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Journal articles on the topic 'Inventory Control Management'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

Freeman, Jim, and C. D. J. Waters. "Inventory Control and Management." Journal of the Operational Research Society 44, no. 3 (March 1993): 316. http://dx.doi.org/10.2307/2584204.

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2

Freeman, Jim. "Inventory Control and Management." Journal of the Operational Research Society 44, no. 3 (March 1993): 316–17. http://dx.doi.org/10.1057/jors.1993.59.

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3

Gelders, L. F. "Inventory control and management." European Journal of Operational Research 62, no. 3 (November 1992): 386–87. http://dx.doi.org/10.1016/0377-2217(92)90136-w.

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4

Waldichuk, Michael. "Inventory management for pollution control." Marine Pollution Bulletin 18, no. 7 (July 1987): 368. http://dx.doi.org/10.1016/0025-326x(87)90291-8.

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5

White, A. S. "Management of inventory using control theory." International Journal of Technology Management 17, no. 7/8 (1999): 847. http://dx.doi.org/10.1504/ijtm.1999.002754.

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6

Sinha, Diptendu, Nasir Ghiaseddin, and Khalil Matta. "Expert systems for inventory control management." Computers & Industrial Engineering 17, no. 1-4 (January 1989): 425–29. http://dx.doi.org/10.1016/0360-8352(89)90099-5.

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7

RathinaKumar, V., K. Lalitha Priya, Prasanna Kumar.I, and C. Ravekumar. "Construction Material Management through Inventory Control Techniques." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 899. http://dx.doi.org/10.14419/ijet.v7i3.12.16558.

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Objectives: A research has shown that the construction materials account for more than 55%-60% of the total expenditure of a project. Efficient material planning plays a major role in the successful handing over of a project within the estimated cost and schedule. Methods: This paper mainly focuses on materials planning and inventory control as these are the major aspects of material management. S-curve analysis is performed to measure the fluctuation between estimated materials cost and market materials cost. The major reasons for this are identified by interviewing engineers and contractors. Inventory control techniques such as ABC classification as well as EOQ analysis are conducted. Findings: The result of the S-curve analysis show that the actual materials cost is higher than the planned materials cost in most cases. ABC and EOQ analysis are applied to maintain sufficient stock in inventory and any given point of time, to protect the materials in the inventory against damages, to reduce inventory holding costs, to overcome stock-out problems and to maintain the inventory in an optimal level. Sensitivity check is applied to the results of EOQ analysis. The stock-out of A class and B class material problems faced in the construction site can be reduced by the application of ABC classification and BOQ analysis. The total expenditure of inventory is less after the adoption of these simple inventory control techniques. Conclusion: Instead of using costly software for inventory management, the engineers and contractors may use these simple inventory control techniques which are equally beneficial and economical.
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8

Brinkman, Peter, Christine Seipel, and Alison Juers. "Prosthetic inventory management." Australian Health Review 24, no. 1 (2001): 120. http://dx.doi.org/10.1071/ah010120.

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An improved approach to inventory management in the Operating Theatre has been initiated at Princess AlexandraHospital. A Clinical Resource Co-ordinator (CRC) position was created to provide access to expertise in purchasingand materials management at the clinical level. A review of existing inventory management practices conducted bythe CRC revealed reporting inadequacies, lack of product specialisation and inadequate control over pricing, stocklevels and product usage. Through liaison with key stakeholders, a competitive tendering process was introduced whichresulted in a standing offer arrangement being installed for three specialty orthopaedic areas. Outcomes of thisarrangement are discussed. The importance of raising the area of prosthetic inventory management for debate in theAustralian literature is also highlighted.
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9

Chalotra, Vipul, and Neetu Andotra. "Inventory Management Strategies And Control Techniqies: An Empirical Investigation Of Small Scale Industries." Indian Journal of Applied Research 1, no. 5 (October 1, 2011): 28–30. http://dx.doi.org/10.15373/2249555x/feb2012/12.

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10

Vinod, Ben. "Advances in inventory control." Journal of Revenue and Pricing Management 4, no. 4 (January 2006): 367–81. http://dx.doi.org/10.1057/palgrave.rpm.5170159.

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11

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

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12

Zhang, Xiu Juan. "Inventory Control of Supply Chain Environment." Advanced Materials Research 971-973 (June 2014): 2346–49. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.2346.

