Automated defect detection
Reduced rework costs
Audit-ready quality records
Consistent inspection standards
Process deviation prediction
What Manual QC Is Actually Costing Your Plant
Most precast plants inspect visually, record on paper or spreadsheets, and escalate defects when they’re found — usually after the element has been cast, stripped, and moved. By that point, the cost of rework is maximised.
Manual inspection is also inherently inconsistent. Different inspectors make different calls on borderline cases. End-of-shift judgement differs from start-of-shift. Inspection records are incomplete. There’s no usable trend data to identify recurring process issues before they become rework problems.
Computer Vision doesn’t get tired, doesn’t make different calls on Friday afternoon, and generates a complete data record of every inspection automatically.
Rework Cost
Elements failing inspection after stripping — requiring additional finishing, patching, or rejection. Direct material and labour cost with no revenue return.
Inspection Time
Experienced QC staff spending hours on manual checking that could be partially or fully automated — time better spent on exception handling and process improvement.
Compliance Exposure
Paper-based QC records that are incomplete, inconsistent, or hard to retrieve during audits — creating compliance risk and client relationship problems.
No Root Cause Data
Defects caught late with no data trail to identify whether the root cause is mould wear, material variability, process deviation, or operator error.
From Assessment to Deployed System — Our Implementation Process
Deploying Computer Vision in a precast plant is not simply installing cameras. The system needs to be trained on your specific products, defect types, and production conditions — and it needs to integrate with your existing QC workflow, not replace it wholesale on day one.
We work in partnership with DAC.digital for the technical development of AI and Computer Vision systems — combining their machine learning expertise with LPS’s production floor knowledge to deliver systems that work in real precast environments, not just in controlled laboratory conditions.
QC Process Audit
Review of your current inspection procedures, defect history, rework rates, and quality record-keeping. Identifies which inspection tasks are most suitable for automation and what data exists to train the system.
System Design
Camera placement, lighting, and capture configuration designed for your specific production environment — accounting for the geometry of your elements, lighting conditions, and production flow.
Model Training
The AI model is trained on images of your specific products and defect types — surface voids, honeycombing, dimensional deviations, rebar cover, and other inspection criteria relevant to your production.
Integration & Deployment
The system is integrated with your existing production records and quality management workflow — ensuring inspection results feed into your documentation automatically without a separate manual step.
Refinement & Support
The model continues to improve with operational data. We provide ongoing refinement and support — adjusting detection thresholds, retraining on new defect types, and monitoring system performance.