Holonic Systems and Latent Dirichlet Allocation for Enhancing Digital Economy Strategies in High-Quality Manufacturing Development

The accelerated development of the digital economy requires flexible manufacturing systems that can optimize resource planning and decision-making efficiently. Unfortunately, conventional methods are not only inefficient in their scalability but also in making dynamic adjustments in real time. An integrated system introduced in this study employs Holonic Systems (HS) and Latent Dirichlet Allocation (LDA) to enhance manufacturing flexibility, strategic knowledge, and resource handling. The methodology uses holonic architectures for self-organization in production and LDA for topic modeling to support data-driven optimizations. Empirical analysis, by way of mathematical and computational frameworks, measures the performance of systems in terms of accuracy, precision, recall, F1-score, and efficiency in resource management (RME). The outcome shows a high improvement with the RME down to 4.2%, accuracy of 95.6%, and an improved decision-making ability. The research concludes that holonic systems enabled by LDA enhance industrial adaptability and operational efficiency, delivering a sound platform for sustainable digital manufacturing. Future research must further investigate reinforcement learning to enhance predictive decision-making and industrial scalability.