Economic Order Quantity Research Articles

Analisis Pengendalian Persediaan Bahan Baku Kacang Koro pada Produk Abon Menggunakan Metode Economic Order Quantity dan Desain Kemasan di UMKM Surya Abon

This research aims to produce compatibility between the policies of Abon Surya with the Economic Order Quantity raw material control method, namely the purchase quantity of the Abon Surya policy has an average purchase of 770 kg, for the frequency of purchasing raw materials for jack beans 5 times, for security and point supplies. There is no reordering policy with a total inventory cost of IDR 251,640. Meanwhile, the EOQ method has an average purchase of 1,313 kg. the purchase frequency is only 3 purchases, the security inventory is 90 Kg and the number of reorders is 204 Kg with a total cost of inventory required of IDR 173,254. The cost difference between Abon Surya and the EOQ method is IDR 78,386,417. This shows that there is a saving or reduction in the total cost of inventory needed if Abon Surya can implement the EOQ raw material control method. Apart from that, the packaging used at UMKM Surya Abon for shredded products still uses transparent plastic with stickers, which is less attractive to buyers. Therefore, a new design is needed to attract more buyers.

Analisis Pengendalian Persediaan Bahan Baku Dengan Menggunakan Metode Economic Order Quantity (Eoq) Pada Pt. Bazt Inti Bersama

A very important component in the production process at the company is the raw material. To deal with market demand, accurate raw material inventory planning is needed to determine optimal and economical purchasing levels. This is to ensure that there are no excess or shortage of inventory, which allows for cost savings and disruption of raw material stockouts in the production process. The purpose of this study is to determine the management of existing inventory stock at PT. Bazt Inti Bersama, as well as to find out the optimal number of orders at the most economical cost using the Economic Order Quantity (EOQ) method, and the application of safety stock. This study uses a qualitative descriptive method with data collection techniques through library research, observation, interviews, documentation related to the research problems studied. The results showed that the raw material inventory control for fresh salmon at PT. Bazt Inti Bersama is quite good, but controlling raw material inventories using the economic order quantity (EOQ) method has been proven to be able to reduce the cost of inventories used with a difference in costs of 19,074,116 rupiah. So it can be more economical. Meanwhile, the safety stock needed by the company requires 5,324 kg.

Analisa Efektifitas Persediaan Usaha Mikro Kecil Dan Menengah (UMKM) Dinara Bolen’s Menggunakan Metode Economic Order Quantity (EOQ)

UMKM Dinara Bolen's Karawang is one of the culinary that has succeeded in providing a touch of distinctive taste of Karawang Regency. The development of this business has an impact on the number of materials that must be ready to be used for production. Failure to manage inventory can have an impact on production delays which can disappoint buyers because product stocks are empty. This research was conducted to analyze whether the inventory system used by the company has reached the maximum and effective number, which can then be used as a consideration for Dinara Bolen's Karawang MSMEs to be applied as a basis for procuring inventory. This type of research is descriptive qualitative.The data to be collected by researchers is based on information obtained when meeting directly with informants. Primary data were obtained from observations and interviews with research subjects. This research also has the result that using the Economic Order Quantity (EOQ) method can produce a reference for accommodating inventory by only spending 35% of the total usual expenditure, which also helps in calculating stored inventory to be called Safety Stock. From the inventory cost analysis, Dinara Bolen's Karawang MSMEs can apply the time and amount of purchasing production materials.

Multi-item order quantity optimization through stochastic goal programing

AbstractManaging multi-item economic order quantity (MIEOQ) problems within an uncertain business environment is a critical challenge. Decision-makers, with a comprehensive understanding of organizational goals and risk tolerances, play a pivotal role in this context. However, existing solutions often inadequately consider decision-maker preferences in MIEOQ problem-solving. The literature suggests that integrating the concept of satisfaction function with stochastic goal programming (SGP) can address this issue. However, the existing SGP approaches struggle with the challenge of effective goal setting. Additionally, employing distinct satisfaction functions for each uncertain goal can complicate threshold setting, diminishing their effectiveness. To tackle these challenges, we introduce a straightforward, yet effective approach called aspiration-free goal programming (AFGP) and integrate it with a unified satisfaction function. AFGP operates by minimizing expected values of deviation variables, eliminating the challenging task of goal setting under uncertainty. A unified satisfaction function is a singular metric applied uniformly across multiple goals, offering a consistent framework for evaluating performance across diverse objectives. This integration forms a preference-sensitive framework that not only captures nuanced trade-offs between conflicting objectives but also enhances decision quality and stakeholder satisfaction. By emphasizing the importance of decision-maker’s preferences and addressing identified issues, our research introduces a practical and effective approach for achieving balanced solutions in uncertain MIEOQ environments.

