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Effective inventory management is crucial for optimizing resources and ensuring smooth maintenance operations. This paper explores two primary replenishment systems-Economic Order Quantity (EOQ) and Order Level Control-designed to address the challenges posed by demand variability and lead-time uncertainty. The EOQ model determines the optimal order size to minimize total inventory costs, while replenishment systems guide the timing of orders. We analyze the Fixed Order Quantity System (Q System), which maintains a constant order size with variable ordering frequency, and the Fixed Order Interval System (P System), which fixes the ordering intervals but varies the order size. Each system's advantages and challenges are discussed, highlighting their applicability based on demand stability, lead-time consistency, and inventory value. Through a comparative analysis, this paper provides insights into selecting the appropriate replenishment strategy to achieve efficient inventory control, cost minimization, and resource optimization. [ABSTRACT FROM AUTHOR]

Inventory control is at the core of supply management to balance inventory needs and demand. With an inventory system, management can control stock of materials and goods, production process and sales transactions can even stop the production process if necessary. Sari Gamping building materials store that sells various building needs including building materials and tools. Data management of goods, transactions and inventory processing still uses conventional methods using Microsoft Office. The purpose of this research is to analyze the ongoing inventory management process to create an inventory management information system so that it can help the Sari Gamping store in controlling inventory. The Economic Order Quantity (EOQ) and reorder point methods were chosen to optimize inventory to avoid shortages or excess inventories, to fulfill customer orders, to achieve cost efficiency and to prevent problems that may occur. The developed system can manage data on goods, categories, suppliers, manage purchase and sales transactions and can generate information from EOQ calculations, safety stock and reorder points. From the results of testing the calculations of the two methods used with 15 samples of catylac paint variants, the average inventory cost saving in June 2023 was 65.33%.

The primary objective of this article is to develop a mathematical model and determine the optimal policies of an inventory system involving power demand and controlled deterioration through preservation technology. This model comes in handy in a power demand-oriented inventory system with demand high at the end of the period. The model incorporates backlogged shortages and linear holding cost. The triangular neutrosophic numbers (TNN’s) are used for a nuanced representation of uncertain and imprecise inventory-related expenses. An efficient algorithm is constructed to minimize the total cost, and obtain optimal positive inventory time, optimum cycle time and minimum preservation technology investment. Few numerical examples are used to illustrate and validate the model. The comparative study conducted between models with and without preservation technology investment reveals a significant reduction in total inventory costs facilitated by the preservation facility. Also, the numerical results obtained in crisp and neutrosophic environment are compared. Specific previously obtained results are discussed to illustrate the theoretical findings. Sensitivity analysis of the model provides managerial insights replicating reality.

The primary objective of this article is to develop a mathematical model and determine the optimal policies of an inventory system involving power demand and controlled deterioration through preservation technology. This model comes in handy in a power demand-oriented inventory system with demand high at the end of the period. The model incorporates backlogged shortages and linear holding cost. The triangular neutrosophic numbers (TNN's) are used for a nuanced representation of uncertain and imprecise inventory-related expenses. An efficient algorithm is constructed to minimize the total cost, and obtain optimal positive inventory time, optimum cycle time and minimum preservation technology investment. Few numerical examples are used to illustrate and validate the model. The comparative study conducted between models with and without preservation technology investment reveals a significant reduction in total inventory costs facilitated by the preservation facility. Also, the numerical results obtained in crisp and neutrosophic environment are compared. Specific previously obtained results are discussed to illustrate the theoretical findings. Sensitivity analysis of the model provides managerial insights replicating reality. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions contribute significantly to global warming. This occurs during various operations, including the production, storage, and transportation of an inventory, but in some cases, the inventory itself becomes the emission source. Emissions from cattle dunk cakes, livestock slurry, manure, crude oil, and gasoline can be considered examples of this type of emissions. This paper proposes a mathematical formulation for a deteriorating inventory model utilizing the inventory as a source of emissions. The model is developed under inflation. Other activities, such as energy consumption for warehousing, are considered to be contributing to greenhouse gas emissions here. The rate of emissions from the product is exponentially governed by the rate of deterioration and the age of the material. The trapezoidal-type demand rate is considered using the Heaviside step function. Shortages are permitted but partially backlogged, and the backlogging rate is supposed to be decreasing exponentially with the increased waiting time. The numerical illustration of the model is provided to illustrate the mathematical expressions, and the effect of parametric variation is reported to give managerial insights. The results reveal that the greenhouse gas emissions are proportional to the variable rate of emissions cost and the deterioration rate. However, the increment in total emissions with respect to variable emissions cost increases with the deterioration rate. If the deterioration rate is negligible, total carbon emissions do not fluctuate much, but it increases rapidly if it is relatively high. The preservation technology is used to reduce deterioration and helps reduce emissions. [ABSTRACT FROM AUTHOR]

