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Journal articles on the topic 'INVENTORY-APPROACH'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Walton, J. H. D. "Inventory Control-A Soft Approach?" Journal of the Operational Research Society 45, no. 4 (1994): 485. http://dx.doi.org/10.2307/2584225.

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2

Walton, J. H. D. "Inventory Control-A Soft Approach?" Journal of the Operational Research Society 45, no. 4 (1994): 485–86. http://dx.doi.org/10.1057/jors.1994.72.

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3

Baharom, Nuridawati, and Pa’ezah Hamzah. "Inventory Optimization using Simulation Approach." Journal of Computing Research and Innovation 3, no. 2 (2018): 38–47. http://dx.doi.org/10.24191/jcrinn.v3i2.93.

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Inventory creates a significant cost to a firm in the form of the ordering cost, shortage cost, holding cost and also the cost of the goods itself. Managing inventory is always a big challenge for firms in order to balance these operating costs and maintain customer’s service. In this paper, a case study of an electronics manufacturing firm was used to illustrate the use of the Monte Carlo simulation to improve the current inventory system for sensor cable. A simulation model mimicking the current inventory system was developed, and used to study the current system and alternative reorder point policies. Various reorder points were experimented to determine the reorder policy that results in the lowest average total inventory cost per week. The simulation experiments allow the decision maker to make good purchasing decisions in order to avoid ordering excessive raw materials which lead to higher inventory cost to the company.   Keywords: inventory, optimization, Monte Carlo Simulation

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4

Borade, Atul B., and Satish V. Bansod. "An approach for inventory routing under vendor managed inventory system." International Journal of Services Sciences 3, no. 4 (2010): 293. http://dx.doi.org/10.1504/ijssci.2010.035761.

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5

Sui, Zheng, Abhijit Gosavi, and Li Lin. "A Reinforcement Learning Approach for Inventory Replenishment in Vendor-Managed Inventory Systems With Consignment Inventory." Engineering Management Journal 22, no. 4 (2010): 44–53. http://dx.doi.org/10.1080/10429247.2010.11431878.

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6

Gholamian, M. R., and M. Heydari. "An inventory model with METRIC approach in location-routing-inventory problem." Advances in Production Engineering & Management 12, no. 2 (2017): 115–26. http://dx.doi.org/10.14743/apem2017.2.244.

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7

Brodetskiy, Gennady L. "The new approach to inventory optimisation." International Journal of Logistics Systems and Management 22, no. 3 (2015): 251. http://dx.doi.org/10.1504/ijlsm.2015.072282.

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8

Joshi, Rajani R. "Immune network memory: An inventory approach." Computers & Operations Research 22, no. 6 (1995): 575–91. http://dx.doi.org/10.1016/0305-0548(94)00057-f.

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9

Fors, Nashat. "Mathematical programming approach to inventory control." Computers & Industrial Engineering 11, no. 1-4 (1986): 166–69. http://dx.doi.org/10.1016/0360-8352(86)90071-9.

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10

Schwartz, Jay D., and Daniel E. Rivera. "A process control approach to tactical inventory management in production-inventory systems." International Journal of Production Economics 125, no. 1 (2010): 111–24. http://dx.doi.org/10.1016/j.ijpe.2010.01.011.

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11

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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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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12

Wang, Dja-Shin, and Tong-Yuan Koo. "Inventory Management Practices Approach to Reverse Logistics." Industrial Engineering and Management Systems 9, no. 4 (2010): 303–11. http://dx.doi.org/10.7232/iems.2010.9.4.303.

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13

Farsad, B. "A measured approach to food-inventory management." Cornell Hotel and Restaurant Administration Quarterly 34, no. 3 (1993): 90–95. http://dx.doi.org/10.1016/0010-8804(93)90092-w.

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14

Zeng, Amy Z. "A partial backordering approach to inventory control." Production Planning & Control 12, no. 7 (2001): 660–68. http://dx.doi.org/10.1080/09537280010016008.

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15

Bertsimas, Dimitris, and Aurélie Thiele. "A Robust Optimization Approach to Inventory Theory." Operations Research 54, no. 1 (2006): 150–68. http://dx.doi.org/10.1287/opre.1050.0238.

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16

M�ller, Christian Max. "A point process approach to inventory models." Applied Stochastic Models in Business and Industry 16, no. 2 (2000): 111–26. http://dx.doi.org/10.1002/1526-4025(200004/06)16:2<111::aid-asmb412>3.0.co;2-1.

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17

Maksic, Radovan. "Logistic approach to dislocating mobile items inventory." Vojnotehnicki glasnik 49, no. 2 (2001): 147–57. http://dx.doi.org/10.5937/vojtehg0102147m.

