Order Quantity Research Articles

Analysis of Production and Inventory Control of Batik Raw Materials According to Islamic Economic Perspective (Case Study at SME Santi Batik)

This study aims to find out how the batik production process and batik raw material inventory control in UKM Santi Batik using the EOQ (economic order quantity) method, safety stock, reorder point and total cost. . In this study, researchers used a descriptive-comparative method with a quantitative approach and used primary data collection techniques in the form of a questionnaire specifically for UKM Santi Batik. The data analysis technique used is the approah formula, namely the method of determining the number of economic orders by reducing them in the formulas. The results of the research and discussion can be concluded that the EOQ (economic order quantity) method is more effectively used in raw material inventory control than the existing policies in UKM Santi Batik. Because UKM Santi Batik has not used optimal raw material inventory control, namely only using estimates in purchasing batik raw materials. In addition, by using safety stocks, UKM Santi Batik can avoid shortages of raw materials in production, reorder points so that UKM Santi Batik knows when to buy raw materials again and obtain them with minimum total inventory costs.

Demand Allocation and Lot-Sizing Solution of Single Product Deterministic Demand

In this study, a two-phase algorithm has been developed to address the single-product supplier selection and order allocation problem (SP-SSOAP). In the first phase, a novel demand allocation algorithm is developed using a recursive demand equation. Subsequently, an adaptive genetic algorithm is employed in the second phase, with additional constraints for capacity and minimum order quantity, to determine the best lot-sizing plan for each supplier. Experimental results demonstrate that, on average, the proposed heuristic achieves an objective value within 1% of the optimal solution for small-sized instances and outperforms the previous heristic solution by an average of 12% in terms of objective value for larger problems. Additionally, the numerical results emphasize that the proposed method performs better when input parameters such as price, ordering cost, and holding cost exhibit lower fluctuations. This suggests that managers should focus on improving supplier relationships and coordination strategies to reduce variability in price, order cost, and holding cost.

Analisis Persediaan Suku Cadang Dengan Metode Economic Order Quantity Pada Bengkel Asia Jaya Motor Karawang

The development of hardware and software technology has developed rapidly in recent years. Currently, companies are still experiencing difficulties in processing data and information. Spare parts that are not available when needed can hinder maintenance and repair plans. For this reason, controlling spare parts inventory in the Asia Jaya Motor workshop company is very necessary to maintain the quality of goods. Asia Jaya Motor Workshop is a business operating in the automotive sector, where this place cannot be separated from an accurate information system. However, data management still uses manual methods, seeing the problems that occur at the Asia Jaya Motor Karawang Workshop, very high accuracy, convenience and speed are needed. The aim of this research is to obtain an effective and efficient spare parts inventory analysis so that spare parts procurement and control plans can be accurate in supporting maintenance and repair programs. The data collection techniques for this research are library research and field research. So, the total cost of spare parts inventory in the 2011-2014 period according to the Economic Order Quantity method for each spare part has increased. The total cost (total cost) of spare parts inventory according to the Economic Order Quantity method is lowest in the 2021 period and the total cost (total cost) of spare parts inventory is the highest in the 2023 period.

Analisis Pengendalian Persediaan Bahan Baku dengan Metode Economic Order Quantity Pada Klontang Coffe & Resto

The aim of this research is to find out and determine the optimal order quantity and frequency of ordering raw materials using the method Economic Order Quantity (EOQ) at Klontang Coffee & Resto. This research uses qualitative methods using data collection techniques through library research, field research including observation, interviews, documentation studies. The calculation results for Klontang Coffee & Resto based on data on raw material requirements and storage cost data as well as ordering costs using the Economic Order Quantity method are 14.4 kg, for the frequency of ordering raw materials is 0.83 orders in one year.

