In discrete manufacturing facilities in Turkey, unplanned equipment downtimes, scrap from quality errors, and bottlenecks in CNC/assembly lines seriously hamper production efficiency. With Corius's predictive maintenance agents and ML quality control models, increase OEE by %15, reduce unplanned downtimes by %60, and decrease quality scrap costs by %35 — integrate with your existing MES/ERP infrastructure.
Discrete manufacturing's unique challenges — CNC programming deviations, assembly line cycle time variations, and multi-level BOM complexity — are areas that standard ERP reports cannot solve. Corius develops customized solutions that provide measurable production improvements by combining IoT/OPC-UA sensor data collected from the field with ML models.
Let's Discuss Your Production ProcessesTechnical infrastructure capable of processing CNC machine parameters, pneumatic sensors, assembly line cycle time, and BOM data structures. Natively supports OPC-UA and MQTT protocols; integration with legacy SCADA systems is also available.
REST API integration with SAP PP, Aras PLM, and custom MES systems. We enable transition from pilot to production without stopping the existing production line; integration architecture is designed at the beginning of the project.
In the Mine Colours plastic/masterbatch production project, FTR rate increased from %68 to %93, FPY increased from %74 to %91. Full ROI achieved in 5 months.
Unplanned equipment downtimes, scrap from quality errors, manual quality control delays, and unscheduled maintenance costs are the most common pain points in Turkey's manufacturing sector. Industry research shows that %72 of manufacturers experience at least three of these problems simultaneously.
Unplanned downtimes in discrete and assembly line machines consume %20-30 of total production time. Predictive maintenance models convert this rate into planned maintenance windows.
Out-of-tolerance part rate in CNC machining and assembly operations ranges from %8-15 depending on operation scale. ML quality prediction model enables part acceptance decision to be made before cutting or assembly begins.
Maintenance intervention after a failure increases the cost of the same job by 3-5 times. Predictive maintenance agent detects failure 48-72 hours in advance, enabling planned intervention.
Of quality errors are detected only at the final stage of assembly or during customer delivery. Real-time ML model moves this detection point to the beginning of production, eliminating rework costs.
Unplanned setup changes and operator-dependent cycle time variations reduce line efficiency 2-3 times over time. Time series analysis detects bottlenecks in advance, providing actionable recommendations.
MES, ERP, CNC controller logs, sensor data, and quality tracking systems don't communicate with each other. Real-time OEE calculation is impossible, decision-making remains reactive.
Each solution provides measurable production improvements by integrating with your existing MES, ERP, and CNC infrastructure.
AI agent that tracks vibration, current, and temperature data from CNC machines, presses, assembly robots, and conveyor sensors in real-time; detects anomalies and notifies failures 48-72 hours in advance. Reduces unplanned maintenance rate by %60.
ML model that processes machine parameters and sensor data; predicts deviations that will cause out-of-tolerance part production before cutting or assembly begins. Image processing integration also supports optical quality control.
Dashboard that calculates Availability, Performance, and Quality components in real-time via OPC-UA; automatically detects bottlenecks, setup times, and micro-stop patterns. Provides actionable recommendations to transform production planning into proactive actions.
Demand forecasting model that combines customer order history, supplier lead times, and capacity constraints to minimize excess capacity loading and delivery schedule overruns. Integrates with MRP and ERP systems.
AI agent system that automatically prepares quality reports, NCR (Non-Conformance Report), maintenance work orders, and customer delivery documents. Pulls data from ERP and produces documents in ISO 9001 audit format.
Do you have a different manufacturing problem?
We also develop solutions for manufacturing problems not on the list. Share your production data and goals, let's evaluate together.
DESCRIBE YOUR NEEDMine Colours
FTR rate increased from %68 to %93, scrap cost decreased by %38.
We now catch quality deviations in production line before they occur — this is revolutionary for us.Review Our Case Study
Enart Energy
Fault detection accuracy reached %91 with ML model in epoxy composite blade material production.
ML model detects micro-defects in material testing that manual review cannot see.Review Our Case Study
In a free preliminary analysis meeting, we listen to your production processes — understanding your machine park, line data, and goals — to determine the starting point with the highest ROI potential.
Kızılırmak Mah. Dumlupınar Bulvarı Next Level No:3 A Kat:4 D.No:10 PK:06520 Çankaya / Ankara - Türkiye
