基于模糊证据推理的汽车制造产业网络环境复杂性测度

环境复杂性是汽车制造产业网络复杂性的主要诱因。利用指标波动程度测度复杂性的思想,建立表征汽车制造产业网络外部环境的指标体系,构建指标变动程度的模糊信度结构模型,运用模糊证据推理算法结合熵值理论对多指标进行信息融合,并利用灰色关联分析方法评估复杂性测度结果。通过2002~2012年汽车行业数据的实证研究,结果表明:经济环境与国际环境的波动是导致汽车制造产业网络复杂性的最主要因素,技术环境和市场环境次之;近10年来我国汽车制造产业网络外部环境经历了两次较大波动,总体复杂性呈现增长趋势。本研究可为汽车产业环境复杂性测度及应对提供思路参考。

Complexity Measurement of Automobile Manufacturing Industry Network Environment Based on Fuzzy Evidential Reasoning

The complexity of the environmental is the main cause of the complexity of automobile manufacturing industry network. With the thought to measure the complexity through the environmental index volatility, the indicator system of the automobile manufacturing industry network environment and the fuzzy belief structure model for measuring the volatility degree are established, the multiple indexes of environment complexity are transformed into fuzzy belief structures using the fuzzy evidential reasoning algorithm combining the entropy value theory, and then the results of environment complexity of automobile manufacturing industry network are evaluated by grey relational analysis method. Through the empirical research on the data of 2002~2012 in the automotive industry, the results show that the fluctuations of economic environment and international environment are the main factors leading to the complexity of automobile manufacturing industry network, followed by the technical environment and market environment. Over the past 10 years of China’s automobile manufacturing industry, network environment has experienced two large fluctuations, and overall complexity presents a growth trend. This study can provide a reference for complexity measure and control of automotive industry environment.