SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD

Tomasz Rymarczyk

tomasz@rymarczyk.com
Research and Development Center, Netrix S.A., Lublin; University of Economics and Innovation in Lublin, (Poland)

Grzegorz Kłosowski


Lublin University of Technology, Faculty of Management, Department of Organization of Enterprise (Poland)

Abstract

In this paper, the conceptual model of risk-based cost estimation for completing tasks within supply chain is presented. This model is a hybrid. Its main unit is based on Monte Carlo Simulation (MCS). Due to the fact that the important and difficult to evaluate input information is vector of risk-occur probabilities the use of artificial intelligence method was proposed. The model assumes the use of fuzzy logic or artificial neural networks – depending on the availability of historical data. The presented model could provide support to managers in making valuation decisions regarding various tasks in supply chain management.


Keywords:

project management, decision support systems, neural networks, fuzzy logic

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Published
2017-12-21

Cited by

Rymarczyk, T. ., & Kłosowski, G. . (2017). SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 7(4), 20–23. https://doi.org/10.5604/01.3001.0010.7244

Authors

Tomasz Rymarczyk 
tomasz@rymarczyk.com
Research and Development Center, Netrix S.A., Lublin; University of Economics and Innovation in Lublin, Poland

Authors

Grzegorz Kłosowski 

Lublin University of Technology, Faculty of Management, Department of Organization of Enterprise Poland

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