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Simulation Study of Inventory Management in Supply Chains

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Języki publikacji
EN
Abstrakty
EN
Well-organized management of a supply chain involves the control of inventory levels and fast response to the changing customer demands. Enterprises cannot cope with this problem which contributes to the growing stock levels and therefore increases costs. The main goal of the article is to present a simulation approach to the problem of inventory management in a supply chain. The work shows the use of computer simulation techniques in order to emulate a supply chain system and its stochastic behaviour. The procedure for the usage of simulation modelling was described in a case study containing an analysis of an online store. The simulation results are presented using statistical parameters, which means that managers can get not only information concerning the expected value of the parameter looking decision-making, but also statistics to the characteristics of the risk associated with the decision associated with possible uncertainty.
Rocznik
Strony
41--48
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • The University of Szczecin, Poland
Bibliografia
  • [1] Banks J. (1999), Discrete Event Simulation, Proceedings of the 1999 Winter Simulation
  • [2] Beamon B.M. and Chen V.C.P. (2001), Performance analysis of conjoined supply chains, International Journal of Production Research, Vol. 39, No. 14, pp.3195-3218.
  • [3] Chodak G., Latus L. (2011), Metody prognozowania popytu i zarządzanie gospodarką magazynową w polskich sklepach internetowych - wyniki badań, Gospodarka Materiałowa i Logistyka, Vol. 9, pp. 11-18.
  • [4] Christiansen P.E., Kotzab H and Mikkola J,H. (2007), Coordination and sharing logistics in leagile supply chains, International Journal of Procurement Management Vol. 1, No. 1/2, pp. 79-96.
  • [6] Dooley F. (2005), Logistics, Inventory Control, and Supply Chain Management, CHOICES: The magazine of food, farm and resource Issues, Vol. 20, No. 4., pp. 287-291.
  • [7] Duong L.N.K., Wood L.C. and Wang W.Y.C. (2015), Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems, Procedia Manufacturing, pp.266-276.
  • [8] Fisher M. (1997), What is the right supply chain for your product?, Harvard Business Review, pp. 105-116.
  • [9] Fliedner G.(2003), CPFR: an emerging supply chain tool", Industrial Management & Data Systems, Vol. 103, No. 1, pp.14-21.
  • [10] Holweg M.D.S., Holmström J and Smäaros J. (2005). Supply chain collaboration: Making sense of the strategy continuum, European Management Journal, Vol. 23, No. 2, pp. 170-181.
  • [11] Houé T., Guimaraes R.(2017), The variety of supply chain design: from a standard typology to a relational pragmatism, Logistics and Transport, Vol. 34, No. 2, pp.5-14.
  • [12] Jammernegg W., Reiner G. (2007), Performance improvement of supply chain processes by coordinated inventory and capacity management, Int. J. Production Economics, Vol.108, pp.183190,
  • [13] Jung I.Y., Blau G., Pekny J., Gintaras F., Reklaitis V. and Eversdyk D. (2004), A simulation based optimization approach to supply chain management under demand uncertainty, Computers and Chemical Engineering, Vol. 28, No.10, pp.2087–2106.
  • [14] Kemme, N.(2013), Design and Operation of Automated Container Storage System, PhysicaVerlag, Springer-Verlag, Berlin, Heidelberg.
  • [15] Kostrzewski M. (2014) Simulation Method in Research on Material-Flow in a Warehouse, Logistics and Transport, Vol. 21, No. 1, pp. 21-32
  • [16] Kristianto N.Y.(2011), Production ramp up in built-to-order supplier chain, Journal of Modelling in Management, Vol.6, No. 2, pp.143-163.
  • [17] Krzyżaniak S. (2009), O skutkach wynikających z błędnego założenia o typie rozkładu popytu w cyklu uzupełnienia zapasu, Między teorią a praktyką zarządzania zapasami - o znaczeniu prawidłowego określania czasu cyklu uzupełnienia, Logistyka, Vol. 1, pp. 21-25.
  • [18] Law, A.M. and Kelton, W.D.(2000), Simulation modelling and analysis, third edition, Boston, MA: McGraw Hill
  • [19] Liu J. and Hou Y.R. (2011), Time based strategy in distribution logistics: gaining competitive advantages in IKEA, Bachelor‟s Thesis in Industrial Management & Logistics.
  • [20] NarmadhaS. and Selladurai V. (2009), Multifactory, Multi-Product Inventory Optimization using Genetic Algorithm for Efficient Supply Chain Management, JCSNS International Journal of Computer Science and Network Security, Vol.9 No.12, pp. 203-212.
  • [21] Park Y.-B., Kim H.S. (2016), Simulation-based evolutionary algorithm approach for deriving the operational planning of global supply chains from the systematic risk management, Computers in Industry, Vol.83, pp.68–77.
  • [22] Scheuffele G. and Kulshreshta A. (2007),Inventory Optimization: A Necessity Turning to Urgency, SETLabs Briefings Vol.5, No.3, pp.15-24.
  • [23] Schmitt A.J., Singh M. (2012), A quantitative analysis of disruption risk in a multi- echelon Supply chain, Int. J. Production Economics, Vol.139, No.1, pp.22–32.
  • [24] Thierry C., Narahari Y. and Thomas A. (2010), The role of modelling and simulation in supply chain management, SCS M&S Magazine, Vol. 1, No. 4, pp. 1-8.
  • [25] Torkul O., Yılmaz R., Selvi I.H., Cesur M.R. (2016), A real-time inventory model to manage variance of demand for decreasing inventory holding cost, Computers & Industrial Engineering, Vol.102, pp.435–439.
  • [26] Tubis A., Nowakowski T., Werbińska-Wojciechowska S. (2017), Supply Chain Vulnerability and Resilience – Case Study of Footwear Retail Distribution Network, Logistics and Transport, Vol. 33, No. 1, pp. 15-24
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
PL
W numeracji bibliografii brak pozycji 5.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f94df2aa-b170-4952-b4d5-d3c11c4c60c9
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