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18

Knaap, Gerrit J., and Lewis D. Hopkins. "The Inventory Approach to Urban Growth Boundaries." Journal of the American Planning Association 67, no. 3 (2001): 314–26. http://dx.doi.org/10.1080/01944360108976238.

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19

Farsad, Behshid, and Stephen LeBruto. "A Measured Approach to Food-Inventory Management." Cornell Hotel and Restaurant Administration Quarterly 34, no. 3 (1993): 90–95. http://dx.doi.org/10.1177/001088049303400315.

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20

Wagner, Michael R. "Robust Inventory Management: An Optimal Control Approach." Operations Research 66, no. 2 (2018): 426–47. http://dx.doi.org/10.1287/opre.2017.1669.

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21

Song, Jin-Hwa, and Kevin C. Furman. "A maritime inventory routing problem: Practical approach." Computers & Operations Research 40, no. 3 (2013): 657–65. http://dx.doi.org/10.1016/j.cor.2010.10.031.

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22

Yule, I. J., P. J. Cain, E. J. Evans, and C. Venus. "A spatial inventory approach to farm planning." Computers and Electronics in Agriculture 14, no. 2-3 (1996): 151–61. http://dx.doi.org/10.1016/0168-1699(95)00045-3.

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23

Rubinstein, Y. R., and J. Kreimer. "Inventory models under uncertainty: an adaptive approach." Mathematics and Computers in Simulation 28, no. 3 (1986): 169–88. http://dx.doi.org/10.1016/0378-4754(86)90025-x.

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24

Edirisinghe, Lalith. "Smart Container Inventory Management: A Conceptual Approach." CINEC Academic Journal 2 (December 30, 2017): 105. http://dx.doi.org/10.4038/caj.v2i0.64.

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25

Pulido-Rojano, Alexander, Andrea Pizarro-Rada, Miguel Padilla-Polanco, Milton Sánchez-Jiménez, and Ladianys De-la-Rosa. "An optimization approach for inventory costs in probabilistic inventory models: A case study." Ingeniare. Revista chilena de ingeniería 28, no. 3 (2020): 383–95. http://dx.doi.org/10.4067/s0718-33052020000300383.

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26

Ni'mah, Zaidatun, and Yuniar Farida. "Multi Unit Spares Inventory Control-Three Dimensional (MUSIC 3D) Approach to Inventory Management." Jurnal Matematika "MANTIK" 5, no. 1 (2019): 19–27. http://dx.doi.org/10.15642/mantik.2019.5.1.19-27.

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Inventory control is a series of efforts that need to be done for each company to generate the maximum profit from existing inventory. In this study inventory control was conducted through the Multi-Unit Spares Inventory Control – Three Dimensional (MUSIC 3D) approach at PT Fajar Mas Murni Surabaya using three analysis, namely ABC analysis, SDE analysis, and FSN analysis. The result of ABC analysis show that category A consists of 6 items (3%) which contribute 81% to company income, category B consists of 16 items (8%) which contributes 15% to company income, while category C consists of 190 items (89%) which contribute 4% to company income. The result of SDE analysis shows that category S consists of 127 items (60% of all items), category D consists of 43 items (20% of all items), while category E consist of 42 items (20% of all items). The result of FSN analysis show that category F consists of 15% (7% of all items), category S consists of 41 items (19% of all items) and category N consists of 156 items (74% of all items).

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27

Kadiyala, Bharadwaj, Özalp Özer, and Alain Bensoussan. "A Mechanism Design Approach to Vendor Managed Inventory." Management Science 66, no. 6 (2020): 2628–52. http://dx.doi.org/10.1287/mnsc.2019.3297.

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This paper studies an inventory management problem faced by an upstream supplier that is in a collaborative agreement, such as vendor-managed inventory (VMI), with a retailer. A VMI partnership provides the supplier an opportunity to manage inventory for the supply chain in exchange for point-of-sales (POS)- and inventory-level information from the retailer. However, retailers typically possess superior local market information and as has been the case in recent years, are able to capture and analyze customer purchasing behavior beyond the traditional POS data. Such analyses provide the retailer access to market signals that are otherwise hard to capture using POS information. We show and quantify the implication of the financial obligations of each party in VMI that renders communication of such important market signals as noncredible. To help institute a sound VMI collaboration, we propose learn and screen—a dynamic inventory mechanism—for the supplier to effectively manage inventory and information in the supply chain. The proposed mechanism combines the ability of the supplier to learn about market conditions from POS data (over multiple selling periods) and dynamically determine when to screen the retailer and acquire his private demand information. Inventory decisions in the proposed mechanism serve a strategic purpose in addition to their classic role of satisfying customer demand. We show that our proposed dynamic mechanism significantly improves the supplier’s expected profit and increases the efficiency of the overall supply chain operations under a VMI agreement. In addition, we determine the market conditions in which a strategic approach to VMI results in significant profit improvements for both firms, particularly when the retailer has high market power (i.e., when the supplier highly depends on the retailer) and when the supplier has relatively less knowledge about the end customer/market compared with the retailer. This paper was accepted by Gad Allon, operations management.

