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Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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1

Xabibullayev, Ibrokhim, Ruslana Zhovnovach, and Mariia Petrova. "MODEL OF ASSORTMENT OPTIMIZATION AND INVENTORY MANAGEMENT IN ENTERPRISE." Economics and Finance 9, no. 1 (2021): 31–46. http://dx.doi.org/10.51586/2311-3413.2021.9.1.31.46.

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Анотація:
The actual problems of work in the sphere of organization of supply and sale are considered, the existing developments in the sphere of modeling and optimization of commercial activity of the wholesale trading enterprises are analyzed. The necessity of a comprehensive approach to improving the commercial activities of wholesalers is substantiated. The composition of the solutions included in the integrated approach is determined by the sole purpose, practical possibilities of its implementation and implementation at the wholesale enterprises and is based on the analysis of actual problems of the industry as a whole, interdependence in the work of departments, development of a single optimization criterion. The effectiveness of the integrated approach is based on the fact that for the sake of maximum result it is important not to isolate the development of individual operations, but to improve the entire purchasing system of the wholesale enterprise as a whole. The scientific and methodological approach of carrying out the integrated ABC-XYZ analysis of a range of a trading enterprise by its combination with R/S analysis, which acts as a criterion for the effectiveness of the XYZ analysis and an indicator of the possibility of forecasting the dynamics of sales of individual product groups, has been improved. XYZ analysis, based on the calculation of the coefficient of variation, when there are deterministic factors such as seasonality, cyclicality or trend in a series of determinants, shows erroneous results. Therefore, it is suggested to use R/S analysis to evaluate the quality of the XYZ analysis and to pre-process the data. This will allow us to draw more adequate conclusions about the possibility of forecasting the dynamics of sales of certain product groups in the future.
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2

Boucher, Thomas O., William E. Stuckey, and Layek Abdel-Malek. "An Inventory Model for International Inventory Management." Engineering Economist 30, no. 3 (January 1985): 245–68. http://dx.doi.org/10.1080/00137918508902910.

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3

Panday, Rorim, and Dovina Navanti. "Inventory Management Evaluation and Inventory Forecast Using EOQ." Syntax Literate ; Jurnal Ilmiah Indonesia 6, no. 1 (February 28, 2021): 395. http://dx.doi.org/10.36418/syntax-literate.v6i1.2286.

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Анотація:
The fashion industry that is gamis in Indonesia is growing rapidly because the majority of the population is Moslem. Elzatta is a company that does business on Moslem clothing, one of its products is the gamis. The company is experiencing problems with stockpiling in warehouses, because of models that were not sold, as well as outdated models. With the accumulation of products in warehouses in 2017 and 2018, many products will be damaged. For this reason, the company runs a buy one get one business strategy and sells products at low prices. As a result, the company suffered a substantial loss. For this reason, it is necessary to evaluate the inventory management that has been carried out using the EOQ model. For 2019, it is necessary to plan the number of products to be sold and apply the EOQ model. The results of evaluations in 2017 and 2018, by using EOQ the company could save 64.78% for 2017 and 63.40% for 2018. Whereas for 2019, after forecasting the number of sales using the seasonal model, sales projections are similar to the number of sales in the previous years, so that the number of products needed for a single order is 1364 pcs.
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4

Michalski, G. "Corporate inventory management with value maximization in view." Agricultural Economics (Zemědělská ekonomika) 54, No. 5 (June 13, 2008): 187–92. http://dx.doi.org/10.17221/251-agricecon.

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Анотація:
The basic financial purpose of the firm is maximization of its value. An inventory management should also contribute to the realization of this basic aim. Many current assets management models which we can find in the literature relating to financial management were constructed with the assumption of book profit maximization as the basic aim. These models could be lacking what relates to another aim, i.e., maximization of the enterprise value. This article presents the value based inventory management model modification.
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5

Przasnyski, Zbigniew H. "Spreadsheet Simulation Model For Inventory Management." SIMULATION 63, no. 1 (July 1994): 32–43. http://dx.doi.org/10.1177/003754979406300105.

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6

Michalski, Grzegorz. "Value based management approach in inventory management." Medjunarodni problemi 61, no. 1-2 (2009): 36–47. http://dx.doi.org/10.2298/medjp0902036m.

