面向产品模块化研发知识协调的最优资源投入策略研究

Research on Optimal Resource Input Strategy for ProductModular R&D Knowledge Coordination

产品模块化研发中模块间存在知识互依性,需要模块供应商与系统集成商进行以知识学习为核心的知识协调活动。分析了模块供应商的知识协调机理及其知识存量增长原理后,借鉴最优控制理论构建了资源投入的最优控制模型,进而探讨了其个体学习与协同学习的终止时刻、资源的主要投入对象以及资源最优投入强度。研究表明,在产品模块化研发过程中,应综合考虑模块间的知识互依性、模块供应商的知识存量以及知识学习的边际作用等关键因素来制定合理的知识协调资源投入策略,以优化模块化研发绩效。

Since product R&D is a relatively complex knowledge-based task,including the analysis of consumer preferences,market trends,technical routes,process realization,etc.,the knowledge required for R&D is distribute d among different functional module suppliers.For practical considerations,system integrators can only divide modules according to product functions and hand them over to these suppliers for research and development,resulting in coordination requirements based on division of labor.Therefore,modular R&D is a cognitive process in which module suppliers and system integrators gradually form a consensus on module R&D and integration based on the knowledge division of product functional modules based on the knowledge interdependence of modules.The process of knowledge coordination activities centered on knowledge learning between module suppliers and system integrators plays a key role.This paper firstly analyzes the knowledge coordination mechanism of module suppliers and the growth principle of knowledge stock.Module suppliers should not only apply and develop their core module R&D knowledge,but also master the systematic knowledge of interaction and integration between modules.Therefore,this paper proposes that module suppliers’knowledge learning includes individual learning and collaborative learning.Among them,individual learning refers to self-learning for the knowledge needs of the module itself,with the purpose of improving one’s own core knowledge.Collaborative learning refers to interactive learning with system integrators for knowledge interdependence between modules,with the purpose of mastering systematic knowledge about interaction and integration between modules.With continuous learning,the depth of knowledge and the breadth of the knowledge of module suppliers are constantly changing until they meet the knowledge needs of module development.Based on existing research,this paper believes that there is a positive correlation between knowledge depth and module supplier R&D performance,while there is an inverted U-shaped relationship between knowledge breadth and module R&D performance.Both individual learning and collaborative learning require resource input,and changes in module suppliers’knowledge stock(including two dimensions of knowledge width and knowledge depth)will affect their R&D capabilities and performance.Therefore,module suppliers must consider how to dynamically invest limited resources in the process of improving their knowledge width and depth based on the interdependence of knowledge among R&D modules,and on this basis seek the optimal resource investment strategy.Referring to the optimal control theory,with the goal of maximizing the performance of module R&D,the optimal control model of module supplier resource investment is constructed,and resource investment strategies in different situations are analyzed and summarized,including the termination of individual learning and collaborative learning.time,the main input objects of resources,and the optimal input intensity of resources.The research analysis draws the following important conclusions:(1)In order to achieve effective knowledge coordination in modular R&D,module suppliers should determine the optimal resource investment strategy based on the interdependence of knowledge between modules.The strategies include:Shifting from individual learning to collaborative learning;Collaborative learning-based shift to individual learning-based;Individual learning-led;Collaborative learning-led.(2)After determining the optimal resource input strategy,the module supplier should determine the corresponding changes in resource input intensity according to the marginal effect relationship between individual learning and collaborative learning on knowledge depth and knowledge breadth,and the marginal contribution of knowledge breadth.Module providers cannot commit excessive resources to a particular study.When the comprehensive marginal income of continuous individual learning is zero,the resource investment in individual learning should be terminated;When the comprehensive marginal income of continuous collaborative learning is zero,the resource investment in collaborative learning should be terminated.