摘要
将情景规划融合在技术路线图的设计过程中,可以较好解决单一线性预测的技术路线图由于缺乏鲁棒性而在动态不稳定环境下难以实施的问题。针对目前基于情景的技术路线图研究现处于定性化理论探索阶段,以关键技术创新等管理规划为目的的技术路线图设计缺乏定量化分析方法的决策支持,且基于情景的技术路线图研究大都处于企业层面,鲜有产业层面的分析等问题,提出一种通过在产业层次的情景技术路线图设计中融合贝叶斯网络分析,指导产业关键技术创新模式选择的方法。首先,通过基于情景的产业技术路线图设计框架分析并构建相对应的贝叶斯网络拓扑结构模型,并结合领域专家知识构造体现产业环境及产业目标和关键技术创新模式间的逻辑关系的条件概率表;其次,基于贝叶斯网络概率推理方法,提出了产业关键技术创新模式选择和路径选择等的分析及计算模型;最后,基于广东省LED产业的案例研究对所提方法进行了验证。
Scenario-based Technology Roadmapping（STRM） can solve the issue that single linear prediction technology roadmapping is difficult to be implemented in a dynamic and unstable environment due to its lack of robustness. Currently, STRM is still limited to theoretical research, and it has the following limitations. First, STRM targets at enterprise-level technology roadmapping. Few analyses and researches are given to scenario-based Industrial Technology Roadmapping（SITRM）. Second, there is no analysis about industrial technology innovation model. Third, the effects and update of STRM are rarely discussed. In order to find a new method for planning industrial technology innovation, this paper presents Bayesian Network（BN） quantitative analysis in SITRM on the basis of incorporating the idea of STRM into industrial technology roadmapping. According to the research of SITRM, BN analysis has three advantages. First, BN can learn layers or the interrelation in SITRM. In addition, BN can express and conduct analysis of these levels and interrelations by using the Directed Acyclic Graph（DAG）. Second, the Conditional Probability Table（CPT） can demonstrate the system＇s multiple status and the uncertainty of the logical relation between multiple status. Third, BN can also display the relation between industrial scenarios and industrial critical technologies even when the data is incomplete. This paper investigates the following aspects. First, this paper constructs a topology model of SITRM and displays the direct or indirect relation between random variables in ITRM（industrial environments, industrial targets and industrial critical technologies） through the DAG. It solves the issue that the relation between cross-level random variables cannot be displayed in ITRM design process. It also uses the CPT to demonstrate multiple status and the uncertainty of the logical relation between multiple statuses in SITRM in order to enhance the robustness of SITRM design. Second, based on probability deduction methods of BN, this paper performs a quantitative analysis of the planning of industrial technology innovation model and innovation path. The paper also provides specific analysis process and calculation model. Third, this paper presents the probability algorithm of the occurrence of relevant nodes based on posterior probability. It also discusses how to reconstruct and update SITRM based on this algorithm. Fourth, this paper verifies the proposed methods based on the case study of the Light Emitting Diode（LED） industry in Guangdong. However, this paper also leaves some issues to be further discussed. Those issues include the time dimension of ITRM, the timeliness of industrial critical technologies, and providing quantitative information for ITRM update.
作者
李剑敏
李从东
汤勇力
胡欣悦
Claudio Petti
LI Jian-min;LI Cong-dong;TANG Yong-li;HU Xin-yue;Claudio Petti(School of Economics and Management,Jiangsu University of Science and Technology,Zhenjiang 212003,China;School of Management,Jinan University,Guangzhou 510632,China;Department of Innovation Engineering,University of Salento,Lecce 73100,Italy)
出处
《管理工程学报》
CSSCI
CSCD
北大核心
2018年第3期226-233,共8页
Journal of Industrial Engineering and Engineering Management
基金
国家自然科学基金资助项目(71401063)
欧盟FP7资助项目(PIRSES-GA-2013-610350)
关键词
产业技术路线图
基于情景的技术路线图
贝叶斯网络
产业技术创新模式
创新路径
Industrial technology roadmapping
Scenario-based technology roadmapping
Bayesian network
Industrial technological innovation model
Iinnovation path
