C3 IoT Case Studies Dynamic Optimization of Inventory Management in Aerospace Manufacturing

	Dynamic Optimization of Inventory Management in Aerospace Manufacturing C3 IoT

Dynamic Optimization of Inventory Management in Aerospace Manufacturing

C3 IoT

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
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. Read More
About 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. Read More
Customer Name	Not Disclosed Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

Customer Name

Not Disclosed

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.

Impact #1	The trial of the C3 AI Inventory Optimization application resulted in significant operational improvements for the company. The application's ability to provide a real-time view of inventory levels and supplier risks allowed the company to reduce inventory levels while maintaining or improving service levels. This led to significant savings in inventory holding costs. The application also accurately predicted supplier delays, enabling the company to better manage its supply chain and mitigate the impact of these delays. The company was able to integrate data from six supply chain systems to create a unified federated data image, further streamlining its operations. The creation of a production-ready application to detect supplier de-commit risk and streamline workflow and analytics for supply chain managers also improved the efficiency of the company's operations.

Impact #1

The trial of the C3 AI Inventory Optimization application resulted in significant operational improvements for the company. The application's ability to provide a real-time view of inventory levels and supplier risks allowed the company to reduce inventory levels while maintaining or improving service levels. This led to significant savings in inventory holding costs. The application also accurately predicted supplier delays, enabling the company to better manage its supply chain and mitigate the impact of these delays. The company was able to integrate data from six supply chain systems to create a unified federated data image, further streamlining its operations. The creation of a production-ready application to detect supplier de-commit risk and streamline workflow and analytics for supply chain managers also improved the efficiency of the company's operations.

Benefit #1	$180–240M of potential value identified for inventory reduction
Benefit #2	80% accuracy predicting supplier delays or de-commits
Benefit #3	30-40% reduction in inventory levels

Benefit #1

$180–240M of potential value identified for inventory reduction

Benefit #2

80% accuracy predicting supplier delays or de-commits

Benefit #3

30-40% reduction in inventory levels

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Overview

Dynamic Optimization of Inventory Management in Aerospace Manufacturing

Operational Impact

Quantitative Benefit