Manufacturing, run on data from the shop floor up.
Ageing ERP and MES, disconnected OT and IT, and unplanned downtime keep plants reactive and margins thin. CodeFacts connects the shop floor to the enterprise, turns sensor data into predictive insight, and automates the workflows that drive OEE.
What's changing in manufacturing.
Manufacturers face volatile demand, supply-chain shocks and a widening skills gap while running on control systems and ERP that were never designed to talk to each other. The plants that win are turning OT and IT data into real-time visibility — predicting failures, catching defects early, and lifting OEE without pouring capital into new lines.
What makes manufacturing hard.
The specific, structural challenges we build against — not generic pain points.
Legacy ERP and MES
Production planning and execution run on rigid, heavily customised systems that resist change.
OT/IT silos
Shop-floor PLCs and SCADA are cut off from enterprise systems, so data never flows both ways.
Unplanned downtime
Assets fail without warning and reactive maintenance eats capacity and margin.
Blind supply chains
Limited visibility into suppliers and inventory drives stockouts, expedites and excess buffer.
Quality and scrap
Manual inspection misses defects late, when rework and waste are most expensive.
Sensor data at scale
IoT telemetry floods in faster than legacy systems can store, model or act on it.
From problem to platform.
Each challenge has an engineering answer. This is how we turn them into working systems.
Rigid ERP/MES
Modular services and clean APIs wrapped around the core, so you extend it without a risky rip-and-replace.
OT/IT disconnect
A unified data layer that streams from PLCs, SCADA and historians into the enterprise in real time.
Unplanned downtime
Predictive maintenance models that score asset health from vibration, temperature and cycle data.
Supply-chain blind spots
End-to-end visibility with demand and inventory signals surfaced on one control tower.
Late defect detection
Computer-vision quality inspection that flags defects inline, before scrap compounds.
IoT data overload
A cloud-native pipeline that ingests telemetry at scale and turns it into OEE and yield analytics.
Systems built for manufacturing.
OEE and plant dashboards
Real-time availability, performance and quality by line, cell and shift.
Predictive maintenance
ML on sensor telemetry that forecasts failures and schedules work before breakdowns.
Unified IoT platform
Edge-to-cloud ingestion connecting OT assets to enterprise analytics.
Vision quality inspection
Computer-vision defect detection built into the production flow.
Supply-chain control tower
One view of suppliers, inventory and demand for faster, tighter planning.
MES and ERP integration
Bridging shop-floor execution and enterprise systems with governed APIs.
The service offerings we bring.
One partner across strategy, AI, engineering, cloud, data and growth — mapped to what this sector needs.
Outcomes we engineer for.
Higher OEE across lines and shifts
Less unplanned downtime and lower maintenance cost
Fewer defects caught earlier, less scrap and rework
One connected view from shop floor to enterprise
Questions we hear.
Can you connect our OT systems without disrupting production?+
Yes — we integrate via edge gateways and read-only historian feeds first, so lines keep running while data starts flowing.
Do we need to replace our ERP or MES?+
No — we build around the core with modular services and APIs, modernising incrementally rather than ripping it out.
How does predictive maintenance actually work here?+
We model asset health from vibration, temperature and cycle data, scoring failure risk so maintenance is scheduled before breakdowns.
Can vision inspection handle our defect types?+
Yes — models are trained on your parts and defect classes, and run inline with humans reviewing edge cases.
Where should we start?+
A single line or asset class where downtime or scrap is costliest, proving OEE gains before scaling across the plant.