Onboard a virtual team
Overview
The Infosys Aerospace and Defense practice boosts enterprise performance via business process automation. We implement process bots as part of a digital transformation program to enhance processes and automate repetitive, rules-based tasks across business functions. Automation standardizes effort and time-intensive processes such as procurement, sales, inventory, supply chain, payments, auditing, customer service, and data management.
Infosys combines Artificial Intelligence (AI), deep learning, natural language processing, speech / image / facial recognition, machine learning, and text analytics to enhance the responsiveness of aircraft manufacturers. Software robots activate workflows and trigger alerts/ responses based on pre-defined criteria. Automated verification of airworthiness reports, aircraft lease documents, and aviation safety records helps identify and address gaps.
Infosys Nia, our AI-powered virtual agent, can be trained to address queries for document, work order, and workforce management. Intelligent automation, the amalgamation of knowledge and machine learning, enables our chatbot to retrieve files from aircraft technical records and installation labels, shop floor task cards, digital MRO manuals, supply / labor contracts, parts release notes, and compliance statements to address specific queries. Cognitive capabilities enable Infosys Nia to proactively manage tasks and resolve issues.
Infosys AssistEdge automates high-touch workloads such as aftermarket sales and support service. Our Robotic Process Automation (RPA) software creates a unified processing queue to accelerate generation of sales as well as purchase orders and advance shipment notification labels. Significantly, it streamlines the procure-to-pay, hire-to-retire and customer service processes.
Our digital bots process aircraft component / spare parts return deliveries by reconciling labels, service orders, release statements, and certificates.
Talk to our expertsOur experts discuss the system architecture of a robust vehicle health monitoring mechanism, and provide a use case to showcase how it addresses functional requirements.
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