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Анотація:
The basic financial purpose of the firm is maximization of its value. A inventory management should also contribute to realization of this basic aim. Many current assets management models which we can find in the literature relating to financial management were constructed with the assumption of book profit maximization as basic aim. These models could lacking what relates to another aim, i.e., maximization of enterprise value. This article presents the value based inventory management model modification.
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7

Ordoñez Castano, Andrés, Juan Pablo Orejuela Cabrera, and Juan José Bravo. "Inventory management model in a butcher shop." Revista científica Pensamiento y Gestión, no. 39 (June 1, 2015): 30–51. http://dx.doi.org/10.14482/pege.39.8441.

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8

Toh, R. "Hotel room-inventory management: an overbooking model." Cornell Hotel and Restaurant Administration Quarterly 43, no. 4 (August 2002): 79–90. http://dx.doi.org/10.1016/s0010-8804(02)80044-1.

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9

Abdelaziz, Fouad Ben, and Sameh Mejri. "Decentralised bilevel model for shared inventory management." Production Planning & Control 24, no. 8-9 (March 21, 2012): 684–701. http://dx.doi.org/10.1080/09537287.2012.666857.

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10

Edirisinghe, Lalith, Zhihong Jin, and A. W. Wijeratne. "Container inventory management: introducing the 3F model." International Journal of Logistics Systems and Management 31, no. 3 (2018): 363. http://dx.doi.org/10.1504/ijlsm.2018.095825.

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11

Wijeratne, A. W., Lalith Edirisinghe, and Zhihong Jin. "Container inventory management: introducing the 3F model." International Journal of Logistics Systems and Management 31, no. 3 (2018): 363. http://dx.doi.org/10.1504/ijlsm.2018.10016864.

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12

Anthony, Biri, and Degbe Sewodo Augustin. "Intra-Firm Inventory Management Model with Transshipments." American Journal of Industrial and Business Management 07, no. 01 (2017): 15–26. http://dx.doi.org/10.4236/ajibm.2017.71002.

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13

Poo, Mark Ching-Pong, and Tsz Leung Yip. "An optimization model for container inventory management." Annals of Operations Research 273, no. 1-2 (November 15, 2017): 433–53. http://dx.doi.org/10.1007/s10479-017-2708-8.

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14

Feng, Xi Wen, Hui Wen Jia, and Qian Feng. "Study on Inventory Management System Dynamics Model of Successive Manufacturing Enterprises." Advanced Materials Research 433-440 (January 2012): 2730–36. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2730.

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Анотація:
This paper is based on the analyzing the features and the questions of inventory management of successive manufacturing enterprises, using the inventory management theory and the system dynamics theory, on the assumption of combining actual situation of inventory management of Hongyu Corporation, analyzes the relations of kinds of factors which affect the inventory management, using the system dynamics method, establishes the causality graph of kinds factors which affect the inventory management in Hongyu Corporation, including the causality graph of cooperation part inventory management, semi products inventory management, profit and the whole inventory management in Hongyu Corporation. Based on the causality graphs, the paper establishes the system dynamics model of inventory management of Hongyu Corporation. The system dynamics model of inventory management in Hongyu Corporation which was built in this paper is accurate and steady, especially satisfied to the practical production of Hongyu Corporation and successive manufacturing enterprises, it greatly helps to raise the inventory management level of Hongyu Corporation, so this model has great significance in both theory and reality for inventory management of successive manufacturing enterprises.
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15

Xu, Gui Bin, Xu Zhao, and Xue Ting Li. "Dynamic Planning Model for Port Iorn Ore Inventory." Applied Mechanics and Materials 409-410 (September 2013): 1429–32. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1429.

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Анотація:
Port inventory is a very important part in iron ore supply chain. Through the inventory management, we can get the best inventory strategy to minimize the total inventory cost. The inventory management of the port iron ore can get a reasonable and feasible inventory management strategy through the dynamic planning model.
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16

Sinha, Diptendu, and Khalil F. Matta. "Multiechelon (R, S) Inventory Model." Decision Sciences 22, no. 3 (July 1991): 484–99. http://dx.doi.org/10.1111/j.1540-5915.1991.tb01276.x.

