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Journal articles on the topic 'Inventory control management system'

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Published: 4 June 2021
Last updated: 16 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

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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5

Potamianos, J., and A. J. Orman. "An Interactive Dynamic Inventory-Production Control System." Journal of the Operational Research Society 47, no. 8 (August 1996): 1017. http://dx.doi.org/10.2307/3010409.

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6

Potamianos, J., and A. J. Orman. "An Interactive Dynamic Inventory-Production Control System." Journal of the Operational Research Society 47, no. 8 (August 1996): 1017–28. http://dx.doi.org/10.1057/jors.1996.128.

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7

Pons, Dirk. "System model of production inventory control." International Journal of Manufacturing Technology and Management 20, no. 1/2/3/4 (2010): 120. http://dx.doi.org/10.1504/ijmtm.2010.032895.

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8

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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9

Gardner, Everette S. "Evaluating Forecast Performance in an Inventory Control System." Management Science 36, no. 4 (April 1990): 490–99. http://dx.doi.org/10.1287/mnsc.36.4.490.

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10

Krajčovič, Martin, and Dariusz Plinta. "Adaptive inventory control system for material items with continuous non-stationary demand." Management and Production Engineering Review 5, no. 1 (March 1, 2014): 11–20. http://dx.doi.org/10.2478/mper-2014-0002.

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Abstract This paper describes an algorithm of dynamic inventory control system for large numbers of material items with continuous non-stationary demand. It uses principles of pull inventory control systems, statistical inventory analysis and joint replenishment inventory systems. This algorithm was processed in software module (VBA for Excel) and applied in practice. The target of described algorithm is to keep an optimum inventory level and optimum customer service level in terms of inventory control of items with non-stationary demand.
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11

Mohammaditabar, Davood, Seyed Hassan Ghodsypour, and Chris O'Brien. "Inventory control system design by integrating inventory classification and policy selection." International Journal of Production Economics 140, no. 2 (December 2012): 655–59. http://dx.doi.org/10.1016/j.ijpe.2011.03.012.

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12

Arda, Yasemin, and Jean-Claude Hennet. "Inventory control in a multi-supplier system." International Journal of Production Economics 104, no. 2 (December 2006): 249–59. http://dx.doi.org/10.1016/j.ijpe.2004.09.008.

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13

Ghiaseddin, Nasir, Khalil Matta, and Diptendu Sinha. "A Structured Expert System for Model Management in Inventory Control." ORSA Journal on Computing 6, no. 4 (November 1994): 409–22. http://dx.doi.org/10.1287/ijoc.6.4.409.

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14

Deb, Mahuya, Prabjot Kaur, and Kandarpa Kumar Sarma. "Fuzzy Approach to Decision Support System Design for Inventory Control and Management." Journal of Intelligent Systems 28, no. 4 (September 25, 2019): 549–57. http://dx.doi.org/10.1515/jisys-2017-0143.

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Abstract The ubiquitous nature of inventory and its reliance on a reliable decision support system (DSS) is crucial for ensuring continuous availability of goods. The DSS needs to be designed in a manner that enables it to highlight its present status. Further, the DSS should be able to provide indications about subtle and large-scale variations that are likely to occur in the supply chain within the context of the decision-making framework and inventory management. However, while dealing with the parameters of the system, it is observed that its operations and mechanisms are surrounded by uncertain, imprecise, and vague environments. Fuzzy-based approaches are best suited for such situations; however, these require assistance from learning systems like artificial neural network (ANN) to facilitate automated decision support. When ANN and fuzzy are combined, the fuzzy neural system and the neuro-fuzzy system (NFS) are formulated. The model of the DSS reported here is based on a framework commonly known as adaptive neuro-fuzzy inference system (ANFIS), which is a version of NFS. The configured model has the advantages of both the ANN and fuzzy systems, and has been tested for the design of a DSS for use as part of inventory control. In this work, we report the design of an ANFIS-based DSS configured to work as DSS for inventory management. The system accepts demand as input and generates procurement, ordering, and holding cost to control production and supply. The system deals with a certain profitability rating required to quantify the changes in the input and is combined with the day-to-day inventory records and demand-available cycle. The effectiveness of the system has been checked in terms of number and types of membership used, accuracy generated, and computational efficiency accounted by the computation cycles required.
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15

Ismail, Siti Zubaidah, and Abdulsalam Khaled Abdullah Al-Hadi. "Developing Palm Oil Inventory Control System Using Ms Excel Macro." Journal of Modern Manufacturing Systems and Technology 5, no. 1 (March 19, 2021): 51–55. http://dx.doi.org/10.15282/jmmst.v5i1.6102.

