15–25%
Average Scrap Rate
High Scrap and Rework Costs
Scrap rates in chemical and plastics production range from 15–25% depending on the sector. ML quality prediction models reduce this rate by an average of 38%.
In chemical and plastics production, scrap rates, formulation deviations, and REACH compliance burdens put significant pressure on profitability. With Corius's ML quality prediction models and AI agent systems, raise your First Time Right rate to 93%, reduce scrap costs by 38%, and automate compliance processes.
The REACH compliance burdens, spectrophotometer data complexity, and strict quality standards of chemical production are areas where standard software falls short. Corius is the only software company that understands these requirements, integrates with existing MES and ERP infrastructure, and sets measurable ROI targets before starting every project.
Let's Talk About Your Chemical Production ProcessesA technical infrastructure that consolidates spectrophotometer, LIMS, MES, and ERP data into a single analytics pipeline. We process all dimensions of chemical data, from color measurement to quality prediction.
REST API and OPC-UA integration with SAP, Oracle, and custom ERPs. No need to change your existing production infrastructure; our models are embedded within your system.
In the Mine Colours project, the FTR rate rose from 68% to 93% and FPY from 74% to 91%. Full ROI was achieved by month 5.
Scrap from quality deviations, intuition-based formulation decisions, reactive maintenance costs, and increasing regulatory compliance burdens are the common pain points of Turkey's chemical sector. Research shows that 78% of manufacturers experience at least three of these problems simultaneously.
Scrap rates in chemical and plastics production range from 15–25% depending on the sector. ML quality prediction models reduce this rate by an average of 38%.
Switching to a new pigment or raw material supplier involves a 2–4 week trial-and-error process. Data-driven formulation models reduce this to days.
Equipment failures halt production and emergency maintenance costs are 3–5 times higher than planned maintenance. Predictive maintenance agents reduce this rate by 60%.
Manual compliance processes consume resources and time; each new REACH registration or SDS update means 3–5 days of workload. Automation reduces this process to 4 hours.
Post-production spectrophotometer measurement catches errors too late; the problem is noticed only after the entire batch is completed. A real-time prediction model reverses this process.
MES, ERP, LIMS, and laboratory systems don't communicate with each other. Real-time decision-making becomes impossible, and data remains siloed.
Every solution is integrated into your existing MES and ERP infrastructure to deliver measurable production improvements.
An XGBoost-based quality prediction model analyzes pigment ratio, temperature, and resin parameters to forecast color deviation (ΔE) before a batch starts. Validated at MAE=0.18 precision in the Mine Colours project, raising the FTR rate from 68% to 93%.
An AI agent that monitors reactor, extruder, and mixer sensor data in real time detects anomalies and notifies 72 hours before a failure. Only necessary interventions take place instead of scheduled maintenance.
An ML model that optimizes temperature profile, pressure, and mixing speed parameters in real time simultaneously reduces raw material usage and energy consumption. It recommends optimum production conditions for each formulation.
An agent system that automatically prepares compliance reports by matching LIMS data with REACH, SDS, and ISO 9001 requirements. Reduces post-production manual documentation time from 5 days to 4 hours.
A forecasting model that combines seasonal cycles, raw material lead times, and customer order patterns calculates the optimum order point on an SKU basis, balancing overstocking and stockouts.
Do you have a different production challenge?
We also develop solutions for problems not on the list that are specific to the chemical and plastics sector. Share your dataset and goals, let's evaluate together.
TELL US YOUR NEEDMine Colours
FTR rate rose from 68% to 93%, scrap cost reduced by 38%.
We now catch quality deviations on the production line before they occur — this is revolutionary for us.View Our Case Study
Enart Enerji
Managing turbine blade material selection with data. Blade lifespan extended by 30%, annual maintenance cost reduced by 22%.
The model now catches micro-defects in material testing that manual inspection couldn't see.View Our Case Study
In a free preliminary analysis meeting, we listen to your production processes and jointly identify 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