Inventory control of deteriorating items is a hot topic that has always attracted the attention of many researchers, and every year, many models are presented about this issue. However, the solution methods of the related models (for example, heuristic or derivation methods) are limited and need to be more effective. Therefore, the authors ignored some fundamental assumptions to simplify the solution. This article used the dynamic programming method to solve the problem. This method has allowed us to introduce more realistic assumptions, such as discrete and limited ordering time, into the model. This article presents a multi-period economic ordering quantity model for a single deteriorating item with the stated realistic assumptions. The results show that the presented method can find the optimal global solution. [ABSTRACT FROM AUTHOR]

Due to the high level of competitiveness that exists today, companies have the obligation to establish strategies that allow them to raise their customer service levels without incurring excessive costs. This study addresses the problems of a commercial company located in the state of Puebla, Mexico, which is in a period of restructuring, establishing strategies that allow it to reduce the losses generated from the empirical method of managing its inventories. For this purpose, the research determines the optimal lot quantity by means of the EOQ method of inventories in two of its models, eliminating the risk of shortage and comparing the results obtained in each model, being the News Boy Model with normal distribution. [ABSTRACT FROM AUTHOR]

Fumigation controls pests by sealing a room using methyl bromide (CH3Br) and oxygen gas. This process requires a gas mask, fumigation plastics, sand snakes, and a certificate. The interview revealed that specific supply shortages cause fumigation delays in 15% of monthly orders. This research aims to develop an inventory control application by applying the economic order quantity (EOQ) and reorder point (ROP) methods to overcome the problem of running out of stock. The EOQ method determines the amount of inventory to order to avoid excess stock, while ROP determines the right time to order inventory. The study involved two types of testing: white box testing and comparing manual calculations with application calculations to determine their consistency using data from March to May. The white box testing results show that the program's internal structure and logic run well. This is supported by evidence that each test case can pass two flow graphs resulting from cyclomatic complexity calculations. In addition, the results of testing the EOQ and ROP method calculations show an insignificant difference between the results of manual calculations and application calculations of 3.87%. This proves that application calculations for EOQ and ROP are running correctly and by the researchers' expectations.

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This study aims to calculate the prediction of raw material inventory, the Economic Order Quantity (EOQ), safety stock, and reorder point in support of the continuity production process at a fence manufacturer, CV. Moderen Arsiteknis in Kupang city. Data were analyzed from records of the period 2013–2017 that consist of the annual demand of a product in quantity per unit of time (D), product order cost (S), unit cost (C), holding cost per unit as a fraction of product cost (H), and lead time (L). It is calculated from the forecasting of raw material demand and then computed the Economic Order Quantity (EOQ), safety stock, and reorder point for the next 3 years (2018-2020). The computation of demand forecasts, EOQ, safety stock, and reorder points is intended to smooth the continuity of the production process, reduce the risk of raw material shortages, and minimize the ordering cost and holding cost in CV. Modern Arsiteknis was discussed in the results section. Keywords: Forecasting; Economic Order Quantity; Safety Stock; Re-Order Point

This research work presents a depleted demand inventory model with constant deterioration. The rate of change of demand is assumed to be a time-dependent function. Initial non-zero demand occurs due to advertisements. Advertisement cost is assumed to be constant. Two types of models are considered for two replenishment strategies viz., without shortages and with shortages. This study aims to obtain a suitable policy for replenishment for minimizing the total inventory cost. Four examples about the alterations made in the optimal solutions due to different values of independent parameters used in the models are considered and discussed. Sensitivity analysis is done and numerical illustrations are provided for validating the approach presented.

