In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD...In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.展开更多
Objective To empirically analyze the relationship between Government R&D funding and R&D investment of the enterprises in different sub industries of pharmaceutical industry,and to provide reference for the de...Objective To empirically analyze the relationship between Government R&D funding and R&D investment of the enterprises in different sub industries of pharmaceutical industry,and to provide reference for the development of policies related to R&D funding input.Methods Granger causality test was performed using the data of relevant indicators in different sub industries of China’s pharmaceutical industry from 1995 to 2019 based on the theory of covariance.Results and Conclusion The funding of R&D from the government had a significant positive effect on their R&D funding inputs to enterprises with chemo products,Chinese patent products,and biological products.It means the improvement of government funding was beneficial in promoting the R&D investment from various sub industries of pharmaceutical industry.The order of this influence was biological products,chemo products,and Chinese patent drugs.As to chemical drugs and biological products,the government’s R&D funding and enterprises R&D funding input showed a good trend of mutual promotion in a certain lag period.The government can fully leverage its funding to promote the investment of all sub industries of pharmaceutical industry.Meanwhile,regulatory mechanisms should be refined for government funding.For the inheritance,innovation,and development of traditional Chinese medicine,the government should give more policy support than financial support.展开更多
文摘In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.
文摘Objective To empirically analyze the relationship between Government R&D funding and R&D investment of the enterprises in different sub industries of pharmaceutical industry,and to provide reference for the development of policies related to R&D funding input.Methods Granger causality test was performed using the data of relevant indicators in different sub industries of China’s pharmaceutical industry from 1995 to 2019 based on the theory of covariance.Results and Conclusion The funding of R&D from the government had a significant positive effect on their R&D funding inputs to enterprises with chemo products,Chinese patent products,and biological products.It means the improvement of government funding was beneficial in promoting the R&D investment from various sub industries of pharmaceutical industry.The order of this influence was biological products,chemo products,and Chinese patent drugs.As to chemical drugs and biological products,the government’s R&D funding and enterprises R&D funding input showed a good trend of mutual promotion in a certain lag period.The government can fully leverage its funding to promote the investment of all sub industries of pharmaceutical industry.Meanwhile,regulatory mechanisms should be refined for government funding.For the inheritance,innovation,and development of traditional Chinese medicine,the government should give more policy support than financial support.