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The palm oil sector is just one of the popular sectors in Malaysia that offer one of the highest exports to Malaysia's economy. This study handles the research study of inventory control for Malaysian palm oil plantation. The existing inventory control cannot be running smoothly when the inventory are managed in different user interface systems which is by utilizing manual inventory control like Microsoft Excel and handwriting documents. Data collection was gathered with the targeted respondents, they are core members of the management team who have a great experience as well as understanding of their inventory system. In order to make PSS inventory control much easier as well as organized, an inventory application system is developed utilizing MS Excel Macro. The outcomes of this research study are a computerized supply control system that beneficial, crucial, and also much better substitute for a hand-operated administration system and quick handling.
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16

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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17

., Mustafid, Alzena Dona Sabila, and Suryono . "Sistem Pengendali Inventori Supply Chain Dengan Pendekatan Probabilitas Pada Industri Pakaian." JURNAL SISTEM INFORMASI BISNIS 8, no. 1 (April 30, 2018): 17. http://dx.doi.org/10.21456/vol8iss1pp17-24.

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Inventory control systems have an important role in supply chain management to control process of production, inventory and distribution of products in accordance with rapidly changing consumer demand. The research aims to develop inventory control system on supply chain that can manage the number of production and supply of products based on variables of consumer demand and lead time. Supply chain inventory control systems are designed using model of supply chain inventory control within the information system framework. Model of inventory control using variable of demand lead time is designed by probability approach. Input to the system are data of demand and lead time sent by retail. The results of research provide theory of supply chain inventory control system with probability approach, especially on developing the theory of probability distribution for combined variables from two random variables of normal distributed. Inventory control system used to determine safety stock and reorder point as the basis for determining the replenishment stock. Furthermore by the system, supply chain management can manage the estimation of production number, and manage inventory and stock number of products in warehouse, supplier and retail in accordance with the consumer demand.
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18

Sumaida, Dalia. "Pharmacy inventory control ordering web application." Journal of Clinical Oncology 30, no. 34_suppl (December 1, 2012): 330. http://dx.doi.org/10.1200/jco.2012.30.34_suppl.330.

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330 Background: Many departments within the hospital system were complaining of errors in the carousel system and the quantity of drugs ordered vs. received. Methods: Development of a communication tool between purchasing, inventory control, materials management, finance and accounts payable to streamline the process of ordering and receiving throughout the hospital system. Results: Communication between the departments in ordering and receiving. Reduction in errors of drug quantities ordered vs drug quantity received. Automating the receiving log system and the packing slip process. The web app cut down the time to process a drug by 50%. Conclusions: The use of IT to reduce waste, increase productivity, and improve quality of the system.
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19

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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20

Nelly, Nelly, Stevanus Stevanus, Rosna Rosna, and Ivan Haryono. "EVALUASI SISTEM INFORMASI PERSEDIAAN PADA CV. SARANA TELEMAXINDO." CommIT (Communication and Information Technology) Journal 4, no. 2 (October 30, 2010): 98. http://dx.doi.org/10.21512/commit.v4i2.542.

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Evaluation objectives of inventory information systems at CV. Facilities Telemaxindo was to evaluate the weaknesses of the implementation of internal control, as well as estimating and assessing possible risks that might arise. The research method used is literature study of reference books in the library, and field study consisted of observation, interviews with relevant parties, creating check lists, check the inventory information systems documentation, and testing of inventory application systems. The results are findings of control weaknesses in the implementation of security management, operational management control, boundary control, input control, and output control on the present inventory information system. These weaknesses could pose a risk and if not dealt soon it could hurt the company. The resulting conclusion of the security management control, boundary control, control input, and output control is not running well so that it needs to be improved in accordance with the recommendations, while operational management control has been running fairly well.Keywords: evaluation, information system, inventory
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21

Subramoniam, Suresh, and K. V. Krishnankutty. "EDSIM: expert database system for inventory management." Kybernetes 34, no. 5 (June 2005): 721–33. http://dx.doi.org/10.1108/03684920510595409.