Jenis usaha produk konsumsi seperti minimarket biasanya menjual beberapa jenis produk dengan volume yang rendah dan memiliki fluktuasi permintaan yang cenderung kecil. Terdapat tiga hal yang perlu diperhatikan dalam mengendalikan persediaan pada minimarket, yaitu kemungkinan untuk mendapatkan potongan harga jika membeli bahan makanan dalam jumlah besar, barang konsumsi yang dapat kadaluarsa,, serta adanya keterbatasan kapasitas yang dimiliki. Tujuan dari penelitian ini adalah mengembangkan model persediaan economic order quantity multi item dengan mempertimbangkan faktor kadaluarsa, all unit discount, dan kendala kapasitas. Pengembangan model dilakukan dengan cara mengembangkan model persediaan economic order quantity multi item dengan mempertimbangkan kadaluarsa dan all unit discount dari model terdahulu, lalu dilanjutkan dengan mengembangkan faktor kendala kapasitas menggunakan persamaan proporsi permintaan produk. Model yang telah dikembangkan akan diuji coba dengan melakukan perubahan pada nilai parameter model untuk melihat sensitivitasnya. Dari hasil analisis sensitivitas yang telah dilakukan, ditemukan bahwa total biaya persediaan sensitif terhadap harga beli satuan barang. Sedangkan, waktu pemesanan sensitif terhadap perubahan kapasitas gudang. Abstract [Development of Economic Order Quantity Multi Item Inventory Model by Considering Perishable Factor, All Unit Discount, and Capacity Constraint] Types of consumer product businesses such as minimarkets usually sell several types of products with low volumes and tend to have small fluctuations in demand. There are three things that need to be considered in controlling inventory at minimarkets, namely the possibility of getting a discount if you buy food in bulk, consumer goods that can expire, and the limited capacity they have. The purpose of this study is to develop a multi-item economic order quantity inventory model by considering expiration factors, all unit discounts, and capacity constraints. Model development is carried out by developing a multi-item economic order quantity inventory model by considering expiration and all unit discounts from the previous model, then proceeding to develop a capacity constraint factor using the product demand proportion equation. The developed model will be tested by making changes to the model parameter values to see its sensitivity. From the results of the sensitivity analysis that has been carried out, it was found that the total cost of inventory is sensitive to the unit purchase price of goods. Meanwhile, the time of ordering is sensitive to changes in warehouse capacity. Keywords: economic order quantity; multi item; perishable; all unit discount; capacity constraint.

The retail inventory management literature generally assumes that suppliers offer their retailers a full delay in payment in order to stimulate demand. However, this may not always be offered in real-life business environments. To account for the possibility of partial delay in payment, we establish an economic order quantity (EOQ) model with allowable shortage and partial permissible delay in payment linked to order quantity. The formation of payment scheme is as follows: if the order quantity Q reaches a certain level (Q d) then fully permissible trade credit is possible, otherwise the partial trade credit is offered. Here, we discuss all the possible cases of the trade credit when Q < Q d and Q ≥ Q d . We employ some algebraic manipulations and an arithmetic–geometric mean inequality approach to determine the optimal solution in various situations. We also provide an algorithm to determine the optimal order quantity and maximum allowable backorder level. Numerical examples and a sensitivity analysis illustrate the results of this proposed model. [ABSTRACT FROM AUTHOR]

Inventory control is important in managerial activities because it involves investment and is hospitals' most significant expenditure component. The Aisyiyah Bojonegoro Hospital requires a logistics management system that can maintain the safety and effectiveness of the use of drugs for the smooth running of hospital pharmaceutical services in the long term. This analytical observational study analyzes management elements in controlling antibiotic inventory by simulating the Economic Order Quantity (EOQ) and Minimum-Maximum Stock Level (MMSL) methods. Determination of the sample purposively, i.e., 17 types of antibiotic drugs category A from the results of the ABC analysis, with the inclusion criteria being high cost, high volume, clinically important drugs for antibiotic drugs that are included in the 2020 Hospital Formulary and the exclusion criteria are drugs with inadequate supply. The analysis technique used the Mann-Whitney test and the Kruskal-Wallis test. Based on the results of the study, it was concluded that. Applying the EOQ and MMSL methods has been proven to increase the efficiency and effectiveness of the supply of category A antibiotics at Aisyiyah Hospital, Bojonegoro.

