AWS re:Invent 2025 - Accelerate product development lifecycle with a product digital twin (IND371)

Accelerating Product Development with a Product Digital Twin

Overview

• Manufacturers are facing increasing pressure to rapidly innovate and bring new products to market faster
• Traditional product development lifecycles of 5-7 years are being compressed to 2 years or less, driven by competition from new market entrants
• Leveraging software, AI, and a "product digital twin" approach can help manufacturers reduce development costs and time-to-market

Challenges in Modern Product Development

• Product data is siloed across different systems and stages of the product lifecycle
• Lack of visibility and traceability from design to manufacturing to field service
• Difficulty identifying root causes of quality issues and product failures

The Product Digital Twin Approach

• Connecting disparate data sources across the product lifecycle into a "product data fabric"
• Using a knowledge graph to establish relationships between design, manufacturing, and field data
• Enabling traceability from product requirements to defects, and collaboration across the supply chain
• Providing a unified view of the product throughout its lifecycle

Implementing the Product Data Fabric

1. Ingest data from various systems of record (PLM, MES, CRM, etc.) into an S3 data store
2. Build a knowledge graph using Amazon Neptune to model relationships between product data
3. Leverage generative AI (AWS Bedrock) to enable natural language querying of the knowledge graph
4. Provide secure, role-based access to the product data fabric through web interfaces and APIs
5. Integrate the product data fabric with engineering tools and development workflows

Business Benefits

• Reduced product development lifecycle from 5-7 years to 2 years or less
• Lower total cost of product development through increased efficiency and reduced rework
• Improved product quality and reliability by identifying and addressing issues earlier
• Enhanced collaboration across the supply chain and with customers
• Ability to make data-driven decisions throughout the product lifecycle

Real-World Examples

• Automotive manufacturers using the product digital twin to compete with new market entrants
• Consumer electronics companies reducing time-to-market for new product features
• Aerospace manufacturers improving traceability and quality in aircraft engine development