Enhancing Economic Development through Inventory Management Optimization in Agro-based Industries in Bihar: A Comparative Study of EOQ and EPQ Models

Inventory management is crucial for optimizing operational efficiency and cost-effectiveness in agro-based industries, particularly in regions like Bihar,known for their agricultural resources. This study compares the effectiveness of two inventory management models, the Economic Order Quantity (EOQ) and Economic Production Quantity (EPQ), in addressing the unique challenges faced by Bihar's agro-based industries. Data from Taruwar Agro Industries Private Limited in Ashiyana, Patna, use to calculate EOQ and EPQ values for three product categories: Tomato Chili, Peri Peri, and Punjabi Tadka. The study employs relevant formulas to compute optimal order quantities and total inventory costs, assuming constant demand, quality, price, holding costs, and ordering costs. Both the EOQ and EPQ models offered viable approaches to inventory optimization. EOQ focused on minimizing holding and ordering costs, while EPQ integrated production-related costs to optimize production flow. The study underscored the importance of aligning inventory management strategies with operational capabilities, market demand, and financial objectives. This study contributes to advancing theoretical understanding and practical applications of inventory management in agro-based economies like Bihar. Recommendations for empirical validation, contextual adaptation, technology integration, and long-term impact assessment are provided to enhance inventory management practices. Integration of the BCG matrix into decision-making processes offers a comprehensive approach to prioritizing inventory management strategies and guiding strategic implications.

A multi-period vaccines supply chain network design with capacity expansion and different replenishment cycles under uncertain demand

The World Health Organization (WHO) proposes guidelines for vaccine distribution networks that incorporate economic order quantity and risk pooling principles, specifically through implementing different replenishment cycles between different levels of the distribution network. The substantial impact of different replenishment cycles on inventory levels is demonstrated in the existing literature. However, current optimization network design research has paid limited attention to this aspect. This article aims to address this research gap by investigating a two-stage stochastic programming for a multi-period perishable vaccine network design problem, taking into account the different replenishment cycles characteristic and capacity expansion under uncertain demand conditions. Given the inherent difficulty of solving the proposed problem, a matheuristic approach based on Variable Neighborhood Search (VNS) is developed. To illustrate the practical application and analyze the impact of uncertain demand, we apply the proposed model and its solution method using the Indonesia dataset in the COVID-19 situation. The computational results indicate that lower replenishment cycles leads to higher inventory levels, total costs, and capacity requirements for the top-level distribution center. Optimum replenishment cycles and further managerial implications and future potential research are also presented.

Inventory Management in Pharmaceutical Industry

This research paper explores the intricacies of managing inventories of medicines in the pharmaceutical industry, focusing on meeting the healthcare needs of both humans and animals. Through a comprehensive review of existing literature and analysis of real-world case studies, this paper identifies key challenges, strategies, and best practices associated with pharmaceutical inventory management. The literature review reveals the complexities of inventory management in the pharmaceutical sector, including demand variability, regulatory compliance, and the need for precise temperature control for perishable medicines. Strategies such as economic order quantity (EOQ) and just-in-time (JIT) inventory management are discussed for managing normal inventories, while specialized approaches like cold chain management are highlighted for managing perishable inventories. Technological innovations, such as RFID technology and predictive analytics, are recognized for their potential to enhance inventory visibility and mitigate supply chain risks. Case studies provide valuable insights into successful inventory management practices within the pharmaceutical industry. Examples demonstrate the importance of collaboration among stakeholders, investment in advanced inventory management systems, and adherence to regulatory compliance measures. Furthermore, case studies underscore the significance of maintaining optimal inventory levels, minimizing stockouts, and ensuring medication safety and efficacy. This research paper contributes to the ongoing discourse on pharmaceutical supply chain optimization, patient-centric care delivery, and the pursuit of sustainable healthcare solutions. By synthesizing insights from literature and real-world examples, stakeholders can develop strategies to address challenges, enhance inventory management practices, and improve patient outcomes in the pharmaceutical industry. KEYWORDS Pharmaceutical Inventory Management, Medicine Inventory Control, Perishable Inventory, Normal Inventory, Supply Chain Optimization, Regulatory Compliance, Demand Variability, Cold Chain Management, RFID Technology, Patient Safety, Supply Chain Efficiency, Case Study Analysis, Healthcare Logistics, Sustainable Healthcare Solutions.

