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Dynamic Optimization of Inventory Management in Aerospace Manufacturing
Technology Category
- Application Infrastructure & Middleware - Event-Driven Application
- Functional Applications - Inventory Management Systems
Applicable Industries
- Aerospace
Applicable Functions
- Logistics & Transportation
- Warehouse & Inventory Management
Use Cases
- Inventory Management
- Picking, Sorting & Positioning
The Challenge
A global manufacturer of aircraft engines, avionics, and other aviation products was grappling with the complexity of managing its supply chain. The company maintains 90 major product lines that require tens of thousands of parts from hundreds of manufacturers spread across the globe. The costs of maintaining inventory were significant, with just two components of its aircraft engines accounting for $600 million in parts inventory, including $400 million in fast-moving inventory. The company was seeking ways to optimize inventory levels to mitigate supplier delays and improve gross margins and revenue.
The Customer
Not Disclosed
About The Customer
The customer is a Fortune 100 software-industrial company with $16 billion in worldwide aerospace sales. The company employs 40,000 people and operates 100 manufacturing sites. It maintains six supply chain systems and produces 90 major product lines with variations that require tens of thousands of parts from hundreds of manufacturers spread across the globe. The company's products include aircraft engines, avionics, and other aviation products.
The Solution
The company decided to trial the C3 AI Inventory Optimization application as part of its push to optimize operations and invest in software. The application uses artificial intelligence to build a real-time view of inventory levels and supplier risks, enabling the company to reduce inventory levels while maintaining or even improving service levels. The trial lasted 10 weeks and demonstrated significant savings in inventory holding costs and accurately predicted supplier delays. The company's project objectives included integrating data from six supply chain systems to create a unified federated data image, applying AI to dynamically optimize inventory levels, and creating a production-ready application to detect supplier de-commit risk and streamline workflow and analytics for supply chain managers.
Operational Impact
Quantitative Benefit
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