Digital twins in manufacturing: Getting started
A digital twin in manufacturing is a dynamic, real-time representation of your operations that mirrors the current state of physical assets, processes, and relationships. Unlike traditional reporting systems that show what happened hours ago, a digital twin reflects what’s happening right now across your entire operation.
The power lies in modeling complex relationships between manufacturing entities—production lines, inventory levels, supplier deliveries, quality metrics—in business language rather than raw database tables. When a machine adjustment affects throughput or a quality issue triggers a production halt, these changes propagate through the digital twin within seconds.
Core requirements for manufacturing digital twins
Manufacturing digital twins must meet two fundamental requirements. First, they must stay perfectly synchronized with reality. In manufacturing, small changes have cascading effects—a single machine adjustment can impact downstream processes, quality metrics, inventory levels, and delivery schedules. Your digital twin must capture these ripple effects immediately.
Second, they must support the scale demands of modern manufacturing operations. As manufacturers deploy more sensors, automated systems, and AI agents, the volume of data queries increases dramatically. Your infrastructure must handle this machine-generated traffic economically while maintaining performance.
Architectural foundations
Traditional data warehouses operate on batch schedules that leave manufacturers working with stale information. When your digital twin updates every few hours, operators make decisions based on outdated conditions, leading to suboptimal outcomes.
Operational databases provide better freshness but struggle with the complex transformations needed for meaningful business views. Building manufacturing insights directly from raw tables creates expensive, brittle solutions.
The solution is incremental view maintenance (IVM) technology. IVM keeps transformed views continuously updated as source data changes, without expensive full reprocessing. This eliminates the traditional tradeoff between data freshness and query performance, enabling complex manufacturing models that update in real-time while remaining cost-effective.
Best practices for implementation
Start small with high-impact use cases
Begin by focusing on a single manufacturing process or production line where real-time visibility would provide immediate value—perhaps a bottleneck process, quality-critical operation, or high-variability workflow. Define views over relevant systems (ERP, MES, sensor data) and build initial data products representing key concepts like work orders, equipment status, or inventory levels. This focused approach demonstrates value quickly while building organizational confidence.
Design for AI agent integration
Modern manufacturing increasingly relies on automated systems and AI agents for optimization and predictive maintenance. Rather than forcing AI agents to construct complex queries against raw database tables, expose manufacturing data as well-defined data products through standardized interfaces like the Model Context Protocol (MCP). This ensures agents receive reliable, semantically meaningful data while protecting operational systems from expensive queries.
Build cross-system visibility progressively
Manufacturing involves complex interactions between multiple systems—ERP, MES, quality management, supply chain, and maintenance. Expand your digital twin incrementally by adding new data sources and relationships as you identify valuable cross-system insights. Stream updates from these systems into your IVM engine using change data capture (CDC), message queues, or direct integrations.
Implement governance with agility
As your digital twin expands, governance becomes critical. Manufacturing data products must be discoverable, well-documented, and properly permissioned. However, governance shouldn’t slow innovation. Implement frameworks that allow teams to rapidly create and deploy new data products while maintaining oversight. Document data products in natural language that both humans and AI agents can understand.
Real-world applications
Manufacturing organizations achieve significant value from digital twins across multiple areas. Real-time process monitoring enables rapid response to inventory changes, quality issues, and equipment performance variations while supporting optimization of routing, scheduling, and resource allocation.
Live inventory tracking improves customer satisfaction by providing accurate delivery updates and enabling proactive communication about potential delays. Quality management benefits from immediate visibility into issues as they emerge, enabling faster root cause analysis and corrective action.
Most importantly, digital twins provide the foundation for AI-driven manufacturing optimization by offering curated, real-time views that are both safe and semantically meaningful for automated decision-making.
Implementation roadmap
Begin with a focused pilot addressing a specific manufacturing challenge using data from limited systems. This demonstrates clear value while providing practical experience with underlying technologies.
Next, expand to cross-system integration by connecting additional data sources and building comprehensive views of manufacturing operations. This stage unlocks more sophisticated optimization and automation use cases.
Finally, evolve toward a comprehensive operational data mesh where multiple teams can contribute to shared digital twin capabilities while maintaining appropriate governance and control.
Materialize is a platform for creating agent-ready digital twins, just using SQL. It is built around a breakthrough in incremental-view maintenance, and can scale to handle your most demanding context retrieval workloads. Deploy Materialize as a service or self-manage in your private cloud.
