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.


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

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Published : 2017-12-21

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.

Tomasz Rymarczyk
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