An EOQ inventory model with nonlinear stock dependent holding cost, nonlinear stock dependent demand and trade credit

Cárdenas–Barrón, Leopoldo Eduardo; Shaikh, Ali Akbar; Tiwari, Sunil; Treviño-Garza, Gerardo

doi:10.1016/j.cie.2018.12.004

Cited by 86 publications

(27 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The EOQ formula can be modified to determine different production levels or order intervals or high variable costs. This inventory model is also applicable in conditions of nonlinear stock dependent demand and nonlinear holding cost [15]. EOQ is also an important cash flow tool.…”

Section: Methodsmentioning

confidence: 99%

Estimating inventory stocks for greenhouse heating needs

Kurpaska¹,

Krakowiak-Bal²,

Gliniak³

et al. 2019

Technium

View full text Add to dashboard Cite

Paper deals with the problem of supply logistic for greenhouse energy purposes. It is an attempt to estimate the proper supplies of coal, to reduce the costs of warehouse management in the analyzed horticultural enterprise. The analysis includes estimation of the object's heat demands of the, an optimal order quantity and calculation of costs of maintaining inventory. The research object is a greenhouse complex (10 ha) with tomatoes cultivation. We calculated that, the proposed inventory shaping method, to determine the order quantity of energy raw material for the needs of the greenhouse, would reduce inventory financing costs by 33.5%.

show abstract

Section: Methodsmentioning

confidence: 99%

Estimating inventory stocks for greenhouse heating needs

Kurpaska¹,

Krakowiak-Bal²,

Gliniak³

et al. 2019

Technium

View full text Add to dashboard Cite

show abstract

“…In this study, (Q, r) model is studied and its performance is analyzed in terms of average inventory investment, fill rate, and backorder level and the results are compared with the company's existing model. The (Q, r) model parameters used are provided in Table 3 and they are calculated using Equations (1)- (17) from the study of W. J. Hopp et al [20]. The (Q, r) model is shown in Table 2, calculated based on real data using Equations (1)- (17) as mentioned in the article.…”

Section: The (Q R) Modelmentioning

confidence: 99%

“…In material management, spare parts channel the focus of the researchers to a separate field of spare parts management (SPM) due to its primary role in reducing plant downtimes and achieving companies' planned goals of production and meet market demands. The total cost associated with spare parts supply chain networks cannot be controlled only by traditional material management policies and models, but considering the warehouse location optimizing and policy integration is the leading path to total cost reduction [19,20]. In the inventory management system, one of the critical inventory levels controlling parameters regarding total cost reduction is the optimal ordering quantity (Q).…”

Section: Introductionmentioning

confidence: 99%

Improved MRO Inventory Management System in Oil and Gas Company: Increased Service Level and Reduced Average Inventory Investment

et al. 2020

View full text Add to dashboard Cite

This study proposes a methodology for the oil and gas businesses to keep their production plant productive with a minimum investment in carrying maintenance, repair, and operating inventory planning. The goal is to assist the exploration and production companies in minimizing the investment in keeping maintenance, repair, and operating (MRO) inventory for improving production plant uptime. The MRO inventory is the most expensive asset and it requires substantial investment. It helps in keeping the oil and gas production plant productive by performing planned and unplanned maintenance activities. A (Q, r) model with a stock-out and backorder cost approach is combined with a continuous inventory review policy for the analysis of class A items of oil and gas production plant MRO inventory. The class A items are identified through popular ABC analysis based on annual dollar volume. The demand for the inventory is modeled through Poisson distribution with consideration of constant lead time. The (Q, r) model in both stock-out cost and backorder cost approaches assigned higher order frequency and lower service level to low annual demand and highly expensive items. The stock-out cost approach shows an 8.88% increase in the average service level and a 56.9% decrease in the company average inventory investment. The backorder cost approach results in a 7.77% increase in average service level and a 57% decrease in average inventory investment in contrast to the company’s existing inventory management system. The results have a direct impact on increasing plant uptime and productivity and reducing company maintenance cost through properly managing maintenance stock. The analysis is carried out on the oil and gas production plant’s MRO inventory data, but it can be applied to other companies’ inventory data as well. All the results reflected in this research are based on the inventory ordering policy of two orders per year. The inventory ordering frequency per year may be other than two orders per year depending on the type of organization.

show abstract

“…Yao and Minner (2017) presented an inventory model that includes different supply options (multiple-sourcing, transhipment, supplier selection methods) and summarized their influence from managerial perspective. Cárdenas-Barrón et al (2018) proposed an inventory model by assuming that ending inventory level can be negative. Their work formulated an economic order quantity model to maximize the retailer's total profit per unit time under nonlinear holding cost and demand.…”

Section: Introduction and Background Of The Studymentioning

confidence: 99%

An inventory model for a three-stage supply chain with random capacities considering disruptions and supplier reliability

et al. 2020

View full text Add to dashboard Cite

This study develops an inventory model to solve the problems of supply uncertainty in response to demand which follows a Poisson distribution. A positive aspect of this model is the consideration of random inventory, delivery capacities and supplier's reliability. Additionally, we assume supplier capacity follows an exponential distribution. This inventory model addresses the problem of a manufacturer having an imperfect production system with single supplier and single retailer and considers the quantity of product (Q), reorder points (r) and reliability factors (n) as the decision variables. The main contribution of our study is that we consider supplier may not be able to deliver the exact amount all the time a manufacturer needed. We also consider that the demand and the time interval between successive availability and unavailability of supplier and retailer follows a probability distribution. We use a genetic algorithm to find the optimal solution and compare the results with those obtained from simulated annealing algorithm. Findings reveal the optimal value of the decision variables to maximize the average profit in each cycle. Moreover, a sensitivity analysis was carried out to increase the understanding of the developed model. The methodology used in this study will help manufacturers to have a better understanding of the situation through the joint consideration of disruption of both the supplier and retailer integrated with random capacity and reliability.

show abstract

An EOQ inventory model with nonlinear stock dependent holding cost, nonlinear stock dependent demand and trade credit

Cited by 86 publications

References 58 publications

Estimating inventory stocks for greenhouse heating needs

Estimating inventory stocks for greenhouse heating needs

Improved MRO Inventory Management System in Oil and Gas Company: Increased Service Level and Reduced Average Inventory Investment

An inventory model for a three-stage supply chain with random capacities considering disruptions and supplier reliability

Contact Info

Product

Resources

About