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22

Dong, Hai, Wei Ling Zhao, and Yan Ping Li. "A Novel Predictive Control Strategy for Supply Chain Management under Networked Manufacturing." Advanced Materials Research 421 (December 2011): 548–52. http://dx.doi.org/10.4028/www.scientific.net/amr.421.548.

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A novel predictive control strategy is applied to dynamic supply chain management under networked manufacturing. The optimisation-based control scheme aims at a complete management framework for production-inventory systems that is based on model predictive control (MPC) and on a neural network time series forecasting model. The model proves to be very useful in production-inventory systems that is enough to keep production and inventory balance. A move suppression term that penalises the rate of change in the transported quantities through the network increases the system’s robustness. The results show that the proposed scheme can improve significantly the performance of the production-inventory system, due to the fact that more accurate predictions are provided to the formulation of the MPC optimization problem in real time.
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23

Yang, ll-Sun, Alyce M. Fanslow, and DoriAnn Finley. "Inventory control system achievement test for dietetics and foodservice management students." Journal of the American Dietetic Association 91, no. 6 (June 1991): 691–94. http://dx.doi.org/10.1016/s0002-8223(21)01206-2.

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24

M, Ashika, and Monisha V. "A Material Management in Construction Project Using Inventory Management System." International Journal for Modern Trends in Science and Technology 6, no. 5 (May 26, 2020): 32–40. http://dx.doi.org/10.46501/ijmtst060506.

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Material management is a critical component of the construction industry. The goal of materials management is to insure that construction materials are available at their point of use when needed. A proper implemented material management program can achieve the timely flow of materials and equipment to the jobsite, and thus facilitate improved work face planning, increased labour productivity, scheduling and minimize the cost. Material management is an important function in order to improve productivity in construction project. It is defind materials management function which take off, vendor evaluation and selection , purchasing, expenditure, shipping, material receiving, where housing and inventory, and material distribution. In this project we have prepare scheme of material management in the construction industry for building project also conducting survey of industry and determine the various format for construction material management. In conclusion the wastage of material, control cost of material and time should be analyzed by using inventory management system.
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25

Chou, Chien Chang, J. M. Yih, J. F. Ding, T. C. Han, Y. H. Lin, L. J. Liu, and W. K. Hsu. "Application of a Fuzzy EOQ Model to the Stock Management in the Manufacture System." Key Engineering Materials 499 (January 2012): 361–65. http://dx.doi.org/10.4028/www.scientific.net/kem.499.361.

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In a manufacture system, the inventory control is one of important issues. This paper proposes a fuzzy economic order quantity (EOQ) inventory model for solving the inventory control problem in the manufacture system. Firstly, this paper discusses the square roots of a trapezoidal fuzzy number. Secondly, the square root of fuzzy number is applied to the EOQ inventory model in the manufacture system.
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26

Al Kattan, Ibrahim, and Taha Al Khudairi. "Simulation of Inventory Control System in a Supply Chain Using RFID." International Journal of Information Systems and Supply Chain Management 3, no. 1 (January 2010): 68–86. http://dx.doi.org/10.4018/jisscm.2010092905.

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This paper employs a simulation model in a Supply Chain Management (SCM) system. This study is one of the first to present simulation model of inventory control system in supply chain management using barcode and Radio Frequency Identification (RFID). The main objective of this model is to compare two inventory systems in a supply chain, one using RFID, versus the barcode. The model will help company to consider moving from a barcode system to the RFID application. A quantitative analysis based on a simulation model is developed. The model runs for both systems using ARENA simulation software with a comparison between the two systems. Furthermore, the simulation model is tested by applying three different types of demand for both scenarios. The results have shown that regardless of demand distribution pattern and customer order rate, the outcomes of the model are consistent and provide promising RFID technology adoption to improve inventory control of the entire supply chain system. The installation and unit cost of RFID implementation were estimated and considered to be the main barrier. Such model can offer the policymakers insight into how RFID might improve SCM system performance. Additional test has been conducted for demand with normal and triangular distributions using real data provided by ABC-Dubai Company. The results obtained from running the two models for these distributions are consistent with the original results.
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27

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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28

Kiesmüller, G. P., and S. Minner. "Simple expressions for finding recovery system inventory control parameter values." Journal of the Operational Research Society 54, no. 1 (January 2003): 83–88. http://dx.doi.org/10.1057/palgrave.jors.2601480.