Reauditing and dual sourcing? Implication of collusion deterrence measures in ethical sourcing

AbstractWe consider a supply chain in which a buyer sources from a supplier for the production of a product. The supplier's production process is subject to probable violations of either government regulations or ethical requirements. The sourcing contract offered by the buyer demands that the supplier exerts an effort to improve its production process, thus reducing the probability of the occurrence of supplier violation. Because the supplier's effort is hardly observable, the buyer has to send an auditor to perform an audit of the supplier's facility. The auditor in turn reports whether the supplier has exerted the effort or not. If the supplier fails to pass the audit, she may offer a side contract to the auditor, thus engaging in a supplier‐auditor collusion. We study how such supplier–auditor collusion may affect the buyer's outsourcing strategy. We find that the buyer should reduce the order quantity to deter the supplier–auditor collusion when the collusion penalty is relatively low. We further examine reaudit and dual sourcing, two mechanisms which could probably prevent supplier–auditor collusion. We find that a reaudit may complement the collusion penalty under certain conditions; using dual sourcing to deter collusion is viable only if supplier‐improvement techniques or procedures are effective. Finally, we extend our model by considering false positive audit error and linear penalty function. Our analytical and numerical results show that our insights are robust.

A Fuzzy Inventory Model Using Pentagonal Fuzzy Numbers

Transportation expenses may have an effect on significant business sectors. They could affect a company's price of goods as well as its capacity to remain competitive in the market. Another way to maximize transportation expenses is through inventory management. Prevent needless overstocking and incomplete shipments by keeping an accurate inventory and managing stock levels. This lowers the price of shipping extra items or broken shipments. Nonlinear Programming Kuhn Tucker Method is used to find the optimal solution, which has an impact on the monthly payment. The signed distance approach is used for defuzzification in the proposed model, and the Pentagonal fuzzy number is used to find the lowest cost. To test the inventory model, we create a CSV file and use Weka software to analyse the Economic Order Quantity and total cost.

An inventory model with price- and stock-dependent demand and time- and stock quantity-dependent holding cost under profitability maximization

This paper focuses on inventory models with a broad framework for the storage cost and the demand rate. The cumulative storage cost is modelled with a power function, depending on both time and stock quantity, by using two elasticity coefficients. Similarly, the demand rate has an isoelastic dependence on sale price and stock quantity, modelled with another two elasticity coefficients. These four elasticity coefficients allow many real practical situations to be modelled. A reference price is used to measure the effect of the sale price on the demand rate. The goal is to maximize the income expense ratio (IER), and the sale price, the order level and the reorder point are the decision variables. The operating expense ratio (OER) of the system, defined as the quotient cost/income, is used to solve the problem. The optimum values are obtained with explicit expressions, which is an interesting result for inventory managers. Under the optimum policy, the reorder point is always equal to zero and the order quantity depends on the replenishing cost, the purchase price and the four elasticity coefficients. However, the optimum ordering policy does not depend on the scale parameters of the storage cost and the demand rate. A complete sensitivity analysis for most of the model parameters is performed. A numerical example is used to compare the optimum policies for the maximum income expense ratio and the maximum profit per unit time. Finally, some managerial insights derived from the results are given.

Integrated Decision Making to Determine the Optimal Order Quantity for Raw Materials Using Genetic Evolutionary Algorithms

Decision Making in economic organizations, especially the productivity, is one of the problems that researchers are interested in. It is important for the organizations that seek global competition and the integration of their decision will lead to coordination of decisions between the departments of these organizations to determine optimal order quantity and establish a correct inventory policy to warrant production with least total costs (costs of transportation, holding and purchasing), these is the objective of any company to achieve an adequate and enough inventory level to meet the future needs. In this paper, integrated decision making with three stages, first stage is forecasting with demands to final products using time series, second stage determine required quantities for raw material to manufacture these final products, and formulated a mathematical model reduces the total cost in third stage, when the transportation and purchase are variable costs with order quantity, while in just-in-time model or economic order quantity model are a fixed demand and purchase cost. The aim of this paper is integrated decision making to reduce total cost of required raw materials for the manufacturing processes and without and determine the optimal order quantity using genetic algorithms. This study was applied in Wasit company for cotton products in the textile factory, the results shown Holt-Winter method is the best method to forecasting because has least mean absolute error and the percentage of purchasing cost 73%, Transportation cost 8%, and holding cost 19%. The percentage of purchasing cost of cotton is biggest value, more 99% of purchasing cost.