Pengendalian Persediaan Bahan Baku Petis Udang dengan Menggunakan Metode Economic Order Quantity (Eoq) di Ud. Agung Jaya

Raw material inventory is very important for companies. UD. Agung Jaya is a company that operates in the food sector, namely Petis Udang. Problems at UD. Agung Jaya has raw material stock problems, namely a shortage or excess of raw materials. The aim of this research is to help optimize UD costs of UD. Agung Jaya. This research uses raw material purchase data for shrimp heads, sugar, and flour which is processed by forecasting calculations for 12 periods or 1 year into the future using the single exponential smoothing (SES) time series forecasting method. The aim is to determine the need for raw materials so that there are no shortages or excesses of raw materials during the ongoing production process, and the efficiency of raw material needs in this research. The results of the research show that the raw material for shrimp heads has a mape error value of 24, sugar has a mape error value of 24, and flour has a mape error value of 26.1. Furthermore, the raw material decomposition method for shrimp heads has a mape error value of 15, sugar has a mape error value of 15, and flour has a mape error value of 16.42. The second result of forecasting shows that the decomposition method obtains a smaller mape value or the lowest error value. This research also uses the Economic Order Quantity (EOQ) method in the 2022-2023 period with raw material yields of 515.28 kg of shrimp heads, 488.54 kg of sugar and 188.09 kg of flour.

Inventory Management Improvement at PT Inalum, Kuala Tanjung, Sumatra Utara

This research aimed to analyze the inventory issue in PT Inalum. The method used in this research is a quantitative method to collect inventory historical data from January 2022 to December 2022 obtained from the company’s ERP SAP. The data contains information of 6028 items of the MRO inventory. The collected data was analyzed using Economic Order Quantity (EOQ), Reorder Point (ROP), Safety Stock (SS) and Average Inventory Level (AIL). By using Fixed Order Quantity model, the MRO inventory is calculated to compare the ideal and actual average inventory levels. From the analysis, the researcher found that the MRO inventory in Kuala Tanjung, PT Inalum, has a service level of 0.52, which is still far below the service level target of 0.95. At the same time, PT Inalum has the potential to reduce inventory by USD 5,402,389 or 58.2% of the inventory value. This research aims to provide guidance for the management of PT Inalum in making inventory policies that can increase service levels as well as minimize inventory costs.

A Stochastic Goal-Based Economic Order Quantity Model for Dairy Products

Resilient efforts are still sought to optimize the inventory of dairy products under demand uncertainty. Several industries operate under uncertainties that severely affect company performance. In this paper, a multi-objective goal programming model is proposed to optimize the economic order quantity of dairy products under stochastic demand. Adopting a Markov chain approach, the model initially defines the demand transition matrix, inventory cost matrix, objective function, priorities, and goal constraints of dairy products. The model seeks to minimize the deviation variables from the targeted economic order quantity and total inventory costs of dairy products over a finite period planning horizon. The sum of deviations is minimized so that the actual economic order quantity and inventory costs meet the targeted levels of dairy products. Using the simplex method, an analytical solution is obtained and a numerical example is presented for illustration; demonstrating the overachievement or underachievement of the targeted economic order quantity and inventory costs of dairy products. Results indicate how priority-based goal programming solution is sensitive to the highest priority to be achieved. The proposed model can be effective where the relevant economic order quantity and inventory costs can be prioritized if necessary.