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29

Mustafid, Dwi Ispriyanti, Sugito, and Diah Safitri. "Inventory control systems for stochastic lead time demand." E3S Web of Conferences 73 (2018): 13021. http://dx.doi.org/10.1051/e3sconf/20187313021.

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Product inventory control can be used to provide information related to processes of production and distribution products based on lead time and product demand from consumers. The research aims to design product inventory control systems for stochastic lead time demand, and using exponential smoothing to make lead time demand prediction. The design of the control system uses the probability method based on the estimated number of product demand from the market or consumer. This research provides theoretical framework for control systems of products that can determine the size of inventory parameters in supply chain management, that is reorder point and fixed order quantity, so it can manage the production amount and supply of products according to market or consumer demand. The benefits of a product control systems in the inventory management are to provide the necessary inventory allocation information in making decisions to determine the amount of production and distribution of products to consumers.
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30

Herawati, Evy. "AUDIT SISTEM INFORMASI APLIKASI PERSEDIAAN PADA PT SS." CommIT (Communication and Information Technology) Journal 2, no. 2 (October 31, 2008): 95. http://dx.doi.org/10.21512/commit.v2i2.497.

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The research objective was to evaluate and determine how far the application of information system inventories that are running (including management controls and application controls) have been able to provide reliable information and where time and reduce risk to an acceptable level by the company also provides recommendations for companies in order to minimize the risk that existed at the moment. The research method used “around the computer” is testing and evaluating audit through management control, in performing input and output controls for application systems based on the quality of input supply and output that will produce a report. The results of this study was to determine the inventory of information system applications that use the SS company can provide adequate and reliable information and timely, thus simplifying the management to make sound decisions related to inventory policy.Keywords: audit, information systems inventory, control, corporate
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31

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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32

DAI, YUE, XIULI CHAO, SHU-CHERNG FANG, and HENRY L. W. NUTTLE. "CAPACITY ALLOCATION AND INVENTORY POLICY IN A DISTRIBUTION SYSTEM." Asia-Pacific Journal of Operational Research 23, no. 04 (December 2006): 543–71. http://dx.doi.org/10.1142/s0217595906001054.

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We consider a single-period distribution system with one supplier and two retailers. The supplier may have infinite or finite capacity. The demand at each retailer is random. When a stockout occurs at one retailer the customer may go to the other retailer. We study both the decentralized and centralized inventory control problems. For the decentralized problem we show that a unique Nash equilibrium exists when the capacity at the supplier is infinite. However, when the capacity is finite, only under certain conditions does the Nash equilibrium exist. For centralized inventory control we obtain the optimal allocation that maximizes the expected profit of the entire supply chain. For the case of decentralized controls we also design channel coordination mechanisms, i.e., a decentralized cost structure resulting in a Nash equilibrium with chain-wide profits equal to those achieved under a fully centralized system. We compare the performance of two retailers in the decentralized and centralized controls and analyze the impact of channel coordination on the whole supply chain.
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33

Mak, K. L. "Determining optimal production-inventory control policies for an inventory system with partial backlogging." Computers & Operations Research 14, no. 4 (January 1987): 299–304. http://dx.doi.org/10.1016/0305-0548(87)90067-0.

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34

ZHANG, B. L., and C. YANG. "DELAYED STATE FEEDBACK CONTROL FOR PERIODIC-REVIEW INVENTORY SYSTEMS." ANZIAM Journal 58, no. 3-4 (April 2017): 397–405. http://dx.doi.org/10.1017/s1446181117000153.

