modeling and simulation of an intelligent technology commercialization process based on ai algorithms, with a focus on transitioning from mass production to economic value creation

Technology commercialization is a complex and multidimensional process that requires coordination among product development, resource allocation, and alignment with market needs. traditional mass production models are unable to respond effectively to dynamic environments and rapid market changes, and focusing solely on production volume often results in low economic value. this study aims to develop an intelligent framework for technology commercialization by integrating system dynamics (sd), agent-based modeling (abm), and artificial intelligence algorithms, including genetic algorithms and reinforcement learning. the proposed model consists of three main layers: the data and input layer, which encompasses investment indicators, r&d metrics, and market data; the processing and simulation layer, which simulates actor behaviors, feedback loops, and resource allocation; and the output and decision-making layer, which provides key performance indicators including economic value (ev), market adoption (a), profitability (p), and customer satisfaction (cs). the simulation examined three primary scenarios: mass production, value-oriented, and hybrid. results indicated that the value-oriented scenario generates the highest economic value, market adoption, and customer satisfaction, while the mass production scenario demonstrates limited performance and low flexibility. the hybrid scenario offers a balance between profitability and adaptability and can serve as an intermediate approach for organizations that cannot fully transition to a value-oriented model. this study demonstrates that applying ai in modeling and simulation of the technology commercialization process enables the prediction of scenario outcomes and the optimization of resource allocation, and facilitates the transition from mass production to economic value creation. the findings provide organizational decision-makers and technology policymakers with a powerful tool for designing innovative strategies and reducing the risk of failure.