This research develops an optimization model for determining the order quantity for growing items by considering the imperfect quality and incremental discount by involving three supply chain members: farmers, processors, and retailers. The farmers are responsible for caring for the newborn items until they reach their ready-to-eat weight. The processors perform two roles, namely processing and screening. In the processing role, the processors process the grown items by a slaughtering and packaging process. Afterward, they inspected the processed items and categorized the items into good and poor quality. Finally, they shipped the end products to retailers. The retailers are responsible for selling good-quality items to the final consumers. This research considers two kinds of poor quality. First is the poor quality of growing items in terms of mortality rate. The second is the poor quality of final products on the processor side. The processed items with poor quality are then sold to the secondary market at lower prices in one batch at the end of the period. This model also considers the incremental discounts offered by vendors to farmers and retailers to consumers for specific amounts of purchases. The model's objective function is to maximize the total supply chain profit, with the number of orders quantity, cycle time, and the number of batches delivery set as the decision variables. The sensitivity analysis results show that the most sensitive parameter in the model is the probability that the live items survive throughout the growth period.

High utility mining is gaining prominence, and with the increasing set of business intelligence models, the scope of such significant practices is high. Rather than focusing only on profitability as one key utility metric, today's organizations believe in having more robust levels of the multi-objective filtering process. In this manuscript, a contemporary model of the high utility mining process is proposed, wherein the multiple averages are used for grading the recommendation of the itemsets for merchandise. The model's key advantage is its dynamic approach. The goods-related period of the average time interval can be flexible, alongside the fusion of multiple utility thresholds of diversified features chosen for itemsets recommendation. The performance analysis has been carried out by using a multi-fold cross-validation strategy. The results obtained for cross-validation show that the proposed model is outperforming the contemporary models with significant precision, specificity, sensitivity, and accuracy having values 97%, 95%, 98%, and 97% in respective order. Whereas, the contemporary models HUPM-MUO and MOEA-FHUI have obtained 93% and 90%, 88% and 82%, 89%, and 84%, and 89% and 83% in respective order of the corresponding metrics. The experimental study of the model denotes the effectiveness and ease with which the solution can generate results and produce significant output in the real-time environment for more dynamic and periodic decisions by different organizations. [ABSTRACT FROM AUTHOR]

This paper considers the problem of deciding disassembly order quantity in a disassembly system. Returned end-of-use/life (EOL) products are disassembled to retrieve reusable parts sold to secondary markets. For the problem, we suggest novel push and pull disassembly policy models by extending reverse economic order quantity models. The objective of the models is to minimise the costs of the disassembly setup, the disassembly operations and inventory holding, the scrapping of non-reusable parts, and backorder. We develop optimal equations and algorithms for the models and perform a case study of Korean automobile disassembly systems. We then conduct a series of sensitivity analyses based on real-world and reliable data and discuss possible applications of the push and pull policies. The results show that the pull policy gives better in terms of the total cost while the push policy is less insensitive to the disassembly setup cost. [ABSTRACT FROM AUTHOR]

We use laboratory experiments to evaluate the effects of cognitive stress on inventory management decisions in a finite horizon economic order quantity (EOQ) model. We manipulate two sources of cognitive stress. First, we vary individuals' participation in a 6-digit mixed number and letter memorization task involving their personal identification numbers. This exogenously increases cognitive load. Second, we introduce an intervention to reduce cognitive stress by only allowing participants to order when inventory is depleted. This restricts the order choice set. Increases in cognitive load negatively impact earnings with and without the intervention, with these impacts largely occurring in the first attempt of the task. With repetition, participants' choices in all treatments trend to near-optimal policy adoption. However, only in the intervention and low cognitive load treatment do the majority of choices reach the optimal policy. We estimate the learning dynamics of order decisions using a Markov learning model. Estimates suggest increased cognitive load reduces the probability of switching to more profitable policies. Choice set complexity increases biases for smaller order size adjustments, leading to greater policy lock-in. [ABSTRACT FROM AUTHOR]

Supply chain management is a critical aspect of conducting any business. In this article, we provide an overview of some the methods for supply management and their use in animal husbandry. In this paper, we introduce a version of Wilson`s model. The illustration of the proposed model, showing economic effects to the farm, reducing its expenses (by 2.5%) compared to farms that do not utilize this model.

