State-Owned Enterprises IPD R&D Management Optimization Using Data-Driven Decision-Making Models

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.