Why UNS Matters
The business case for a Unified Namespace in manufacturing.
The Cost of Data Silos
Every manufacturing operation generates vast amounts of data — machine states, production counts, quality measurements, maintenance logs. But in most factories, this data is fragmented across disconnected systems:
| System | Knows | Doesn't Know |
|---|---|---|
| SCADA | Machine status | Production targets |
| MES | Production orders | Real-time machine state |
| ERP | Planned schedules | Actual throughput |
| Maintenance | Work orders | Machine alarm history |
| Quality | Inspection results | Which machine/operator was involved |
Getting a complete picture requires manual data extraction, spreadsheet merging, and significant engineering effort. By the time the data is assembled, it's already stale.
What the UNS Enables
Real-time Visibility
Every data point is available the moment it's published. No batch exports, no overnight ETL jobs, no waiting for reports. A machine goes into ALARM — every subscribed system knows immediately.
Single Source of Truth
One topic, one current value. No conflicting data between systems. v1.0/enterprise/site1/area1/cnc-01/status always reflects the machine's actual state.
Reduced Integration Cost
Adding a new consumer (dashboard, analytics tool, AI model) means subscribing to existing topics. No new point-to-point connections. No API development. No vendor negotiations.
Faster Time to Insight
With all data flowing through a common structure, building new analytics is straightforward. This project computes utilisation, availability, throughput, MTBF, MTTR, and stoppage pareto — all from the same UNS data, using simple SQL queries.
Scalability
The UNS scales horizontally. Add more machines — they publish to new topics. Add more consumers — they subscribe. The architecture doesn't change.
Measurable Outcomes
| Metric | Before UNS | With UNS |
|---|---|---|
| Time to answer "what's machine utilisation?" | Hours (manual data pull) | Seconds (API call) |
| New integration setup | Weeks (custom development) | Minutes (subscribe to topic) |
| Data freshness | Hours/days (batch) | Real-time (< 1 second) |
| Systems with access to machine data | 1-2 (SCADA, historian) | Unlimited (any MQTT subscriber) |
| Cost of adding a new KPI | Significant (cross-system) | Minimal (new SQL query) |
Who Benefits
| Role | Value |
|---|---|
| Operations Managers | Real-time dashboards showing utilisation, production progress, and stoppage reasons across the entire shop floor |
| Maintenance Teams | Automatic MTBF and MTTR calculations. Alarm history with durations. Stoppage pareto charts |
| Production Planners | Actual throughput data compared against planned schedules. Identify bottleneck machines |
| Quality Engineers | Scrap tracking linked to specific machines, programs, and operators |
| Continuous Improvement | Data-driven kaizen. Every state change, stoppage, and production run recorded with timestamps |
| IT & Engineering | Clean, maintainable architecture. Independent functions, version-controlled, deployable via git push |