Abstract
This work attempts to discover how complex order fulfillment processes of a supply chain can be analyzed effectively and efficiently. In this context, complexity is determined by the number of process elements and the degree of interaction between them, as well as by the extent variability is influencing process performance. We show how the combination of analytic methods and simulation can be utilized to analyze complex supply chain processes and present a procedure that integrates queuing theory with discrete event simulation. In a case study, the approach is applied to a real-life supply chain to show the practical applicability.
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References
Chung C (2004) Simulation modeling handbook: A practical approach. CRC Press, Boca Raton
Gnoni M, Iavagnilio R, Mossa G, Mummolo G, Di Leva A (2003) Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry. International Journal of production economics 85(2):251–262
Granger J, Krishnamurthy A, Robinson S (2001) Stochastic modeling of airlift operations. In: Proceedings Winter Simulation Conference 2001, IEEE Computer Society Washington, DC, USA, vol 1, pp 432–440
Jain S, Lim C, Gan B, Low Y (1999) Criticality of Detailed Modeling in Semiconductor Supply Chain Simulation. In: Proceedings of the Winter Simulation Conference 1999, ACM New York, NY, USA, vol 1, pp 888–896
Jammernegg W, Reiner G (2001) Ableitung und Bewertung von Handlungsalternativen in einem Unternehmen der Elektroindustrie. In: Jammernegg W, Kischka PH (eds) Kundenorientierte Prozessverbesserungen, Konzepte und Fallstudien, Springer, Berlin, Berlin, pp 237–247
Kelton W, Sadowski R, Sturrock D (2002) Simulation with ARENA, 2nd edn. McGraw-Hill Science/Engineering/Math, Boston
Ko H, Ko C, Kim T (2006) A hybrid optimization/simulation approach for a distribution network design of 3PLS. Computers & Industrial Engineering 50(4):440–449
Law A, Kelton W (2000) Simulation modeling and analysis, 3rd edn. McGraw Hill, New York
Lee Y, Kim S (2002) Production–distribution planning in supply chain considering capacity constraints. Computers & Industrial Engineering 43(1):169–190
Merkuryev Y, Petuhova J, Grabis J (2003) Analysis of dynamic properties of an inventory system with service-sensitive demand using simulation. In: Proceedings of the 15 th European Simulation Symposium-Simulation in Industry, Delft, The Netherlands, pp 509–514
MPX (2003) MPX WIN 4.3 - For use with Windows, User Manual. Network Dynamics Inc, Framingham
Nolan R, Sovereign M (1972) A recursive optimization and simulation approach to analysis with an application to transportation systems. Management Science 18(12):676–690
Raturi A, Meredith J, McCutcheon D, Camm J (1990) Coping with the build-toforecast environment. Journal of Operations Management 9(2):230–249
Sargent R (1994) A historical view of hybrid simulation/analytic models. In: Proceedings of the Winter Simulation Conference, pp 383–386
Shanthikumar J, Sargent R (1983) A unifying view of hybrid simulation/analytic models and modeling. Operations Research 31(6):1030–1052
Supply Chain Council (2009) SCOR Model. URL http://www.supplychain.org/cs/root/s/scor model/scor model
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Schodl, R. (2009). The Best of Both Worlds - Integrated Application of Analytic Methods and Simulation in Supply Chain Management. In: Reiner, G. (eds) Rapid Modelling for Increasing Competitiveness. Springer, London. https://doi.org/10.1007/978-1-84882-748-6_13
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DOI: https://doi.org/10.1007/978-1-84882-748-6_13
Publisher Name: Springer, London
Print ISBN: 978-1-84882-747-9
Online ISBN: 978-1-84882-748-6
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