The Infosys Aerospace and Defense practice leverages predictive and cognitive modeling to augment capabilities of aircraft manufacturers. Military and commercial aircraft stream megabytes of data every second, which can be used to enhance the lifespan of assets by manufacturers and maintenance service providers.
Our knowledge-based predictive models convert real-time information from avionics systems, flight data recorders and ERP systems into actionable insights to mitigate risks and preempt potential issues. Timely action may range from production rescheduling due to a supply chain disruption to reallocation of workforce due to grounding of aircraft for preventive maintenance.
Our IoT Gateway Framework enables seamless interaction of enterprise systems with Augmented Reality (AR) and Virtual Reality (VR) platforms to boost maintenance and ensure industrial safety. We maintain a library of analytical models and reusable datasets and attributes to gather predictive insights across processes.
Infosys Nia, our artificial intelligence-driven chatbot, can be trained to extract contextual information from design specifications, maintenance manuals, and repair service records. Our AI platform combines data and a self-learning mechanism to automate resolution of asset-related issues. Our cognitive knowledge model integrates data across the enterprise to streamline asset management by automating tasks and guiding workflows. Significantly, Infosys Nia improves the quality of service while minimizing human intervention.
Infosys develops integrated spare parts forecasting models and warranty profitability models to rationalize inventory costs, cultivate customer loyalty, and grow revenue. Accurate forecasting of spare parts based on real-time asset performance and global demand improves supply chain management and facilitates last mile optimization.
White paper: Boost the aircraft landing gear lifecycle
Our experts discuss technologies and analytical tools to address the challenges in developing landing gear.
Knowledge models assimilate data from condition monitoring systems, correlate events, and undertake root cause mapping to identify issues and predict maintenance requirements.
Algorithms simplify data interpretation, enabling planning and scheduling managers, maintenance engineers, and service technicians to address issues and avoid downtime.
Big data solutions mitigate risks by recognizing patterns across parameters, and spotting trends and anomalies in metallic as well as composite structures / components across aircraft models and fleet.