SUMMARYWe propose a business scenario evaluation method using a qualitative/quantitative hybrid model. In order to evaluate business factors with qualitative causal relations, we introduce statistical values based on propagation and combination of effects of business factors by Monte Carlo simulation. In propagating an effect, we divide a range of each factor by landmarks and decide an effect to a destination node based on the divided ranges. In combining effects, we determine an effect of each arc using contribution degree and sum all effects. Through applied results to practical models, it is confirmed that there are no differences between results obtained by quantitative relations and results obtained by the proposed method at the risk rate of 5%.
In the proposed production control system, inventories are concentrated at the position where supply lead time and demand lead time are equal, and inventory and production planning are made at this position. 'This position is defined as "Coupling Point".The Coupling Point is moved as demand lead time and supply lead time change. Inventory and production planning are made at that new Coupling Point. This makes the forecasted inventories unnecessary on the demand side cif the Coupling Point, and this also makes quick response to orders possible.This new system has been applied to an electronic device manufacturing process successfully.
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.
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