SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD

Tomasz  Rymarczyk; Grzegorz  Kłosowski

doi:10.5604/01.3001.0010.7244

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Published: Dec 21, 2017

DOI: https://doi.org/10.5604/01.3001.0010.7244

Issue Vol. 7 No. 4 (2017)

Articles

SELECTED PROBLEMS OF EVALUATION AND CLASSIFICATION OF HISTORICAL BUILDINGS USING ROUGH SETS
Krzysztof Czajkowski

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LABORATORY STAND FOR SMALL WIND TURBINE SIMULATION
Wojciech Matelski, Eugeniusz Łowiec, Stanisław Abramik

11-14
DEVELOPMENT OF AN AUTOMATED DIAGNOSTICS AND CONTROL SYSTEM FOR BIOGAS COMBUSTION PROCESSES
Oxana Zhirnova

15-19
SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD
Tomasz Rymarczyk, Grzegorz Kłosowski

20-23
THE USE OF PETRI NETS IN DECISION SUPPORT SYSTEMS BASED ON INTELLIGENT MULTIPLY SOURCE DATA ANALYSIS
Tomasz Rymarczyk, Grzegorz Kłosowski, Tomasz Cieplak

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APPLICABILITY ANALYSIS OF REST AND SOAP WEB SERVICES
Tomasz Zientarski, Marek Miłosz, Marek Kamiński, Maciej Kołodziej

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A REVIEW OF CONTROL METHODS OF WIND TURBINE SYSTEMS WITH PERMANENT MAGNET SYNCHRONOUS GENERATOR
Piotr Gajewski

32-37
DIRECT TORQUE CONTROL OF MULTI-PHASE INDUCTION MOTOR WITH FUZZY LOGIC SPEED CONTROLLER
Jacek Listwan

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IMAGE COMPLETION WITH LOW-RANK MODEL APPROXIMATION METHODS
Tomasz Sadowski, Rafał Zdunek

44-48
APPROXIMATION OF ELECTRIC PROPERTIES OF PERIODIC LAYERED COMPOSITE MATERIALS
Adam Steckiewicz, Bogusław Butryło

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BOOST QUASI-RESONANT CONVERTERS FOR PHOTOVOLTAIC SYSTEM
Michał Harasimczuk

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RESEARCH OF FLOW AROUND SELECTED SENSORS PROFILES FOR METROLOGY FLOWS
Piotr Zgolak

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MAXIMUM SUBARRAY PROBLEM OPTIMIZATION FOR SPECIFIC DATA
Tomasz Rojek

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ANALYSIS OF POWER LOSS IN THE LOW-SPEED PNEUMATIC ENGINE
Adam Ilnicki, Mariusz Rząsa

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APPLICATION OF FUZZY COGNITIVE MAP TO PREDICT OF EFFECTIVENESS OF BIKE SHARING SYSTEMS
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FUZZY COGNITIVE MAP AS AN INTELLIGENT RECOMMENDER SYSTEM OF WEBSITE RESOURCES
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MODELING OF THE ARTIFICIAL BLOOD CHAMBER AND THE MICROPUMPS PULSATILE DRIVE FOR BLOOD TRANSFUSION
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CONTROL A SMALL WIND TURBINE WITH ASYNCHRONOUS GENERATOR
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MECHANICAL PROPERTIES OF SELECTED EPOXY ADHESIVES
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POLYNOMIAL APPROXIMATION FOR T WAVE PARAMETER RECOGNITION IN ECG PROCESSING
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THE IMPACT OF WINDOW FUNCTION ON IDENTIFICATION OF SPEAKER EMOTIONAL STATE
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DOI

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

Grzegorz Kłosowski

g.klosowski@pollub.pl

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

References

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].

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].

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.

Hu J., Shen E., Gu Y.: Evaluation of Lighting Performance Risk Using Surrogate Model and EnergyPlus. Procedia Engineering 2015, 522–529.

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.

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.

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.

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.

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.

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.

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.

Sentia P. D., Mukhtar M., Shukor S. A.: Supply chain information risk management model in Make-to-Order (MTO). Procedia Technology 2013, 403–410.

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.

Taylan O. et al.: Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing 2014, 105–116.

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

SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD

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