OPTIMAL INVENTORY POLICY USING ASSIGNMENT MODEL AS MIXED INTEGER LINEAR PROGRAMMING PROBLEM

This article discusses the assignment model as a mixed integer linear programming problem used in determining inventory policy for the multi-item case with backorder using the branch-and-bound method. The assignment model used must meet the specified constraints. The LINGO 18.0 application is used to solve mixed integer linear programming problems from the inventory problem assignment model. The optimal solution obtained from the assignment model uses the LINGO 18.0 application, which is based on the branch-and-bound method compared to the EOQ model with backorder. The results obtained from the comparison of these two models can be concluded to show that the inventory problem provides more optimal value using the assignment model as a mixed integer linear programming problem compared to using the EOQ model with backorder.

Optimizing the Economic Order Quantity Using Fuzzy Theory and Machine Learning Applied to a Pharmaceutical Framework

In this article, we present a novel methodology for inventory management in the pharmaceutical industry, considering the nature of its supply chain. Traditional inventory models often fail to capture the particularities of the pharmaceutical sector, characterized by limited storage space, product degradation, and trade credits. To address these particularities, using fuzzy logic, we propose models that are adaptable to real-world scenarios. The proposed models are designed to reduce total costs for both vendors and clients, a gap not explored in the existing literature. Our methodology employs pentagonal fuzzy number (PFN) arithmetic and Kuhn–Tucker optimization. Additionally, the integration of the naive Bayes (NB) classifier and the use of the Weka artificial intelligence suite increase the effectiveness of our model in complex decision-making environments. A key finding is the high classification accuracy of the model, with the NB classifier correctly categorizing approximately 95.9% of the scenarios, indicating an operational efficiency. This finding is complemented by the model capability to determine the optimal production quantity, considering cost factors related to manufacturing and transportation, which is essential in minimizing overall inventory costs. Our methodology, based on machine learning and fuzzy logic, enhances the inventory management in dynamic sectors like the pharmaceutical industry. While our focus is on a single-product scenario between suppliers and buyers, future research hopes to extend this focus to wider contexts, as epidemic conditions and other applications.

Effects of Inventory Management Practices on Operational Performance in Mombasa County Government, Kenya

This study aimed at establishing the effect of inventory management practices on the operational performance of Mombasa County government. The specific objectives included the following: to examine the impact of JIT model on the operational performance of Mombasa County government; to assess the effect of Activity Based Costing (ABC) on the operational performance of Mombasa County government; to determine the impact of Economic Order Quantity (EOQ) on the operational performance of Mombasa County government and to establish the impact of stock taking practice on the operational performance of Mombasa County government. The research study reviewed Just in Time Theory, Systems theory and the transactional theory. This study used a descriptive survey design. The target population was all the 250 employees working in Mombasa County government that included ICT officers, Finance officers, Budgeting officers, Internal Audit officers and Procurement officers. Sample size of 96 employees was drawn from and was selected by the use of stratified random sampling. Data was collected using a questionnaire. Data analysis was done with the aid of SPSS version 28. Inferential statistics was displayed by the use of correlations and regression analysis. The ethical considerations for this study included confidentiality, informed consent, privacy and anonymity. Correlation analysis revealed that there was a significant correlation between the Operational Performance of the County Government of Mombasa and Activity Based Costing practice (β = 0.321, p˂ 0.05). This is followed by Economic Order Quantity X3 (β = 0.289, p˂ 0.05), Stocktaking X4 (β = 0.251, p˂ 0.05), and Just in Time Model X1 (β = 0.243, p˂ 0.05), in that order.

An EOQ Model for Temperature-Sensitive Deteriorating Items in Cold Chain Operations

To improve the inventory management of cold chain logistics, we propose an economic order quantity (EOQ) inventory model for temperature-sensitive deteriorating products. Considering that the products are temperature-sensitive, the deterioration rate of the proposed model is a function of the temperature. In addition, the transportation cost, which is a function of the quantity ordered, is considered in this study. This article aims to find the optimal value of the total profit, selling price, and the length of the ordering cycle. Numerical examples are provided; the sensitivity analysis shows that the total profit is much more sensitive to transportation costs, compared with ordering and holding costs.