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17

Mulya, Yudhia. "Inventory simulation-optimization model for small business." International Journal of Business, Economics, and Social Development 1, no. 2 (June 26, 2020): 55–60. http://dx.doi.org/10.46336/ijbesd.v1i2.36.

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Анотація:
Simulation-optimization inventory models are widely used in inventory management studies, including for perishable good. A good inventory management is required to save inventory cost. We use simulation approach to take into consideration of uncertain demand and lead time to obtain a better result of optimal order quantity that minimizes inventory cost. Simulation result shows that simulation-optimization models generates lower total inventory cost. The simulation provides information of objectives in inventory management: (1) how much to order and (2) when to order with minimum cost which gives valuable information in business decision making.
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18

Soshko, Oksana, Vilmars Vjakse, and Yuri Merkuryev. "Modelling Inventory Management System at Distribution Company: Case Study." Scientific Journal of Riga Technical University. Computer Sciences 42, no. 1 (January 1, 2010): 87–93. http://dx.doi.org/10.2478/v10143-010-0047-1.

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Анотація:
Modelling Inventory Management System at Distribution Company: Case Study The paper presents a case study on improving inventory management at the distribution company which operates in Latvia. The case study is focused on application of different modelling approaches in inventory management under uncertain demand, namely inventory models, simulation models and optimization model. The functionality of each model as well as its benefits for the current problem is discussed in the end of the paper.
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19

Baek, Jung Woo, and Yun Han Bae. "A queuing-inventory model for manufacturing systems with fluid-type inventory." Omega 111 (September 2022): 102674. http://dx.doi.org/10.1016/j.omega.2022.102674.

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20

Sasireka, A., A. H. Nandhu Kishore, J. Femila Mercy Rani, and N. Martin. "INVENTORY MODEL TO COMBAT E-WASTE MANAGEMENT COSTS." Advances in Mathematics: Scientific Journal 9, no. 5 (July 4, 2020): 3059–65. http://dx.doi.org/10.37418/amsj.9.5.65.

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21

Asllani, Arben, Elizabeth Culler, and Lawrence Ettkin. "A simulation-based apheresis platelet inventory management model." Transfusion 54, no. 10pt2 (February 17, 2014): 2730–35. http://dx.doi.org/10.1111/trf.12570.

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22

Nakandala, Dilupa, Henry Lau, and Paul K. C. Shum. "A lateral transshipment model for perishable inventory management." International Journal of Production Research 55, no. 18 (April 6, 2017): 5341–54. http://dx.doi.org/10.1080/00207543.2017.1312587.

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23

Venkataraman, Sri Vanamalla, and Dipankar Mandal. "A dynamic programming model for perishable inventory management." International Journal of Operational Research 35, no. 2 (2019): 147. http://dx.doi.org/10.1504/ijor.2019.10022433.

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24

Lin, Jennifer, Henry Chao, and Peterson Julian. "A demand independent inventory model." Yugoslav Journal of Operations Research 23, no. 1 (2013): 129–35. http://dx.doi.org/10.2298/yjor120127021l.

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Анотація:
This paper is an extension of Deng et al. (2007) that was published in the European Journal of Operational Research. We have generalized their model from ramp type demand to arbitrary positive demand while theoretically discovering an important phenomenon: the optimal solution is actually independent of the demand as pointed out by Wou (2010), Hung (2011) and Lin (2011). We extend their inventory models in which the deteriorated rate is any non-negative function and backlog rate is inversely linearly related to the waiting time. Our findings will provide a new inventory system to help decision makers decide the optimal ordering quantity and replenishment policy.
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25

Dong, Li Li. "The Study on the Model of Vendor Management Inventory in Supply Chain." Applied Mechanics and Materials 644-650 (September 2014): 6105–8. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.6105.

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Анотація:
In order to reduce inventory cost effectively, the vendor management inventory (VMI) is studied in this paper. The inventory problem of supply chain is described, the model of VMI is established, and the optimal solution is obtained. The research in this paper can enrich the supply chain management theory, and help enterprises improve inventory management level.
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26

Kawakami, Kosuke, Hirokazu Kobayashi, and Kazuhide Nakata. "Seasonal Inventory Management Model for Raw Materials in Steel Industry." INFORMS Journal on Applied Analytics 51, no. 4 (July 2021): 312–24. http://dx.doi.org/10.1287/inte.2021.1073.