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We investigate delayed state feedback control of a periodic-review inventory management system with perishable goods. The stock under consideration is replenished from multiple supply sources. By using delayed states as well as current states of the inventory system, a delayed feedback $H_{\infty }$ control strategy is developed to mitigate bullwhip effects of the system. Some conditions on the existence of the delayed feedback $H_{\infty }$ controller are derived. It is found through simulation results that the proposed delayed $H_{\infty }$ control scheme is capable of improving the performance of the inventory management significantly. In addition, the delayed controller is better than the traditional delay-free $H_{\infty }$ controller.
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35

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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36

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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37

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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38

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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39

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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40

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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41

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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42

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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43

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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44

Franco, C. Eugine, and S. Rubha. "AN OVERVIEW ABOUT JIT (JUST-IN-TIME) - INVENTORY MANAGEMENT SYSTEM." International Journal of Research -GRANTHAALAYAH 5, no. 4(SE) (April 30, 2017): 14–18. http://dx.doi.org/10.29121/granthaalayah.v5.i4(se).2017.1944.

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A stock out is undesirable for manufacturers because it halts the production process. An inventory is the stock of ideal resources in a firm for future use. Manufacturing organizations typically have raw materials, components, sub-assemblies, tools and equipment’s; semi-finished goods etc., the inventory of an item should not be neither too high nor too less. It should be optimal. The recent development in inventory management is JIT (Just-in-Time). JIT implies handling of inventory in a much disciplined way. It requires changes in culture. It also encompasses the Japanese managerial characteristics i.e., Lifetime employment, implicit control mechanisms, collective decision making, collective responsibility and holistic concern for employees. JIT applies to all functions of a company not just the operations.
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45

Stoia, Claudiu-Leonardo. "An Analysis Regarding the Possibility of Using Fuzzy Logic in Inventory Management." ACTA Universitatis Cibiniensis 64, no. 1 (November 1, 2014): 76–81. http://dx.doi.org/10.2478/aucts-2014-0014.

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Abstract The paper presents a brief state-of-the-art survey regarding the use of fuzzy logic in inventory management. Its goal is to motivate enthusiastic entrepreneurs to take into account the benefits of using fuzzy logic inventory control systems. It offers a guide to model an inventory system having a free fuzzy tool as starting point
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46

Swaszek, Rebecca M. A., and Christos G. Cassandras. "Receding Horizon Control for Station Inventory Management in a Bike-Sharing System." IEEE Transactions on Automation Science and Engineering 17, no. 1 (January 2020): 407–17. http://dx.doi.org/10.1109/tase.2019.2928454.

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47

Yang, Jiankui, David D. Yao, and Heng-Qing Ye. "Technical Note—On the Optimality of Reflection Control." Operations Research 68, no. 6 (November 2020): 1668–77. http://dx.doi.org/10.1287/opre.2019.1935.

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The so-called reflection control is easy to implement and widely applied in many applications such as inventory management and financial systems. To apply reflection control in a production-inventory system, for example, production will stop when the finished-goods inventory reaches a certain level. What is the best level for this control? In what sense is it optimal? In their paper, “On the Optimality of Reflection Control,” Jiankui Yang, Heng-Qing Ye, and David D. Yao have established the optimality of reflection control under an exponential holding cost in three settings—namely, a Brownian motion model, a single-server system, and a birth–death queue model. The study provides a thorough understanding of the control and extends significantly its domain of applications.
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48

Hopp, Wallace J., Niranjan Pati, and Philip C. Jones. "Optimal inventory control in a production flow system with failures." International Journal of Production Research 27, no. 8 (August 1989): 1367–84. http://dx.doi.org/10.1080/00207548908942628.

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49

SIONG, LIM BENG, T. IMAO, H. YOSHIDA, K. GOTO, S. L. KOH, D. LIM, L. CHIN, and S. C. GAN. "Integrated modular fixture design, pricing and inventory control expert system." International Journal of Production Research 30, no. 9 (September 1992): 2019–44. http://dx.doi.org/10.1080/00207549208948136.

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50

GHOBBAR, A. A., and C. H. FRIEND. "Aircraft maintenance and inventory control using the reorder point system." International Journal of Production Research 34, no. 10 (October 1996): 2863–78. http://dx.doi.org/10.1080/00207549608905063.

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