Improving supply chain efficiency through simulations - Literature and methodological review


  • Csaba Péterfi Hungarian University of Agriculture and Life Sciences
  • Balázs Gyenge Hungarian University of Agriculture and Life Sciences



Supply chain, logistics, simulation, process development, efficiency improvement


Increased competition, efficiency and profit orientation largely determine the operation of today’s companies. While the XX. In the twentieth century, the emphasis was on mass production, large batch numbers, and economies of scale, until today, consumer demand has shifted significantly toward individualized products and services. The consumer expects almost perfect quality, reasonable price and immediately available stocks. That is why it is necessary to tailor the supply chain to the needs of consumers as perfectly as possible. Every small change in the way a company operates can have different effects that managers need to respond to immediately.

In a company where there is some flow of auxiliary fuels, semi-finished or finished products, the proper design of the supply chain is a focal point that largely determines its operation and efficiency, as logistics costs significantly affect the consumer prices of products and services. The solid foundation of my topic is provided by the logistics knowledge and the supply chain management that extends it, which already examines the entire supply chain instead of one player at a time. A XXI. The development of information and IT in the 21st century makes it possible to apply new approaches in this field as well and to develop an approach in which we do not assess the effects of changes in retrospect, but prepare for them in advance.

In my research work, the optimization of logistics activities through simulation is the focus. The simulation has been an integral part of technical sports and engineering work for many years, this methodology is not yet used in the field of supply chain management, which would provide an opportunity to analyze the effects of changes without risk and would be a tool for companies to model and measure their activities. And measurability would provide an opportunity for correction and improvement, which plays an essential role in increasing efficiency and effectiveness. Based on this line of reasoning, I believe that the combination of supply chain management and simulation holds untapped potentials.

In my article, I will review the currently known and useful simulation methodologies, which shows the processing of the issue and the prevalence of the methodologies.

Szerző életrajzok

  • Csaba Péterfi, Hungarian University of Agriculture and Life Sciences


  • Balázs Gyenge, Hungarian University of Agriculture and Life Sciences



A. Rozinat1, M. T. Wynn, W. M. P. van der Aalst, A. H. M. ter Hofstede, and C. J. Fidge, 2008: Workflow Simulation for Operational Decision Support Using Design, Historic and State Information, file:///C:/Users/User/Downloads/Workflow_Simulation_for_Operational_Decision_Suppo.pdf

Backus, J. 1978. THE HISTORY OF FORTRAN I, II, AND III, IBM Research Laboratory, San Jose, California, ACM SlGPLAN Notices, VoI. 13, No. 8, August 1978

BUFFON, G 1777., Essai d'arithmetique morale, Supplement al'Histoire naturelle, 4

Conway, R. W., B. M. Johnson, and W. L. Maxwell. 1959. Some problems of digital systems simulation. Management Science 6 (1): 92–110.

Conway, R. W. 1963. Some tactical problems in digital simulation. Management Science 10 (1): 47–61.

Cooper, N. C., ed. 1988. From cardinals to chaos: Reflections on the life and legacy of Stanislaw Ulam. Cambridge: Cambridge University Press

David Goldsman Richard E. Nance James R. Wilson, 2009: A BRIEF HISTORY OF SIMULATION, Proceedings of the 2009 Winter Simulation Conference

Forrester, J. 1961. Industrial Dynamics. Vol. 1: Cambridge Mass: MIT Press

Goldratt research labs. 2018: Improving Reliability and Profitability of Integrated Steel Supply Chain with Simulation. profitability-of-integrated-steel-supply-chain-with simulation/?utm_source=anl_mkto_email&utm_medium=email&utm_campaign=cs manufacturing&mkt_tok=MDM4LU5GUC0zMzYAAAF80cKOcBy11jJ bWln_mwVX3LvdebP2t_cF_bOfue3XnctLZaXVJqb6DRU5McBiKQ0a5tThrg303HWYKzLDPQFAZjUeRVw4lY0kdEcP-Nv5g

Gordon, G. 1961. "A General Purpose Systems Simulation Program". In Proceedings of the December 12-14, 1961 Eastern Joint Computer Conference: computers-Key to Total Systems Control, edited by W. H. Ware, 87-104. ACM.