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28

Specht, James, Albert Kagan, and Kin-Nam Lau. "Inventory Valuation Under Cyclical Demand: A Modelling Approach." Journal of Applied Business Research (JABR) 10, no. 4 (2011): 97. http://dx.doi.org/10.19030/jabr.v10i4.5912.

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Traditional EOQ models are limited in use because of the constant demand assumption. Firms rarely face constant demand and, therefore, these firms have restrictive use for the EOQ model. The proposed MIPM model in contrast can project orders and schedules for both variable and constant demand. The MIPM model capitalizes on state of the art programming techniques and current computer software. It permits firms to determine the minimum inventory costs in all product demand situations. In addition, it can be used to improve cash planning and to assist in optimized product contribution to firm profits through sensitivity analysis.

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29

Lee, Ellen. "A Piggy-Back Approach to Archaeological Resource Inventory." APT Bulletin 22, no. 1/2 (1990): 17. http://dx.doi.org/10.2307/1504268.

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30

Rubino, S., G. Mossa, and S. Digiesi. "Spare Parts inventory reduction: a multi-attribute approach." IFAC Proceedings Volumes 43, no. 3 (2010): 62–67. http://dx.doi.org/10.3182/20100701-2-pt-4012.00012.

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31

Venkitasamy, Seshapriya, and B. Vijay Bhaskar. "Emission Inventory-A Preliminary Approach to Primary Pollutants." Current Science 111, no. 11 (2016): 1831. http://dx.doi.org/10.18520/cs/v111/i11/1831-1835.

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32

Lei, Jun Cheng, Yan Peng Wu, and Wen Fei Zeng. "Optimization Approach for Multi-Stork Inventory Routing Problem." Advanced Materials Research 268-270 (July 2011): 1637–40. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1637.

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Inventory routing problem is one of the key issues to achieve integrated management of logistics. Solving this problem effectively, we can improve vehicle utilization, and reduce distribution costs. This paper, concerning the problem in inventory routing of multi-variety, multi-vendor to multi-customers, proposed heuristic algorithm based on greedy rules. The core strategy of the algorithm is to choose circularly the current lowest unit cost routine ---Hamilton delivery routes. Simulation shows that the algorithm reduces the unloaded ratio of truck, raises the efficiency of truck delivery and saves transport costs.

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33

Hu, Jian-Qiang, Soracha Nananukul, and Wei-Bo Gong. "A new approach to (s, S) inventory systems." Journal of Applied Probability 30, no. 4 (1993): 898–912. http://dx.doi.org/10.2307/3214521.

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In this paper, we consider period review (s, S) inventory systems with independent and identically distributed continuous demands and full backlogging. Using an approach recently proposed by Gong and Hu (1992), we derive an infinite system of linear equations for all moments of inventory level. Based on this infinite system, we develop two algorithms to calculate the moments of the inventory level. In the first one, we solve a finite system of linear equations whose solution converges to the moments as its dimension goes to infinity. In the second one, we in fact obtain the power series of the moments with respect to s and S. Both algorithms are based on some very simple recursive procedures. To show their efficiency and speed, we provide some numerical examples for the first algorithm.(s, S) INVENTORY SYSTEMS; DYNAMIC RECURSIVE EQUATIONS; INFINITE LINEAR EQUATIONS; MACLAURIN SERIES

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34

Partovi, Fariborz Y., and Murugan Anandarajan. "Classifying inventory using an artificial neural network approach." Computers & Industrial Engineering 41, no. 4 (2002): 389–404. http://dx.doi.org/10.1016/s0360-8352(01)00064-x.

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35

Adelman, Daniel. "A Price-Directed Approach to Stochastic Inventory/Routing." Operations Research 52, no. 4 (2004): 499–514. http://dx.doi.org/10.1287/opre.1040.0114.

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36

Hu, Jian-Qiang, Soracha Nananukul, and Wei-Bo Gong. "A new approach to (s, S) inventory systems." Journal of Applied Probability 30, no. 04 (1993): 898–912. http://dx.doi.org/10.1017/s002190020004465x.