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Supply chain management , so that enterprises not only to face the fierce competition in the international market , and can withstand demand uncertainty and rapid technological innovation and other factors that impact . Production and inventory management is always an important part of business process indispensable , is the added value chain to achieve the important part. In supply chain management, inventory level not only affects the overall cost of a single enterprise , but also restricts the performance of the entire supply chain. Based on the analysis of the traditional inventory management , supply chain theory combined with the latest developments , the use of vendor-managed inventory , joint management of inventory control, inventory control, multi-level strategy, inventory control, supply chain environment for further improvements , making inventory control of the supply chain environment more perfect .
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13

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

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14

Daellenbach, H. G. "Inventory Control and Trade Credit." Journal of the Operational Research Society 37, no. 5 (May 1986): 525. http://dx.doi.org/10.2307/2582676.

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15

Dave, Upendra, and Robert Goodell Brown. "Advanced Service Parts Inventory Control." Journal of the Operational Research Society 38, no. 5 (May 1987): 466. http://dx.doi.org/10.2307/2582739.

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16

Mitropoulos, Costas, Dennis W. McLeavey, and Seetharama L. Narasimhan. "Production Planning and Inventory Control." Journal of the Operational Research Society 36, no. 6 (June 1985): 545. http://dx.doi.org/10.2307/2582831.

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17

Mitropoulos, Costas. "Production Planning and Inventory Control." Journal of the Operational Research Society 36, no. 6 (June 1985): 545. http://dx.doi.org/10.1057/jors.1985.94.

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18

Daellenbach, H. G. "Inventory Control and Trade Credit." Journal of the Operational Research Society 37, no. 5 (May 1986): 525–28. http://dx.doi.org/10.1057/jors.1986.88.

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19

Dave, Upendra. "Advanced Service Parts Inventory Control." Journal of the Operational Research Society 38, no. 5 (May 1987): 466–67. http://dx.doi.org/10.1057/jors.1987.78.

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20

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

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21

ANDERSSON, JONAS, SVEN AXSÄTER, and JOHAN MARKLUND. "DECENTRALIZED MULTIECHELON INVENTORY CONTROL." Production and Operations Management 7, no. 4 (January 5, 2009): 370–86. http://dx.doi.org/10.1111/j.1937-5956.1998.tb00130.x.

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22

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 (May 2010): 111–24. http://dx.doi.org/10.1016/j.ijpe.2010.01.011.

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23

Lewis, Colin D. "Advanced service parts inventory control." International Journal of Forecasting 2, no. 1 (January 1986): 122. http://dx.doi.org/10.1016/0169-2070(86)90039-7.

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24

Bachman, Andreas. "Inventory Control with Price Fluctuations." International Journal of Operations & Production Management 6, no. 3 (March 1986): 58–64. http://dx.doi.org/10.1108/eb054767.

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25

Zhang, Qing Ying, Ying Chi, Yu Liu, and Qian Shi. "Enterprise Inventory Control Based on Supply Chain Management." Advanced Materials Research 472-475 (February 2012): 3273–76. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3273.

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The main target of supply chain management is to control inventory of each node enterprise effectively with the minimum cost. In this paper, the control strategies and methods of inventory based on supply chain management are put forward, which are significant for saving the cost of supply chain and improving the overall benefits of the whole chain.
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26

Huiskonen, Janne. "Service parts management: demand forecasting and inventory control." Production Planning & Control 25, no. 6 (November 5, 2012): 513–14. http://dx.doi.org/10.1080/09537287.2012.734449.

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27

Angane, Vinayak Ramesh, and Abhiraj Kachare. "Inventory Management and Control using ‘Probability Distribution Function’." SAE International Journal of Materials and Manufacturing 7, no. 1 (January 15, 2014): 173–83. http://dx.doi.org/10.4271/2013-01-9098.

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28

Kim, C. O., J. Jun, J. K. Baek, R. L. Smith, and Y. D. Kim. "Adaptive inventory control models for supply chain management." International Journal of Advanced Manufacturing Technology 26, no. 9-10 (July 7, 2004): 1184–92. http://dx.doi.org/10.1007/s00170-004-2069-8.

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29

Yang, Ya, Huihui Chi, Wei Zhou, Tijun Fan, and Selwyn Piramuthu. "Deterioration control decision support for perishable inventory management." Decision Support Systems 134 (July 2020): 113308. http://dx.doi.org/10.1016/j.dss.2020.113308.

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30

Ouyang, Xiao Xun. "Stochastic Optimization of Inventory Control." Applied Mechanics and Materials 58-60 (June 2011): 2063–67. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2063.

