Maximization of the return on inventory management expense in a system with price- and stock-dependent demand rate

Pando, Valentín; San-José, Luis A.; Sicilia, Joaquı́n; Pablo, David Alcaide López de

doi:10.1016/j.cor.2020.105134

Cited by 31 publications

(16 citation statements)

References 38 publications

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“…The deployment of industrial IoT in intelligent manufacturing environment mainly contains the equipment perception layer, data resource layer, service application layer, and operation and maintenance management layer, which work together to maintain all data links [ 30 , 31 ]. From the specific business point of view, the cloud components are mainly responsible for the formation of the model of the collected data, and the peripheral components are basically responsible for obtaining the model from the data dictionary, providing timely services for factory equipment in real time.…”

Section: Related Theories and Research Methodsmentioning

confidence: 99%

Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

Ran¹

2021

PLoS ONE

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The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.

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Section: Related Theories and Research Methodsmentioning

confidence: 99%

Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

Ran¹

2021

PLoS ONE

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“…Autonomation inspection can perform error-free inspection. Price along with stock-level of the product is crucial for industry Pando et al [31]. In the similar direction, Xu et al [32] proposed a "dual channel SCM", where online demand takes a major part to make decisions.…”

Section: Selling Price Dependent Demandmentioning

confidence: 99%

Intelligent servicing strategy for an online-to-offline (O2O) supply chain under demand variability and controllable lead time

et al. 2022

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With the advancement of technologies, industries tries to adopt the advantages of the technology. Customers are busy in their daily life, and the online platform is the best option for retail, whereas traditional customers still prefer to visit the retail shop. Few customers choose the product online but buy it online or vice-versa. Owing to all those circumstances, current study focuses on an intelligent dual channel (online-to-offine) strategy in industry to arrange the optimal services for customers. The selling price of the product vary with different channel, which helps to determine the demand of product for entire supply chain. Two important factors, backorder and lead-time are examined precisely through marginal value which helps to arrange optimal service and calculate the exact profit. The profit for a centralized and decentralized case are computed for both the players. Some propositions are developed to prove the global optimality. Numerical results prove that a centralized case provides 7.77% better pro t than a decentralized case due to bonding between the players.

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“…Finally, the following corollary shows the sensitivity analysis of the optimal lot size q * , which is given by the expression (22). Corollary 4.…”

Section: Proof Please See the Proof In Appendix Amentioning

confidence: 99%

“…Recently, Pando et al [22] devised the term return on inventory management expense (ROI ME), defined as the ratio between the net profit (income minus total costs) and the total costs involved in managing the inventory (purchasing cost, ordering cost, holding cost, etc.). Then, the minimum value of the ROI ME is −1 (when there are no sales), with a profitability index of PI = 0, because there is no income.…”

Section: Introductionmentioning

confidence: 99%

Profitability Index Maximization in an Inventory Model with a Price- and Stock-Dependent Demand Rate in a Power-Form

et al. 2021

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This paper presents the optimal policy for an inventory model where the demand rate potentially depends on both selling price and stock level. The goal is the maximization of the profitability index, defined as the ratio income/expense. A numerical algorithm is proposed to calculate the optimal selling price. The optimal values for the depletion time, the cycle time, the maximum profitability index, and the lot size are evaluated from the selling price. The solution shows that the inventory must be replenished when the stock is depleted, i.e., the depletion time is always equal to the cycle time. The optimal policy is obtained with a suitable balance between ordering cost and holding cost. A condition that ensures the profitability of the financial investment in the inventory is established from the initial parameters. Profitability thresholds for several parameters, including the scale and the non-centrality parameters, keeping all the others fixed, are evaluated. The model with an isoelastic price-dependent demand is solved as a particular case. In this last model, all the optimal values are given in a closed form, and a sensitivity analysis is performed for several parameters, including the scale parameter. The results are illustrated with numerical examples.

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Maximization of the return on inventory management expense in a system with price- and stock-dependent demand rate

Cited by 31 publications

References 38 publications

Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

Intelligent servicing strategy for an online-to-offline (O2O) supply chain under demand variability and controllable lead time

Profitability Index Maximization in an Inventory Model with a Price- and Stock-Dependent Demand Rate in a Power-Form

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