Miroslav Špaček
Business Process Risk Modelling in Theory and Practice
Číslo: 1/2021
Periodikum: Quality Innovation Prosperity
DOI: 10.12776/qip.v25i1.1551
Klíčová slova: process modelling; probabilistic approach; stochastic simulation
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Anotace:
Purpose: The purpose of the paper is to introduce SW based decision-making tool that helps managers cope with risks and uncertainties of selected industrial processes. The solution is substantiated by the theoretical background.
Methodology/Approach: The research is based on combination of contextual interviews with process management experts and Business Process Modelling Notion (BPMN). The former is aimed at the identification of industrial processes with highest risk exposure the latter is conducive to the design of processes to be subjected to stochastic simulation.
Findings: The findings show that the risks and uncertainties in the management of industrial processes can be kept under control when using advanced tools of risk analysis as simulation approaches. The solution proposed comes in handy to risk analysts or process managers.
Research Limitation/Implication: The library of process models which were included into stochastic simulation includes selected processes as investments, service providing or economic value-added engineering. Additional processes are being included on ongoing basis.
Originality/Value of paper: The paper offers the solution to industrial process risk management which goes far beyond academic sphere and provides industrial practitioners SW tool that facilitates process risk management.
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Methodology/Approach: The research is based on combination of contextual interviews with process management experts and Business Process Modelling Notion (BPMN). The former is aimed at the identification of industrial processes with highest risk exposure the latter is conducive to the design of processes to be subjected to stochastic simulation.
Findings: The findings show that the risks and uncertainties in the management of industrial processes can be kept under control when using advanced tools of risk analysis as simulation approaches. The solution proposed comes in handy to risk analysts or process managers.
Research Limitation/Implication: The library of process models which were included into stochastic simulation includes selected processes as investments, service providing or economic value-added engineering. Additional processes are being included on ongoing basis.
Originality/Value of paper: The paper offers the solution to industrial process risk management which goes far beyond academic sphere and provides industrial practitioners SW tool that facilitates process risk management.