Economic Order Quantity Research Articles

A Hybrid Fuzzy Mathematical Programming Approach for Manufacturing Inventory Models with Partial Trade Credit Policy and Reliability

This study introduces an inventory model for manufacturing that prioritizes product quality and cost efficiency. Utilizing fuzzy logic and mathematical programming, the model integrates fuzzy numbers to describe uncertainties associated with manufacturing costs and quality control parameters. The model extends beyond conventional inventory systems by incorporating a dynamic mechanism to halt production, employing fuzzy decision variables to optimize the economic order quantity and minimize total costs. Key innovations include the application of approaches related to graded mean integration for defuzzification and the use of Kuhn–Tucker conditions to ensure optimal solutions under complex constraints. These approaches facilitate the precise management of production rates, inventory levels, and cost factors, which are essential in achieving a balance between supply and demand. A computational analysis validates the model’s effectiveness, demonstrating cost reductions while maintaining optimal inventory levels. This underscores the potential of integrating fuzzy arithmetic with traditional optimization techniques to enhance decision making in inventory management. The model’s adaptability and accuracy indicate its broad applicability across various sectors facing similar challenges, offering a valuable tool for operational managers and decision makers to improve efficiency and reduce waste in production cycles.

A Genetic algorithm approach for improving the probabilistic inventory model with the continuous review: A practical application in the department of pharmacy

This study investigates to shed light on artificial intelligence techniques, specifically the genetic algorithm, to improving traditional solutions, as well as finding the best policy for the problem of probabilistic inventory with continuous review by finding the optimal reorder point and the optimal economic quantity to avoid the risk of stock out (shortage), reduce total costs, and reach the optimal solution with little time and effort. The research was conducted in the stores of the department of pharmacy of the Ninawa health department for the period from January 1, 2021, to January 1, 2023, on a sample of three drugs (most demanded drugs). An analysis of the demand data for the study sample was conducted using the statistical program (SPSS Statistics Version 22) to determine the type of inventory. it was found that the type of inventory is probabilistic and the demand data follows a normal distribution. Based on this foundation, the mathematical model for the study problem was built. Given the complexity of the steps and iterations of the traditional solution, the solution steps were applied using the R programming language to reach the model's solution. Additionally, the solution steps were programmed for the genetic algorithm and applied using the R programming language. The results of the genetic algorithm showed an improvement in the traditional solution results by reducing total costs. Based on the results of the genetic algorithm, the economic order quantity, safety stock, reorder period, and safety period were found. Paper type: Research Paper

Inventory Management System Using Economic Order Quantity And Reorder Point

The inventory management system for car workshops is still using Excel, which makes it inefficient and inaccurate for complex stock calculations. The solution is to design an inventory application that uses the Economic Order Quantity and Reorder Point methods. The application will provide timely stock replenishment information and optimize order quantities, increasing inventory management efficiency and accuracy. The Software Development Life Cycle used in this study is Rapid Application Development, a process emphasizing short development cycles. User Acceptance Test results show that users received the inventory management system very well, with an average satisfaction level of 98.44% for Admins and 97% for general users, indicating high satisfaction with the system's features and functionality.

Fuzzy inventory model for breakable items, including damage costs and inspection errors in a green environment.

Abstract Conventional Economic Order Quantity (EOQ) models operate on the implicit assumption that all received products are flawless. Current EOQ models for items of defectiveness implicitly suggest that suppliers do not conduct a comprehensive assessment, even though they loosen this presumption and are more applicable to scenarios in reality.This paper presents a significant EOQ model. This model takes into account errors in determining whether an item is perfect or broken. Because things are piled high in stock, breaking of products takes place. These goods are stored and sold at once, with damage costs included. Moreover, Governments have prioritized reducing carbon emissions as their main goal. With the aim of creating a greener environment, we have taken carbon emission costs into account in order to minimize overall costs and maximize overall profit. This model is distinct because it closes the research gap by taking into account breakable items under inspection errors with damage cost, and carbon emission cost.The ideal total cost of the EOQ model is produced by the crisp version. It is important to note that this model is constructed in a fuzzy manner by using a triangular fuzzy number to represent the cost of accepting a broken object.Applications of this idea include shipping commodities. A numerical illustration of the ideal solution is given and the current study addresses how changes in a few parameters influence the ideal overall cost.