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In this paper, we focus on the interaction between inventory investment and market demand, and consequent effect on discount profit when stochastic disturbance is introduced into the system. To this end, we set up optimal decision-making models on inventory cross-time management , in which inventory is considered as firms investment and Market demand follows SDE , as well as inventory investment. Helpful results and three propositions are obtained.
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31

Fortvin, Leonard. "Production planning and inventory control." European Journal of Operational Research 26, no. 2 (August 1986): 314–15. http://dx.doi.org/10.1016/0377-2217(86)90198-0.

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32

Murdoch, John. "Forecasting and Inventory Control on Micros." Journal of the Operational Research Society 36, no. 7 (July 1985): 607. http://dx.doi.org/10.2307/2582478.

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33

Chung, Kee H. "Inventory Control and Trade Credit Revisited." Journal of the Operational Research Society 40, no. 5 (May 1989): 495. http://dx.doi.org/10.2307/2583622.

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34

Murdoch, John. "Forecasting and Inventory Control on Micros." Journal of the Operational Research Society 36, no. 7 (July 1985): 607–8. http://dx.doi.org/10.1057/jors.1985.104.

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35

Chung, Kee H. "Inventory Control and Trade Credit Revisited." Journal of the Operational Research Society 40, no. 5 (May 1989): 495–98. http://dx.doi.org/10.1057/jors.1989.77.

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36

Sun, Pei Hong, Lei Tang, and Li Ying Tang. "Application of Optimal Control in Inventory Management of Production." Applied Mechanics and Materials 29-32 (August 2010): 2503–8. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2503.

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Countering the inventory management problem of manufacturing enterprise, according to the optimal control theory, considering the numbers of products as control variables and the stocks as the state variables, this essay establishes systemic real-time dynamic model, gives the objective function, and makes use of dynamic programming method to solve the optimal control and obtains the optimal inventory, which provides a theoretical foundation for the production and inventory management of manufacturing enterprise.
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37

Federgruen, Awi, and Aliza Heching. "Multilocation Combined Pricing and Inventory Control." Manufacturing & Service Operations Management 4, no. 4 (October 2002): 275–95. http://dx.doi.org/10.1287/msom.4.4.275.5730.

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38

Tarish, Hiba A. "Optimized computational techniques-based inventory control process in supply chain management." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 11, 2021): 374–82. http://dx.doi.org/10.17762/turcomat.v12i6.1402.

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Now-a-days, supply chain management is one of the crucial factors in business for identifying the flow of goods, raw material storage, movement of products and inventory process so on. The main aim of the supply chain management process is to provide the valuable business services, infrastructure, logistic control and inventory control to the business for improving the business. Among the various supply chain factors, inventory control is one of the difficult tasks in supply chain management process because of high cost inventory, consistent stockouts, low rate of inventory turnover, high value of obsolete inventory, maximum working capital, huge cost for storage and lot customers. Then, several supply chain management techniques are introduced in traditional to control the inventory process but they are failing to detect with greatest accuracy due to the low convergence rate in computation techniques. So, in this paper introduce the effective and optimized neural network computational approach for managing the inventory control. In addition to this, the system ability to select the product based on the few factors that improve the inventory control system accuracy. Then the excellence of the system is evaluated using experimental analysis and respective performance metrics.
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39

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 (May 31, 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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40

Dona Sabila, Alzena, Mustafid Mustafid, and Suryono Suryono. "Inventory Control System by Using Vendor Managed Inventory (VMI)." E3S Web of Conferences 31 (2018): 11015. http://dx.doi.org/10.1051/e3sconf/20183111015.

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The inventory control system has a strategic role for the business in managing inventory operations. Management of conventional inventory creates problems in the stock of goods that often runs into vacancies and excess goods at the retail level. This study aims to build inventory control system that can maintain the stability of goods availability at the retail level. The implementation of Vendor Managed Inventory (VMI) method on inventory control system provides transparency of sales data and inventory of goods at retailer level to supplier. Inventory control is performed by calculating safety stock and reorder point of goods based on sales data received by the system. Rule-based reasoning is provided on the system to facilitate the monitoring of inventory status information, thereby helping the process of inventory updates appropriately. Utilization of SMS technology is also considered as a medium of collecting sales data in real-time due to the ease of use. The results of this study indicate that inventory control using VMI ensures the availability of goods ± 70% and can reduce the accumulation of goods ± 30% at the retail level.
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41

Bulinskaya, E. V. "Inventory control and investment policy." International Journal of Production Economics 81-82 (January 2003): 309–16. http://dx.doi.org/10.1016/s0925-5273(02)00355-9.