Analyzing EOQ model for imperfect items with time dependent demand under substitution of two products

The inventory model is based on the quantity of order and the time at which orders for particular goods are to be placed. In this paper, models with defective items have been discussed and the EOQ approach has been shown. One-way substitution of item is used in case of scarcity of the first item. The demand for the items is based on the Heaviside function. It is noticed that the presence of defective items hampers the demand of the two items differently. To establish the virtue of our model, a numerical example and sensitivity analysis is imparted.

Is Partial Sourcing Disadvantageous to Startup Suppliers Under Capacity-Shortage Threat?

This article investigates the joint influence of a startup supplier's market-growth capability and its capacity-shortage threat on the attractiveness of partial sourcing (PS) over complete sourcing (CS) to that supplier. We develop an analytical model that accounts for this shortage threat and a positive relationship between the startup supplier's order quantity and its buyers’ product demand, while considering two forms—bounded versus unbounded order-induced effect—of this market-growth capability. We derive optimal order quantities under each sourcing structure, show how a revenue-sharing agreement coordinates the supply chain, and identify conditions under which the startup supplier should favor PS over CS. We find that PS delivers greater value to the startup supplier relative to CS if the order-induced effect is unbounded and capacity-shortage likelihood is high. Furthermore, when employing PS, the value to the startup supplier from a capacity-shortage threat appears to be greater when the supply chain is uncoordinated than when it is coordinated.

The Impact of Big Data Analytics Capabilities on the Diversification of E-Commerce Firms

Abstract With the arrival of the big data era, e-commerce enterprises are facing more and more competition and market changes. A fuzzy clustering model is created by combining fuzzy set theory and fuzzy logic with a clustering algorithm in this paper. Then, for the shortcomings of the fuzzy C-mean clustering algorithm, the genetic simulated annealing algorithm is used to weight the data points, the fuzzy C-mean clustering model is improved, and a fuzzy clustering algorithm based on the genetic simulated annealing optimization is proposed. By using this algorithm, e-commerce enterprises’ diversification is being analyzed. The results show that the mean and standard deviation of each supply chain inventory, production orders, supplier ordering, and platform ordering quantity after optimization are reduced to different degrees, some of which reach more than 60%. Stabilizing the development of e-commerce enterprises can be achieved by reducing supply chain inventory and order quantity. E-commerce enterprises that excel in their enterprise production quality (0.741), credibility (0.748), and product sales competitiveness (0.726) are more competitive. E-commerce enterprises can utilize the ability of big data analysis to achieve diversification.

Optimization of Neutrosophic EOQ Model for Effective Demand Management in Uncertain Environment Using Genetic Optimization

Inventory management is characterized by a continuous struggle to lower goods levels and related costs while also providing customers with the goods they need. However, reducing costs while simultaneously striving for ideal inventory levels is difficult, notably in the current situation of high unpredictability of goods demand and lead time. Traditional inventory models are not strong enough to endure changes like goods demand and lead-time demand. As a result, it must be adjusted to achieve results. The oeuvre below presents a new kind of inventory model that deals with uncertainty in the demand for goods and lead time. In this regard, the presented work, the novel Neutrosophic Economic Order Quantity approach is a mechanism to account for the likely imprecision in the model. Specifically, the Neutrosophic set theory is integrated into the EOQ model so that it can handle variations in the demand and lead-time pattern successfully. An objective function is established for obtaining economical order quantities that include demand, lead-time, and other necessary components’ irregularities. The process variables in the model are given the final values using genetic algorithms and simulated annealing. To highlight the impact of the proposed Neutrosophic approach, it is then applied to several realistic examples. This will provide the audience a sense of how effective inventory management may be in high-uncertainty situations. The rapid evolution of organizations necessitates innovative inventory control tactics to meet growing demands

