Aerospace companies are known for building aircraft but, in reality, they do so much more. They are a central cog in aircraft manufacturing supply chains, delivering parts, people, and possibilities to manufacturers around the globe.
Today’s airplanes are incredibly sophisticated – one count found a single aircraft can have between four and six million parts – but stockpiling unused parts is a prohibitively expensive strategy. The solution? Predictive maintenance.
When done well, connected devices leverage artificial intelligence and machine learning (AI/ML) to proactively provide crucial data and insights about real-time asset performance, estimated replacement timelines, and supply-chain agility, with ample lead time for suppliers and airlines. This requires agility and scalability across organizations, and that means moving away from legacy systems to a flexible cloud-based model, which often begins with the process of application modernization. It is a considerable undertaking, but it is the first step in true digital transformation.
We worked with a leading aerospace manufacturer facing legacy-system challenges that wanted to modernize and improve predictive maintenance of its supply chain for after-market service-parts management. The objective was to adopt a cloud-first approach to improve lead time, forecasting accuracy, inventory optimization, and, above all, planning, leveraging Red Hat OpenShift and the Kubernetes Orchestration Platform.
Download the success story now to discover the steps we took and the powerful results that followed.
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