How Manufacturers Use a Semantic Layer:
1. Supply Chain Optimization
Federate ERP, IoT, and supplier data to enable real-time inventory visibility, improve demand forecasts, and streamline logistics.
2. Production Efficiency Analytics
Surface bottlenecks, downtime trends, and root causes across plants using governed OEE and performance metrics.
3. Quality Control and Compliance
Standardize inspection, defect, and audit reporting with unified, traceable data for regulatory compliance and ISO certification.
4. Customer 360 & After-Sales Support
Connect CRM, IoT telemetry, and service data to enable predictive maintenance and data-driven service workflows.
Challenges Solved by a Semantic Layer
- Data Silos: Fragmented data across ERP, MES, CRM, and IoT systems hinders a unified view.
- Outdated Tools: Legacy tools like Excel and OLAP cubes limit scale and adaptability.
- Inconsistent Metrics: Different teams interpret KPIs differently, reducing trust in analytics.
- Collaboration Barriers: Siloed systems slow cross-functional decision-making.
Why AtScale for Manufacturing?
AtScale’s Universal Semantic Layer bridges the gap between raw data and real-time insights, enabling manufacturers to modernize analytics, power AI agents, and unify data across plants, platforms, and teams.
With AtScale, you can:
- Simplify analytics with consistent, business-friendly data models
- Empower self-service across BI tools, Excel, and GenAI platforms
- Scale with sub-second performance—no data movement required
- Govern everything with fine-grained access controls, Git-based versioning, and RBAC
And you get:
✔ Live queries—no duplication, no extracts
✔ Standardized metric definitions across all tools
✔ Built-in support for RLS, CLS, and platform impersonation
✔ One source of truth for GenAI agents and business users alike
Why Now? The future of analytics is composable, agentic, and governed. Manufacturers need real-time, AI-ready insights to stay competitive.
With AtScale, your semantic model becomes the foundation for analytics, decision intelligence, and explainable AI.
Real-World Examples
- Global Manufacturer: Improved supply chain efficiency by integrating data from multiple sources and enabling self-service analytics, reducing reliance on IT teams.
- Multinational Industrial Company: Unified data across platforms and replaced outdated reporting workflows, significantly enhancing operational efficiency.
- Leading Automotive Company: Created a comprehensive “Customer 360” view, enabling seamless self-service analytics while decommissioning legacy systems.
- Bicycle Manufacturer: Accelerated data query performance, minimized data duplication, and improved reporting agility with a modern semantic layer solution.