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Анотація:
We developed a seasonal inventory management model for raw materials, such as iron ore and coal, for multiple suppliers and multiple mills. The Nippon Steel Corporation imports more than 100 million tons of raw material annually by vessels from Australia, Brazil, Canada, and other countries. Once these raw materials arrive in Japan, they are transported to domestic mills and stored in yards before being treated in a blast furnace. A critical problem currently facing the industry is the limited capacity of the yards, which leads to high demurrage costs while ships wait for space to open up in the yards before they can unload. To reduce the demurrage costs, the inventory levels of the raw materials must be kept as low as possible. However, inventory levels that are too low may lead to inventory shortage resulting from seasonal supply disruptions (e.g., a cyclone in Australia) that delay the supply of raw materials. Because both excess and depleted inventory levels lead to increased costs, optimal inventory levels must be determined. To solve this problem, we developed an inventory management model that considers variations on the supply side, differences that should be observable upon looking at the ship operations. The concept is to model the probability distribution of ship arrival intervals by brand groups and mills. We divided ship operations into two stages: arrival at all mills (in Japan) and arrival at individual mills. We modeled the former as a nonhomogeneous Poisson process and the latter as a nonhomogeneous Gamma process. Our proposed model enables inventory levels to be reduced by 14% in summer and 6% in winter.
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27

Datta, T. K., and A. K. Pal. "A Note on an Inventory Model with Inventory-Level-Dependent Demand Rate." Journal of the Operational Research Society 41, no. 10 (October 1990): 971. http://dx.doi.org/10.2307/2583275.

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28

Datta, T. K., and A. K. Pal. "A Note on an Inventory Model with Inventory-level-dependent Demand Rate." Journal of the Operational Research Society 41, no. 10 (October 1990): 971–75. http://dx.doi.org/10.1057/jors.1990.149.

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29

Thompson, Paul D., Roberto Soares, Hong Jae Choung, Fazil T. Najafi, and Richard Kerr. "User Cost Model for Bridge Management Systems." Transportation Research Record: Journal of the Transportation Research Board 1697, no. 1 (January 2000): 6–13. http://dx.doi.org/10.3141/1697-02.

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Анотація:
User cost models are used in bridge management systems to quantify, in economic terms, the potential safety and mobility benefits of functional improvements to bridges. One component of many such models is a formula to predict traffic accident risk on the basis of bridge characteristics such as roadway width and approach roadway alignment. A critical analysis of the user cost model used in AASHTO’s Pontis and Bridgit systems found that the model was overly sensitive to extremes of roadway width, yielding unrealistically high benefit estimates. A new model was developed using Florida data on bridge characteristics and traffic accidents. The new model has superior behavior and statistical characteristics on a full inventory of state highway bridges. Since it relies solely on National Bridge Inventory data items, the model is readily transferable to many different bridge management systems.
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30

Kim, Myungsoo, Jaehyeong Lee, Chaegyu Lee, and Jongpil Jeong. "Framework of 2D KDE and LSTM-Based Forecasting for Cost-Effective Inventory Management in Smart Manufacturing." Applied Sciences 12, no. 5 (February 24, 2022): 2380. http://dx.doi.org/10.3390/app12052380.

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Анотація:
Over the last decade, the development of machine-learning models has enabled the design of sophisticated regression models. For this reason, studies have been conducted to design predictive models using machine learning in various industries. In particular, in terms of inventory management, forecasting models predict historical market demand, predict future demand, and enable systematic inventory management. However, in most small and medium enterprise (SMEs), there is no systematic management of data, and because of the lack of data and the volatility of random data, it is difficult for prediction models to work well. Since the predictive model is a core function derived from the management of the enterprise’s inventory data, the poor performance of the model causes the company’s inventory data-management system to be degraded. Companies that have poor inventory data because of this vicious cycle will continue to have difficulty introducing data-management systems. In this paper, we propose a framework that can reliably predict the inventory data of a firm by modeling the volatility of a firm stochastically. The framework makes the prediction using the point prediction model by means of LSTM(Long Short Term Memory), the 2D kernel density function, and the prediction result reflecting inventory-management cost. Through various experiments, the necessity of interval prediction in demand prediction and the validity of the cost-effective prediction model through the readjustment function were shown.
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31