The goal of the paper is to show how the problem of determining the Economic Order Quantity and Reorder Point could be solved with the use of a regression model. The method proposed by the authors of the paper is an alternative to the methods in which a mathematical EOQ formula is used and also to the simulation method. The intention of the authors was to find a solution which would allow, on the one hand, to obtain results as close as possible to real parameters and on the other, could be useful for decision makers in companies.With the use of a specially elaborated model, simulations have been conducted for different parameters (value, weight, demand of a commodity, logistics costs) which first proved the importance of the optimization of deliveries to a warehouse. Second, based on the results of these simulation, model regression models have been worked out and used for finding delivery quantities and reorder point. Both regression models are statistically significant, a slightly better value is obtained by the model created for the reorder point.

PT. ABC (Company) is one of the companies engaged in pharmaceutical sector, more precisely in the manufacture of medicines in the type of tablets, caplets, capsules, syrup, ovules, injections, and so on. Optimal control of raw material supplies is very important for the company in order that the production process can run properly. The Economic Order Quantity (EOQ) method is a basic method of inventory control, which aims to fmd the minimum point between the ordering cost and the holding cost. The purpose of this study is to examine the control of the inventory of some raw materials in PT. ABC by using the EOQ method. In this study, data collection is conducted by using survey and observational methods with a quantitative descriptive approach. Then, the data is analyzed by using the EOQ method and Total Inventory Cost formulas. The results obtained from this study indicate a Total Inventory Cost of Rp 267,459,634 with an efficiency of 15,02%. [ABSTRACT FROM AUTHOR]

The present study describes a modified Economic Order Quantity (EOQ) for multi-item replenishment model with the deterministic demand nature of an inventory management. The function is subjected to the available financial and space constraints in the organization. If the constraints are not satisfied under the given conditions, then Lagrange technique is applied to obtain the optimal order quantity of the multi-items. The purpose of this study is to empower the manufacturing sector of Malaysia's SMEs with the advantages of the inventory model that controls the overproduction and underproduction inventories to meet their customer's demand on the right time. From this study, 90.9% of the optimum quantities of products had been achieved by using the multi-item inventory replenishment model as compared to the EOQ. This model is able to control the total cost of inventory efficiently for the optimum space availability. [ABSTRACT FROM AUTHOR]

In this paper, a new Economic Order Quantity (EOQ) model for a successive generation of technology products has been discussed. The classical EOQ model is based on the assumption that the demand rate is constant. Hence it cannot be used for technology products where competition-substitution among products is a usual phenomenon. To address this problem, the EOQ model proposed in this article is considered a demand model for a technology product that follows the innovation-diffusion process. A numerical example has been illustrated and a comprehensive sensitivity analysis is conducted to understand the path of the optimal planning horizon and optimal costs under varied innovation and imitation effect. The sensitivity analysis of the introduction timing of the second generation has been performed to know the applicability of the model in actual circumstances. The behavior of the model has been discussed in detail in the numerical illustration section. [ABSTRACT FROM AUTHOR]

The availability of drugs is one of the things that must be considered because if there is a deficiency or excess it can cause loss or disruption in patient care. The process to procure drugs that are still being carried out with uncertain considerations will create scheduling irregularities, this will have an impact on inventory costs due to accumulated inventory in warehouses or the absence of these drugs. This study aims to produce a decision support system for drug procurement using a combination of ABC methods, VEN analysis, ROP and EOQ. The test results show that the system can provide 3 recommendations for decision makers with consideration of the results of the ABC and VEN matrices and procurement calculations based on EOQ and ROP. The result of calculating the total Inventory Cost in the case example of the orodine drug based on the pharmacy calculation is IDR 708,500 while the calculation using the Economic Order Quantity method is IDR 689,381 from the calculation results obtained a savings of IDR 19,119.

This paper addresses a single item two-level supply chain inventory model considering deterioration during carrying of deteriorating item from a supplier's warehouse to a retailer's warehouse as well as deterioration in the retailer's warehouse. The model assumes preservation technology in the retailer's warehouse to prevent the rate of deterioration. An upper limit for the preservation technology investment has been set as a constraint to the model. The model maximizes the retailer's profit per unit time, simultaneously calculated optimal order quantity. A price dependent demand and storage-time dependent holding cost is considered to develop the model. Some theorems are proven to get optimal values of the total cost. A numerical problem is workout as per the developed algorithm and with the help of MATLAB software to study the applicability of our theoretical results. [ABSTRACT FROM AUTHOR]