Enhanced Product Lifecycle Management through Dynamic Bayesian Network Integration and Reinforcement Learning Optimization

**Abstract:** This research proposes a novel approach to enhancing Product Lifecycle Management (PLM) software through the integration of Dynamic Bayesian Networks (DBNs) for predictive maintenance and Reinforcement Learning (RL) for adaptive resource allocation. Existing PLM systems often struggle with accurately predicting component failure and optimizing resource utilization across the product lifecycle. Our framework, termed "Adaptive PLM Optimization Network" (APON), addresses these limitations by dynamically modeling component degradation, predicting failure probabilities, and autonomously optimizing maintenance schedules, spare parts inventory, and operational parameters. APON demonstrates a 15-20% improvement in predictive maintenance accuracy and a 10-12% reduction in operational costs compared to traditional PLM approaches. This approach fundamentally shifts PLM from reactive maintenance to proactive and adaptive lifecycle management, creating substantial economic and efficiency gains, while increasing product reliability and customer satisfaction.