Academic literature on the topic 'Inventory in supply chain'

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Journal articles on the topic "Inventory in supply chain"

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Jane, S. Priscilla, Dr C. Elango Dr.C.Elango, and Dr A. Nagarajan Dr.A.Nagarajan. "Markov Decision Process in Supply Chain Inventory Systems." International Journal of Scientific Research 2, no. 12 (June 1, 2012): 350–53. http://dx.doi.org/10.15373/22778179/dec2013/108.

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Singh, Deepesh, and Ajay Verma. "Inventory Management in Supply Chain." Materials Today: Proceedings 5, no. 2 (2018): 3867–72. http://dx.doi.org/10.1016/j.matpr.2017.11.641.

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Drakaki, Maria, and Panagiotis Tzionas. "Investigating the impact of inventory inaccuracy on the bullwhip effect in RFID-enabled supply chains using colored petri nets." Journal of Modelling in Management 14, no. 2 (May 10, 2019): 360–84. http://dx.doi.org/10.1108/jm2-08-2017-0081.

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PurposeInformation distortion results in demand variance amplification in upstream supply chain members, known as the bullwhip effect, and inventory inaccuracy in the inventory records. As inventory inaccuracy contributes to the bullwhip effect, the purpose of this paper is to investigate the impact of inventory inaccuracy on the bullwhip effect in radio-frequency identification (RFID)-enabled supply chains and, in this context, to evaluate supply chain performance because of the RFID technology.Design/methodology/approachA simulation modeling method based on hierarchical timed colored petri nets is presented to model inventory management in multi-stage serial supply chains subject to inventory inaccuracy for various traditional and information sharing configurations in the presence and absence of RFID. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results.FindingsThe bullwhip effect is increased in RFID-enabled multi-stage serial supply chains subject to inventory inaccuracy. The information sharing supply chain is more sensitive to the impact of inventory inaccuracy.Research limitations/implicationsInformation sharing involves collaboration in market demand and inventory inaccuracy, whereas RFID is implemented by all echelons. To obtain the full benefits of RFID adoption and collaboration, different collaboration strategies should be investigated.Originality/valueColored petri nets simulation modeling of the inventory management process is a novel approach to study supply chain dynamics. In the context of inventory errors, information on RFID impact on the dynamic behavior of multi-stage serial supply chains is provided.
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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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Sbai, Nouçaiba, and Abdelaziz Berrado. "A literature review on multi-echelon inventory management: the case of pharmaceutical supply chain." MATEC Web of Conferences 200 (2018): 00013. http://dx.doi.org/10.1051/matecconf/201820000013.

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Inventory management remains a key challenge in supply chain management. Many companies recognize the benefits of a good inventory management system. An effective inventory management helps reaching a high customer service level while dealing with demand variability. In a complex supply chain network where inventories are found across the entire system as raw materials or finished products, the need for an integrated approach for managing inventory had become crucial. Modelling the system as a multi-echelon inventory system allows to consider all the factors related to inventory optimization. On the other hand, the high criticality of the pharmaceutical products makes the need for a sophisticated supply chain inventory management essential. The implementation of the multi-echelon inventory management in such supply chains helps keeping the stock of pharmaceutical products available at the different installations. This paper provides an insight into the multi-echelon inventory management problem, especially in the pharmaceutical supply chain. A classification of several multi-echelon inventory systems according to a set of criteria is provided. A synthesis of multiple multi-echelon pharmaceutical supply chain problems is elaborated.
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Kegenbekov, Zhandos, and Ilya Jackson. "Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning." Algorithms 14, no. 8 (August 15, 2021): 240. http://dx.doi.org/10.3390/a14080240.

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Adaptive and highly synchronized supply chains can avoid a cascading rise-and-fall inventory dynamic and mitigate ripple effects caused by operational failures. This paper aims to demonstrate how a deep reinforcement learning agent based on the proximal policy optimization algorithm can synchronize inbound and outbound flows and support business continuity operating in the stochastic and nonstationary environment if end-to-end visibility is provided. The deep reinforcement learning agent is built upon the Proximal Policy Optimization algorithm, which does not require hardcoded action space and exhaustive hyperparameter tuning. These features, complimented with a straightforward supply chain environment, give rise to a general and task unspecific approach to adaptive control in multi-echelon supply chains. The proposed approach is compared with the base-stock policy, a well-known method in classic operations research and inventory control theory. The base-stock policy is prevalent in continuous-review inventory systems. The paper concludes with the statement that the proposed solution can perform adaptive control in complex supply chains. The paper also postulates fully fledged supply chain digital twins as a necessary infrastructural condition for scalable real-world applications.
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Orjuela-Castro, Javier Arturo, Miguel Angel Cardona-Rojas, and Laura Vanessa Castañeda-López. "Agricultural Supply Chain Mango Inventory Model." Revista Ingeneria Industrial 16, no. 2 (August 1, 2017): 141–50. http://dx.doi.org/10.22320/s07179103/2017.09.

