25%–40%
Scrap & Rework Reduction
Predictive quality analytics cut scrap 25–40% by detecting viscosity deviations mid-batch. Each 1 pp reduction = hundreds of thousands saved annually.
AI-Assisted Operator Guidance
Overview
Manufacturing quality issues rarely stem from a single failure. More often, they emerge through process variability, delayed decisions, and inconsistent operator actions. LTM’s AI-Assisted Operator Guidance solution combines AVEVA MES, InTouch OMI, and an edge-deployed Small Language Model (SLM) to predict quality risks in real time and guide operators with contextual recommendations before deviations impact production.
The Challenge
In batch manufacturing, quality deviations like are often detected after production is complete, leading to rework, scrap, and inconsistent outcomes.
Operators frequently rely on experience over real-time intelligence, while process variability materials, timing, and manual interventions makes maintaining consistent quality increasingly difficult.
The challenge for manufacturers today is not the lack of data. Modern plants already collect massive volumes of operational information through PLCs, historians, MES systems, and sensors. The real challenge is improving decision quality during execution — when the process is still recoverable and corrective action is still cost-effective.
Key Pain Points
- Late detection of quality deviations
- Limited real-time process intelligence
- Operator decisions driven by intuition
- High rework and scrap costs
- Inconsistent batch quality
- Knowledge dependency across operators and shifts
LTM Solution: SLM-Powered Predictive Quality
LTM introduces a Small Language Model (SLM)-driven edge solution that predicts product quality in real time and guides operators during the process. This SLM is deployed in the AVEVA MES installed in LTM server for local and faster processing based on raw data or parameters provided.
How It Works
- Captures real-time signals such as torque, temperature, and RPM
- Runs SLM inference locally at the edge (no cloud latency)
- Provides actionable guidance via operator dashboard
- Logs decisions in MES for continuous learning
- Enables human-in-the-loop decision-making
| FROM | TO |
| Fixed Processing parameter | SLM predicts viscosity from energy input signals |
| Experience-based operator decisions | AI-backed, explainable real-time guidance |
| Post-batch investigation only | In-process quality assurance at edge |
| One-time model, static rules | Continuous SLM retraining from batch outcomes |
Closing Point of View
“The next improvement in manufacturing quality will not come from more alarms or more reports. It will come from better decisions during execution.”
Plants that operationalize AI-assisted guidance well will improve consistency not because they have more data, but because they act on the right signals earlier and with more confidence.
Business Benefits
+30%–50%
First-Time-Right Improvement
Today 20–50% of paint/coating batches need post-mix viscosity adjustment. Real-time SLM guidance enables mid-batch correction, lifting first-time-right rates by 30–50%.
1%–3%
Throughput / OEE Gain
Eliminating rework cycles and re-mixing frees reactor capacity. AI-optimized batch timing delivers 1–3% throughput uplift without additional equipment.
5%–15%
Energy Cost Reduction
Optimized mixing endpoints eliminate unnecessary run time. AI-driven process control cuts energy consumption 5–15% through precise endpoint detection.
200–300%
Expected ROI
AI quality-control in manufacturing delivers 200–300% ROI. Typical payback of 12–15 months, with edge SLM keeping infrastructure costs minimal.
3×
Faster Deviation Response
Operators with real-time AI guidance respond 3× faster to batch deviations. Consistent quality across shifts, reducing variability from operator experience gaps.
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