The Infosys Aerospace and Defense practice streamlines Supply Chain Management (SCM) by leveraging data analytics, value engineering and optimization techniques. Our digital tools provide run-time adaptability to address the challenges in managing multi-tiered supply chains and global supplier networks. Spend aggregation, visibility into potential cost savings, and best practices for contracts enable aircraft manufacturers to better manage pricing, product development, capacity, and inventory, thereby facilitating on-time delivery of orders.
Infosys blends knowledge models, open source architecture, automation tools, and infrastructure resources to predict, monitor and control product as well as supply chain costs. We capitalize on Process Failure Modes and Effects Analysis (PFMEA) for supply chain risk management. We quantify the probability and severity of risk factors, which helps mitigate risks and prevent cost escalation. Moreover, PFMEA facilitates supplier assessment and identification. We evaluate the capabilities, performance and capacity of suppliers prior to selection and onboarding for transfer of work.
We implement Production Part Approval Process (PPAP) as well as first and last article inspection to accelerate supply of machined components and materials. It helps manufacturers approve supplier data in real time, while ensuring the reliability of mass produced parts. Significantly, it addresses regulatory compliance and quality requirements by eliminating calibration errors and meeting stringent dimensional tolerance limits of aircraft components.
White paper: Interdisciplinary collaboration accelerates product enhancement
A structured knowledge-based engineering methodology distils experiential knowledge and enables concurrent development.
Infosys cost maintenance application includes a centralized supplier database and repositories of material, labor and process rates, which enables scenario-specific cost optimization.
Smart sourcing solutions enable data-driven price re-negotiation, alternative sourcing, and smooth transfer of work, while mitigating supply chain risks.
Analytical frameworks with machine learning capabilities determine the ‘right cost’ across aeronautical machinery and spacecraft parts even in the absence of historical data.