This paper addresses a new approach to find business risk factors based on business scenarios derived from qualitative-quantitative hybrid simulation. To investigate business risk factors under uncertain business environment, cause-effect interpretation of business factors along with time is significant for decision-makers. Business factors are represented either qualitatively or quantitatively. Our proposed simulation model involves such aspects with structural model representation and generates possible scenarios by using Monte Carlo simulation. However, possible scenarios as probability distribution of target factors inherently involve risk scenarios and non-risk scenarios from decision-makers point of views. Identifying risk factors still resorts to decision-makers without computational analysis support on derived outputs. Therefore we propose a finding method of business risk factors on target models.
In order to decide an effective management plan, managers often draw up and evaluate business scenarios. To make the evaluation, a simulation method on the qualitative and quantitative hybrid model represented as causal graph has been proposed. There is a strong need to get optimal input values for the target outputs in the simulation, but exhaustive search can not be realistically applied to it from considering the processing time. Therefore, we propose a quick search method for optimal input values cencerning the qualitative and quantitative hybrid simulation. Our approach is to get optimal values of input nodes by inverse propagation of effects from the value of target output nodes on the simulation model. However, it generates the contradiction that the value of a separated node in the causal graph decided from one of destination nodes is different from the value of the other destination nodes. Therefore, we re-execute the inverse propagation repeatedly from the nearest qualitative node connecting to a quantitative node for solving the contradiction. By experimental results about the proposed method, time could be reduced for reaching the solution. We also could confirm a certain level of accuracy about the solution.
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