Hybrid model integrating predictive analytics and environmental adaptation

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Abstract

The research investigates industrial maintenance strategies in the oil and gas sector and proposes a hybrid model that adapts maintenance schedules based on real-time environmental data. Comparative analysis shows that traditional strategies either cause inefficiencies or incur high failure risks. The hybrid approach integrates predictive analytics with environmental inputs such as corrosion and weather conditions. Simulations demonstrate a 15% reduction in failure rates and annual cost savings of up to $15 million per facility. The model enhances operational resilience, optimizes resource use, and aligns with the industry's transition toward safer and more sustainable practices.