recommended that managers optimize fairness incentives and green benefit-sharing mechanisms, improve information-sharing platforms, and promote collaborative upgrading of green supply chains to better integrate social responsibility with business performance.

Sales competition and recycling rivalry are critical factors affecting the operation of closed-loop supply (CLSC). The existing research on competitive CLSCs primarily analyzes the impact of competition between two sales entities and/or two recycling entities on management decisions. To make the study more realistic, this study constructs a Stackelberg game model with the manufacturer as a leader, and analyzes the impacts of competition among n retailers (where n≥2) and rivalry among m third-party recyclers (where m≥2) on the decision-making and profits of both node enterprises and the supply chain system, and proposes a linear transfer-payment contract to coordinate the CLSC from an economic perspective. Numerical analyses are conducted to visualize the effects of competition on the decisions and profits. The key findings are as follows: (1) In the centralized system, inter-retailer competition reduces optimal order quantities but does not affect optimal retail prices. In the decentralized system, however, it decreases both optimal order quantities and retail prices. (2) Rivalry among recyclers reduces their optimal recycling volumes but does not affect their optimal recycling prices in the centralized system. In the decentralized system, however, such rivalry not only decreases recycling volumes but also increases optimal recycling prices. (3) The manufacturer’s product wholesale price and used product recycling price remain independent of competitive interactions among retailers and recyclers in the decentralized system. (4) Competition among retailers and recyclers positively affects the profits of the CLSC and the manufacturer, but negatively impacts those of retailers and recyclers. (5) When the reward–penalty factors for product order and used product recycling fall within a specific range, the linear transfer-payment contract can coordinate the CLSC in the presence of competition in both retail and recycling. (6) All enterprises’ profits are sensitive to the penalty–reward factor, but this sensitivities also gradually decrease as the number of retailers and (or) recyclers increases.

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.