Mejia, Jose, Liliana Avelar-Sosa, Boris Mederos, and Jorge L. García-Alcaraz. "Inventory Model with Stochastic Demand Using Single-Period Inventory Model and Gaussian Process." Processes 10, no. 4 (April 16, 2022): 783. http://dx.doi.org/10.3390/pr10040783.

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Анотація:
Proper inventory management is vital to achieving sustainability within a supply chain and is also related to a company’s cash flow through the funds represented by the inventory. Therefore, it is necessary to balance excess inventory and insufficient inventory. However, this can be difficult to achieve in the presence of stochastic demand because decisions must be made in an uncertain environment and the inventory policy bears risks associated with each decision. This study reports an extension of the single-period model for the inventory problem under uncertain demand. We proposed incorporating a Gaussian stochastic process into the model using the associated posterior distribution of the Gaussian process as a distribution for the demand. This enables the modeling of data from historical inventory demand using the Gaussian process theory, which adapts well to small datasets and provides measurements of the risks associated with the predictions made. Thus, unlike other works that assume that demand follows an autoregressive or Brownian motion model, among others, our approach enables adaptability to different complex forms of demand trends over time. We offer several numerical examples that explore aspects of the proposed approach and compare our results with those achieved using other state-of-the-art methods.
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32

Pan, Feng Shan, Chun Ming Ye, and Jiao Yang. "Inventory Optimization in Supply Chains." Applied Mechanics and Materials 66-68 (July 2011): 1305–10. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1305.

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Анотація:
This paper reviews joint inventory management models and discusses its contribution to improve the overall supply chain competitiveness, after discussing suppliers and agents’ plan model respectively. Further, based on the background of Company F, and combined the modern scientific management theory of supply chain inventory with practical case, it improves the inventory management method. Finally, the conclusion of calculation is presented in detail.
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33

Riezebos, J., and G. J. C. Gaalman. "A single-item inventory model for expected inventory order crossovers." International Journal of Production Economics 121, no. 2 (October 2009): 601–9. http://dx.doi.org/10.1016/j.ijpe.2006.10.004.

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34

Wang, Yun Rui. "Enterprise Inventory Management and Simulation." Advanced Materials Research 933 (May 2014): 874–78. http://dx.doi.org/10.4028/www.scientific.net/amr.933.874.

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Анотація:
Inventory is an important link in supply chain, according to characteristics and objectives of inventory control, the reasonable safety inventory, order point, the highest inventory and inventory strategy were determined based on historical sales data. Using Witness simulation software, the optimal strategy was selected from nine inventory strategies by running simulation model for one year. If goods shortage was allowing, the seventh schemes (72000,158100) was better ,the minimum total cost was 888158 yuan ; considering allowing a small amount of shortage, the cost of eighth schemes ( 82000,158100) was least of 1095167; the third scenarios (92000,138100) was selected if without shortage, and the minimum total cost was 1254909 yuan. This shows, the application of computer simulation to assist in the management of inventory is scientific and feasible, it can help enterprises to save the order and operation cost.
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35

Sánchez-Sierra, Shaira-Tatiana, Santiago-Omar Caballero-Morales, Diana Sánchez-Partida, and Jose-Luis Martínez-Flores. "FACILITY LOCATION MODEL WITH INVENTORY TRANSPORTATION AND MANAGEMENT COSTS." Acta logistica 5, no. 3 (September 30, 2018): 79–86. http://dx.doi.org/10.22306/al.v5i3.98.

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36

Prokofieva, Oksana S., and Yana V. Yushchuk. "OPTIMIZATION MODEL OF INVENTORY MANAGEMENT AT AN INDUSTRIAL ENTERPRISE." Proceedings of Irkutsk State Technical University 21, no. 7 (July 2017): 158–63. http://dx.doi.org/10.21285/1814-3520-2017-7-158-163.