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Kapuscinski, Roman, Rachel Q. Zhang, Paul Carbonneau, Robert Moore, and Bill Reeves. "Inventory Decisions in Dell's Supply Chain." Interfaces 34, no. 3 (June 2004): 191–205. http://dx.doi.org/10.1287/inte.1030.0068.

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Hayya, Jack C., Jeon G. Kim, Stephen M. Disney, Terry P. Harrison, and Dean Chatfield. "Estimation in supply chain inventory management." International Journal of Production Research 44, no. 7 (April 2006): 1313–30. http://dx.doi.org/10.1080/00207540500338039.

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Hartwig, Robin, Karl Inderfurth, Abdolkarim Sadrieh, and Guido Voigt. "Strategic Inventory and Supply Chain Behavior." Production and Operations Management 24, no. 8 (January 26, 2015): 1329–45. http://dx.doi.org/10.1111/poms.12325.

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Dissertations / Theses on the topic "Inventory in supply chain"

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Felch, Jennifer Ann. "Supply chain modeling for inventory analysis." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/46323.

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Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management; and, (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
Includes bibliographical references (p. 97).
by Jennifer Ann Felch.
M.S.
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Sakhaii, Mandana. "Inventory Policy for a Hospital Supply Chain with Perishable Inventory." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1480597038799773.

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Meng, Yue. "The effect of inventory on supply chain." Thesis, Växjö University, School of Technology and Design, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-958.

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Supply chain management addresses the management of materials and

information across the entire chain from suppliers to producers,

distributors, retailers, and customers. In the past few decades, scholars

gave ample attention about the impact of inventory on Supply Chain

Management (SCM). Roughly speaking, research on supply chain management has

been mainly focused on three major issues. One is the behavior of

information flow; the second issue deals with inventory management; the

third issue is orientated to planning and operations management. In this

paper the second issue, namely inventory management will be discussed. The

author will follow the phases of classifying inventory; identify cost

factors; assess cost components; calculate EOQ; giving suggestion and

effect of inventory on supply chain will be discussed. The result is going

to become clear under the analysis of two alternatives by using MCDM

(Multiple Criteria Decision Making) method. The conclusion is when

optimizing the inventory management, both up stream and down stream

activities will run effectively.

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Boone, Christopher A. Hanna Joe B. Craighead Christopher W. "Managing service inventory in the supply chain." Auburn, Ala., 2006. http://repo.lib.auburn.edu/Send%208-7-07/BOONE_CHRISTOPHER_34.pdf.

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Shin, Hojung. "Inventory coordination in the industrial supply chain." Connect to resource, 2001. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1261244702.

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Stremler, David Jay. "Constraint-based supply chain inventory deployment strategies." Master's thesis, Mississippi State : Mississippi State University, 2001. http://library.msstate.edu/etd/show.asp?etd=etd-11102001-070052.

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Khadilkar, Yogesh S. "Reverse supply chain life cycle inventory analysis /." Cincinnati, Ohio : University of Cincinnati, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1098665167.

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Lo, Billy S. (Billy Si Yee). "Inventory optimization in an aerospace supply chain." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39678.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Manufacturing Program at MIT, 2007.
Includes bibliographical references (p. 66-67).
Strategic inventory management has become a major focus for Honeywell Aerospace as the business unit challenged itself to meeting cost reduction goals while maintaining a high level of service to its customers. This challenge has become particularly important as customers have steered their purchase decisions from focusing only on capability and quality to including cost performance as well. To do so, Honeywell Aerospace's Planning and Asset Management group is undertaking a three-year effort to re-engineer its inventory planning systems with the goal of increasing planner productivity, improving supply chain responsiveness, and reducing overall inventory. This internship forms the building blocks of this strategy by leveraging existing software available in the industry and applying it to Honeywell's supply chain. Through two pilot programs with different supply chain designs, this internship analyzed the cost and benefit of transforming the company's inventory management strategy. In addition, this internship attempts to identify the challenges associated with such an enormous change, compare them with challenges with implementation in other industries in order to prepare management for full implementation across all product lines. These challenges range from leadership buy-in and information readiness to implementation feasibility both within Honeywell manufacturing and its suppliers.
by Billy S. Lo.
S.M.
M.B.A.
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KHADILKAR, YOGESH SUDHIR Mr. "REVERSE SUPPLY CHAIN: LIFE CYCLE INVENTORY ANALYSIS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1098665167.