A Fuzzy Economic Order Quantity Model on Manufacturer-Retailer considering delay in payments: A Non-Cooperative Game Theoretical Approach

A Fuzzy Economic Order Quantity Model on Manufacturer-Retailer considering delay in payments: A Non-Cooperative Game Theoretical Approach

An economic order quantity model under constant purchasing price increments

An economic order quantity model under constant purchasing price increments

Integrated assembled production decision support inventory system

In the effective supply chains development, the interaction between financial and inventory decisions is important for suppliers and manufacturers. In addition, implementing effective coordination between supply chain participants has become a related research issue. Considering a two-echelon model of multi-suppliers and one manufacturer in this study, coordinate their situations to maximize the total supply chain profits. One or more components required for the final product produced are provided by each supplier. The payments which are permissible delay is coordinated to order quantity between two echelons in the proposed inventory level model. This study is to establish an integrated inventory model to minimize the joint total expected inventory cost and develop a decision support system (DSS).

Fuzzy inventory modelling: addressing uncertainty in economic order quantity analysis within nonlinear science

Fuzzy inventory modelling: addressing uncertainty in economic order quantity analysis within nonlinear science

VENDOR MANAGED INVENTORY POLICY FOR PERISHABLE PRODUCTS UNDER A COMBINATION OF FIFO-LIFO STOCK CONTROL

For retailing systems, arranging the perishable products has always been a challenge. Although more profit is made when products are sold with a FIFO policy, now customers taste in a competitive market which requires new products to be offered to them has changed the game. This paper considers a hybrid of FIFO-LIFO strategies for controlling a retailer's stock and its impact on the profits of the vendor managed inventory contracts. The problem is formulated as a multi-period, multi-store integer programming model which aims to maximize the total profit. The proposed model considers different selling prices and different holding costs for perishable products according to their remaining shelf life. A dynamic programming with a backward strategy is proposed to find the optimal order quantity for each store. The numerical experiments confirm the applicability and efficiency of the model illustrating impact of the crucial parameters of the model on the stocking policy.

Economic order quantity model for Pareto distributed decaying products with quadratic demand, shortage and salvage value

Economic order quantity model for Pareto distributed decaying products with quadratic demand, shortage and salvage value

An economic order quantity model under constant purchasing price increments

An economic order quantity model under constant purchasing price increments

Investigation of a Fuzzy Production Inventory Model with Carbon Emission using Sign Distance Method

Reducing costs associated with inventory is the primary goal of conventional inventory models like the models of economic order quantity and economic production quantity. However, these models fall short of addressing defective goods or revising them. Imperfections in the manufacturing process result in flawed products alongside the final goods. To convert these flawed components into finished goods, rework is necessary. In manufacturing and reworking process produces carbon emissions which are harmful for the earth. To determine the optimal quantity for a single product manufactured in a single-stage manufacturing system that yields partially defective products that are reworked in the same cycle, is determined in real-life situations, where the inventory characteristics and objectives are not exact. Such a type of uncertainty may be characterized by fuzzy numbers. A pentagonal fuzzy number has been used to define the cost parameters. Due to fuzzy parameters, the model becomes a fuzzy quantity, and it is defuzzied by the sign distance method. this article formulates a model of manufacturing inventories with planned backorders. Furthermore, a closed form for the inventory’s system total cost is determined, and a range of actual values for defective products for which an appropriate method exists is also provided. A proper mathematical model is created to accomplish the goal, and the manufacturing lot size that reduces the overall cost is determined. The ideal amount of a production batch to reduce total cost is established in to attain this goal using an appropriate mathematical model. While formulating and solving the relevant model, the necessary and sufficient conditions for a single globally optimal solution have been determined. Examples used as visualizations are given and confirmed by data.