PENGENDALIAN PERSEDIAN PAKAN AYAM PETELUR MENGGUNAKAN METODE EONOMIC ORDER QUANTITY (EOQ) PADA PETERNAKAN XYZ DI BANYUWANGI

Delays in the supply of chicken feed at XYZ Farm resulted in a decrease in chicken egg productivity, resulting in chicken death. To ensure that production runs smoothly, XYZ Farms must guarantee the availability of raw materials for chicken feed. The aim of this research is to determine the ideal number of orders for chicken feed supplies to minimize feed shortages due to delivery delays. This research applies the Economic Order Quantity (EOQ) method to help XYZ farms manage the purchase of raw materials for chicken feed, and Single Exponential Smoothing to forecast supplies that are updated continuously by considering the latest data. The forecasting calculation uses an accuracy of 0.6 for the need for katul, corn and concentrate in January 2024. The results of the Economic Order Quantity (EOQ) calculation show that the optimal order for Katul is 6,702 kg, corn 29,190 kg, concentrate 25,402 kg. Orders are made 4 times a month, and Reorder Point is made when 1,586 kg of katul remains, 6,494 kg of corn and 8,742 kg of concentrate. Reordering or Reorder Point (ROP) is needed to overcome the potential for stock out during lead time TRANSLATE with x English Arabic Hebrew Polish Bulgarian Hindi Portuguese Catalan Hmong Daw Romanian Chinese Simplified Hungarian Russian Chinese Traditional Indonesian Slovak Czech Italian Slovenian Danish Japanese Spanish Dutch Klingon Swedish English Korean Thai Estonian Latvian Turkish Finnish Lithuanian Ukrainian French Malay Urdu German Maltese Vietnamese Greek Norwegian Welsh Haitian Creole Persian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back //

The Neutrosophic Economic Order Quantity: Backlogged Shortages and Quality Issues

The Neutrosophic Economic Order Quantity: Backlogged Shortages and Quality Issues

An EOQ Model for Delayed Deteriorating Items: Analysis of Three-Stage Consumption, Dual Storage Facilities, and Variable Partial Backlogging Rates

Study’s Excerpt An Economic Order Quantity (EOQ) model that accounts for items with delayed deterioration by integrating three distinct stages of consumption rates, dual storage facilities, and partially backlogged shortages was proposed. The model can minimize overall variable costs and establish the conditions for the existence and uniqueness of the optimal solution by optimizing cycle length, order quantity, and depletion timing. The model could help in inventory management of perishable goods, particularly in scenarios involving multiple storage options and consumption phases. Full Abstract This paper presents an Economic Order Quantity (EOQ) model for items with delayed deterioration, incorporating three distinct stages of consumption rates, dual storage facilities, and shortages. The model assumes that the average consumption rates before and after the onset of deterioration, as well as during shortages, are constant but differ from one another. The shortages are partially backlogged and vary according to the waiting time until the next replenishment. The model aims to minimize the overall variable cost per unit of time by optimizing the cycle length, order quantity, and the timing at which the inventory in the owned warehouse is depleted. The paper establishes the necessary and sufficient conditions to ensure the existence and uniqueness of the optimal solution. A sensitivity analysis is conducted, and the findings are used to derive managerial insights.

Mitigating Consumer Returns through Omnichannel Retail Operations: An EOQ Model under Stochastic Demand

Mitigating Consumer Returns through Omnichannel Retail Operations: An EOQ Model under Stochastic Demand

Optimal ordering strategy for deteriorating items with maximum lifetime using trade credit financing under imprecise environments

In many business scenarios, a retailer is permitted specific credit period to pay back for the products bought earlier. This facility enables retailers to continue their business operations even when they are unable to raise funds or secure a business loan. To boost the market's demand, promotional effort is a very effective business strategy to the retailer for maximizing the profit. On the other hand, a sudden and significant rise in customer demand for an inventory leads to shortages. Moreover, inventory relevant unit cost coefficients become imprecise due to insufficient data, human error etc. Nowadays, neutrosophic set quantifies the impreciseness more realistically. Considering these facts, an imprecise EOQ model for deteriorating items with maximum lifetime is formulated under trade credit facility. In addition, this article allows shortages, which are linearly time-dependent partially back-logged. Here, the unit cost coefficients are expressed as single-valued trapezoidal neutrosophic numbers. Furthermore, particular models are derived under different environments - intuitionistic, fuzzy, and crisp. Step-by-step solution procedures are suggested for all models to obtain optimal solutions. Models are numerically illustrated with real-life data, and some sensitivity analyses are performed. Managerial insights demonstrate that depletion time always depends on demand. Again, the present study suggests to reduce demand by halting the promotional activities during the shortage period and choose products with a larger lifetime.