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37

Willoughby, Keith A. "Project procurement and disposal decisions: An inventory management model." International Journal of Production Economics 71, no. 1-3 (May 2001): 467–72. http://dx.doi.org/10.1016/s0925-5273(00)00146-8.

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38

Roy, Dilip, and Debdip khan. "Integrated model for line balancing with workstation inventory management." International Journal of Industrial Engineering Computations 1, no. 2 (June 15, 2010): 139–46. http://dx.doi.org/10.5267/j.ijiec.2010.02.003.

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39

Bretthauer, Kurt, Bala Shetty, Siddhartha Syam, and Susan White. "A Model for Resource Constrained Production and Inventory Management." Decision Sciences 25, no. 4 (July 1994): 561–77. http://dx.doi.org/10.1111/j.1540-5915.1994.tb01860.x.

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40

SAKAGUCHI, Tatsuhiko, Hiroaki FUJIWARA, Jia Yee Chai, and Keiichi SHIRASE. "3501 MRP Based Inventory Management for Supply Chain Model." Proceedings of the JSME annual meeting 2008.4 (2008): 293–94. http://dx.doi.org/10.1299/jsmemecjo.2008.4.0_293.

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41

Huang, Tingliang, and Jan A. Van Mieghem. "Clickstream Data and Inventory Management: Model and Empirical Analysis." Production and Operations Management 23, no. 3 (July 19, 2013): 333–47. http://dx.doi.org/10.1111/poms.12046.

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42

Badri, Masood A. "A Simulation Model for Multi-Product Inventory Control Management." SIMULATION 72, no. 1 (January 1999): 20–32. http://dx.doi.org/10.1177/003754979907200103.

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43

YADAVALLI, V. S. S., M. JEEVA, and RAJALAKSHMI RAJAGOPALAN. "MULTI-ITEM DETERMINISTIC FUZZY INVENTORY MODEL." Asia-Pacific Journal of Operational Research 22, no. 03 (September 2005): 287–95. http://dx.doi.org/10.1142/s0217595905000637.

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Анотація:
In this note, a fuzzy inventory model with multiple items is considered. Economic order quantities (EOQs) are developed for these items. The costs involved in inventory are always assumed to be exact, otherwise called as crisp or hard, but in practice these costs are not exact or precise but vary over a range. In such cases an application of fuzzy set theory plays a vital role in obtaining the EOQ. An application of this model to manpower planning is studied with an example; sensitivity analysis is also made.
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44

Sanguri, Kamal, and Sri Vanamalla Venkataraman. "Inventory model based upon order criticality." International Journal of Operational Research 40, no. 3 (2021): 380. http://dx.doi.org/10.1504/ijor.2021.114304.

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45

Venkataraman, Sri Vanamalla, and Kamal Sanguri. "Inventory model based upon order criticality." International Journal of Operational Research 40, no. 3 (2021): 380. http://dx.doi.org/10.1504/ijor.2021.10036890.

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46

Alkhedher, M. J., M. A. Darwish, and Abdulrahman R. Alenezi. "Stochastic inventory model for imperfect production processes." International Journal of Logistics Systems and Management 15, no. 1 (2013): 32. http://dx.doi.org/10.1504/ijlsm.2013.053237.

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47

Mahdi Tajbakhsh, M., Chi-Guhn Lee, and Saeed Zolfaghari. "An inventory model with random discount offerings." Omega 39, no. 6 (December 2011): 710–18. http://dx.doi.org/10.1016/j.omega.2011.02.003.

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48

Madu, Christian N. "The sensitivity of (Q, S) inventory model." Omega 16, no. 1 (January 1988): 61–64. http://dx.doi.org/10.1016/0305-0483(88)90009-6.

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49

AUCAMP, DONALD V. "An inventory model with expedited stockouts." International Journal of Production Research 24, no. 1 (January 1986): 27–32. http://dx.doi.org/10.1080/00207548608919708.

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50

Dave, U. "The rational expectations equilibrium inventory model." European Journal of Operational Research 45, no. 2-3 (April 1990): 373–74. http://dx.doi.org/10.1016/0377-2217(90)90204-o.

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