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Mauro, Joseph (Joseph Peter). "Strategic inventory management in an aerospace supply chain." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44295.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Manufacturing Program at MIT, 2008.
Includes bibliographical references (p. 79-80).
This paper introduces multiple methods to set and optimize inventory levels. These methods are then classified based on the complexity involved to implement them. As an organization develops a deeper understanding of inventory, it becomes more mature and can apply more complex methods. This sequencing of methods is defined as a three phase maturity model. First, a foundational level of maturity is defined, which quantifies inventory levels based on future demand and business requirements. Second, a transitional level of maturity defines safety stock positioning in a single-echelon supply chain. Finally, the maturity model concludes with an optimal level of maturity that is based on principles of multi-echelon inventory optimization: safety stock at multiple positions of a supply chain. The setting for this paper was the Aerospace industry. Honeywell Aerospace is in the middle of a 3-year effort to re-engineer Sales, Inventory and Operations Planning (SIOP) systems. At the same time, Honeywell Aerospace is standardizing on a uniform implementation of an ERP system. Through SIOP, standard inventory and planning practices aided by the uniform ERP backbone and a strategic inventory program executive management hopes to reduce what is seen as a disproportionate contribution of inventory to Honeywell International.
by Joseph Mauro.
S.M.
M.B.A.
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Books on the topic "Inventory in supply chain"

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Goetschalckx, Marc. Supply Chain Engineering. Boston, MA: Springer Science+Business Media, LLC, 2011.

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RFID in the supply chain. New York: McGraw-Hill, 2011.

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Tayur, Sridhar, Ram Ganeshan, and Michael Magazine. Quantitative models for supply chain management. New York: Springer Science, 1999.

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Materials management: An executive's supply chain guide. Hoboken, N.J: John Wiley & Sons, 2009.

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1938-, Proth Jean-Marie, and SpringerLink (Online service), eds. Supply Chain Engineering: Useful Methods and Techniques. London: Springer-Verlag London, 2010.

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Parts management models and applications: A supply chain system integration perspective. New York: Springer, 2005.

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Davis, Robert A. Demand-driven inventory optimization and replenishment: Creating a more efficient supply chain. Hoboken, New Jersey: Wiley, 2013.

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Silver, Edward A., David F. Pyke, and Douglas J. Thomas. Inventory and Production Management in Supply Chains. Fourth Edition. | Boca Raton : Taylor & Francis, 2017. | Revised: CRC Press, 2016. http://dx.doi.org/10.1201/9781315374406.

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The supply chain in manufacturing, distribution, and transportation: Modeling, optimization, and applications. Boca Raton, FL: CRC Press, 2011.

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Arkes, Jeremy. Estimating the benefits of the Air Force purchasing and supply chain management initiative. Santa Monica, CA: RAND Project Air Force, 2008.

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Book chapters on the topic "Inventory in supply chain"

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Waters, Donald. "Inventory Management." In Supply Chain Management, 335–70. London: Macmillan Education UK, 2009. http://dx.doi.org/10.1007/978-1-137-26234-9_11.

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Goetschalckx, Marc. "Inventory Systems." In Supply Chain Engineering, 431–93. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-6512-7_10.

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Rehman Khan, Syed Abdul, and Zhang Yu. "Inventory Management." In Strategic Supply Chain Management, 109–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15058-7_5.

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Ganesan, Ramnath. "Inventory Planning." In The Profitable Supply Chain, 23–85. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0526-6_2.

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Ganesan, Ramnath. "Inventory Margin Analysis." In The Profitable Supply Chain, 267–71. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0526-6_11.

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Wild, Tony. "Supply chain inventory management." In Best Practice in Inventory Management, 217–37. 3 Edition. | New York: Routledge, 2017. | Revised edition of the author’s Best Practice in Inventory Management, [2002]: Routledge, 2017. http://dx.doi.org/10.4324/9781315231532-16.

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Mauergauz, Yuri. "Supply Chain Inventory Dynamics." In Advanced Planning and Scheduling in Manufacturing and Supply Chains, 273–311. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27523-9_9.

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Ross, David Frederick. "Supply Chain Inventory Management." In Competing Through Supply Chain Management, 193–246. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-4816-1_6.

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Brown, Alexander O., Markus Ettl, Grace Y. Lin, Raja Petrakian, and David D. Yao. "Inventory Allocation at a Semiconductor Company." In Supply Chain Structures, 283–309. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-6635-6_9.