An EOQ model incorporating consumer behavior towards marketing policy change

An EOQ model incorporating consumer behavior towards marketing policy change

Analisis Persediaan Bahan Baku Susu pada Produksi Keju British Cheddar dengan Metode Economic Order Quantity ( EOQ ) di PT. Mazaraat Lokanatura Indonesia Pasuruan

To achieve profit, companies usually process raw materials into finished or semi-finished goods. Therefore, in this company, controlling and managing raw materials becomes an important factor influencing the smooth production process to achieve desired goals. Cow's milk is one of the popular agricultural products, and cheese is a dairy product consumed globally. PT Mazaraat Lokanatura Indonesia is a company that produces local Yogyakarta cheese and creamery products. The instability of organic milk quality from local farmers often negatively impacts the production process and the final quality of the company's British Cheddar Cheese products. This instability also affects raw material inventory planning, so the company must be more careful in maintaining the quality and quantity of organic milk stock to ensure optimal production. To address this issue, an analysis of raw milk inventory using the Economic Order Quantity (EOQ) method was conducted. This research began with observations and identification of problems related to raw material inventory management that could affect production efficiency and operational costs. Then, data collection was conducted regarding the demand amount, order time, ordering cost, and milk storage cost. The research results show that the application of EOQ is proven effective in optimizing inventory costs. Using EOQ allows the company to manage the optimal order quantity, significantly reducing total ordering costs by decreasing the order frequency.

Analisa Pengendalian Persediaan Bahan Baku pada CV. Es Mitra Jaya Surabaya

Manual data collection of raw materials causes inventory problems such as data collection errors, duplicate data, and warehouse loading processes that consume a lot of time. This research was conducted on a milktea boba frenchise company that is very maintaining quality. Raw materials are provided and arranged by the central company, the time of marking goods can only be ordered according to a predetermined schedule. By implementing the Qr-Code Scan system using the Scan-IT to Office application, the warehouse goods data collection process becomes more effective. The results of system implementation were evaluated using the PIECES method with an average satisfaction of 3.58 (SATISFIED) from the six domains tested. The results of the total cost calculation that have been carried out prove that the decrease in inventory costs using Material Requirement Planning (MRP) with the Economic Order Quantity (EOQ) method has decreased. The calculation carried out by the company shows the total inventory cost of Rp.32,346,426.25. After calculations using the MRP method, it decreased by Rp.1,460,780.23.

Optimizing substitution of two products model for exponentially increasing demand under inflation and shortages

This study addresses the limited attention given to product substitution in inventory models. Incorporating product substitution is crucial for determining reorder points, and safety stock, enabling businesses to optimize inventory levels, reduce costs, and maintain customer satisfaction. This study introduces an economic order quantity model tailored to an inflationary environment with shortages and one-way substitution between two deteriorating product types. Through comprehensive testing, this study evaluates the model under various substitution scenarios, including partial substitution. Findings highlight the significance of product substitution in inventory management, allowing businesses to optimize inventory levels, manage costs, and ensure customer satisfaction in dynamic environments with inflation and fluctuating product availability. This model provides the firm with the necessary information to determine the optimal ordering quantity of both products to optimize total benefit and enhance supply chain efficiency. The model demonstrates substantial cost advantages, with partial substitution resulting in an average cost reduction of approximately 9% compared to no substitution and about 45% compared to full substitution. Numerical experiments validate the applicability of the proposed model.

