Production Optimisation

Manufacturing optimisation requires understanding which process parameters causally influence quality, yield, and efficiency, not just correlation. Our causal machine learning solutions replace trial-and-error tuning and statistical approximations with precision engineering informed by causal discovery.

We applyCausal Machine Learningto solve complex business challenges.

Using causal discovery algorithms on production telemetry data, we identify the true process dependencies and feedback loops. Causal inference methods then quantify how changes to temperature, pressure, feed rates, and other parameters affect outcomes while controlling for confounding factors like raw material variation and equipment aging. This goes beyond traditional design of experiments (DOE) by scaling to high-dimensional settings and discovering relationships DOE matrices might miss, then learning continuously as production runs accumulate data. Our approach builds on research in [causally learning optimal policies](/research#optimal-rework-policy).

Industrial manufacturers deploying our platform achieve measurable yield improvements through optimized parameter settings, reduce scrap and rework by identifying true quality drivers, and extend equipment life through predictive maintenance informed by causal relationships between sensor readings and failure modes. Even small reductions in downtime in high-volume facilities deliver significant value. Multi-site manufacturers use our platform to identify best practices from one facility and confidently transfer them to others, accounting for local differences.

Real-time dashboards show process engineers exactly which variables matter most and which interventions will improve the next batch.