SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD
Tomasz Rymarczyk
tomasz@rymarczyk.comResearch 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 logicReferences
Ameyaw E.E., Chan A.PC: Evaluation and ranking of risk factors in public–private partnership water supply projects in developing countries using fuzzy synthetic evaluation approach. Expert Systems with Applications 42(12), 2015, 5102–5116 [doi: 10.1016/j.eswa.2015.02.041].
Google Scholar
Elfaki A., Alatawi O., Abushandi E.: Using intelligent techniques in construction project cost estimation: 10-Year Survey. Advances in Civil Engineering 2014, Article ID 107926 [doi: 10.1155/2014/107926].
Google Scholar
Fragiadakis N.G., Tsoukalas V.D., Papazoglou V.J.: An adaptive neuro-fuzzy inference system (ANFIS) model for assessing occupational risk in the shipbuilding industry. Safety Science 63/2014, 226–235.
Google Scholar
Hu J., Shen E., Gu Y.: Evaluation of Lighting Performance Risk Using Surrogate Model and EnergyPlus. Procedia Engineering 2015, 522–529.
Google Scholar
Idrus A., Nuruddin M.F., Rohman M.A.: Development of project cost contingency estimation model using risk analysis and fuzzy expert system. Expert Systems with Applications 2011, 1501–1508.
Google Scholar
Kłosowski G., Gola, A.: Risk-based estimation of manufacturing order costs with artificial intelligence. Computer Science and Information Systems (FedCSIS), Federated Conference on. IEEE, 2016, 729–732.
Google Scholar
Paul S.K., Sarker R., Essam D.: Managing risk and disruption in production-inventory and supply chain systems: A review. Journal of Industrial and Management Optimization 12.3/2016, 1009–1029.
Google Scholar
Radke A.M., et al.: A risk management-based evaluation of inventory allocations for make-to-order production. CIRP Annals-Manufacturing Technology 2013, 459–462.
Google Scholar
Rudnik K., Deptuła A.M.: System with probabilistic fuzzy knowledge base and parametric inference operators in risk assessment of innovative projects. Expert Systems with Applications 2015, 6365–6379.
Google Scholar
Rush C., Roy R.: Analysis of cost estimating processes used within a concurrent engineering environment throughout a product life cycle. 7th ISPE International Conference on Concurrent Engineering, Lyon, France, July 17th-20th, Technomic Inc., Pennsylvania USA, 2000 58–67.
Google Scholar
Schwarz I.J., Sandoval-Wong J.A., Sánchez P.M.: Implementation of artificial intelligence into risk management decision-making processes in construction projects, 2015, 361–362.
Google Scholar
Sentia P. D., Mukhtar M., Shukor S. A.: Supply chain information risk management model in Make-to-Order (MTO). Procedia Technology 2013, 403–410.
Google Scholar
Taroun A., Yang J. B., Lowe D.: Construction risk modelling and assessment: Insights from a literature review. The Built and Human Environment Review 2011, 93.
Google Scholar
Taylan O. et al.: Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing 2014, 105–116.
Google Scholar
Authors
Tomasz Rymarczyktomasz@rymarczyk.com
Research and Development Center, Netrix S.A., Lublin; University of Economics and Innovation in Lublin, Poland
Authors
Grzegorz KłosowskiLublin University of Technology, Faculty of Management, Department of Organization of Enterprise Poland
Statistics
Abstract views: 268PDF downloads: 94
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Tomasz Rymarczyk, Przemysław Adamkiewicz, MONITORING DAMAGE AND DAMPNESS IN FLOOD EMBANKMENT BY ELECTRICAL IMPEDANCE TOMOGRAPHY , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 7 No. 1 (2017)
- Tomasz Rymarczyk, Paweł Tchórzewski, Jan Sikora, DETECTION OF AIR GAPS IN COPPER-MINE CEILING BY ELECTRICAL IMPEDANCE TOMOGRAPHY , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 7 No. 1 (2017)
- Konrad Kania, Tomasz Rymarczyk, METHODS FOR DETECTION ANALYSIS IN QUALITY CONTROL SYSTEM , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 8 No. 3 (2018)
- Tomasz Rymarczyk, LEVEL SETS AND COMPUTATIONAL INTELLIGENCE ALGORITHMS TO MEDICAL IMAGE ANALYSIS IN E-MEDICUS SYSTEM , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 7 No. 1 (2017)
- Tomasz Rymarczyk, Jan Sikora, SCATTERING BY CIRCULAR VOIDS WITH RIGID BOUNDARY: DIRECT AND INVERSE PROBLEMS FOR OPEN AND CLOSE DOMAINS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 12 No. 4 (2022)