Gordon, G. 1981. The development of the General Purpose Simulation System. In History of programming languages, ed. R. L. Wexelblatt, 403–437. New York: Academic Press.

Gyenge B., Mészáros K., Tari K., 2019; ÜZLETI INTELLIGENCIA (BI) ALKALMAZÁSA A LOGISZTIKÁBAN, Studia Mundi – Economica, Vol. 6. No. 2.(2019)

Ilya Grygoyev, 2018: Anylogic in three days – A quick course in simulation modelling

Kiviat, P. J. 1967. "Digital Computer Simulation: Modeling Concepts". RM-5378-PR, August, The Rand Corporation.

Káposzta, J.; Illés, B.; Nagy, H. (2017): Examination of impact of economic policy on quality of life in regions of some european countries with global perspective.ENGINEERING FOR RURAL DEVELOPMENT 16:1 pp. 236-241., 7 p. (2017)

Káposzta, J; Nagy, H. (2015): Status report about the progess of the Visegrad Countries in relation to Europe 2020 targets. EUROPEAN SPATIAL RESEARCH AND POLICY 22:1 pp. 81-99., 19 p. (2015)

Kozma T.- Pónusz M., (2016): Ellátásilánc-menedzsment elmélete és gyakorlata –alapok, Károly Róbert Kutató – Oktató Közhasznú Nonprofit Kft., Gödöllő

Kristen Nygaard. SIMULA: An Extension of ALGOL to the Description of Discrete Event Networks. Proceedings of the IFIP congress 62, Munich, Aug 1962. North Holland Publ., pages 520–522.

Laplace, P. S 1812: Théorie analytique des probabilités. Paris: Veuve Courcier,

Markowitz, H. M. 1979. SIMSCRIPT: Past, present, and some thoughts about the future. In Current Issues in Computer Simulation, ed. N. R. Adam and A. Dogramaci, 27–60. New York: Academic Press.

Mohammad Asif Salam Sami A Khan, (2016),"Simulation based decision support system for optimization", Industrial Management & Data Systems, Vol. 116 Iss 2 pp. 236 – 254,

Nance, R. E. 1996. A history of discrete event simulation programming languages. In History of programming languages II, ed. T. J. Bergin and R. J. Gibson, 369–427. New York: ACM Press; Reading, MA: Addison-Wesley.

North, M. J., and C. M. Macal. 2007. Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation: Oxford University Press

Nygaard, K., and O.-J. Dahl. 1981. The development of the SIMULA languages. In History of programming languages, ed. R. L. Wexelblatt, 439–491. New York: Academic Press.

Pidd, M. 2004. Computer Simulation in Management Science: John Wiley and Sons Ltd

Roberts S. D., Pegden D. 2017: THE HISTORY OF SIMULATION MODELING, Proceedings of the 2017 Winter Simulation Conference

Shannon, C. E. 1949. "W. Weaver the Mathematical Theory of Communication". Urbana: University of Illinois Press 29.

Schruben, L. 1983. "Simulation Modeling with Event Graphs". Communications of the ACM 26 (11):957-963.

Sterman, J. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, Irwin/McGraw-Hill

Student. (1908). The probable error of a mean. Biometrika, 6, 1–25.

Tocher, K. D., and D. G. Owen. 1960. The automatic programming of simulations. In Proceedings of the Second International Conference on Operational Research, ed. J. Banbury and J. Maitland, 50–68. London: The English Universities Press Ltd.

Tocher, K. D., and M. Splaine. 1966. Computer control of the Bessemer process. Journal of the Iron and Steel Institute 204 (February): 81–86




Hasonló cikkek

1-10 a 214-ból/ből

You may also Haladó hasonlósági keresés indítása for this article.