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Atan, Zümbül, and Lawrence V. Snyder. "Inventory Strategies to Manage Supply Disruptions." In Supply Chain Disruptions, 115–39. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-778-5_5.

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Conference papers on the topic "Inventory in supply chain"

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Nuamchit, Kittiphan, and Navee Chiadamrong. "OPTIMAL INVENTORY CONTROL POLICY OF A HYBRID MANUFACTURING – REMANUFACTURING SYSTEM USING A FUZZY LINEAR PROGRAMMING APPROACH." In World conference on Supply Chain Management. The International Institute of Knowledge Management - TIIKM, 2019. http://dx.doi.org/10.17501/24246697.2018.4102.

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Chang, Guangshu. "Supply Chain Inventory Level with Procurement Constraints." In 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2007. http://dx.doi.org/10.1109/wicom.2007.1208.

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Guo, Ruey-Shan, David Chiang, and Po-Chun Yang. "Supply Chain Inventory Control in Semiconductor Manufacturing." In 2006 International Symposium on Semiconductor Manufacturing (ISSM). IEEE, 2006. http://dx.doi.org/10.1109/issm.2006.4493024.

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Chuantao Wang, Shouwen Ji, Jinsheng Shen, and Wei Wei. "Supply chain model in vendor managed inventory." In 2008 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). IEEE, 2008. http://dx.doi.org/10.1109/soli.2008.4682882.

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El Haoud, Naima, and Zineb Bachiri. "Stochastic Artificial Intelligence benefits and Supply Chain Management inventory prediction." In 2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA). IEEE, 2019. http://dx.doi.org/10.1109/logistiqua.2019.8907271.

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Sbai, Noucaiba, and Abdelaziz Berrado. "A Strategic Framework for Multi-Echelon Inventory System Selection." In 2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA). IEEE, 2019. http://dx.doi.org/10.1109/logistiqua.2019.8907306.

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Cheng, Tiexin, Jingbo Yue, and Tao Guo. "Inventory Modeling in Supply Chain Management: A Review." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.1498.

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Li, Jizi, Peiling Guo, and Zhijun Zuo. "Inventory Control Model for Mobile Supply Chain Management." In 2008 International Conference on Embedded Software and Systems Symposia (ICESS Symposia). IEEE, 2008. http://dx.doi.org/10.1109/icess.symposia.2008.85.

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Bai, Shizhen, and Min Li. "Supply chain optimization of combine transporting with inventory." In 2011 International Conference on Management Science and Industrial Engineering (MSIE). IEEE, 2011. http://dx.doi.org/10.1109/msie.2011.5707575.

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Huo hong, Huang Zhipeng, and Xin Shen. "Research on supply chain inventory of agricultural products." In 2011 International Conference on Management Science and Industrial Engineering (MSIE). IEEE, 2011. http://dx.doi.org/10.1109/msie.2011.5707576.

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Reports on the topic "Inventory in supply chain"

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Iyer, Ananth, Ahmed Soliman, and Amanda Thompson. Indiana Furniture Supply Chain. West Lafayette, IN: Purdue University, 2006. http://dx.doi.org/10.5703/1288284313373.

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ADVANTECH INC ANNAPOLIS MD. Recruit Clothing Supply Chain Assessment. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada410577.

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Fleischer, Mitchell, Ronald Kohler, Thomas Lamb, H. B. Bongiorni, and Nathan Tupper. Shipbuilding Supply Chain Integration Project. Fort Belvoir, VA: Defense Technical Information Center, November 1998. http://dx.doi.org/10.21236/ada361426.

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Umeda, Shigeki, and Y. Tina Lee. Integrated supply chain simulation system - a design specification for a generic supply chain simulator. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7146.

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Visker, Edward R. Improving the DoD Supply Chain Can Commercial Supply Chain Management Software Do the Job"". Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada378230.

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Kernodle, Joseph W. Nomex Supply Chain Total Asset Visibility. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444053.

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Harvilla, Andrew John, Jonathon G. Greenwood, Alexander Joseph Masucci, and Robert Ray Rhodehouse. Detonator Production Overview - Supply Chain Collaboration. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1605117.

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Booten, Charles, Margaret Mann, Ayyoub Momen, and Omar Abdelaziz. Critical Material Supply Chain Analysis: Magnetocalorics. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1659867.

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Morales, Diane K. DoD Supply Chain Materiel Management Regulation. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada430976.

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Helinski, Ryan, Gio Kao, Jason Hamlet, Joshua Letchford, Philip Campbell, and Benjamin Anthony. Macro Supply Chain Lifecycle Decision Analytics. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1761979.

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