A MATHEMATICAL MODEL WITH INVENTORY- AND SELLING PRICE-DEPENDENT DEMANDS CONSIDERING ALL-UNITS DISCOUNT AND CARBON EMISSION

Customer satisfaction is a crucial aspect that consistently takes center stage in business operations. In light of this, companies must devise appropriate strategies to fulfill customer demands. Consequently, this study delves into examining various factors that facilitate the supply process, including the application of discounts. Moreover, in line with the advancements in eco-green concepts, businesses have begun considering carbon emission factors concerning storage and distribution, which is further supported by the United Nations Framework Convention on Climate Change (UNFCCC). In this context, the paper presents an enhanced version of the economic order quantity model encompassing all-unit discount and carbon emission factors. The developed model entail inventory management approaches where demand relies on inventory levels, inventory levels coupled with selling prices, time-dependent demand, and exponentially declining demand patterns. The primary objective is to aid companies in optimizing their inventory management by determining the optimal quantity of goods while minimizing overall costs. Sensitivity analysis conducted to observe the influence on the reorder point (T), total inventory cost (TC), and total carbon emission (TE) reveals that lower unit purchase prices, driven by high demand, correspond to larger order quantities. Furthermore, it is worth noting that the higher average carbon emission within warehouses result in increased carbon emissions overall.

Penerapan Algoritma Artificial Neural Network dan Economic Order Quantity dalam Memprediksi Persediaan Pengendalian BBM

Motor vehicle production in Indonesia increases every year along with increasing demand for fuel as a raw material. Generally, gas stations carry out the process of ordering fuel from Dempo on an irregular basis, the frequency of orders does not have a certain time, orders depend on sales transactions and the amount of fuel inventory available depends on the fuel in storage. Regarding prediction and control of fuel supplies, the risk at gas stations is that the volume of fuel received is different from that ordered. It is suspected that tank trucks carrying fuel during delivery from the depot to gas stations tend to experience evaporation in the tank (loses), so that the fuel quantity decreases. Requests for fuel filling are only based on monitoring without any special calculations resulting in stock being maintained and not covering consumer demand. This research is to analyze the Artificial Neural Network algorithm in predicting fuel, and determine inventory control using Economic Order Quantity. The research was conducted using data from November 2020 - October 2023. The data was processed using the ANN algorithm using Google Colab, and continued with EOQ using Microsoft Excel. The ANN parameters are 1 hidden layer with 100 units, Adam optimizer, learning rate 0.001, batch size 8 and epoch 200. Pertalite ANN test results are MSE 248852593.81 and MAE 12749.45, while Pertamax Turbo MSE 803842.94 and MAE 672, 74 provides predictions for November and December of 11,1436.82 L and 11,1960.83 L and Pertamax Turbo of 3,782.46 L and 3,660.70 L. Furthermore, in 2023 the fuel EOQ of Pertalite and Pertamax Turbo will be 8,445 L and 5,261 L, Safety Stock 3,516 L and 1,064 L, Maximum Inventory 6,042 L and 5,153 L, Re order point 2,403 L and 108 L, Order frequency 149 times and 6 times with Total Inventory Cost Rp. 178,830,302 and Rp. 7,700,459.

The role of effective inventory management practices on organizational performance: a case of Tarkwa municipal hospital, western region, Ghana

