Manufacturing Knowledge Graph for Operational and Engineering Decisions
An enterprise Knowledge Graph for manufacturing. Connecting fragmented operational, engineering, and supply chain data into a shared model of meaning that enables faster decisions and explainable AI insights.
- Connect production, engineering, and supply chain data
- Understand relationships across assets, components, and suppliers
- Enable explainable AI insights through Aluna
- Improve operational and engineering decision speed
Manufacturing use cases • Architecture walkthrough • No infrastructure replacement
Why Manufacturing Data Alone Doesn’t Deliver Operational Insight
Manufacturers generate massive volumes of data across:
- Production systems
- Engineering platforms
- Supply chain systems
- IoT and asset telemetry
Yet many organizations struggle to understand how production systems, components, and suppliers relate to each other.
Because while the data exists, the relationships between systems, assets, and processes are rarely modeled explicitly.
Manufacturing complexity lives in relationships. Most data platforms don’t model them.
From Fragmented Manufacturing Data to a Unified Knowledge Graph
Traditional manufacturing data platforms focus on storing or moving data.
d.AP focuses on modeling meaning and relationships.
The platform builds an ontology-grounded Knowledge Graph representing:
- Machines and assets
- Components and materials
- Suppliers and supply chains
- Production processes and dependencies
This shared knowledge layer enables teams and AI systems to reason across manufacturing systems.
Traditional Manufacturing Data Platforms
- Data stored across multiple systems
- Relationships inferred manually
- Operational analysis recreated repeatedly
Manufacturing Knowledge Graph with d.AP
- Explicit operational relationships
- Shared manufacturing context
- Reusable reasoning across teams
Step 1: Data Integration
Production systems, engineering tools, supply chain platforms, and IoT systems connect into the Knowledge Graph.
Step 2: Ontology Modeling
Manufacturing entities and relationships are modeled explicitly e.g., assets, components, suppliers, production processes.
Step 3: Knowledge-Based Reasoning
Relationships and constraints allow reasoning across manufacturing processes and dependencies.
Step 4: Explainable AI via Aluna
Teams ask questions in natural language and receive answers grounded in manufacturing knowledge.
Real results, real impact.
End-to-End Operational Visibility
Understand relationships between machines, components, suppliers, and production processes.
Explainable AI for Manufacturing Decisions
Aluna delivers answers grounded in the Knowledge Graph so operational insights remain transparent and trustworthy.
Faster Root Cause and Dependency Analysis
Trace dependencies across systems, assets, and processes without weeks of manual investigation.
Questions d.AP Can Help Answer
Which suppliers and components are connected to this production issue?
What operational dependencies exist between this machine, production line, and downstream process?
Which products or customers are affected by this manufacturing disruption?
How are engineering changes connected to production outcomes?
Explainable AI for Manufacturing Operations
d.AP exposes the manufacturing Knowledge Graph through Aluna, enabling teams to ask questions in natural language and receive answers grounded in operational relationships.
Every answer returned through Aluna is:
- Derived from the Knowledge Graph
- Grounded in manufacturing ontologies
- Traceable to systems, assets, and processes
This ensures AI supports manufacturing decisions without hallucination or opaque reasoning.
Designed for Manufacturing Enterprise Architectures
d.AP integrates with existing industrial systems rather than replacing them.
Deployment options include:
- EU-hosted SaaS (VPC)
- Customer-managed cloud environments
- Hybrid deployments
Most organizations begin with a focused domain such as asset relationships, supply chain dependencies, or production systems.
Who d.AP is for
Manufacturing operations leaders
Industrial data architecture teams
Engineering and product lifecycle teams
Supply chain analytics teams
Frequently Asked Questions
We answer your questions in advance. We've missed something? Let us know.
Is d.AP suitable for complex manufacturing environments with many interconnected systems?
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Yes. d.AP is designed for environments where systems, assets, and processes are deeply interconnected. Its ontology-driven Knowledge Graph models relationships across machines, components, suppliers, and production systems so teams can understand operational dependencies clearly.
How does d.AP integrate with existing manufacturing systems like ERP, MES, and PLM?
d.AP integrates with existing systems such as ERP, MES, PLM, and IoT platforms. These systems remain the systems of record while d.AP connects their data into a Knowledge Graph that captures relationships across manufacturing operations.
Our manufacturing data platform already supports analytics. Why introduce a knowledge graph layer?
Traditional analytics platforms analyze datasets but often lack a shared model of how operational entities relate to each other. d.AP creates a knowledge layer that connects machines, components, suppliers, and processes so teams can reason across manufacturing systems rather than analyzing data in isolation.
How does d.AP support AI initiatives in manufacturing?
d.AP provides the structured knowledge foundation needed for reliable AI. Through Aluna, users can ask questions in plain English and receive answers derived from the Knowledge Graph, ensuring insights are grounded in operational relationships rather than probabilistic guesses.
Can d.AP scale across large manufacturing operations and global supply chains?
Yes. d.AP is designed for enterprise-scale environments where knowledge spans production systems, supply chains, and engineering platforms. Its Knowledge Graph architecture enables organizations to model and reason across large, interconnected datasets.