Valuation of Memory Effect of Fuzzy EOQ Model with Constant Demand Rate

Valuation of Memory Effect of Fuzzy EOQ Model with Constant Demand Rate

Artificial Neural Network Model For Optimization of Forecasting Material Inventory

The increasing competition in the fast-moving consumer goods (FMCG) industry leads to demand fluctuations, negatively impacting the accuracy of demand forecasts and determining optimal lot sizes in material inventory planning. Many companies struggle to adopt appropriate forecasting models, resulting in poor accuracy and higher material costs. This study aims to develop an integrated model for forecasting and material planning using simulation. The artificial neural network (ANN) method is proposed to improve forecasting accuracy, with performance evaluated through mean percentage error (MAPE), mean absolute deviation (MAD), and mean squared error (MSE). The forecast results are then applied to optimize material inventory using the economic order quantity (EOQ) model, considering warehouse capacity constraints. The EOQ model is applied to adjust lot sizes under time-varying demand. The findings highlight the importance of integrating forecasting with inventory planning to provide accurate demand predictions and optimal lot sizing, ultimately minimizing material costs in the FMCG industry. This research contributes to better decision-making in supply chain management by enhancing forecasting accuracy and inventory optimization.

The Continuous-Time Joint Replenishment Problem: ϵ-Optimal Policies via Pairwise Alignment

The main contribution of this paper resides in developing a new algorithmic approach for addressing the continuous-time joint replenishment problem, termed [Formula: see text]-pairwise alignment. The latter mechanism, through which we synchronize multiple economic order quantity models, allows us to devise a purely combinatorial algorithm for efficiently approximating optimal policies within any degree of accuracy. As a result, our work constitutes the first quantitative improvement over power-of-2 policies, which have been state-of-the-art in this context since the mid-1980s. Moreover, in light of recent intractability results, by proposing an efficient polynomial-time approximation scheme for the joint replenishment problem, we resolve the long-standing open question regarding the computational complexity of this classical setting. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported by the Israel Science Foundation [Grant 1407/20].

Logistics Model for Indonesia’s National Freight Model System: From a Deterministic to a Stochastic Framework

One of the components of a new model, called INTRAMOD, for Indonesia’s domestic freight transport is the logistics model. The logistics model describes shipment size choice and the choice between five different transport chain alternatives involving four main modes: truck, train, vessel, and plane. This paper presents the work to forecast the disaggregate transport chain and shipment size choices for Indonesia’s domestic shipments by applying a deterministic and a stochastic approach. Using a standard economic order quantity model with a consolidation assumption, a deterministic approach is used to determine the transport chain and shipment size, minimizing total logistics cost. As an alternative for this, a stochastic model aims to improve the logistics choice modeling by employing data on the manufacturer’s revealed preferences and stated preferences about only the transport chain choice. The chosen specification for the stochastic approach is utilizing the multinomial logit model. Using the demand elasticities for all alternatives with respect to changes in its transport cost, a comparison will be made between the two approaches. In addition, it is concluded that the deterministic model is susceptible to sticky and flip-flop behaviors. In contrast, this characteristic is absent from the stochastic approach.

An EOQ model for joint ordering, pricing, and return policy with advance booking discount

An EOQ model for joint ordering, pricing, and return policy with advance booking discount

Research on raw material inventory management of liquor production enterprises based on EOQ model

With the recovery of the economy, China's liquor industry has shown strong recovery force, the scale of the liquor industry has been rising since 2018, but the output and sales volume have declined, and the competition in the liquor industry has become increasingly fierce, which has put forward higher operating requirements for various liquor companies. This paper analyzes the raw material (wheat) demand and inventory cost data of "Hengkang" liquor enterprises in the past three years, and uses the EOQ model to calculate its economic order quantity. Compared with the fixed order quantity method currently adopted by "Hengkang", it is found that the order quantity obtained by the EOQ model can reduce the inventory management cost of the enterprise. On this basis, some suggestions for inventory management of liquor enterprises are proposed.

ANALYSIS OF INVENTORY MANAGEMENT IN A BAKERY INDUSTRY

This case study analyzes the wheat stock management, a crucial input in the bakery industry, in a company located in the Greater Vitória region, Espírito Santo, Brazil. Using the case study methodology, the objective is to identify ways to save and reduce costs, considering the company's logistics and production needs. The Economic Order Quantity (EOQ) system through the Continuous Review Model is employed in the analysis of Inventory Management. Prioritizing the French bread, the best-selling product, the application of the ABC method highlights wheat flour as essential. The calculation of lead time, safety stock, and economic order quantity proved promising; however, delivery logistics pose a challenge. The 600-bag limit per truck and the location in a residential area hinder larger orders. The proposed safety stock does not even cover a day's production, requiring adjustments to prevent interruptions. Although the continuous review model presents advantages, logistical and operational limitations must be considered. A hybrid approach may be more suitable to ensure operational efficiency and continuity of operations.