The study aimed at investigating the role of effective inventory management practices on organisational performance; a case study of Municipal Hospital Tarkwa in the Western Region of Ghana. The specific objectives of the study were to establish the inventory management practices used by organisations, to find the effect of inventory management practices on organisational performance, and to determine the challenges of implementing inventory management practices in organizations. The study adopted descriptive and causal research designs and used questionnaires as the data collection instrument. Data were collected from 200 respondents and STATA 13 analytical tool was used to perform the analysis. The results were presented using frequency tables and descriptive statistics tables. The study also used Probit regression analysis to establish the relationship between the dependent and the independent variables. The findings show that the stakeholders of the hospital are generally happy with the service delivery of the hospital. Thus, the results show that the hospital adopts various inventory management practices including; activity-based costing, economic order quantity, material requirement planning, materials resource planning, enterprise resource planning, distribution requirement planning, and just in time. The study concludes that effective inventory management positively affects the performance of organizations, in this case, the quality of service delivery of organizations. The study recommends organizations successfully implement an effective inventory management system. This will help reduce the risks of shortages. In addition, the concept of inventory management could be simplified for organizations in Ghana. This can be done through education or training to all staff in organizations to enlighten them on the need for effective inventory management practices for their organization’s performance.

Multi-Item Inventory Control Using Economic Order Quantity (EOQ) Model with Safety Stock, Reorder Point, and Maximum Capacity in Retail Business

The presence of retail businesses in Indonesia has many positive impacts on the community, especially in improving the economy. The existence of buying and selling transactions involving suppliers, retailers, and the community as consumers can play a role in improving the national economy. Retail can be called a bridge for suppliers and consumers to meet their needs. The diversity of consumer needs requires retailers to provide a variety of products from many suppliers. Efforts that need to be made by retail businesses in order to minimize costs incurred are by controlling inventory. To prevent excessive expenditure, the inventory control method used is to apply the Economic Order Quantity (EOQ) model. The EOQ model can provide the optimum total inventory cost by adjusting the frequency of orders placed over a period of time. After obtaining the total inventory cost, the calculation of safety stock, reorder point, and maximum capacity can also be applied so that the inventory costs incurred can be minimal.

Pengendalian Persediaan Bahan Baku Untuk Lemari Tipe Sliding Door Glass (Studi Kasus di PT Alba Unggul Metal)

PT Alba Unggul Metal faced several operational problems in its production activities. In this study, the focus is on standard products owned by PT Alba Unggul Metal, especially SDG type cabinets, due to delays in delivery schedules caused by scarcity of raw materials, most are SDG type cabinets. The purpose of this study was to plan the raw material requirements for SDG type cabinets so that there is no scarcity of raw materials in the warehouse. The method used is the Economic Order Quantity method, the Period Order Quantity method and the Min-Max method. After analysis and discussion, it is known that the Economic Order Quantity method is the most optimal method compared to the Period Order Quantity method and the Min-Max method. Then the Company can better control inventory and delivery time of raw materials by using the EOQ method. As an example of the calculation for an SD SPCC plate measuring 0.7mm x 2440mm x 1220mm with an optimal order quantity of 512 pieces for one order, 157 safety stocks, reorder point when stock is 493 sheets, and order frequency 24 times a year. After implementation and data sampling for three months in April, May and June 2023 there was no shortage of raw materials which caused a delay in the delivery schedule for SDG type cabinets.Keywords : Inventory control, Economic Order Quantity, Period Order Quantity, Min-Max, Reorder Point, Safety Stock.

A Planning Purchasing Policies for Raw Material Welding Wire Using Economic Order Quantity (EOQ) Method to Reduce Overstock

This study aims to determine the optimal order quantity of K71T welding wire for PT BMI's boiler production process to reduce storage and inventory costs. The Economic Order Quantity (EOQ) method is applied to calculate the optimal amount of material to be ordered in one period. The results show that the total inventory cost using the EOQ method is Rp. 89,060,000,- compared to the company policy of Rp. 290,840,000,-. The optimal number of orders is 407 boxes with a purchase frequency of 4 times per year and a safety stock of 181 boxes. The findings of this study can be used as a reference for other manufacturing companies that face similar procurement challenges to optimize their inventory management and reduce costs.
 Highlight :
 
 The study determines the optimal order quantity of K71T welding wire, reducing storage and inventory costs.
 The Economic Order Quantity (EOQ) method is applied, resulting in a total inventory cost of Rp. 89,060,000,- compared to the company policy of Rp. 290,840,000,-.
 The findings can be used as a reference for other manufacturing companies facing similar procurement challenges to optimize their inventory management and reduce costs.
 
 Keywords: welding wire, optimal order quantity, inventory management, procurement challenges, manufacturing companies