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In this paper, we consider period review (s, S) inventory systems with independent and identically distributed continuous demands and full backlogging. Using an approach recently proposed by Gong and Hu (1992), we derive an infinite system of linear equations for all moments of inventory level. Based on this infinite system, we develop two algorithms to calculate the moments of the inventory level. In the first one, we solve a finite system of linear equations whose solution converges to the moments as its dimension goes to infinity. In the second one, we in fact obtain the power series of the moments with respect to s and S. Both algorithms are based on some very simple recursive procedures. To show their efficiency and speed, we provide some numerical examples for the first algorithm. (s, S) INVENTORY SYSTEMS; DYNAMIC RECURSIVE EQUATIONS; INFINITE LINEAR EQUATIONS; MACLAURIN SERIES

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37

Campbell, Ann Melissa, and Martin W. P. Savelsbergh. "A Decomposition Approach for the Inventory-Routing Problem." Transportation Science 38, no. 4 (2004): 488–502. http://dx.doi.org/10.1287/trsc.1030.0054.

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38

Puhan, Biranchi N. "Projective-Inventory: An Indigenous Approach to Personality Assessment." Psychology and Developing Societies 7, no. 2 (1995): 115–31. http://dx.doi.org/10.1177/097133369500700202.

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39

Kazaryan, R. R., and P. I. Andreeva. "Aspects of the System Approach to Inventory Management." IOP Conference Series: Materials Science and Engineering 753 (March 7, 2020): 042038. http://dx.doi.org/10.1088/1757-899x/753/4/042038.

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40

Nikbakht, Mohammad Reza, ali rahmani, and Mehrdad Sadr Ara. "The Evaluation of Inventory Management with Behavioral Approach." Iranian journal of Value and Behavioral Accounting 1, no. 2 (2017): 97–126. http://dx.doi.org/10.18869/acadpub.aapc.1.2.97.

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41

Maloney, Billy M., and Cerry M. Klein. "Constrained multi-item inventory systems: An implicit approach." Computers & Operations Research 20, no. 6 (1993): 639–49. http://dx.doi.org/10.1016/0305-0548(93)90118-3.

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42

KAPLAN, ALAN J. "Bayesian Approach to Inventory Control of New Parts." IIE Transactions 20, no. 2 (1988): 151–56. http://dx.doi.org/10.1080/07408178808966164.

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43

Kenski, Donna M., Richard A. Wadden, Peter A. Scheff, and William A. Lonneman. "Receptor Modeling Approach to VOC Emission Inventory Validation." Journal of Environmental Engineering 121, no. 7 (1995): 483–91. http://dx.doi.org/10.1061/(asce)0733-9372(1995)121:7(483).

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44

Maloney, Billy M., та Cerry M. Klein. "Constrained multi-item inventory systeṁs an implicit approach". Computers & Industrial Engineering 23, № 1-4 (1992): 279–82. http://dx.doi.org/10.1016/0360-8352(92)90116-2.

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45

Kabbaj, F., J. L. Menaldi, and E. Rofman. "Variational approach to serial multilevel production/inventory systems." Journal of Optimization Theory and Applications 65, no. 3 (1990): 447–83. http://dx.doi.org/10.1007/bf00939561.

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46

Ward, David. "Triangular tessellation: a new approach to forest inventory." Forest Ecology and Management 44, no. 2-4 (1991): 285–90. http://dx.doi.org/10.1016/0378-1127(91)90014-m.

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47

Chen, Youhua Frank. "Fractional programming approach to two stochastic inventory problems." European Journal of Operational Research 160, no. 1 (2005): 63–71. http://dx.doi.org/10.1016/j.ejor.2003.06.020.

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48

Kwak, Choonjong, Jin Sung Choi, Chang Ouk Kim, and Ick-Hyun Kwon. "Situation reactive approach to Vendor Managed Inventory problem." Expert Systems with Applications 36, no. 5 (2009): 9039–45. http://dx.doi.org/10.1016/j.eswa.2008.12.018.

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49

Zhao, Qiu-Hong, Shou-Yang Wang, and K. K. Lai. "A partition approach to the inventory/routing problem." European Journal of Operational Research 177, no. 2 (2007): 786–802. http://dx.doi.org/10.1016/j.ejor.2005.11.030.

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50

Demiralp, Berna, Bonnie B. Gantt, and David D. Selover. "Modeling unemployment as an inventory: A multicointegration approach." Journal of Macroeconomics 33, no. 4 (2011): 724–37. http://dx.doi.org/10.1016/j.jmacro.2011.06.005.

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