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42

Ehrenberg, Dieter. "Expert systems for inventory control." Decision Support Systems 6, no. 4 (October 1990): 293–98. http://dx.doi.org/10.1016/0167-9236(90)90024-l.

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43

Katanyukul, Tatpong, and Edwin K. P. Chong. "Intelligent Inventory Control via Ruminative Reinforcement Learning." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/238357.

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Inventory management is a sequential decision problem that can be solved with reinforcement learning (RL). Although RL in its conventional form does not require domain knowledge, exploiting such knowledge of problem structure, usually available in inventory management, can be beneficial to improving the learning quality and speed of RL. Ruminative reinforcement learning (RRL) has been introduced recently based on this approach. RRL is motivated by how humans contemplate the consequences of their actions in trying to learn how to make a better decision. This study further investigates the issues of RRL and proposes new RRL methods applied to inventory management. Our investigation provides insight into different RRL characteristics, and our experimental results show the viability of the new methods.
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44

Hopp, Wallace J., Mark L. Spearman, and Rachel Q. Zhang. "Easily Implementable Inventory Control Policies." Operations Research 45, no. 3 (June 1997): 327–40. http://dx.doi.org/10.1287/opre.45.3.327.

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45

Huggins, Eric Logan, and Tava Lennon Olsen. "Inventory Control with Generalized Expediting." Operations Research 58, no. 5 (October 2010): 1414–26. http://dx.doi.org/10.1287/opre.1100.0820.

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46

Wirth, Andrew. "Inventory Control and Inflation: A Review." International Journal of Operations & Production Management 9, no. 1 (January 1989): 67–72. http://dx.doi.org/10.1108/eum0000000001219.

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47

de Kok, Ton, and Karl Inderfurth. "Nervousness in inventory management: Comparison of basic control rules." European Journal of Operational Research 103, no. 1 (November 1997): 55–82. http://dx.doi.org/10.1016/s0377-2217(96)00255-x.

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48

Pomeroy, Kimball, and Mark Marcon. "Reproductive Tissue Storage: Quality Control and Management/Inventory Software." Seminars in Reproductive Medicine 36, no. 05 (September 2018): 280–88. http://dx.doi.org/10.1055/s-0038-1676851.

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AbstractThe recent failure of two liquid nitrogen storage tanks at two separate facilities in the United States has highlighted the need to reexamine our approach to how reproductive laboratories handle the storage of tissue. If we wish to truly understand how we can increase the security and safety of stored reproductive tissue, we really need to understand how often these events (tank failure) occur and what are the ultimate causes of these failures. Are there design flaws in the storage tanks or are these really failures in protocols and following protocols? Unfortunately, because these cases often involve serious litigation, most do not want to or are forbidden to talk about these failures and so we have little data to base recommendations on. Despite the paucity of data, we must come up with protocols and methods to safeguard tissue stored in these tanks. At the same time, we must be careful that we do not introduce potential solutions to these problems that instead of increasing the safety of stored tissue, we exacerbate the problem. Attention should be paid to where tanks are located so that they can be observed throughout the day. Decisions regarding whether tissue will be stored on-site or off-site, whether small- or large-capacity tanks should be used, or whether a patient's specimens should be split among several tanks should not be based on convenience or cost, but on the ability to reduce risks to these precious specimens. Ongoing quality control (QC) must include checking tanks for both visual clues and analyzing evaporation rates of each tank. Finally, each laboratory must have protocols in place that address all the issues involved in maintaining a safe storage system. These protocols must be understood by all those involved in the safeguard of patients' tissue. We may never eliminate all accidents with stored reproductive tissue, but we can reduce the probability of future accidents. In this article, we will discuss how to manage the risks inherent with the storage of reproductive tissue, where to store tissue, how to store them, how to reduce risks through quality control methods, and finally, options available for inventory management.
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49

Schwartz, Jay D., and Daniel E. Rivera. "PROCESS CONTROL EDUCATION USING INVENTORY MANAGEMENT IN SUPPLY CHAINS." IFAC Proceedings Volumes 39, no. 6 (2006): 631–36. http://dx.doi.org/10.3182/20060621-3-es-2905.00108.

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50

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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